Organizational Change at Work, Employee Turnover, and Health · psykofarmaka (anxiolytica...

U N I V E R S I T Y O F C O P E N H A G E N

F A C U L T Y O F H E A L T H A N D M E D I C A L S C I E N C E S

Johan Høy Jensen

PhD thesis

This thesis has been submitted to the Graduate School of Health and Medical Sciences, Universi ty of Copenhagen

November 29, 2018

Organizational Change at Work,

Employee Turnover, and Health

- a longitudinal study among employees in the Capital Region of Denmark

Johan Høy Jensen

PhD thesis

Graduate School of Health and Medical Sciences, University of Copenhagen

Department of Occupational and Environmental Medicine, Bispebjerg University Hospital

2018

Organizational Change at Work,

Employee Turnover, and Health

- a longitudinal study among employees in the Capital Region of Denmark

Author: Johan Høy Jensen, MSc in Psychology

Department of Occupational and Environmental Medicine Bispebjerg University Hospital, Denmark

Academic advisors: Jens Peter Bonde, MD, PhD, DMSc, Professor

Department of Occupational and Environmental Medicine Bispebjerg University Hospital, Denmark Naja Hulvej Rod, PhD, DMSc, Professor Department of Public Health University of Copenhagen, Denmark Esben Meulengracht Flachs, PhD Department of Occupational and Environmental Medicine Bispebjerg University Hospital, Denmark Janne Skakon, PhD, External Associate Professor Department of Psychology University of Copenhagen, Denmark

Assessment Committee: Kirsten Nabe-Nielsen, PhD, Associate Professor (Chair) Faculty of Health and Medical Sciences University of Copenhagen, Denmark Jussi Vahtera, MD, PhD, Professor Department of Public Health University of Turku, Finland

Johan Hviid Andersen, MD, PhD, Professor Department of Occupational Medicine Regional Hospital West Jutland, Denmark Submitted on: November 29, 2018 Defended on: ISBN: Cover illustration: Nicolai Bruun

February 15, 2019

978-87-970125-3-6

Preface and acknowledgements

This PhD thesis was conducted at the Department of Occupational and Environmental

Medicine at Bispebjerg University Hospital, Copenhagen, Denmark, in the period December

2015 through November 2018. From February to June 2018, I was a visiting research scholar

at the Department of Social and Behavioral Sciences (SBS) at Harvard T. H. Chan School of

Public Health, Harvard University, Boston, MA, USA.

This work was funded by the Danish Working Environment Research Fund (grant number:

13-2015-03). My research stay abroad was financially supported by University of

Copenhagen, Julie von Müllens Fond, Else & Mogens Wedell-Wedellsborgs Fond, and the

Graduate School of Public Health, University of Copenhagen.

My time as a PhD fellow during the past three years has truly been a tremendous experience –

both in terms of educational and personal growth. The subject of organizational change and

employee outcomes branches into several academic disciplines, which has given me the

opportunity to work with an interdisciplinary team of exceptionally talented people to whom I

would like to express my gratitude.

My deepest gratitude goes to my principal supervisor, Professor Jens Peter Bonde. Thanks for

sharing your brilliant insights and for supporting me through the phases of my PhD project. I

have immensely appreciated your contagious enthusiasm and down-to-earth approach. Your

supervision style will undeniably inspire me in my future work.

I would also like to express my sincere thanks to you, Professor Naja Hulvej Rod, for your

original public-health perspectives and insightful comments on the subject. Your ambitious

attitude and high research standards have widened my horizons within psychosocial

epidemiology.

I am especially appreciative of you, Dr. Esben Meulengracht Flachs, for your never-failing

attempts to explain the logics of the most complex statistical methods. This PhD thesis would

not have been possible without your vast knowledge and pedagogical approach to share it.

I also extend my gratitude to you, fellow psychologist Dr. Janne Skakon. Thanks for your

valued inputs on theoretical and interventional perspectives. Our inspiring discussions have

provided me with a broader understanding of the complexities involved in the subject.

To Professor Ichiro Kawachi and everyone in Department of Social and Behavioral Sciences:

Many thanks for warmly welcoming me in your research group, your time and effort in

academic supervision, and for your highly appreciated feedback on my work. My stay with

your research group is one of my fondest memories during this PhD project.

I would also like to thank the working group on the Well-being in Hospital Employees

(WHALE) cohort, and particularly Charlotte Hyldtoft and Dr. Jesper Strøyer Andersen from

the Capital Region of Denmark, for insights and knowledge about the data.

Thanks to the co-authors of Papers I-VI for your collaboration, your valuable ideas, and for

making this PhD thesis possible.

My gratitude also goes to my fantastic – present as well as former – colleagues in Department

of Occupational and Environmental Medicine at Bispebjerg University Hospital. You all

make our department to the best workplace in the world!

My parents, my brother, family, and my friends; thank you all for your emotional and spiritual

support throughout this PhD project and for counterweighting an intensive working life.

Last but not least, I would like to heartfully thank you, my dear Nina, for your love,

unconditional support, and forbearance with my physical and mental absence from time to

another through the past three years.

Johan Høy Jensen

Copenhagen, November 2018

This PhD thesis is based on the following Papers:

Paper I (published)

Hvidtfeldt UA, Bjorner JB, Jensen JH, Breinegaard N, Hasle P, Bonde JP, Rod NH. Cohort

profile: the well-being in hospital employees (WHALE) study. Int J Epidemiol,

2017;46(6):1758-1759h. doi:10.1093/ije/dyx073

Paper II (published)

Jensen JH, Flachs EM, Skakon J, Rod NH, Bonde JP. Dual impact of organisational change

on subsequent exit from work unit and sickness absence: a longitudinal study among public

healthcare employees. Occup Environ Med, 2018;75(7):479-485. doi:10.1136/oemed-2017-

104865

Paper III (in press)

Jensen JH, Flachs EM, Skakon J, Rod NH, Bonde JP. Longitudinal associations between

organizational change, work-unit social capital, and employee exit from the work unit among

public healthcare workers: a mediation analysis. Scand J Work Environ Health,

2019;45(1):53-62, online first [21-Aug-2018]. doi:10.5271/sjweh.3766

Paper IV (published)

Breinegaard N, Jensen JH, Bonde JP. Organizational change, psychosocial work environment,

and non-disability early retirement: a prospective study among senior public employees.

Scand J Work Environ Health, 2017;43(3):234-240. doi:10.5271/sjweh.3624

Paper V (in press)

Jensen JH, Bonde JP, Flachs EM, Skakon J, Rod NH, Kawachi I. Work-unit organizational

changes and subsequent prescriptions for psychotropic medication: a longitudinal study

among public healthcare employees. Occup Environ Med, accepted [28-Nov-2018].

doi:10.1136/oemed-2018-105442

Paper VI (submitted)

Jensen JH, Flachs EM, Skakon J, Rod NH, Bonde JP, Kawachi I. Work-unit organizational

changes and risk of ischemic heart disease: a prospective study of public healthcare

employees in Denmark. BMJ Open

Contents

Resumé på dansk ................................................................................................................... 11

Summary in English .............................................................................................................. 13

Introduction............................................................................................................................ 16

Background ............................................................................................................................ 17

Work, stress, and health ................................................................................................... 17

Organizational change at work ....................................................................................... 19

Differential effects ......................................................................................................... 25

Underlying psychosocial mechanisms ....................................................................... 27

Objectives and aims .............................................................................................................. 29

Methods and Materials ......................................................................................................... 30

Population and data structure ......................................................................................... 30

Inclusion criteria ............................................................................................................ 33

Measures ............................................................................................................................. 35

Organizational changes in the work unit .................................................................. 35

Employee exit from the work unit and sickness absence ........................................ 36

Non-disability early retirement ................................................................................... 37

Prescriptions for psychotropic medication ................................................................ 37

Ischemic heart disease .................................................................................................. 37

Psychosocial work environment ................................................................................. 38

Potential covariates ....................................................................................................... 39

Study designs and timing ................................................................................................ 40

Main statistical analyses ................................................................................................... 41

Main results ............................................................................................................................ 43

Employee turnover, sickness absence, and work-unit social capital ......................... 44

Prescriptions for psychotropic medication and ischemic heart disease .................... 46

Discussions ............................................................................................................................. 49

Key findings ....................................................................................................................... 49

Previous findings and explanations ............................................................................... 50

Employee exit from the work unit and non-disability early retirement ............... 50

Sickness absence ............................................................................................................ 51

Prescriptions for psychotropic medication ................................................................ 52

Ischemic heart disease .................................................................................................. 53

Possible psychosocial mechanisms ................................................................................. 54

Confounding and reverse causation ............................................................................... 56

Methodological considerations ....................................................................................... 57

Representativeness and generalizability ........................................................................ 59

Conclusions ............................................................................................................................ 61

Perspectives ............................................................................................................................ 63

Future research .................................................................................................................. 64

References ............................................................................................................................... 66

Papers I-VI .............................................................................................................................. 75

Abbreviations

ATC Anatomical Therapeutic Chemical classification system

CI Confidence Interval

COPSOQ-II Copenhagen Psychosocial Questionnaire, 2nd version

DREAM Den Registerbaserede Evaluering Af Marginalsamfundet

EFW Exit From the Work unit

HR Hazard Ratio

ICD-10 International Classification of Diseases, 10th revision

IHD Ischemic Heart Disease

MSA Measure of Sampling Adequacy

OR Odds Ratio

RR Rate Ratio

SA Sickness Absence

SD Standard Deviation

WHALE Well-being in Hospital Employees

WHO World Health Organization

WSC Work-unit Social Capital

ZIP Zero-Inflated Poisson

11

Resumé på dansk

Baggrund: Organisatoriske ændringer på arbejdspladsen er almindelige og bliver ofte

iværksat for at imødekomme krav om øget produktivitet og bedre service. Imidlertid lader der

til at være en pris at betale for de berørte ansatte. En stigende mængde forskning konkluderer,

at organisatoriske ændringer har en negativ indflydelse på det psykosociale arbejdsmiljø, og

studier indikerer højere personaleomsætning og øget risiko for dårligt medarbejderhelbred

efter sådanne ændringer. Tidligere forskning i medarbejderkonsekvenser af organisatoriske

ændringer har hovedsagelig fokuseret på større omstruktureringer i virksomheder eller

personalenedskæringer. Denne afhandling evaluerer indflydelsen af specifikke former for

organisatoriske ændringer i arbejdsenheden for efterfølgende personaleomsætning (dvs.

medarbejder-exit fra arbejdsenheden og efterløn) og medarbejderhelbred (dvs. sygefravær,

udskrivelse af psykofarmaka og incident iskæmisk hjertesygdom). Den medierende

(forklarende) betydning af specifikke psykosociale faktorer blev vurderet for associationer

med medarbejder-exit fra arbejdsenheden og iskæmisk hjertesygdom. Potentielle køns- og

tidmæssige forskelle blev undersøgt i relation til udskrivelse af psykofarmaka som udfald.

Metoder og materialer: To arbejdsmiljøundersøgelser blev udført i perioderne fra 12. januar

til 9. februar 2011 (N=35.560; 81% svarede) og gennem hele marts 2014 (N=37.720; 84%

svarede) blandt alle ansatte i Region Hovedstaden. Et selv-rapporteret item målte oplevet

stress. Mål for social kapital, ledelseskvalitet og organisatorisk retfærdighed aggregeret på

arbejdsenheds-niveau var baseret på 16 selv-rapporterede items. I 2013 og 2016 gav lederne

information om hændelse af specifikke former for organisatoriske ændringer i deres

arbejdsenhed mellem januar 2009 og marts 2011 (69% svarede) samt for hvert semester i

2013 (59% svarede): sammenlægninger, opsplitninger, flytning, lederskifte (kun for perioden

2009-2011), afskedigelse af medarbejdere og selektive besparelser. Referencegrupperne

omfattede ansatte, der ikke var eksponeret for nogen organisationsændringer. Data på

medarbejder-exit fra arbejdsenheden, total og langtidssygefravær (≥29 dage), udskrivelse af

psykofarmaka (anxiolytica [ATC-kode: N05B], hypnotica/sedativa [N05C], antidepressiva

[N06A]), og iskæmisk hjertesygdom (ICD-10: I20-I25) i 2014 samt overgang til efterløn

mellem 2011-2012 blev udtrukket via opkobling til regionale løn- og nationale

forskningsregistre. Logistisk-, zero-inflated Poisson- og overlevelses-regressionsanalyser

analyserede sammen med multilevel teknikker relationer mellem organisationsændringer i

2013 og personaleomsætning/medarbejderhelbred i 2014 (Paper II-III og V-VI) samt mellem

organisationsændringer i 2009-2011 og efterløn i 2011-2012 (Paper IV).

12

Resultater: Denne afhandling anvendte data fra WHALE-kohorten (Well-being in Hospital

Employees) og en kohorteprofil blev publiceret til referenceformål (Paper I). Nogle

indikatorer på organisatoriske ændringer, men ikke alle, var forbundet med 10-50% højere

rater for medarbejder-exit fra arbejdsenheden og overgang til efterløn relativt til ingen

ændringer (Papers II-IV). Organisatoriske ændringer var konsistent forbundet med 90-270%

højere relative risiko for lav social kapital i arbejdsenheden. Der var en omvendt dosis-

responssammenhæng mellem lavere social kapital i arbejdsenheden og højere medarbejder-

exit fra arbejdsenheden. Associationen mellem organisatoriske ændringer og medarbejder-exit

fra arbejdsenheden blev ikke reduceret nævneværdigt ved justering for social kapital i

regressionsmodellen (Paper III). Ganske vist blev associationen med efterløn i nogen grad

reduceret ved samtidig justering for arbejdsenhedens sociale kapital, ledelseskvalitet og

organisatorisk retfærdighed (Paper IV). I forhold til ingen ændringer var eksponering for

organisationsændringer associeret med op til 40% højere risiko for sygefravær eller

udskrivelse af psykofarmaka i det følgende år blandt ansatte uanset køn. Sammenhængene

med psykofarmaka var stærkest for ledelsesskift og for udskrivelser i sidste semester af den

12 måneder lange opfølgningsperiode (Papers II og V). Eksponering for flytning, lederskifte

og afskedigelse i arbejdsenheden var forbundet med 120-190% højere risiko for incident

iskæmisk hjertesygdom blandt ansatte i det følgende år sammenlignet med ingen ændringer.

Justering for oplevet stress mindskede ikke disse risikoestimater nævneværdigt (Paper VI).

Konklusioner: Organisatoriske ændringer i arbejdsenheden var longitudinelt associeret med

højere rater for efterfølgende personaleomsætning og højere risici for dårligt helbred blandt

ansatte i forhold til ingen ændringer. Der var ingen overbevisende indikationer på at

specifikke former for organisatoriske ændringer var særligt associeret med samtlige af de

undersøgte medarbejderudfald, om end ændringer, der involverede afskedigelse af ansatte, var

mere konsistent associeret med højere relativ risiko for dårligt medarbejderhelbred.

Arbejdsenhedens sociale kapital forklarede ikke de inkonsistente sammenhænge mellem

organisatoriske ændringer og medarbejder-exit fra arbejdsenheden trods separate

associationer mellem disse faktorer på den indirekte/medierende pathway. Ganske vist tydede

noget evidens på, at associationen mellem organisatoriske ændringer og efterløn blev delvist

forklaret ved arbejdsenhedens sociale kapital, ledelseskvalitet og organisatorisk retfærdighed.

Bias og confounding blev ikke betragtet som sandsynlige forklaringer på nærværende fund.

Politikere og beslutningstager bør øge prioriteringen af strategier til at forebygge

skadevirkninger på ansatte af organisatoriske ændringer, idet sådanne negative virkninger

ikke blot kan være en byrde for den enkelte, men også for samfundet.

13

Summary in English

Background: Organizational change at work is common. Such changes are often

implemented to meet demands for increased productivity and improved service; however,

there seem to be a price to pay among the affected employees. An increasing body of research

conclude that organizational changes have a negative impact on the psychosocial work

environment, and studies indicate higher rates employee turnover and high risk of adverse

health following such changes. Previous research on employee outcomes of organizational

changes has mainly focused on major company restructuring or staff downsizings.

This thesis evaluated the impact of specific types of organizational changes in the work unit

and subsequent employee turnover (i.e., employee exit from the work unit and non-disability

early retirement) and health (i.e., sickness absence, prescription for psychotropic medication,

and incident ischemic heart disease). The mediating (explaining) roles of specific

psychosocial factors were assessed for associations with employee exit from the work unit

and ischemic heart disease. Potential sex and temporal differences were examined in relation

to prescriptions for psychotropic medication as outcome.

Methods and Materials: Two work-environment surveys were conducted in the periods

from 12 January to 9 February 2011 (N=35,560; 81% response) and throughout March 2014

(N=37,720; 84% response) among all employees in the Capital Region of Denmark. One self-

reported item assessed perceived stress. Measures of social capital, quality of management,

and organizational justice aggregated at the work-unit level were based on 16 self-reported

items. In 2013 and 2016, the managers provided information on specific types of

organizational changes occurring in their work unit between January 2009 through March

2011 (69% response) and in each semester of 2013 (59% response): mergers, demergers/split-

ups, relocation, change in management (only in the period 2009-2011), employee layoff, and

selective budget cuts. The reference groups comprised employees not exposed to any

organizational changes. Data on employee exit from the work unit, total and long-term (≥29

days) sickness absence, prescriptions for psychotropic medication (anxiolytics [ATC code:

N05B], hypnotics/sedatives [N05C] or antidepressants [N06A]), and ischemic heart disease

(ICD-10: I20-I25) in 2014 as well as information on transition to non-disability early

retirement between 2011-2012 were extracted via linkage to national research and regional

salary registers. Logistic, zero-inflated Poisson, and hazard/survival regression models as well

as multilevel techniques analyzed associations between organization changes in 2013 and

employee turnover and health in 2014 (Papers II-III and V-VI), and between organizational

14

changes in 2009-2011 and non-disability early retirement in 2011-2012 (Paper IV) relative to

no changes.

Results: This thesis used data from the WHALE cohort (Well-being in Hospital Employees),

and a cohort profile was published for reference purposes (Paper I). Some indicators of

organizational change, but not all, were associations with 10-50% higher rates of employee

turnover relative to no changes (Papers II-IV). Organizational changes were consistently

associated with 90-270% higher relative risk of low work-unit social capital. There was an

inverse dose-response relationship between lower work-unit social capital and higher

employee exit from the work unit. Associations between organizational changes and

employee exit from the work unit did not diminish notably when adjusting for work-unit

social capital in the regression models (Paper III). Indeed, associations with non-disability did

somewhat reduce when adjusting for work-unit social capital, quality of management, and

organizational justice simultaneously (Paper IV).

Relative to no change, organizational changes were associated with up to 40% higher risk of

sickness absence or prescriptions for psychotropic medication in the following year among

employees regardless of sex. Associations with psychotropic prescriptions were strongest for

change in management and for prescriptions in the latter semester of the 12-months follow-up

period (Papers II and V). Finally, exposure to relocation, change in management, or employee

layoff in the work unit was associated with 120-190% higher risk of incident ischemic heart

disease among employees relative to no changes. Adjusting these associations for potential

mediation via perceived stress did not reduce the point estimates notably (Paper VI).

Conclusions: Organizational changes in the work unit were longitudinally associated with

higher rates of subsequent employee turnover and higher risks of adverse among employees

relative to no changes. There were no convincing indications that specific types of

organizational changes were particularly related to all studied employee outcomes, although

changes involving employee layoffs were more consistently associated with higher relative

risk of adverse employee health. Work-unit social capital did not explain the inconsistent

associations between organizational changes and employee exit from the work unit despite

discrete associations between these three factors on the indirect pathway. Indeed, some

evidence suggested that the association between organizational changes and non-disability

early retirement was partially explained by work-unit social capital, quality of management,

and organizational justice. Bias and confounding were not regarded as likely explanations of

the current findings. Policy and decision makers should increase prioritization of strategies to

15

prevent detrimental employee effects of organizational changes as such effects may not only a

burden to the individual, but also to society.

16

Introduction

Organizational change in workplaces is common. Initiatives to changes at work often arise

from challenges faced by the organization. Such challenges may include adaption to shifting

financial or political climates as well as maximization of cost-efficiency to meet demands for

higher productivity and improved service. Managing these challenges is not only an

existential premise for private-sector companies, but also for companies in the public sector,

including healthcare and social enterprises.1–4 During the last two decades, all public hospitals

in Denmark have been imposed by shifting governments to increase treatment rates by 1.5-

2.0% per year without parallel adjustments of budgets.5 There are, however, no indications

that the forces inducing changes at work are diminishing.6

Organizational changes have been referred to as a “[…] difference in form, quality or state

over time in an organizational entity”,7, p. 512 which can take many forms (e.g., mergers, staff

downsizing) at different levels in the workplace. Obviously, organizational change does not

solely affect the organizational structure, but also the working conditions of the employees. It

has been estimated that about half of Danish employees experienced a reorganization that

“substantially affected their work” during a three-year period.8,9 It is thus reasonable to

consider organizational change as a characteristic of modern work life.

Modern work life seems also to be characterized by high levels of occupational stress and job

insecurity.9 There is increasing consensus that work-related stress contributes to various

physical and mental health problems,10 including cardiovascular diseases11,12 and common

mental disorders.13 Worldwide, cardiovascular diseases is the leading cause of death

worldwide,14 while depression and anxiety disorders are among the leading causes of

disability.15 A recent systematic review estimated that work-related stress costs societies up to

USD $187 billion globally, where productivity-related losses account for 70-90% of these

costs.16 High rates of sickness absence persist as a workplace problem in many countries,17,18

including the healthcare sector of Denmark.19 Poor employee health and well-being may

contribute to involuntary exit from paid employment.20,21 In a hospital context, high rates of

employees turnover (i.e., employees leaving the workplace) have been associated with

negative effects on the remaining employees, patients, and the healthcare organizations in

terms of excessive replacement costs.22–24 Meanwhile, the old-age dependency ratio is

increasing in many countries and there is a need to retain capable employees occupationally

active on the labor market to avoid a potential pension crisis.25 Evidently, work-related stress

and excess employee turnover persist as major societal concerns.

17

Background

Work, stress, and health

There are several theories on determinants for psychological stress. The job demand-control

model developed by Robert Karasek in the 1970s has been widely applied in occupational

health research. According to this model, it is particularly detrimental for employees to

experience the combination of high levels of job demands (e.g., excessive workload, time

pressure) and low levels of job control (e.g., poor influence on job tasks, possibilities for

learning new skills). It was later emphasized that high social support may mitigate detrimental

effects of job strain (i.e., high demand, low control).26,27

In the 2000s, another theory emerged and highlighted organizational (in)justice perceived by

the employees as a risk factor for psychological stress. According to this conception, adverse

health arises if employees experience unfairness regarding distribution of resources

(distributive justice), procedures and processes (procedural justice) or distribution of

information and respect from managerial authority (interactional justice).28–30

Moreover, the concept of social capital highlights the importance of positive social

relationships for well-being in a community context. Social capital is defined as the

“resources that are accessed by individuals as a result of their membership of a network or a

group”31, p. 291 and denotes qualities of social cohesion, mutual trust, and reciprocity among

employees (horizontal) and managers (vertical). There has been some disagreement about the

appropriate level of analyzing social capital (individual or workplace); however, studying

social capital as a feature of working groups (workplace) is concurrent with the notion of this

psychosocial factor as a collective ressource.31,32

There are many other theories on job stress focusing on e.g. imbalance in relationships

between perceived effort and reward,33 demands and resources34 etc. Currently, there is

neither a golden standard of measuring stress nor consensus about a general comprehensive

stress theory comprising the main stressor at work. There seems to be some overlap in

contents of current theories on job stress, but no such theory appears explicitly to include the

roles of job insecurity or uncertainty at work.

High job insecurity has been linked to detrimental health and turnover intentions among

employees in both meta-analyses and reviews.35,36 Other meta-analyses have shown that high

levels of job strain and perceived stress were consistently associated with a 1.1-1.6-fold

18

higher relative risk of incident ischemic heart disease and stroke in the general population,37–

39 and researchers have argued in favor of a causal impact of stress on cardiovascular

diseases.40 Excess levels of stress may contribute to an advanced burden of atherosclerotic

plaque in coronary arteries, and high blood pressure may lead to plaque disruption, which can

block or reduce blood flow to the cardiac muscle.38 A meta-analysis demonstrated that high

work stress was associated with a 1.7 times higher relative risk recurrent events of ischemic

heart disease.41

Reviews and studies have also found excess psychological stress at work to predict

depression,42,43 anxiety,44,45 and adverse sleep patterns.46,47 In addition, low organizational

justice has been associated with excess turnover intentions30 and mental health problems

independent of job strain, social support, and effort-reward imbalance.29 Previous research has

found low workplace social capital in a hospital setting to predict lower quality of patient

care,48 work engagement,49 excess emotional exhaustion,50 hypertension (among males

only),51 and long-term sickness absence (SA) among employees.52,53 Not surprisingly, a poor

psychosocial work environment has been associated with excess turnover rates and intention

to quit among employees.22,54 Still employees may also leave the workplace or the labor

market for reasons than ill health.20,55

Most research on work, stress, and health are based on self-reported items;42 however, this

methodology may pose various potential problems. One such problem may arise from using

the same method (e.g., self-report) to gather data on exposure (e.g., job strain) and outcome

(e.g., health status), which is often referred to as common-method bias. Data from same-

method sources (e.g., surveys) are likely to share variance from common factors (e.g., social

desirability, negative/positive affectivity, context). Likewise, the observed associations are

susceptible to be inflated or deflated depending on the correlation of the common factor(s),

which may lead to both Type-I (false positive finding) and Type-II errors (false negative

finding).56–58 It has been claimed that common-method variance, in average, accounts for 41%

of the total variance in attitude measures compared to 11% when no common-method

variance is present. That suggests that common-method bias may play a considerable role in

stress research.56 Common-method bias may indeed have minor impacts in self-reports on

occurrence of factual events, such as organizational changes.58 Indeed, self-reports on

organizational change require that the respondent can be contacted following the changes.

Finally, self-report may potentially introduce response bias, which refers to the notion that

19

people interpret and responds to the same questions differently although having equally

adverse health or being equally stressed.59

Previous research have highlighted the importance of using objective measures of job

stress,42,44 and there seems to be an increasing attention towards employee effects of

organizational changes as a putative stressor at the workplace.60,61 Studying employee effects

of organizational change within psychosocial epidemiology may be empirically superior to

subjective measures of psychological states since a larger group of employees are typically

exposed to the same factual change event simultaneously.

Organizational change at work

Organizational changes at work may potentially have positive as well as negative implications

for employees. Positive consequences could include job enlargement, increased influence on

work procedures, and improvement of poor working conditions.62 On the other hand, negative

consequences may include higher workload intensification, reduced job control, and higher

job insecurity e.g. about future job situation, colleagues or prospects of the workplace.36,63,64

For example, hospital mergers or downsizing (i.e., staff reduction) may reasonably induce

anxiety among employees about being redundant on the workplace following the changes.

Further, goals for productivity goals may not be adjusted to (temporary) changes in staff or

work flows.

Although restructuring of workplaces is a widely performed strategy to align company

operations to changing environments, it has been suggested that hospital mergers have limited

long-term impacts regarding productivity, waiting times, and quality of healthcare.1 A recent

systematic review of 39 longitudinal studies concluded that events of reorganization at work

mainly had immediate negative effects on the psychosocial work environment, such as lower

job satisfaction and trust as well as higher job strain and job insecurity among employees.61

Employees may not understand managerial decisions for changing the workplace, which

could give rise to employee cynicism, perceptions of organizational injustice, lower

organizational commitment, and turnover intentions among the employees.22,65–67

Employees leave workplaces for voluntary and involuntary reasons. Voluntary exit could be

motivated by a poor psychosocial work environment, economic reasons, possibilities for early

retirement, and wish to spend more time with significant others. Involuntary exit routes may

include dismissal or poor physical or mental health.20,21,55 A Danish study found that non-

20

disability early retirement (“efterløn”) was longitudinally associated with 10 of 16

psychosocial factors, including low levels of organizational justice, predictability, quality of

leadership, and trust in management.68

A review of cross-sectional and longitudinal evidence from 162 studies on workplace

rationalization strategies suggested that downsizing and organizational changes

predominantly had negative effects on self-reported health status and well-being (71 negative

and 13 positive studies) among employees, particularly within the healthcare sector (36

negative and 2 positive studies).60 Another review exclusively on longitudinal quantitative

evidence found that 11 of 17 studies linked organizational change to higher relative risk of

subsequent mental-health problems. The authors concluded that more research using a

longitudinal study design is needed to establish this association given the limited number of

studies under review.69

Table 1 shows an overview of published cross-sectional and longitudinal studies on exposure

to organizational change at work presented by outcomes of employee turnover and measures

of health among employees. Studies on associations with psychosocial work environment as

the only outcome were omitted. The overview is not restricted to the field of public health and

epidemiology, but include research studies from other academic disciplines, such as economy,

psychology, and sociology.

21

Table 1. Overview of published studies on associations between organizational changes at work presented by outcomes of employee turnover and

health. Each study only appears once.

First author, year Country Sample frame Participation (follow-up) Type of change Level of change Statistical analysis Outcome (p<0.05)

Employee turnover

Baron, 200170 USA High-technology

start-up firms

(n=101)

59%, (≈5 years) Changes in employment

models or blueprints

Company Multivariate regression,

generalized estimating

equations (GEE)

Turnover ↑

Bauer, 200471 Germany Industrial blue- and

white-collar workers

(n=1,378)

53%, (1 year) Reduction of hierarchy levels,

transfer of responsibilities, and

self-managed teams

investment in IT

Company Tobit models Turnover ↑ (strongest for blue-collar

workers)

Cameron, 198772 USA Higher-education

employees (n=3,406)

55%, (cross-sectional) Organizational decline and

turbulence

Colleges/universities Multivariate analysis of

variance (MANOVA)

Turnover intentions ↑

Ingelsrud, 201773 Norway Hospital employees

(n=54,787)

Register based, (4 years) Mergers Hospital Logistic regression,

average marginal effects

(AME)

Turnover: within hospital sector ↑,

to other sectors ↔ out of

employment ↔

Probst, 200374 USA Public service

employees (n=313)

63%, (6 months) Mergers Company Analysis of variance

(ANOVA)

Turnover intentions ↑, Self-reported

health: longitudinal ↓, cross-

sectional ↔

Sylling, 201475 USA Primary care

providers (n=11,180)

81%, (≈8 years) Changes in workflows Occupational groups Logistic regression,

average marginal effects

(AME)

Turnover ↑

Vahtera, 200576 Finland Municipal employees

(n=19,273)

Register based, (5 years) Downsizing (minor and major) Occupational

group/workplace

Cox proportional hazard

models

Disability retirement ↑

Wahlstedt, 199762 Sweden Postal workers

(n=100)

52%, (1 year) Restructuring Postal sorting

terminal

Multiple regression

models

Turnover ↓. Total sickness absence ↓

de Wind, 201477 Netherlands Senior employees

aged 59-63

(n=2,317)

81%, (1 year) Restructuring (with/without

compulsory redundancies)

Self-reported Logistic regression Non-disability early retirement ↔.

Sickness absence and health status

Bernstrøm, 201578 Norway Health professionals

(n=34,712)

Register based, (2 years) Structural changes and patient

care-related changes

Hospital-aggregated Multilevel logistic

regression

Frequent structural changes:

Sickness absence (≥17 days) ↑.

Frequent patient care-related

changes: Sickness absence (≥17

days) ↔

22


Dragano, 200579 Germany Population (men, n=

12,240; women,

(n=10,319)

61%, (cross-sectional) Employee’s work situation

influenced by downsizing

Self-reported Multivariate logistic

regression

Synergistic interaction between

downsizing and work-related stress:

self-reported health ↓ (comparable

for men/women)

Ingelsrud, 201480 Norway Hospital employees

(n=68,630/15,662)

Register based, (2 years) Merging units, splitting up

units, creating new units,

shutting down units,

reallocation of employees (no

differentiation)

Hospital-aggregated Random/fixed effects

Poisson regression

models

Sickness absence (≥17 days) ↑

Kivimäki, 200081 Finland Municipal employees

(n=764)

86%, (mean: 4.9 years) Downsizing (minor and major) Occupational

group/workplace

Multilevel Poisson

regression models

Sickness absence (≥4 days) ↑.

Mediation: physical demands, skill

discretion, possibilities for

participation, and job insecurity.


(n=550)

67%, (7 years) Downsizing Occupational

group/workplace

Logistic regression

models

Self-rated health ↓. Mediation: job

control, job insecurity, physical

demands


(n=886)

76%, (3 years) Downsizing (minor,

intermediate, and major)

Occupational

group/workplace

Multiple logistic

regression, analysis of

variance (ANOVA)

Health problems ↑ (Worse among

stayers relative to leavers)

Kjekshus, 201484 Norway Hospital employees

(n=107,209)

Register based, (5 years) Mergers Hospital Fixed-effects

multivariate regression

models


Kokkinen, 201385 Finland Hospital employees

(n=2,794)

Register based, (mean: 9.2

years)

Transfer from public to private

sector (no major staff

reduction)

Hospital work-units Cox proportional hazard

models

Sickness absence (≥91 days) ↔

Røed, 200786 Norway Nurses (n=43,167) Register based, (8 years) Downsizing and expansion

(minor, moderate and major)

Occupational group Multivariate mixed

hazards regression model

Sickness absence (≥17 days) ↑ (only

major downsizing)

Theorell, 200387 Sweden Population (n=4,903) 86%, (4 years) Expansion and downsizing Company Multiple logistic

regression

Sickness absence (≥15 days):

women ↓, men ↔

Vahtera, 199788 Finland Local-government

employees (n=981)

Register based, (5 years) Downsizing (minor and major)

- reductions in working hours

Occupational

group/workplace

Poisson regression

models


Westerlund, 200489 Sweden Population

(n=24,036)

34%, (6 years) Expansion, Downsizing,

Mergers, Outsourcing

Company Logistic regression Expansion: sickness absence (≥90

days) ↑, hospital admission ↑ (large

only, whereas moderate ↓).

Downsizing: sickness absence ↑

(moderate only).

Mergers/Outsourcing: sickness

absence, hospital admission ↔.

23


Østhus, 201090 Norway Hospital employees,

(n=1,651,387)

69%, (4 years) Downsizing (minor and major) Hospital Fixed-effects Poisson

regression models


Mental health

Blomqvist, 201891 Sweden Population

(n=2,305,795)

Register based, (9 years) Downsizing (minor and major) Occupational groups Logistic regression

models, generalized

estimating equations

(GEE)

Anxiolytics ↑, sedatives ↑

(especially before event among

stayers and those who become

unemployed) . Sex differences ↔

Brenner, 201492 France,

Hungary,

Sweden, and

UK

Workers excluding

farmers, self-

employed, workers

in micro-businesses,

new-jobbers,

(n=758)

Sweden: 90%; UK: 82%;

France: 62%; Hungary:

19%, (cross-sectional)

Downsizing (medium- and

large-scale)

Self-reported

(telephone interview)

Multiple logistic

regression

Self-reported depressive symptoms ↑

(lay-off survivors, lay-off to

unemployment vs lay-off to

redeployment)

Dahl, 201193 Denmark Population

(n=92,869)

Register based, (6 years) Organizational changes

targeting six different

dimensions

Company Multivariate analysis

with logit models

Psychotropic medication ↑ (multiple

simultaneous changes and changes

targeting cooperation/coordination)

Falkenberg, 201394 UK Civil servants

(n=6,710)

51%, (8 years) Major organizational change Self-reported Logistic regression Self-reported psychiatric symptoms:

Short-term follow-up, anticipated ↑

happened ↑. Long-term follow-up,

anticipated ↑ happened ↔.

Fløvik, 201895 Norway Public/private sector

employees (e.g.,

municipality,

healthcare, finance,

insurance, education,

non-profit),

(n=7,985)

Baseline: 52%, 2nd wave

retention: 66% (2 years)

Separate, co-occurring and

repeated organizational

changes: reorganization,

downsizing, layoffs, partial

disclosure, partial outsourcing

or change of

ownership/merger/acquisition

Self-reported Multilevel logistic

regression

Self-reported mental distress as

outcome.

Separate changes: reorganization ↑,

downsizing ↑, layoffs ↑ (individual

level). Reorganization ↑ (work-unit

level), but ↔ when adjusting for job

strain and support. Co-occurring

changes: ↑ (individual level).

Repeated changes: ↑ (individual

level).

Greubel, 201196 Sweden Police-force

employees (n=1,523)

76%, (cross-sectional) Relocation, extensive

downsizing, extensive job

changes

Self-reported Analysis of variance

(ANOVA)

Self-reported: Depression ↑ Anxiety

↑ Disturbed sleep ↑

Hanson, 201697 Sweden Population,

(n=1,654,259)

Register based, (4 years) Major downsizing Occupational group Logistic regression

models, generalized

estimating equations

Antidepressants ↑. Sex differences

↔.

24


(GEE)


(n=26,682)

Register based, (7 years) Downsizing (minor and major) Occupational

group/workplace

Negative binominal

regression

Psychotropic medication: women ↑,

men ↑

Loretto, 201099 UK Hospital employees

(n=5,385)


retention: 84% (1 year),

3rd wave retention: 77% (2

years)

Organizational changes

(related to

training/development, work

content, peer contact, patient

contact)

Self-reported Logistic regression Amount of work: Self-reported

mental health ↓. Increased training,

promotion, job security: self-

reported mental health ↑

Moore, 2006100 USA Manufacturing

company employees

(n=460)


retention: 63%, 3rd wave

retention: 74% (6 years)

Direct and indirect layoff

contacts (0, 1 or 2)

Self-reported Multivariate analysis of

covariance

(MANCOVA)

Job insecurity ↑, self-reported

depressive symptoms ↑ (some

direct-contact groups), turnover

intention ↑

Netterstrøm,

2010101

Denmark Civil servants

(n=685)

44%, (2 years) Mergers Municipalities/

counties

Logistic regression

models

Self-reported depressive symptoms

↔

Väänänen, 2011102 Finland Industrial employees

(n=6,511/4,096)

82%, (≈5 years) Negative merger appraisals Self-reported Cox proportional hazard

models

Psychiatric events ↑

Cardiovascular outcomes

Drivas, 2013103 Greece Male ex-employees

in public bus

company (n=4,400)

Register based, (13

months)

Company closure Company - Death due to ischemic heart disease

↑

Ferrie, 1998104 UK Civil servants

(n=7,419)

72%, (≈8 years) Transfer from public to private

sector (actual and anticipated

change)

Self-reported Logistic regression Men: Self-rated health ↓, adverse

sleep patterns ↑, blood pressure ↑

(actual change only), ischemia ↔.

Women: Self-rated health ↔,

adverse sleep patterns ↔, blood

pressure ↔, ischemia ↑ (anticipated

change only).

Möller, 2005105 Sweden Population (cases, n=

1,381; referents,

n=1,697)

84% (cases) and 73%

(referents), (≈2 years)

Appraisal of “change of

workplace”

Self-reported Logistic regression “Affected me in a very or fairly

negative way”: Incident non-fatal

myocardial infarction ↔

Pollard, 2001106 UK Local-government

employees (n=184)

65%, (2 years) Workplace reorganization County and district

councils

Multiple regression

models

Blood pressure ↑ Mental distress ↑

Vahtera, 2004107 Finland Municipal employees

(n=22,430)

Register based, (7.5 years) Downsizing (minor and major) Occupational

group/workplace

Cox proportional hazard

models, analysis of

variance (ANOVA)

Major downsizing: cardiovascular

mortality ↑, sickness absence ↑ (only

permanent employees)

25

In line with the direction of findings from systematic reviews,36,60,61,64,69 the studies outlined

in Table 1 suggest that organizational change is predominantly associated with higher rates of

employee turnover (or intentions hereof), including non-disability and disability retirement,70–

76,100 as well as higher risk of SA and poor health status,74,78–84,86,88–90,96,104,107 mental health

problems,91–100,102,106 and cardiovascular outcomes.87,103,106,107 Only few studies found

associations good health status and low turnover rates according to organizational

changes.62,87,99

Previous studies on the association between organizational changes and SA have

predominantly examined employees remaining on the workplace without evaluating the

potential accompanying rates of excess employees turnover.55 Indeed, SA may not solely

reflect ill employee health.108–111 Many previous studies of organizational change and

employee health were based on self-reports and there is currently insufficient evidence to

conclude on longitudinal associations with clinical measures of adverse physical and mental

health among employees.

Differential effects

The literature on employee effects of organizational change has mainly focused on

downsizing or major restructuring (e.g., company mergers) without differentiating between

specific types of changes involved. When two hospitals merge, it is likely that at least some

employees would be relocated or have a new manager. Differentiating between specific types

organizational changes may likely require that exposure to organizational changes is assessed

at a low level in the hierarchical structure of the workplace to meaningfully separate the

changes at the employee level. Organizational changes specified at a low level in the

organization may reasonably increase employee exposure classification of true positive

(actually experiencing the changes) and true negative (not experiencing the changes) rates.

Again, when two hospitals merge (and the exposure variable is aggregated at the hospital

level) some employees (e.g., cleaning staff) may not be personally affected by the changes.

Indeed, relatively few studies have examined objectively measured changes at the lower

work-unit or department level.62,85,101,106

One such study95 assessing various types of organizational changes at the work-unit level

found higher relative risk of clinically relevant mental distress according to work-unit

reorganization (OR 1.70, 95% CI 1.26-2.30) and partial outsourcing (OR 1.90, 95% CI 1.04-

3.44), but not change of ownership/merger/acquisition (OR 0.84, 95% CI 0.40-1.77).

26

Although these associations were based on self-report data, the findings indicate that

organizational change at work is a heterogenous risk factor as difference change types seemed

to have different adverse impacts on the employees.

The magnitude and temporal aspects of organization change may also be important to

consider in evaluating impacts on employee turnover and health.95 A study on changes

targeting specific dimensions and subsequent prescriptions for psychotropic medications (e.g.,

benzodiazepines) concluded that excess prescription rates were particularly observed in the

immediate years after the changes and among employees experiencing broader changes

occurring simultaneously.93 Other studies found that major, but not minor, downsizing was

associated with higher relative risk of long-term SA among nurses in Sweden86 and higher

cardiovascular mortality among permanent municipal employees in Finland.107

Specifically, in the Finnish 10-town study107 there was a doubled mortality from

cardiovascular diseases during the 7.5-year follow-up period after major downsizing relative

to no downsizing. Splitting this follow-up into two halves yielded a 5 times higher

cardiovascular mortality in the former half follow-up period, suggesting that cardiovascular

outcomes may be observed soon after the changes. Supporting this, Pollard et al.106 found that

excess levels of blood pressure peaked among employees just before initiation of substantial

workplace reorganizations, which was especially observed among those with most future job

uncertainty.

Different health trends have also been observed for employees who leave the workplace and

those who remain at work after downsizing. Specifically, better health was found among

redeployed employees relative to those remaining at the workplace and employees laid off to

unemployment.83,92 Indeed, studies among the working population in Sweden found higher

relative risk of prescription for psychotropic medication among employees without history of

substantial SA or disability pension according to major downsizing. Likewise, this association

was particularly observed among employees leaving the workplace to unemployment and

among those remaining at the workplace after the downsizing event.91,97 Mental health

problems requiring medical treatment may indeed develop over an extended period in contrast

to the observation of cardiovascular events.

Some evidence indicate that negative employee effects of organizational change vary by

sex,87,98,112 although this is not consistently demonstrated in the literature.79,81,91 Potential sex

27

differences could be due to heterogeneity in social support and psychological demands,13 but

more research is needed need to address potential sex differences in the negative effects of

organizational changes.

In statistical analysis, single-level regression model assumes that observations (e.g.,

employees) are independently distributed;113 however, this assumption on observation

independence may not necessarily hold in studies on organizational change and employee

outcomes. Workplaces could be considered a setting where attitudes and norms are “socially

contagious” and, thus, employees within workplaces may be more similar than employees

between workplaces.31 Supporting this, a study demonstrated that depressive symptoms were

strongly correlated among members of social groups with associations extending up to three

degrees of friendships (i.e., one’s friends’ friends’ friends).114 Relatively few studies used

multilevel techniques (e.g., multilevel modeling, marginal models) to account for potential

clustering of employee outcomes within the hierarchical workplace structure, which may

increase risk of Type-I error115,116 as previously demonstrated.95

Underlying psychosocial mechanisms

Several factors in the psychosocial work environment have been highlighted as potential

mediators (explanations) of adverse employee effects by organizational changes.36,63,117

Kivimäki et al.81 found that about half of the 2.2-fold higher relative risk of SA was due to

increased job insecurity and physical demands as well as lowered job control after major

downsizing. Impaired social support from spouse or changes in smoking habits did, however,

not seem to mediate the adverse effects.81 Yet in order to gain a better understanding of the

underlying mechanisms of the negative effects of organizational changes, specification of the

change types seems imperative since psychosocial factors may relate differently to different

change content.3,118 For example, it is reasonable to assume that job insecurity may be

stronger related to staff reductions (e.g., due to fear of new downsizing waves) than relocation

or split-ups, whereas workplace mergers may have some lead to pronounced changes in the

social community at work (e.g., due to many new colleagues). Indeed, it is likely that several

psychosocial pathways may be involved in mediating the negative effects of organizational

changes.

Also, organizational changes may induce social disputes among colleagues and management

at work. Downsizing has been associated with subsequent distrust and lack of collaboration

28

between nurses and medical doctors.119 Exposure to downsizing, relocation or

demergers/split-ups of departments may disrupt social friendship ties among employees,

which may induce perceptions of organizational injustice120,121 that could eventually result in

higher voluntary employee turnover from the workplace.120,122–124 Individual-level social

capital has been found to mediate associations between negative work characteristics and

mental distress among Japanese workers.125 Although the literature regarding the relation

between organizational change and workplace social capital is sparse, social capital may

potentially play an important role in mediating adverse effects of organizational changes

together with quality of management and organizational justice. Such mediating properties

may warrant these psychosocial factors as targets for interventions to reduce detrimental

employee outcomes according to organizational changes. This remains, however, to be

investigated.

In sum, organizational change at work seem generally to have immediate negative impacts on

employee turnover, stress-related health, and the psychosocial work environment. These

negative impacts appear somewhat to vary by sex, types of organizational change, and

number of simultaneous changes, although many previous studies have not accounted for

potential multilevel clustering of employee outcomes. There is currently insufficient evidence

on the dual impact of organizational changes on employee turnover and SA, the impact on

non-disability early retirement, and the impact on clinical outcomes of mental health and

cardiovascular disease among employees. To gain a better understanding of how the

detrimental employee effects of organizational changes develop, there is a need to study the

longitudinal associations between specific types of organizational changes at the work-unit

level, psychosocial factors, and employee outcomes retrieved from independent data sources

using multilevel techniques in statistical analysis.

29

Objectives and aims

The overall objective was to evaluate the impacts of organizational changes on subsequent

employee turnover and health. The mediating properties of factors in the psychosocial work

environment were assessed regarding these impacts (Figure 1). A mediator refers to a factor

that, at least partially, explains the relation between two other factors.126 The following

unfolds the overall objectives into six specific aims.

Figure 1. The impacts of organizational changes on subsequent employee turnover or adverse

employee health mediated through factors in the psychosocial work environment.

The present thesis used data from the Well-being in Hospital Employees (WHALE) cohort

study. Aim 1 was to provide a detailed description of the WHALE cohort for reference

purposes (Paper I). Aims 2-6 assessed the impacts of co-occurring and specific types of work-

unit organizational changes (i.e., mergers, demergers/split-ups, relocation, change in

management, employee layoff, budget cuts) and:

2. Subsequent employee exit from the work unit (EFW) and sickness absence (Paper II)

3. The role of work-unit social capital (WSC) on the mediating pathway to subsequent

employee EFW (Paper III)

4. Non-disability early retirement among senior employees and the potential mediating

properties of organizational justice, quality of leadership, and WSC (Paper IV)

5. Temporal aspects of prescriptions for psychotropic medications among employees and

the potential modification of effects by sex (Paper V)

6. Incident ischemic heart disease (IHD) among employees and potential mediating

properties of perceived stress (Paper VI)

In general, organizational change was hypothesized to have negative impacts on employee

turnover and health mediated through the psychosocial factors. Co-occurring changes were

expected to have more adverse employee effects than single changes, but no hypotheses were

made regarding the relative adverse impacts of each specific type of organizational change.

30

Methods and Materials

Population and data structure

Paper I describes the data and rationale for establishing the observational, ongoing Well-being

in Hospital Employees (WHALE) cohort. Papers II-III and V-VI examine the longitudinal

associations between six types of work-unit organizational changes occurring in the last six

months of 2013 (Papers II-III) or throughout 2013 (Papers V-VI), psychosocial factors

assessed through March 2014, and subsequent employee exit from the work unit (EFW) and

health outcomes from baseline at 1 January 2014 to 31 December 2014. Paper IV examines

the longitudinal associations between four types of work-unit organizational changes

occurring between January 2009 and March 2011, psychosocial factors measured between

January-February 2011, and non-disability early retirement from baseline at 4 April 2011 to

31 December 2012.

The source population included all employees in the Capital Region of Denmark, who were

invited to participate in a work-environment survey (“TrivselOP”) conducted from 12 January

through 9 February 2011 (N=35,560; 81% response) and, again, throughout March 2014

(N=37,720; 84% response). The populations for these two surveys were established 5

November 2010 and 13 January 2014. The vast majority of the questionnaires were

distributed through working emails, while paper versions of the questionnaires were

administered to employees without a working email-address (e.g., cleaning staff). Up to three

reminders on completing the questionnaire were sent to employees in each wave. In the 2011-

survey, 46 items concerned the psychosocial work environment, whereas this number was

reduced to 40 in the 2014-survey (37 psychosocial items were overlapping in the two

surveys). The data from the surveys included items responses as well as cross-sectional

occupational background information and organizational affiliations (<1% missing data). Men

and medical doctors/dentists were somewhat underrepresented among respondents. The two

work-environment surveys were not conducted for research purposes to begin with.

In 2014, all 37,720 employees were nested within 2,686 work units (e.g., Research Unit),

which were nested within 440 departments (e.g., Department of Occupational and

Environmental Medicine) nested within 14 institutions (e.g., Bispebjerg & Frederiksberg

31

Hospitals) (Figure 2). The work-unit structure was validated by the work-unit managers prior

to each survey.

Figure 2. All 37,720 healthcare employees nested within the hierarchical organizational

structure of the Capital Region of Denmark by January 2014. In total, 1,105 employees were

not assigned to the department level.

Data on organizational changes occurring before to the work-environment surveys were

obtained by distributing a two-wave Internet survey via working email to the managers of all

work units. The first wave was conducted from October to November 2013 (69% response)

providing information on organizational changes occurring between 1 January 2009 and 31

March 2011 (entire period). The second wave was conducted from April to June 2016 (59%

response) providing information on occurrence of organizational changes in the semesters

between 1 January 2011 and 31 December 2013. In both waves, the managers responded to

occurrence of the following specific types of organizational changes in their work unit:

In the work unit you manage/managed, have there been the following organizational changes

in the period from 1 January 2009 to 31 March 2011 [or] 1 January 2011 to 31 December

2013 [each semester]?”:

32

• Merger with other work unit(s)

• Split up into other work units

• Change of management in work unit

• Physical relocation of the work unit

• Employee layoff(s)*

• Selective budget cuts*

* Items only provided in the second-wave survey regarding organizational changes occurring

between 2011-2013.

Monthly outcome information on employee EFW and sickness absence (SA) in 2014 (Papers

II-III) as well as monthly occupational/sociodemographic information were extracted from

regional salary registers at the employee level via personal and employee identification

numbers. Daily outcome information on non-disability early retirement between 2011-2012

(Paper IV) and prescriptions for psychotropic medication (Paper V) and incident events of

ischemic heart disease (IHD) in 2014 (Paper VI) were extracted from national registers at the

employee level via personal identification numbers. Figure 3 presents a temporal overview of

the applied data on organizational changes, psychosocial factors, and employee outcomes.

Figure 3. Graphical overview of applied data on work-unit organizational changes,

psychosocial work environment, employee turnover, and health outcomes.

33

Inclusion criteria

In this thesis, a work unit was defined as an organizational entity of at least three employees

referring to the same immediate manager. Monthly data on occupational information

extracted from regional salary registers allowed Papers II-III and V-VI to include employees

older than 18 of age who worked more than 18.5 hours per week in the same unit through one

year prior to baseline. Employees from a given work unit were considered eligible for study

participation if at least three of employees and more than 30% of the personnel remained in

the same work unit throughout 2013. For instance, if two work units, each comprising three

employees, merged, all six employees were eligible for study participation. These inclusion

criteria aimed to increase true positive and true negative classifications of exposure to

organizational changes and exclude employees with a short-term work-unit affiliation. The

monthly occupational data from the regional salary registers were, however, not available

during the study of Paper IV.

In Papers II-III and V-VI, the study population comprised at least 15,038 employees (58% of

the eligible population) nested within 1,284 work units across 13 institutions with complete

data on all relevant variables at baseline 1 January 2014. Among the employees in the study

population, 55% were exposed to any organizational changes throughout 2013. Employee

characteristics were comparable between the eligible population, study population, and among

employees exposed to organizational changes, but work units exposed to organizational

change seemed to include a slightly higher number of employees at baseline. Rates of

employee outcomes and WSC levels in 2014 were also comparable between the eligible and

the study population, although the employee-turnover rates were somewhat lower in the study

population (Table 2).

In Paper IV, 3,254 senior employees aged 58-64 at baseline 4 April 2011 were eligible for

non-disability early retirement for at least one week between from baseline to 31 December

2012. Of these senior employees, 642 (19.7%) transferred to the non-disability early

retirement scheme during follow-up between 2011 and 2012.

34

Table 2. Characteristics and outcomes through 2014 among employees nested within work

units nested within institutions presented for the eligible population, the study population, and

among employees exposed to any organizational changes.

Categorical variables Eligible population Study population

Exposed to any

changes

n % of N n % of N n % of N

Employee-level, N

25,897 100 15,038 100 8,242 100

Employee outcomes through 2014

Employee exit from the work unit

4,720 18.2 2,610 17.4 1,485 18.0

Non-disability early retirement*

306 1.2 172 1.1 98 1.2

Total SA percentage, mean (SD)

5 (8.6) - 5 (8.4) - 5 (8.8) -

Long-term SA events

1,524 5.9 881 6.0 516 6.4

Prescription for psychotropic medication

2,776 10.7 1,616 10.7 931 11.3

Ischemic heart disease (prevalence)

91 0.4 59 0.4 35 0.4

Age, mean (SD)

47 (10.7) - 47 (10.6) - 47 (10.7) -

Sex Females 19,808 76.5 11,507 76.5 6,299 76.4

Males 6,089 23.5 3,531 23.5 1,943 23.6

Occupational group Nurses 11,174 43.1 6,534 43.4 3,682 44.7

Medical doctors/dentists 2,791 10.8 1,464 9.7 758 9.2

Social/healthcare workers 3,242 12.5 1,966 13.1 1,055 12.8

Pedagogical workers 761 2.9 401 2.7 217 2.6

Service/technical workers 3,091 11.9 1,864 12.4 975 11.8

Administration workers 4,838 18.7 2,809 18.7 1,555 18.9

Part-time employment No 16,676 64.4 9,613 63.9 5,238 63.6

Yes 9,221 35.6 5,425 36.1 3,004 36.4

Manager status No 24,053 92.9 14,040 93.4 7,591 92.1

Yes 1,843 7.1 998 6.6 651 7.9

Contractual employment No 1,965 7.6 1,066 7.1 487 5.9

Yes 23,932 92.4 13,972 92.9 7,755 94.1

Prior sickness absence, days 0 7,209 27.8 4,132 27.5 2,274 27.6

1-3 5,582 21.6 3,242 21.6 1,760 21.4

4-6 3,928 15.2 2,292 15.2 1,271 15.4

7-13 4,927 19.0 2,877 19.1 1,517 18.4

14≤ 4,251 16.4 2,495 16.6 1,420 17.2

Seniority years, mean (SD)

13 (10.3) - 13 (10.3) - 13 (10.3) -

Personal gross income (€)**, mean (SD)

59,923

(32,239.3) -

59,066

(29,182.7) -

59,066

(29,819.1) -

Work-unit level, N

2,318 100.0 1,284 100.0 642 100.0

No. of employees within work units, mean (SD)

16 (12.9) - 16 (13.3) - 18 (14.3) -

WSC, low-high (0-100), mean (SD)

68 (9.8) - 69 (9.8) - 68 (9.8) -

Institution level, N 13 100.0 13 100.0 13 100.0

The source population included 37,720 employees, 2,696 work units, and 14 institutions.

Abbreviations: SA=Sickness absence, SD=Standard deviation, WSC=Work-unit social

capital.

* Paper IV examined weekly employee transition to non-disability early retirement from 4

April 2011 to 31 December 2012. ** 1 euro (€) = 7.5 Danish kroner (DKK).

35

Measures

Organizational changes in the work unit

Several indicator variables of organizational changes occurring in the work unit prior to the

work-environment surveys were created: one indicator variable for exposure to any changes

(Papers II-III and V-VI), one indicator variable for the number of organizational changes

occurring simultaneously (1, 2 or 3≤ types of changes; Papers II-III and V), and six indicator

variables for each of the specific types of organizational changes (mergers, demergers/split-

ups, relocation, change in management, employee layoff, and budget cuts). The reference

category for all change-indicator variables was non-exposure to any organizational changes.

Papers II-III examined organizational changes occurring only in the last six months of 2013,

whereas Papers V-VI used data on organizational changes occurring throughout 2013.

Paper IV included four change-indicator variables for mergers, demergers/split-ups, relocation

or change in management relative to no changes occurring in the period January 2009 to

March 2011.

Throughout 2013, 55% of the employees in the study population experienced any

organizational changes: 29% experienced one type of change, 15% experienced two types of

changes, and 11% experienced at least three types of changes. Change in management,

employee layoff in the work unit, and mergers were the types of changes experienced most

frequently. None of the specific types of organizational changes were completely overlapping

since co-occurrence rates were 56% or below (Table 3).

Table 3. Distribution of co-occurring types of organizational changes throughout 2013 as

experienced by the employees in the study population (N=15,038).

Employees, n

(% of N) Mergers, %

Demergers/

split-ups, % Relocation, %

Change in

management, %

Employee

layoff, % Budget cuts, %

Any changes 8,242 (55) 31 12 22 46 39 29

Mergers 2,560 (17) 20 41 53 28 25

Demerger/split-ups 956 (6) 54

46 55 31 21

Relocation 1,872 (12) 56 23 46 27 17

Change in management 3,781 (25) 36 14 23

28 22

Employee layoff 3,204 (21) 22 9 16 33 45

Budget cuts 2,401 (16) 27 8 13 35 45

36

Employee exit from the work unit and sickness absence

In Papers II-III, information on employee EFW and SA were calculated based on monthly

data from salary registers in the Capital Region of Denmark. Employee EFW was defined as

the month where an employee was no longer affiliated with the work unit at baseline

regardless of the reason. Since some work units were assumed to undergo changes during

follow-up, employees were not classified as EFW if a significant proportion of the staff (i.e.,

at least 30% and three employees) continued to work in the changed work unit.

Regarding SA, both events (yes/no) and percentages of total SA and long-term SA were

examined. Long-term SA was defined as at least one spell of ≥29 days of SA in keeping with

Danish regulations of public sickness benefits.127 The rationale for analyzing both total and

long-term SA was that total SA comprises all types of SA (e.g., due to short-term SA,

sporadic diseases, non-illness reasons), whereas long-term SA likely reflects long standing

illness.110,128,129 The rationale for examining events and percentages of SA was to assess if

organizational changes were both associated with more employees displaying SA behavior

and the magnitude of SA behavior.

Many previous studies used the number of spells as measure of SA.60,61 However, in the

present study such approach would reasonably inflate the amount of SA observed since

registry of SA in the Capital Region of Denmark was based on both calling in sick and calling

in back to work following sickness. For example, if an employee was sick-listed from work

Thursday through Friday and the employee returns to work as scheduled on the following

Tuesday (i.e., free from work Saturday-Monday), the observed spells of SA would be 5

(Thursday-Monday) although the employee was absent for 2 only working days (Thursday-

Friday). Particularly, such scenario poses a potential bias in a healthcare context because

many hospital employees work on shifting schedules and irregular working hours. The bias

was assumed to inflate the observed short-term SA mostly.

To reduce this potential bias of inflated SA, the percentage of SA during follow-up was

calculated relative to the missed fixed working hours due to total SA and long-term SA until

EFW. For instance, if an employee worked in the same work unit from 1 January 2014 until

EFW at 16 February 2014, the percentage of SA was calculated relative to the fixed working

hours for that period. This approach was chosen since organizational changes and their

accompanying processes may unfold over an extended period and the impacts of moving from

a non-exposed to an exposed work unit during follow-up were unclear.

37

Non-disability early retirement

Paper IV used data on transition to non-disability early retirement (“efterløn”) among eligible

senior employees. Individual-level data on early retirement were obtained via linkage to the

national DREAM (“Den Registerbaserede Evaluering Af Marginalsamfundet”) database,

which holds weekly information on all public transfer payments in Denmark.130

The Danish early retirement scheme is a public welfare benefit allowing eligible employees

aged 60-64 to withdraw voluntarily from the labor market before the nominal retirement age

of 65. The benefit is smaller than the salary previously earned, but the fixed payment rates

increases with age. Eligibility to the early retirement includes membership to an

unemployment-insurance company for a given period prior to early retirement as well as

capability of having a full-time job (i.e., 37 weekly working hours) at age 60.

Prescriptions for psychotropic medication

Paper V used information on prescription for psychotropic medication, including anxiolytics

(World Health Organization's [WHO] Anatomical Therapeutic Chemical [ATC] classification

system codes: N05B), hypnotics and sedatives (ATC: N05C), and antidepressants (ATC:

N06A). These classes of psychotropic medications are used worldwide to treat common

mental health disorders, such as anxiety and depression, and to normalize circadian rhythm. In

Denmark, legal purchase of all psychotropic medications requires a prescription issued by a

physician.

Individual-level data on prescriptions for psychotropic medication were extracted from the

National Prescription Registry, which holds daily information on all medications given at

hospitals or purchased in shops or pharmacies in Denmark. The data on psychotropic

prescriptions were applied regardless of the intended duration or dosage.131

Ischemic heart disease

Paper VI investigated the associations with IHD (International Classification of Diseases,

10th revision, [ICD-10] codes: I20-I25). Individual-level data on IHD were obtained via

linkage to the National Patient Register and the Cause of Death Register. The National Patient

Register holds daily information on patient encounters with all public and private hospitals in

Denmark. These data include date of hospital admission and diagnoses according to ICD-

10.132 The Cause of Death Register holds daily information on all causes of death based on

death certificates issued by a physician.133 However, according to the death register, no

38

employees from the study population in Paper VI died due to IHD during the follow-up

period in 2014.

Psychosocial work environment

Papers III-IV and VI evaluated the mediating roles of factors in the psychosocial work

environment on the pathway from organizational changes to employee turnover or health.

These psychosocial factors included perceived stress measured at the employee level as well

as aggregated measures of social capital, organizational justice, and quality of leadership at

the work-unit level. The psychosocial factors were based on a total of 16 unique self-reported

items from the work-environment surveys using a 5 or 7 point-Likert scale. In total, 12

psychosocial items originated from the Copenhagen Psychosocial Questionnaire, 2nd version

(COPSOQ-II),134 whereas the remaining 4 items were developed by human resource, the

management, and employee representatives (Table 4). Specialists in occupational medicine

selected the items comprising the composite psychosocial scales measuring social capital,

quality of management, and organizational justice, since these psychosocial factors were not

assessed with established questionnaires.

Table 4. Items from work-environment surveys in January-February 2011 and March 2014

used to measure the factors in the psychosocial work environment.

Psychosocial factor Item (5 or 7 point-Likert scale)

"To what degree…

Perceived stress ...have you been stressed for the last six months?"* (5)

Social capital ...are you and your colleagues good at coming up with suggestions for improving work procedures?" (5)

...do you get help and support from your colleagues when needed?"* (5)

...do you and your colleagues take responsibility for a nice atmosphere and tone of communication?" (5)

...does the management trust the employees to do their work well?"* (7)

...can you trust the information that comes from the management?"* (7)

...are conflicts resolved in a fair way?"* (7)

...is the work distributed fairly?"* (7)

...is your staff group respected by the other staff groups at the workplace?"

Quality of management …does the management enough to help employees cope with emotionally demanding situations at work?" (5)

…do your immediate superior gives high priority to job satisfaction?"* (5)

…do your immediate superior is good at work planning?"* (5)

…do you get help and support from your immediate superior when needed?"* (5)

Organizational justice …do you are informed well in advance concerning for example important decisions, changes, or plans for the future?"* (5)

…do you receive all information you need in order to do your job well?"* (5)

…can you trust information coming from the management?"* (7)

…does the management trusts employees to do their job well?"* (7)

…are conflicts resolved in a fair way?"* (7)

…is the work distributed fairly?"* (7)

* Item originated from the Copenhagen Psychosocial Questionnaire, 2nd version (COPSOQ-

II).134

39

5-point response scale: 1: “Not at all”; 2: “To a lesser degree”; 3: “To some degree”; 4:

“To a high degree”; 5: “To a very high degree”.

7-point response scale: 1: “Not at all”; 2; 3; 4; 5; 6; 7: ”To a very high degree”.

Items and responses translated from Danish.

All item responses were recoded to a scale ranging 0-100 (low-high). To establish the

composite scales, employee-level scores were computed as the mean value of each scale for

employees responding to at least half of the items. Next, work-unit level scores were

computed by aggregating the employee-level scores in work units with ≤50% missingness.

Finally, the WSC scores were assigned to all employees within each work unit, including

employees not responding to the psychosocial questionnaire. In Paper III, the WSC variable

was categorized into quartiles.

There has been some disagreement about the appropriate level of analyzing social capital

(e.g., employee, work-unit, department or company level).32,52,53 The current thesis analyzed

WSC in keeping with the conceptualization of social capital as an organizational

characteristic and organizational changes being measured at the work-unit level.

All the composite psychosocial scales showed good internal consistency as Cronbach’s alpha

values ranged 0.8-0.9.135 Principal factor analyses with no rotation showed single-factor

loadings for the psychosocial scales of social capital (Factor 1: 3.46, Factor 2: 0.78, Measure

of Sampling Adequacy [MSA]: 0.86), quality of management (Factor 1: 1.42, MSA: 0.60),

and organizational justice (Factor 1: 2.60, MSA: 0.80) as indicated by Eigenvalues above 1

(Kaiser’s criterion) and acceptable MSA values.136 Promax oblique rotation yielded a two-

factor solution for the social-capital scale (Factor 1: 1.89, Factor 2: 1.06) loading on

justice/trust-related items and collaboration-related items, while rotation was not possible for

the scales on quality of management and organizational justice. These alpha and factor

analyses indicate the reliability and the validity of the composite psychosocial scales.

Potential covariates

Different strategies for confounder adjustments were applied in keeping with the aim of each

paper. The following employee-level variables were used as potential confounders in

regression models for associations with employee turnover or health: age, sex, occupational

group, personal gross income, prior SA, child-related absence, civil status, household gross

income, prior hospitalization for medical diagnosis, seniority, contractual employment,

40

working hours, and manager status. The following variables at the work-unit level were also

used as potential confounders: number of employees within work unit and other types of


In Paper III, the following work-unit-level variables were applied as potential confounders for

the association between organizational changes and WSC (as exposure and outcome were

both measured at the work-unit level): work-unit means of employee age, personal gross

income, and prior SA as well as work-unit proportions of females, employees with child-

related absence, nurses, administrative staff, social/healthcare/pedagogical workers,

service/technical staff, and medical doctors/dentists.

Data on all potential covariates were extracted from regional registers except income, civil

status, and prior hospitalization, which were extracted from national registers.

Study designs and timing

Traditionally, the randomized controlled trial has been considered the golden standard of

study designs for evaluating intervention effects in medical research, since selection bias and

confounding are minimized. It is, however, not always ethically possible to randomly allocate

the exposure/non-exposure to the participants, and observational research studies with time

ordering of exposure and follow-up on outcome may serve as the next best level of

evidence.137,138

Papers II-III and V-VI examined the effects of organizational changes occurring in the last six

months of 2013 and throughout 2013, respectively, on employee turnover and health

outcomes in 2014. Exposure observation was extended to an entire year in Papers V-VI to

increase statistical power. Although more data covering a larger period were available, these

relatively short exposure and follow-up periods were chosen due to following reasons. First,

exposure to organizational changes was limited to occurrence in 2013 to reduce potential

employee EFW for change-related reasons before baseline in 2014, and, second, to assess

psychosocial factors as potential mediators immediately after the changes (March 2014).

Third, baseline was defined following the organizational changes to distinguish the timing in

occurrence of exposure before outcome. Fourth, follow-up on employee EFW and health

outcomes were restricted to one year only (2014) to reduce impacts of behaviors related to

41

new changes (e.g., rumors, announcements, actual changes) during follow-up on employee

outcomes.

Paper IV examined associations between organizational changes and non-disability early

retirement during a four-year study period; however, since non-disability early retirement is

not granted on health grounds and benefit payments increases with later transition, retirement

effects may occur over an extended period.

Main statistical analyses

In Paper II, Cox proportional hazards regression models estimated hazard ratios (HR) and

95% confidence intervals (CI) for months to subsequent employee EFW after organizational

changes relative to no changes. To account for the excess proportion of employees with no

SA observed during follow-up, zero-inflated Poisson (ZIP) regression models analyzed

associations with total and long-term SA according to organizational changes.139 The ZIP

models estimated odds ratios (OR) and 95% CIs for the events of any (total) SA and long-

term SA as well as rate ratios (RR) and 95% CIs for the percentages of missed fixed working

hours due total SA and long-term SA among the sick employees (i.e., four SA outcomes in

total).

In Paper III, logistic regression models weighted by the number of employees within each

work unit assessed the ORs and 95% CIs for lower levels of WSC (than high WSC) following

organizational changes relative to no changes. Cox models with average marginal effects

accounting for clustering140 at the work-unit level analyzed the HRs and 95% CIs for months

to employee EFW according to levels of WSC. Marginal Cox models were also applied to

evaluate the mediating properties of WSC on the association between organizational changes

and subsequent employee EFW as indicated by a drop in the HR point estimate for the

outcome (EFW) when adjusting the potential mediator (WSC) in the regression model.126

In Paper IV, Cox models estimated HRs and 95% CIs for non-disability early retirement

according to organizational changes and WSC among senior employees. Again, the mediating

role of WSC, organizational justice, and quality of management between changes and early

retirement were evaluated by comparing point estimates from Cox models with and without

adjustment for these psychosocial factors.126

42

In Papers V-VI, multilevel mixed-effects survival models analyzed the HRs and 95% CIs for

psychotropic prescriptions and incident IHD after organizational changes compared to no

changes.141 Associations with IHD accounted for clustering at the work-unit and the

institution level, whereas associations with psychotropic prescriptions only accounted for

clustering at the work-unit level for convergence reasons. Intraclass correlation coefficients

evaluated the partitioning of these higher levels in the variance of each health outcome.142 In

addition, Paper V analyzed potential sex differences in psychotropic prescriptions with

additive and multiplicative interaction models, while Paper VI evaluated the mediating

properties of perceived stress on the association between organizational changes and IHD

using the aforementioned approach.126

An alpha-level of statistical significance was set to 0.05. All statistical analyses were

conducted using SAS version 9.4 software (SAS Institute Inc., Cary, North Carolina, USA) or

STATA version 14.2 software (Stata Corporation, College Station, Texas, USA).

43

Main results

Exposure to any type of organizational changes was statistically significantly associated with

excess rate ratios of subsequent employee exit from the work unit (HR 1.10, 95% CI 1.01-

1.19) and total sickness absence (SA) percentage (RR 1.05, 95% CI 1.03-1.06) compared to

no changes. Exposure to any changes was also associated with higher relative risk of long-

term SA event (OR 1.15, 95% CI 1.00-1.33), low work-unit social capital (WSC) (vs. high:

OR 2.04, 95% CI 1.86-2.23), and prescriptions for psychotropic medication (HR 1.14, 95%

CI 1.02-1.26) among the employee remaining in the work unit after the changes. The

direction of these findings was also observed for associations with incident ischemic heart

disease (IHD) (HR 1.50, 95% CI 0.81-2.77) (Figure 4) and non-disability early retirement

(results presented in the section below).

Figure 4. Adjusted rate ratio of employee exit from the work unit and relative risks of adverse

health outcome among employees or low work-unit social capital with 95% confidence

intervals associated with any organizational changes relative to no changes (references: 1.0).

The presented estimates are not comparable between outcomes since exposure and reference

groups differ.

44

Exposure to a greater number of organizational changes occurring simultaneously was not

consistently associated with more adverse employee health or WSC outcomes, but the rate of

employee EFW after ≥3 simultaneous changes were considerably high relative to no changes

(HR 1.52, 95% CI 1.30-1.79) (Figure 5).

Figure 5. Adjusted rate ratio of employee exit from the work unit and relative risks of adverse

health among employees or low work-unit social capital with 95% confidence intervals (CI)

associated with higher number of organizational changes occurring simultaneously relative

to no changes (references: 1.0).


groups differ.

Employee turnover, sickness absence, and work-unit social capital

Regarding exposure to specific types of organizational changes, higher rates of subsequent

employee EFW were found after mergers (HR 1.29, 95% CI 1.12-1.49), demergers/split-ups

(HR 1.41, 95% CI 1.16-1.71), relocation (HR 1.16, 95% CI 1.00-1.35), and change in

management (HR 1.24, 95% CI 1.11-1.38) relative to no change. These associations were also

observed for non-disability early retirement among senior employees following mergers (HR

1.23, 95% CI 1.01-1.49), relocation (HR 1.25, 95% CI 1.01-1.54), change in management

(HR 1.37, 95% CI 1.13-1.66). Exposure to employee layoffs or budget cuts in the work unit

were not associated with subsequent employee EFW, while exposure to demergers/split-ups

(95% CI 0.88-3.46)

45

was not associated with non-disability early retirement. There were indeed no data on work-

unit employee layoffs or budget cuts to analyze associations with employee early retirement

in Paper IV.

All change indicators were statistically significantly associated with low WSC relative to high

WSC as reference, except relocation (OR 1.13, 95% CI 0.96-1.33). Moreover, lower WSC

was associated with higher rate ratios of employee EFW in a dose-response manner (low vs.

high WSC: HR 1.65, 95% CI 1.46-1.86). There were, however, no convincing indications of

WSC mediating the rather inconsistent longitudinal associations between organizational

changes and subsequent employee EFW. Yet, adjusting for WSC, quality of management, and

organizational justice in the regression models for early retirement somewhat reduced HR

point estimates for mergers (HR 1.23 vs. 1.11), relocation (HR 1.25 vs. 1.10), and change in

management (HR 1.37 vs. 1.27). This suggests that the association between specific types of

organizational change and non-disability early retirement is, at least partially, mediated

through these three psychosocial factors (Figure 6).

Figure 6. Adjusted hazard ratios of employee exit from the work unit or non-disability early

retirement with 95% confidence intervals following specific types of organizational changes

relative to no changes (references: 1.0).


groups differ.

46

Relative to no change, exposure to ≥3 types of organizational changes occurring

simultaneously was associated with a lower risk of subsequent events of any (total) SA (OR

0.77, 95% CI 0.63-0.93) in the study population, but a higher rate of total SA percentage (RR

1.13, 95% CI 1.08-1.17) among sick-listed employees only. Other change indicators were also

associated with higher SA percentages among those sick-listed. Specifically, mergers and

employee layoffs were both statistically significantly associated with excess total SA

percentages (RR 1.09, 95% CI 1.06-1.13 and RR 1.16, 95% CI 1.13-1.18, respectively) as

well as a higher relative risk of long-term SA events (OR 1.31, 95% CI 1.00-1.72 and OR

1.31, 95% CI 1.08-1.59, respectively) (Figure 7).

Figure 7. Adjusted risk of sickness-absence event and rate sickness-absence percentage and

95% confidence intervals in the year following specific types of organizational changes

occurring in the last six months of 2013 relative to no changes (references: 1.0).

Prescriptions for psychotropic medication and ischemic heart disease

Only change of management was associated with a higher relative risk of prescription for

psychotropic medications throughout the following year (HR 1.23, 95% CI 1.07-1.41),

whereas the remaining specific types of organizational change were not statistically

47

significantly associated with psychotropic prescriptions. Dividing follow-up period into two

halves showed associations with higher relative risk of psychotropic prescriptions during the

latter 6 months of the 12-months follow-up period after mergers (HR 1.26, 95% CI 1.06-

1.50), change in management (HR 1.42, 95% CI 1.22-1.65), employee layoffs (HR 1.23, 95%

CI 1.03-1.46), and budget cuts (HR 1.19, 95% CI 1.00-1.41) (Figure 8). Associations between

organizational changes and psychotropic prescriptions did not vary by sex as indicated by

additive (Synergy Index: 1.36, 95% CI 0.32-5.84) and multiplicative interaction analyses

(p=0.69).

Throughout the year after organizational changes, there was a higher risk of incident IHD

after relocation (HR 2.91, 95% CI 1.07-7.90), change in management (HR 2.18, 95% CI 1.02-

4.68) and employee layoffs (HR 2.90, 95% CI 1.36-6.16) among employees without

preexisting IHD five years prior to the study relative to no change. Adjusting for perceived

stress did not reduce these associations notably (Figure 9).

The work-unit level seemed to be important contributors to psychotropic prescriptions and

IHD as intraclass correlation coefficients indicated that variation between work units

accounted for 6% and 40%, respectively, of the total variance in each outcome.

Figure 8. Adjusted risk of prescriptions for psychotropic medications in 2014 among the

employees and 95% confidence intervals associated with specific types of organizational

changes occurring in 2013 relative to no changes (references: 1.0).

48

Figure 9. Adjusted relative risk of incident ischemic heart disease (IHD) in 2014 and 95%

confidence intervals associated with specific types of organizational changes occurring in

2013 relative to no changes (references: 1.0) among employees without IHD five years prior

to observation on exposure.

49

Discussions

Key findings

This thesis examined the impacts of co-occurring and specific types of organizational changes

in the work unit on subsequent employee turnover and health. The underlying psychosocial

mechanisms on these associations were also addressed.

In total, 9 of all 14 (64%) main analyses between organizational changes and employee

turnover showed statistically significantly higher rate ratios of subsequent employee EFW or

non-disability early retirement. The remaining 5 main analyses (36%) did not yield significant

associations between organizational changes and employee turnover, although point estimates

suggested such positive relation. Adjusting for work-unit social capital (WSC) in the

regression models predicting EFW did not notably diminish the rather inconsistent

associations with each indicator of organizational changes, suggesting no important mediation

– if any – through this single psychosocial factor. However, WSC, organizational justice, and

quality of management did somewhat diminish associations between specific types of

organizational changes and higher early-retirement rates, suggesting some mediation through

these three psychosocial factors combined.

Regarding the employee health outcomes, 25 of all 68 (37%) main analyses showed

statistically significantly higher risk of adverse employee health according to the studied

indicators of organizational changes relative to no changes. In total, 40 (59%) main analyses

revealed no statistically significant associations between changes and health, but only 3 (4%)

main analyses showed associations with lower risk of adverse health (only in analyses with

sickness absence [SA]). Regarding clinical health outcomes, 10 (36%) of the 28 main

analyses showed higher relative risk of psychotropic prescriptions or ischemic heart disease

(IHD), while the remaining 64% of the analyses yielded insignificant results. Associations

with psychotropic prescription were particularly strong in the latter semester of the follow-up

period. The findings provided no evidence for differential sex effects on associations with

psychotropic prescriptions. There were no convincing indications of perceived stress

mediating associations between organizational changes and incident IHD.

50

In general, the present findings indicate that organizational changes may be associated with

higher rates of subsequent employee turnover and higher risk of detrimental employee health

relative to employees experiencing no changes. The psychosocial factors presently studied did

not seem to have an important role, if any, in mediating these associations. A considerable

number of main analyses yielded statistically insignificant findings, which may be due to

limited statistical power.

Previous findings and explanations

This is apparently one of the first studies to examine the relative impacts of different types of

work-unit organizational changes on subsequent employee turnover and clinical health

outcomes among employees using statistical multilevel techniques. The present studies

additionally contributed to the literature on the adverse effects of organizational changes by

investigating the underlying psychosocial mechanisms using data retrieved from independent

sources.

Employee exit from the work unit and non-disability early retirement

Statistically significant rate ratios of employee were similar for employee EFW (HRs ranged

1.1-1.5) as for non-disability early retirement (HRs ranged 1.2-1.4) according to the different

types of organizational changes studied. The two employee turnover outcomes were indeed

related since retiring senior employees also EFW.

Papers II and IV on employee EFW and early retirement did not account for the multilevel

structure in the data. However, this did not seem to pose a problem since estimates of

employee EFW in Paper II (any change: HR 1.10, 95% CI 1.01-1.10) were equivalent to the

estimates reported in Paper III (vs. HR 1.10, 95% CI 1.01-1.10), which were based on

marginal models to account for clustering at the work-unit level. This is, however, contrary to

a recent study on organizational changes and mental distress showing that point estimates of

associations diminished while the 95% confidence intervals (CI) widened when accounting

for clustering at the work-unit level. These data were indeed self-reported,95 and subjective

item responses may more likely cluster within work units than factual EFW behavior.56,58

Apparently, only one epidemiological study have examined the relation between

organizational changes and early retirement to find that self-reported workplace restructuring

was not statistically significantly associated with transition to the Dutch non-disability early

51

retirement scheme.77 Indeed, in a qualitative research study among early retirees,

organizational changes at work was frequently highlighted as a reason for withdrawing from

the labor market.143

The finding of excess rate ratios of employee EFW following some indicators of

organizational change is concurrent with the existing literature.70–74 Papers II-III showed that

≥3 changes occurring simultaneously was associated with particularly high rate ratios of EFW

in line with prior findings of broader changes having stronger associations with adverse

employee outcomes.93,95,100,144 Hospital mergers were previously found to be associated with

higher employee turnover unrelated to health status.73 This is consistent with the present

associations between mergers and higher rates of non-disability early retirement and

employee EFW, although there were no data on the reasons for EFW. In some cases,

employees may voluntarily EFW as a reaction to changes, whereas in other cases

organizational changes may have the explicit or tacit goal of dismissing specific employees.

Sickness absence

It is likely that some employees left the work unit for health reasons21,76,145 as the studied

organizational changes were also associated with higher risk of SA relative to no changes.

The present associations with excess SA corroborates with findings from existing

publications78,80,81,84–86,88,90 although inconsistent results have been reported.62,85,87

In Paper II, exposure to work-unit mergers was associated with a 1.31-fold higher relative risk

of long-term SA of ≥29 days, which is in line with other findings of a 1.05-fold higher relative

risk of long-term SA.84 The higher risk estimate presently demonstrated may, however, be

explained by measuring mergers at the work-unit level instead of the hospital level as well as

defining long-term SA as ≥29 days instead of ≥91 days.84

Employee layoff in the work unit was associated with both 1.31-fold higher risk of long-term

SA and 1.16-fold higher rate of total SA-percentage relative to no changes, which is in

keeping with findings of downsizings being particularly associated with adverse health

relative to other types of changes (e.g., mergers or outsourcing).89,95 Interestingly, a study

found no excess risk of long-term SA among employees according to transfer from public-

sector organizations to private companies without staff reductions.85 This suggests that

organizational change with staff downsizings is a particular risk factor for SA among

employees relative to other change types.

52

Employees may not necessarily stay away from work when feeling sick (i.e., sickness

presenteeism).111 Higher presenteeism has been related to loss of productivity and higher

employment security146 as a previous study demonstrated that rates of SA increased when

temporary workers were employed permanently.147 However, the present study population

was mostly comprised of permanent employees and the unemployment rate was relatively low

during the study period,148 suggesting that presenteeism was a minor issue.

It has been suggested that about half to two-third of SA from work is due to genuine sickness

or injuries.108–110,149 Unfortunately, there were no data on the reasons for employee SA;

however, findings revealed higher relative risk of specific clinical health outcomes following

organizational changes as discussed in the following.

Prescriptions for psychotropic medication

Exposure to any organizational changes was associated with a 1.14 times higher relative risk

of prescriptions for psychotropic medication through the subsequent year, which corroborates

with other studies on organizational changes,93 including downsizing.91,97,98 This association

was particularly observed for change in management (HR 1.23, 95% CI 1.07-1.41), which has

not been demonstrated previously.

Contrary to prior findings,93,95 a higher number of co-occurring changes was not convincingly

associated with higher risks of psychotropic prescriptions, which may be due to limited

statistical power. In addition, limited statistical power may also have hampered detection of

prescription effects varying by sex. Indeed, these findings of no sex differences corroborate

with post-hoc results from Paper II on associations with employee EFW and SA as well as

other studies on psychotropic prescriptions,91,97 although stronger prescription effects have

been observed among male employees.98

The HR point estimates of all types of organizational changes were higher during follow-up in

the last six months of the subsequent year compared to the first six months. This points to a

general latency period before the observation of excess prescription rates following

organizational changes in keeping with other findings for downsizing among employees

without history of substantial SA.91,97 However, conclusions about the duration of the latency

period are hampered due to unclarity regarding initiation of the organizational changes

studied.

53


Paper VI showed about 2.2-2.9 times higher relative risk of incident IHD among employees

remaining in the work unit during occurrence of relocation, change in management or

employee layoff. There seemed to be no associations with mergers, split-ups or budget cuts;

however, this could be observed due to limited statistical power as indicated by the broad

95% CIs.

The present findings are concurrent with five times higher cardiovascular mortality during a

four-year follow-up period after major (>18% staff reduction) – but not minor (8-18%) –

downsizing relative to no downsizing (<8%) among Finnish municipal employees.107 Another

study also found a five-fold higher mortality from IHD during a 13-month period after closure

of a public bus company among male ex-employees in Greece.103 Möller et al.105 found

borderline significant associations between negative appraisals of change of workplace and

higher relative risk of myocardial infarction among women only, whereas negative appraisals

of conflict at work and increased responsibilities predicted heart attacks statistically

significantly among both sexes. Surprisingly, Paper VI found no convincing indications of

perceived stress as a mediator between specific types of organizational changes and IHD

among employees. Variation between work units explained 40% of incident IHD events.

Indeed, this finding may likely reflect factors related to socio-economic status and lifestyle as

work units were relatively homogenously composed with respect to occupation.

In sum, each type of organizational change was – to varying extents – associated with higher

rates of subsequent employee turnover as well as higher risk of adverse health among

employees compared to no changes. Employee layoffs and budget cuts were only statistically

significantly associated with excess EFW in the first three months of follow-up in 2014

(Supplementary table S2, Paper III), but it was unclear if these types of changes were

associated with non-disability early retirement. Considering findings from previous and the

present studies, organizational changes involving employee layoffs seem to be particularly

associated with adverse health outcomes among those remaining in the workplace after the

changes. Possible psychosocial mechanisms of the demonstrated associations are discussed in

the following.

54

Possible psychosocial mechanisms

Papers III-IV and VI examined whether the longitudinal associations between organizational

changes on adverse employee outcomes were mediated by specific employee- and work-unit-

level factors in the psychosocial work environment. Apparently, Papers III-IV were the first

studies to examine the mediating properties of workplace social capital, quality of

management, and organizational justice on the association between organizational changes

and employee turnover, including employee EFW and non-disability early retirement.

Paper III provided evidence for excess relative risk of low WSC following organizational

changes. Employees within work units may appraise organizational changes as unfair since

they may be treated unequally in the change processes: surplus employees may be dismissed,

while other employees may be relocated for merger purposes, which could result in change of

management. Commitment to the workplace and procedural justice may be diminished if the

employees do not understand the rationale for such changes.92,120,123,150 This may explain

findings of ≥3 changes occurring simultaneously being associated with a particularly high

employee EFW rate ratio. It is also likely that organizational changes will be accompanied by

disruption of social ties, discontinuity of work flows, and lower trust among employees and

managers.120,123,151,152 Low WSC has previously been related to poor employee health, low

work engagement, and emotional exhaustion among employees.48,50,51,53,153 This could

motivate the present findings of lower levels of WSC predicting higher rates of both

employee EFW and non-disability early retirement.

Despite demonstrations of discrete associations between organizational changes and WSC as

well as between WSC and employee EFW, there were no convincing evidence of WSC

mediating the rather inconsistent associations between organizational changes and employee

EFW. Paper III focused on organizational change and EFW during a two-year study period,

but it could be possible that changes in levels of WSC due to reorganizations occur over a

longer time span. Although WSC is conceptualized as a characteristic of the work unit, the

employee composition may alter because of organizational changes, which could hamper

detection of the mediating by WSC, if any. There were, however, some indications that work-

unit organizational justice, quality of management, and WSC partially mediated associations

with non-disability early retirement among senior employees during a longer follow-up

period.

55

It is believed that job insecurity plays a pivotal role in detrimental health effects following

exposure to organizational changes,63,104 which seem particularly relevant for downsizing or

waves of employees layoffs. The mere anticipation of forthcoming organizational changes

and fear of job loss have been associated with long-term adverse health outcomes,94,104,154

pointing to the importance of job uncertainty involved in relation to changes at work.

Previous studies by Kivimäki et al.81,82 showed that associations between major downsizing

and health status were diminished by about 50% when adjusting for the effects of job security

as well as job control and demands, indicating the mediating properties of these psychosocial

factors.

It is reasonable to assume that job strain and job insecurity and may also mediate effects of

other types of organizational changes than major downsizing.66,155 Demands for high quality

patientcare may not be adjusted according to staff reductions or during a workplace

relocation, which could result in greater workload intensification, longer working hours, and,

eventually, higher SA among employees remaining in the given work unit. In addition,

change in management and selective budget cuts may induce anxiety about one’s future job

situation, which can lead to excess mental health problems and higher use of psychotropic

medication among employees as suggested by findings in Paper V. Managers may have a key

role in maintaining a healthy psychosocial work environment,156 which may explain why

change in management was particularly related to psychotropic prescriptions among

employees compared to other types of organizational changes.

Psychological stress at work has been highlighted as a risk factor for development of IHD;12,38

however, Paper VI did not provide evidence of the mediating properties of perceived stress as

mediator on the association between organizational changes and IHD. Indeed, this may be due

to using a single-item measure for perceived stress in combination with the observation of few

incidents cases of IHD (n=49) introducing limited variation in the data for detection of

mediation of effects via perceived stress. Employee perceived stress was measured through

March 2014 during follow-up on IHD from 1 January to 31 December 2014; however,

postponing start of follow-up to 1 April 2014 (following psychosocial assessment) yielded

similar indications of no mediation of IHD effects through perceived stress (data not shown).

There may be multiple plausible psychosocial pathways from organizational changes to

adverse employee health and exit from the workplace. Yet the present Papers III-IV and VI

56

did not provide convincing evidence for WSC and perceived stress as potential mediators of

employee turnover and health outcomes, which might be due to methodological reasons.

Employees with a preexisting high level of stress prior to organizational change may have

fewer mental resources to cope with changing working conditions while maintaining job

demands.34,157,158 Thus, in line with prior findings,36,61 it is likely that employee health and

turnover effects of organizational changes are modified by baseline psychosocial factors, such

as social support,26,159 effort-reward imbalance79 or WSC.125 However, such potential effect

modification of the psychosocial work environment was not addressed in Papers II-VI,

because the psychosocial factors were measured after occurrence of the organizational

changes and, thus, likely to be affected by the changes.

Confounding and reverse causation

Organizational changes may be associated with higher risk of employee turnover and

detrimental health, but association alone is insufficient to infer that organizational changes

have a causal impact on adverse employee outcomes. In fact, associations could be observed

due to residual confounding by factors that influences both the exposure variable (e.g.,

organizational changes) and the outcome variable (e.g., employee turnover, adverse health).160

There were, however, no considerable differences in characteristics of the study population

and employees exposed to any organizational changes, indicating that confounding was not a

major issue.

Psychosocial factors may be regarded as mediators but also confounders of the relationship

between changes and employee turnover/health, since a poor a psychosocial work

environment may give rise to changes. However, given that WSC is robust against changes

over a short-term period (as discussed above), psychosocial factors may not have confounded

associations notably as associations with employees EFW did not seem to be convincingly

influenced by adjustment of WSC.

It is likely that leadership styles and manager personalities at the work-unit level may

influence employee outcomes;161 however, most change initiatives are decided on higher

political and top management levels (mergers, demergers/split-ups, relocation, etc.), and

therefore confounding from these factors are not considered likely in the present context.

Data on preceding organizational changes were available in Papers II-III and V-VI, although

these were not used for adjustment purposes. However, due to the generally high rate of

57

employee EFW (17%), many employees may not have been exposed to preceding

organizational changes in their work unit.

Another explanation of the present associations may be due to reverse causality; that is, high

employee turnover and adverse health among employees causing work-unit organizational

changes. For example, work units with high rates of SA may have a low productivity rate,

which could encourage reorganization of the work unit. However, considering the previous

theoretical and empirical literature as well as the consistency in the present findings, the

presence of reverse causality from excess work-unit turnover rates or adverse health on

organizational changes is regarded as unlikely in the present study.

Methodological considerations

A potential methodological limitation of Papers II-VI may be the relatively short follow-up

periods applied with baseline after the observations on organizational changes, which could

have underestimated associations. Adverse health and turnover effects of organizational

changes could be observed among employees already when rumors about forthcoming

changes at work start to spread.91,94,97,106 The managers provided information on occurrence of

organizational changes and not their initiation or announcement within work units.

Further, it is likely that organizational changes may be associated with detrimental employee

outcomes beyond one year of follow-up. This limitation seemed relevant for Paper V, since

the findings indicated a latency period before observation of higher relative risk of

psychotropic prescriptions. However, employee EFW and health outcomes were not

examined in as such associations would be confounded by organizational changes occurring

in 2014, on which there were no data. Likewise, organizational changes initiated during the

present follow-up periods (2011-2012 and 2014) may have underestimated the findings.

Data on occurrence of organizational changes were obtained via email surveys administered

to the managers, because there were no records on work-unit reorganization in the regional

registers. This approach may be a potential limitation since 31-41% of the managers did not

respond to the questionnaire on organizational changes. Organizational changes could give

rise to managers exiting from the Capital Region of Denmark and, thus, being unable to

provide data on changes in the surveys. Yet working email addresses were not renewed if the

managers changed workplace within the Capital Region of Denmark. Missing data on

58

organizational changes was not considered as a major issue since characteristics among the

eligible population (with incomplete data on changes) and the study population were similar.

Employee turnover rates were somewhat lower in the study population, suggesting some

underestimation of findings with employee EFW and non-disability early retirement.

The applied data on organizational changes were obtained three or four years after their

occurrence, which could introduce recall bias; however, it is likely that the managers executed

the organizational changes themselves and, thus, potential recall bias is regarded as minor.

Finally, it was a limitation that the psychosocial composite scales applied did not originate

from validated questionnaires measuring all aspects of the psychosocial factors. However, the

majority of the items were retrieved from the Copenhagen Psychosocial Questionnaire,134 and

high alpha values and factor analyses indicated the reliability and validity of the scales.

Strengths of this thesis include the large study populations with complete data on follow-up

and background information among all relevant employees. In addition, data on exposure,

outcome, and mediators were retrieved from independent sources, and therefore common-

method bias is not considered as a problem. This is particularly important in mediation

analysis as spurious reductions in point estimates could be observed due to common variance

from applying the same method for data gathering.56 Moreover, all data were retrieved from

reliable sources, including regional and national registers as well as managers responding to a

few simple-phrased items regarding factual change events in their work unit.

It was also a strength that assessments of the psychosocial work environment were based on

surveys with high response rates (81-84%). Moreover, analyzing psychosocial scores

aggregated at the work-unit level, which were assigned to both respondents and non-

respondents, reduced selection bias introduced by employees who did not participate in the

work-environment surveys. In addition, psychosocial factors aggregated at the work-unit level

may likely be less influenced by employee-level factors, such as personality or social

desirability, that could also affect employee outcomes (e.g., employee turnover).56

Examining several types of organizational changes at work-unit level was an additional

strength of the studies. The work-unit approach ensured that the employees did experience the

organizational changes since only employees working in the same work unit during

observation on changes were included. Finally, assessment of several types of organizational

changes enhanced the purity of the reference group of employees not exposed to any changes.

59

Representativeness and generalizability

The source population included all employees in the Capital Region of Denmark, but a

selection of healthy workers could be introduced if previous organizational changes had

removed unhealthy employees from the population.36 Indeed, this potential selection bias do

not seem relevant as organizational change is considered as a characteristic of modern work

life. A considerable proportion of eligible employees were excluded due to missing data on

organizational changes, which could bias representativeness. However, the inclusion criteria

and missingness on organizational changes did not seem to play noteworthy roles in the

representativeness of the study population (e.g., see Papers II and V).

The eligible populations included employees who worked at least 18.5 hours per week in the

same work unit throughout one year as well as senior employees eligible for early retirement

during follow-up. With application of these inclusion criteria, many temporary employees

(e.g., trainees, students) were excluded from the study. However, since these non-eligible

employees did not work on a regular basis in the work unit during the observation on

organizational changes, it was unclear if the employees excluded were even affected by the

studied changes. Eligible employees who left the work unit during the observation on

organizational changes (e.g., potentially due to the changes studied) may potentially introduce

selection of healthy workers into the study population, which could underestimate the

findings. There were, however, no data on the reasons for employee EFW to evaluate this

potential bias.

The study population mainly comprised female employees. The underlying adverse

(psychosocial) mechanisms may differ by sex, although the present findings did not provide

evidence for differential sex effects. The high proportion of female employees is a general

characteristic of the healthcare sector, and generalizations of the findings to other public-

sector enterprises should be made with caution.

Organizational changes are often implemented as rationalization strategies in public and

private sector workplaces, but the underlying psychosocial mechanisms among employees

may likely differ between the two sectors (e.g., job security, effort-reward). It is plausible that

the financial crisis of 2008 may have contributed to excess fear of job loss in the study period

60

from 2009 to 2014. However, working in the Danish healthcare sector is traditionally

considered as a secure and stable employment, and excess fear of job loss may have been

most pronounced among employees in the private sector.

Finally, the presented results are concurrent with prior findings of population-based studies of

public and private sectors in Denmark and Sweden.89,91,93,97 This supports that the findings of

this thesis are generalizable to other occupational contexts than the Capital Region of

Denmark. Generalizability is further supported by the consistency in the findings of

organizational change as risk factor for different adverse employee outcomes all related to

high levels of psychological stress.

61

Conclusions

This thesis demonstrated longitudinal associations between six types of organizational

changes in the work unit (i.e. mergers, demergers/split-ups, relocation, change in

management, employee layoff, budget cuts) and higher rate ratios of subsequent employee

turnover as well as higher risk of detrimental health outcomes among employees relative to no

changes. Specifically, there was up to 50% higher rate ratio/relative risk of subsequent

employee exit from the work unit, non-disability early retirement (Danish: “efterløn”),

sickness absence, and prescriptions for psychotropic medication, whereas 120-190% higher

relative risk of incident ischemic heart disease. Bias and confounding were not considered as

likely explanations of these findings.

There was no strong evidence of specific types of organizational change being particularly

associated with all the employee outcomes studied, yet organizational changes involving

employee layoffs were more consistently associated with higher relative risk of detrimental

health among employees. A greater number of organizational changes occurring

simultaneously was not consistently associated with more adverse health, but there were

indications of a particularly high rate ratio of employee exit from the work unit. Some

evidence suggested that specific indicators of organizational change were related to specific

employee outcomes; however, more research is needed to support this. Findings on

associations with prescriptions for psychotropic medication pointed to a latency period before

the observation of adverse mental health effects.

Organizational change in the work unit was associated with higher relative risk of low work-

unit social capital. Lower levels of work-unit social capital, quality of management, and

organizational justice were associated with higher rate ratios of employee turnover. The

present thesis did not provide convincing evidence of work-unit social capital as a mediator

on the associations between organizational changes and employee turnover. There were

indeed indications that the combination of work-unit social capital, quality of management,

and organizational justice explained some of the association between specific types of

organizational change and higher rate ratios of non-disability early retirement.

It is time for policy and decision makers to consider adverse impacts of organizational

changes on the employees. Findings from this thesis indicate that organizational change is a

substantial and unneglectable characteristic of modern work life, since about half of the

employees were exposed to any changes during a one-year period. Excess rates of employee

62

turnover and sickness absence as well as physical and mental illness may not only be a burden

to the individual, but also to society in terms of excessive costs related to loss of productivity,

healthcare treatment, and public transfer payments.

63

Perspectives

Provided there is a causal effect of organizational changes on excess employee turnover and

adverse employee health, perspectives for diminishing such detrimental effects are outlined in

the following. As for the theories on work-related stress, there seem neither to be consensus

about a general approach to mitigate negative employee effects of organizational changes.

A review on interventions to reduce job stress concluded that a combination of employee- and

organization-focused initiatives had the most promising effect.162 This suggests that it is

important to consider preservation of employee well-being at the organizational level when

planning and executing reorganizations.

In line with the job demand-control-support model26 and empirical findings from a workplace

closure,159 social support from the colleagues and immediate managers may mitigate negative

effects of organizational changes among employees. In addition, involvement of employees to

influence and participate in the change process may increase job control and diminish job

strain.61,162 Such involvement could, for example, include employee influence on the initiation

date of the changes, future workplace location, and activities for skill development. Employee

prospects of redundancy in the post-changed workplace due to lack of skills could give rise to

higher job insecurity and increase competition among colleagues for keeping their job.

Detrimental effects of organizational changes could also be reduced by realistically adjusting

demands for productivity and quality of service to the capabilities of the employees during a

reorganization process.61

Organizational change at work may include many hierarchical levels, and it is reasonable to

expect a psychosocial spillover effect from reorganized work units to other immediate work

units (e.g., within the same department). These spillover effects could, for example, include

workload intensification, perceived unfairness towards colleagues, and anticipation of future

changes or downsizing waves (“Will it be us next time?”). Interventions to diminish

detrimental outcomes of organizational changes should not only target those within the work

unit undergoing changes, but also employees and managers in other relevant workplace

entities. Previous studies concluded that the mere anticipation of organizational changes was

associated with negative employee outcomes,94,104,154 and managerial communication to all

relevant employees about change prospects may reduce job insecurity in this regard.150,163

Although reorganization may lead to positive changes (e.g., improved work environment,

opportunities for promotion, skill development etc.) people tend, in general, to avoid losses

64

over obtaining equivalent gains,164 which could explain the excessive employee focus on

negative expectations to changes at work. Indeed, negative expectations among employees

may also be shaped by internal contextual factors,118 such as the organizational history of

prior restructuring events.100

A prerequisite for mitigating stress-related effects of organizational change is that managers

and decision makers are aware of the occupational hazards of organizational change, the

psychosocial dynamics involved in such changes, and possible options for effective

prevention strategies. However, in a recent survey among members of the Danish Association

of Managers, 61% responded that they were “to some extent” or “to a little extent/not at all”

suited for dealing with stress-related problems among employees, while 96% considered it as

part of their management task to deal with employee stress. Further, only 28% responded that

their workplace offered skill-discretion activities to their managers for prevention or

management of employee stress.165 This points to a need for systematically increasing

managers’ level of competency in dealing with work-related stress to ensure a healthy

psychosocial work environment for their subordinates as well as themselves.

Future research

It remains as a key objective for future research to understand how negative (as well as

positive) effects of organizational changes develop. Employees may react differently to

different types of organizational changes, and future research will likely benefit from focusing

on specific change types and their content. In this regard, it seems imperative to measure the

organizational changes at a lower level in the hierarchical workplace structure to establish

such detailed exposure measure. To better assess immediate and temporality in employee

outcomes, baseline should be set at the exact time point when prospects of organizational

changes were known among employees. It may, however, be practically challenging to define

an exact onset of organizational change for all employees, since rumors about forthcoming

changes may spread within the workplace even before an official announcement.

In addition, the implementation of organizational changes may be important for the

psychosocial and health repercussions among employees. Previous studies have demonstrated

that redeploying and supporting redundant staff were with greater psychological well-

being.92,166 Few epidemiological studies have studied employee impacts of the manner in

65

which the organizational changes are executed. The literature has predominantly focused on

employees as passive recipients of organizational change. Thus, there is a need for elucidating

how employees actively cope with changes at work167 and under which circumstances

positive employee outcomes are observed.168

Also, the various complex processes of organizational changes unfolding over time has

received undeservedly scarce attention within occupational health research; however, this

complexity should be reflected in future occupational health research to gain a better

understanding of the effects of organizational change. It seems reasonable that certain stages

during a change process may be particularly relevant for adverse employee health and

turnover.158 Elucidating such pivotal stages during the change process may qualify the

optimal time points for initiating interventions to diminish negative employee effects.

Finally, more empirical research studies should examine the mediating as well as moderating

factors of organizational change on employee turnover and health. A better understanding of

such factors may likely depart from mixed methods research to integrate qualitative aspects

(e.g., of contextual factors that facilitate or hinder change implementation) and

generalizability of the findings.167 Psychosocial factors with such mediating and/or

moderating properties may comprise specific targets for consideration when planning and

executing organizational changes to hamper negative employee effects. In this regard,

workplace social capital seems a promising target of organizational intervention as an

increasing body of evidence highlights this psychosocial factor as an important determinant

for health and well-being among employees. However, more research is required to evaluate

organizational change as a long-term risk factor for low workplace social capital.

66

References

1. Gaynor M, Laudicella M, Propper C. Can governments do it better? Merger mania and

hospital outcomes in the English NHS. J Health Econ. 2012;31(3):528–543.

2. European Foundation for the Improvement of Living and Working Conditions

(Eurofound). ERM annual report 2014: restructuring in the public sector. Publications

Office of the European Union, Luxembourg; 2015.

3. Gay P du, Vikkelsø S. Reflections: On the Lost Specification of ‘Change’. J Chang

Manag. 2012;12(2):121–143.

4. Kompier MA. New systems of work organization and workers’ health. Scand J Work

Environ Health. 2006;32(6):421–430.

5. Finansministeriet. Sygehusene fritages for produktivitetskrav i 2018 [Internet]. 2017

[cited 2018 Nov 29]. Available from:

https://www.fm.dk/nyheder/pressemeddelelser/2017/10/sygehusene-fritages-for-

produktivitetskrav-i-2018

6. Smaglik P. Job movements. Nature. 2005;433(7028):901.

7. Ven AH. van de, Poole MS. Explaining Development and Change in Organizations.

Acad Manag Rev. 1995;20(3):510–540.


(Eurofound). Third European Company Survey – Overview report: Workplace

practices – Patterns, performance and well-being [Internet]. 2015. Available from:

http://www.eurofound.europa.eu/sites/default/files/ef_publication/field_ef_document/e

f1502en_0.pdf


(Eurofound). Sixth European Working Conditions Survey – Overview report,

Publications Office of the European Union, Luxembourg. [Internet]. 2016. Available

from: https://www.eurofound.europa.eu/data/european-working-conditions-

survey?locale=EN&dataSource=EWCS2016&media=png&width=740&question=y15_

Q88&plot=euBars&countryGroup=linear&subset=agecat_3&subsetValue=All

10. Cohen S, Janicki-Deverts D, Miller GE. Psychological stress and disease. JAMA Am

Med Assoc. 2007;298(14):1685–1687.

11. Schnall PL, Dobson M, Landsbergis P. Globalization, Work, and Cardiovascular

Disease. Int J Heal Serv. 2016;46(4):656–692.

12. Kivimäki M, Kawachi I. Work Stress as a Risk Factor for Cardiovascular Disease.

Curr Cardiol Rep. 2015;17(9).

13. Stansfeld S, Candy B. Psychosocial work environment and mental health - A meta-

analytic review. Scand J Work Environ Heal. 2006;32(6):443–462.

14. Wang H, Naghavi M, Allen C, et al. Global, regional, and national life expectancy, all-

cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a

systematic analysis for the Global Burden of Disease Study 2015. Lancet.

2016;388(10053):1459–1544.

15. Vos T, Allen C, Arora M, et al. Global, regional, and national incidence, prevalence,

and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic

analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388(10053):1545–

1602.

16. Hassard J, Teoh KRH, Visockaite G, Dewe P, Cox T. The cost of work-related stress to

society: A systematic review. J Occup Health Psychol. 2018;23(1):1–17.

17. Gimeno D, Benavides FG, Benach J, Amick BC. Distribution of sickness absence in

the European Union countries. Occup Environ Med. 2004;61(10):867–869.

18. Henderson M, Glozier N, Elliot KH. Long term sickness absence. BMJ Br Med J.

67

2005;330(April):802–3.

19. Krane L, Johnsen R, Fleten N, et al. Sickness absence patterns and trends in the health

care sector: 5-year monitoring of female municipal employees in the health and care

sectors in Norway and Denmark. Hum Resour Health. 2014;12(1):1–8.

20. Kouwenhoven-Pasmooij TA, Burdorf A, Roos-Hesselink JW, Hunink MGM, Robroek

SJW. Cardiovascular disease, diabetes and early exit from paid employment in Europe;

The impact of work-related factors. Int J Cardiol. Elsevier Ireland Ltd;

2016;215(2016):332–337.

21. Rijn RM Van, Robroek SJW, Brouwer S, Burdorf A. Influence of poor health on exit

from paid employment: A systematic review. Occup Environ Med. 2014;71(4):295–

301.

22. Hayes LJ, O’Brien-Pallas L, Duffield C, et al. Nurse turnover: A literature review - An

update. Int J Nurs Stud. Elsevier Ltd; 2012;49(7):887–905.

23. O’Brien-Pallas L, Murphy GT, Shamian J, Li X, Hayes LJ. Impact and determinants of

nurse turnover: A pan-Canadian study. J Nurs Manag. 2010;18(8):1073–1086.

24. Waldman JD, Kelly F, Arora S, Smith HL. The shocking cost of turnover in health

care. Health Care Manage Rev. 2010;35(3):206–211.

25. Sanderson WC, Scherbov S. Remeasuring aging. Science (80- ).

2010;329(5997):1287–1288.

26. Karasek RA, Theorell T. Healthy Work: Stress, Productivity, and the Reconstruction of

Working Life. New York: Basic Books; 1990.

27. Karasek RA. Job Demands, Job Decision Latitude, and Mental Strain: Implications for

Job Redesign. Adm Sci Quaterly. 1979;24(2):285–308.

28. Elovainio M, Kivimäki M, Vahtera J. Organizational justice: Evidence of a new

psychosocial predictor of health. Am J Public Health. 2002;92(1):105–108.

29. Ndjaboué R, Brisson C, Vézina M. Organisational justice and mental health: a

systematic review of prospective studies. Occup Environ Med. 2012;69(10):694–700.

30. Cohen-Charash Y, Spector PE. The role of justice in organizations: A meta-analysis.

Organ Behav Hum Decis Process. 2001;86(2):278–321.

31. Kawachi I, Berkman L. Social Capital, Social Cohesion, and Health. In: Berkman L,

Kawachi I, Glymour M, editors. Soc Epidemiol. 2nd ed. 2014. p. 290–319.

32. Kawachi I, Kim D, Coutts A, Subramanian S V. Commentary: Reconciling the three

accounts of social capital. Int J Epidemiol. 2004;33(4):682–690.

33. Siegrist J. Adverse health effects of high effort-low reward conditions at work. J Occup

Heath Psychol. 1996;(1):27–43.

34. Bakker A, Demerouti E. Job Demands–Resources Theory: Taking Stock and Looking

Forward Arnold. J Occup Health Psychol. 2017;22(3):273–285.

35. Cheng GHL, Chan DKS. Who suffers more from job insecurity? A meta-analytic

review. Appl Psychol. 2008;57(2):272–303.

36. Quinlan M, Bohle P. Overstretched and unreciprocated commitment: Reviewing

research on the occupational health and safety effects of downsizing and job insecurity.

Int J Heal Serv. 2009;39(1):1–44.

37. Richardson S, Shaffer JA, Falzon L, Krupka D, Davidson KW, Edmondson D. Meta-

analysis of perceived stress and its association with incident coronary heart disease. Am

J Cardiol. Elsevier Inc.; 2012;110(12):1711–1716.

38. Kivimäki M, Steptoe A. Effects of stress on the development and progression of

cardiovascular disease. Nat Rev Cardiol. 2018;15:215–229.

39. Kivimäki M, Nyberg ST, Batty GD, et al. Job strain as a risk factor for coronary heart

disease: A collaborative meta-analysis of individual participant data. Lancet.

2012;380(9852):1491–1497.

40. Rod NH, Grønbæk M, Schnohr P, Prescott E, Kristensen TS. Perceived stress as a risk

68

factor for changes in health behaviour and cardiac risk profile: A longitudinal study. J

Intern Med. 2009;266(5):467–475.

41. Li J, Zhang MIN, Loerbroks A, Angerer P, Siegrist J. Work Stress And The Risk Of

Recurrent Coronary Heart Disease Events: A Systematic Review And Meta-Analysis.

Int J Occup Med Environ Health. 2015;28(1):8–19.

42. Bonde JPE. Psychosocial factors at work and risk of depression: A systematic review

of the epidemiological evidence. Occup Environ Med. 2008;65(7):438–445.

43. Netterstrøm B, Conrad N, Bech P, et al. The relation between work-related

psychosocial factors and the development of depression. Epidemiol Rev.

2008;30(1):118–132.

44. Jensen HK, Wieclaw J, Munch-Hansen T, Thulstrup AM, Bonde JP. Does

dissatisfaction with psychosocial work climate predict depressive, anxiety and

substance abuse disorders? A prospective study of Danish public service employees. J

Epidemiol Community Health. 2010;64(9):796–801.

45. Wieclaw J, Agerbo E, Bo Mortensen P, Burr H, Tuchsen F, Bonde JP. Psychosocial

working conditions and the risk of depression and anxiety disorders in the Danish

workforce. BMC Public Health. 2008;8:1–9.

46. Åkerstedt T, Garefelt J, Richter A, et al. Work and Sleep—A Prospective Study of

Psychosocial Work Factors, Physical Work Factors, and Work Scheduling. Sleep.

2015;38(7):1129–1136.

47. Laethem M Van, Beckers DG, Kompier MA, Dijksterhuis A, Geurts SA. Psychosocial

work characteristics and sleep quality: a systematic review of longitudinal and

intervention research. Scand J Work Environ Health. 2013;39(6):535–549.

48. Bogaert P Van, Kowalski C, Weeks SM, heusden D Van, Clarke SP. The relationship

between nurse practice environment, nurse work characteristics, burnout and job

outcome and quality of nursing care: A cross-sectional survey. Int J Nurs Stud. Elsevier

Ltd; 2013;50(12):1667–1677.

49. Kowalski C, Driller E, Ernstmann N, et al. Associations between emotional exhaustion,

social capital, workload, and latitude in decision-making among professionals working

with people with disabilities. Res Dev Disabil. 2010;31(2):470–479.

50. Driller E, Ommen O, Kowalski C, Ernstmann N, Pfaff H. The relationship between

social capital in hospitals and emotional exhaustion in clinicians: A study in four

German hospitals. Int J Soc Psychiatry. 2011;57(6):604–609.

51. Oksanen T, Kawachi I, Jokela M, et al. Workplace social capital and risk of chronic

and severe hypertension: A cohort study. J Hypertens. 2012;30(6):1129–1136.

52. Rugulies R, Hasle P, Pejtersen JH, Aust B, Bjorner JB. Workplace social capital and

risk of long-term sickness absence. Are associations modified by occupational grade?

Eur J Public Health. 2016;26(2):328–333.

53. Török E, Clark AJ, Jensen JH, et al. Work-unit social capital and long-term sickness

absence: A prospective cohort study of 32 053 hospital employees. Occup Environ

Med. 2018;75:623–629.

54. Suadicani P, Bonde JP, Olesen K, Gyntelberg F. Job satisfaction and intention to quit

the job. Occup Med (Chic Ill). 2013;63(2):96–102.

55. Josephson M, Lindberg P, Voss M, Alfredsson L, Vingård E. The same factors

influence job turnover and long spells of sick leave - A 3-year follow-up of Swedish

nurses. Eur J Public Health. 2008;18(4):380–385.

56. Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP. Common method biases in

behavioral research: a critical review of the literature and recommended remedies. J

Appl Psychol. 2003;88(5):879–903.

57. Kasl S. Measuring Job Stressors and Studying the Health Impact of the Work

Environment: An Epidemiologic Commentary. J Occup Health Psychol.

69

1998;34(4):390–401.

58. Spector PE. Method variance in organizational research: Truth or urban legend? Organ

Res Methods. 2006;9(2):221–232.

59. Mellenbergh GJ. Item bias and item response theory. Int J Educ Res. 1989;13(2):127–

143.

60. Westgaard RH, Winkel J. Occupational musculoskeletal and mental health:

Significance of rationalization and opportunities to create sustainable production

systems - A systematic review. Appl Ergon. Elsevier Ltd; 2011;42(2):261–296.

61. Jong T de, Wiezer N, Weerd M de, Nielsen K, Mattila-Holappa P, Mockałło Z. The

impact of restructuring on employee well-being: a systematic review of longitudinal

studies. Work Stress. 2016;30(1):91–114.

62. Wahlstedt KGI, Edling C. Organizational changes at a postal sorting terminal - their

effects upon work satisfaction, psychosomatic complaints and sick leave. Work Stress.

1997;11(3):279–291.

63. Bordia P, Hobman E, Jones E, Gallois C, Callan VJ. Uncertainty during organizational

change: Types, consequences, and management strategies. J Bus Psychol.

2004;18(4):507–532.

64. Oreg S, Vakola M, Armenakis A. Change recipients’ reactions to organizational

change: A 60-year review of quantitative studies. J Appl Behav Sci. 2011;47(4):461–

524.

65. Kernan MC, Hanges PJ. Survivor reactions to reorganization: Antecedents and

consequences of procedural, interpersonal, and informational justice. J Appl Psychol.

2002;87(5):916–928.

66. Rafferty AE, Griffin MA. Perceptions of organizational change: A stress and coping

perspective. J Appl Psychol. 2006;91(5):1154–1162.

67. Knudsen HK, Johnson JA, Martin JK, Roman PM. Downsizing survival: The

experience of work and organizational commitment. Sociol Inq. 2003;73(2):265–283.

68. Thorsen SV, Jensen PH, Bjørner JB. Psychosocial work environment and retirement

age: a prospective study of 1876 senior employees. Int Arch Occup Environ Health.

2016;89(6):891–900.

69. Bamberger SG, Vinding AL, Larsen A, et al. Impact of organisational change on

mental health: a systematic review. Occup Environ Med. 2012;69(8):592–598.

70. Baron JN, Hannan MT, Burton MD. Labor Pains: Change in Organizational Models

and Employee Turnover in Young, High-Tech Firms. Am J Sociol. 2001;106(4):960–

1012.

71. Bauer TK, Bender S. Technological change, organizational change, and job turnover.

Labour Econ. 2004;11(3):265–291.

72. Cameron K, Kim M, Whetten D. Organizational Effects of Decline and Turbulence.

Adm Sci Quaterly. 1987;32(2):222–240.

73. Ingelsrud MH. Hospital Mergers in Norway: Employee Health and Turnover to Three

Destinations. Nord J Work Life Stud. 2017;7(1):107–124.

74. Probst TM. Exploring employee outcomes of organizational restructuring: A solomon

four-group study. Gr Organ Manag. 2003;28(3):416–439.

75. Sylling PW, Wong ES, Liu C-F, et al. Patient-Centered Medical Home Implementation

and Primary Care Provider Turnover. Med Care. 2014;52(12):1017–1022.

76. Vahtera J, Kivimäki M, Forma P, et al. Organisational downsizing as a predictor of

disability pension: The 10-town prospective cohort study. J Epidemiol Community

Health. 2005;59(3):238–242.

77. Wind A de, Geuskens GA, Ybema JF, et al. Health, job characteristics, skills, and

social and financial factors in relation to early retirement - Results from a longitudinal

study in the Netherlands. Scand J Work Environ Heal. 2014;40(2):186–194.

70

78. Bernstrøm VH, Kjekshus LE. Effect of organisational change type and frequency on

long-term sickness absence in hospitals. J Nurs Manag. 2015;23(6):813–822.

79. Dragano N, Verde PE, Siegrist J. Organisational downsizing and work stress: Testing

synergistic health effects in employed men and women. J Epidemiol Community

Health. 2005;59(8):694–699.

80. Ingelsrud MH. Reorganization increases long-term sickness absence at all levels of

hospital staff: panel data analysis of employees of Norwegian public hospitals. BMC

Health Serv Res. 2014;14:411.

81. Kivimäki M, Vahtera J, Pentti J, Ferrie JE. Factors underlying the effect of

organisational downsizing on health of employees: longitudinal cohort study. BMJ.

2000;320(8):971–975.

82. Kivimäki M, Vahtera J, Pentti J, Thomson L, Griffiths A, Cox T. Downsizing, changes

in work, and self-rated health of employees: A 7-year 3-wave panel study. Anxiety,

Stress Coping. 2001;14(1):59–73.

83. Kivimäki M, Vahtera J, Elovainio M, Pentti J, Virtanen M. Human costs of

organizational downsizing: Comparing health trends between leavers and stayers. Am J

Community Psychol. 2003;32(1–2):57–67.

84. Kjekshus LE, Bernström VH, Dahl E, Lorentzen T. The effect of hospital mergers on

long-term sickness absence among hospital employees: a fixed effects multivariate

regression analysis using panel data. BMC Heal Serv Res. 2014;14:50.

85. Kokkinen L, Virtanen M, Pentti J, Vahtera J, Kivimäki M. Does transfer of work from

a public sector organisation to a commercial enterprise without staff reductions

increase risk of long-term sickness absence among the staff ? A cohort study of

laboratory and radiology employees. 2013;8–11.

86. Røed K, Fevang E. Organizational Change, Absenteeism, and Welfare Dependency. J

Hum Ressources. 2007;42(1):156–193.

87. Theorell T, Oxenstierna G, Westerlund H, Ferrie JE, Hagberg J, Alfredsson L.

Downsizing of staff is associated with lowered medically certified sick leave in female

employees. Occup Environ Med. 2003;60(9):E9.

88. Vahtera J, Kivimäki M, Pentti J. Effect of organisational downsizing on health of

employees. Lancet. 1997;350(9085):1124–1128.

89. Westerlund H, Ferrie J, Hagberg J, Jeding K, Oxenstierna G, Theorell T. Workplace

expansion, long-term sickness absence and hospital admission. Lancet.

2004;363(9416):1193–1197.

90. Østhus S, Mastekaasa A. The impact of downsizing on remaining workers’ sickness

absence. Soc Sci Med. 2010;71(8):1455–1462.

91. Blomqvist S, Alexanderson K, Vahtera J, Westerlund H, Hanson LM. Downsizing and

purchases of psychotropic drugs: A longitudinal study of stayers, changers and

unemployed. PLoS One. 2018;13(8):e0203433.

92. Brenner MH, Andreeva E, Theorell T, et al. Organizational downsizing and depressive

symptoms in the European recession: The experience of workers in France, Hungary,

Sweden and the United Kingdom. PLoS One. 2014;9(5).

93. Dahl MS. Organizational Change and Employee Stress. 2011;57(2):240–256.

94. Falkenberg H, Fransson EI, Westerlund H, Head JA. Short- and long-term effects of

major organisational change on minor psychiatric disorder and self-rated health:

Results from the Whitehall II study. Occup Environ Med. 2013;70(10):688–696.

95. Fløvik L, Knardahl S, Christensen JO. Organizational change and employee mental

health: A prospective multilevel study of the associations between organizational

changes and clinically relevant mental distress. Scand J Work Environ Health.

2018;(online first):1–12.

96. Greubel J, Kecklund G. The impact of organizational changes on work stress, sleep,

71

recovery and health. Ind Health. 2011;49(3):353–364.

97. Hanson LLM, Westerlund H, Chungkham HS, Vahtera J, Sverke M, Alexanderson K.

Purchases of prescription antidepressants in the Swedish population in relation to major

workplace downsizing. Epidemiology. 2016;27(2):257–264.

98. Kivimäki M, Honkonen T, Wahlbeck K, et al. Organisational downsizing and

increased use of psychotropic drugs among employees who remain in employment. J

Epidemiol Community Heal. 2007;61(2):154–158.

99. Loretto W, Platt S, Popham F. Workplace change and employee mental health: results

from a longitudinal study. Br J Manag. 2010;21(2):526–540.

100. Moore S, Grunberg L, Greenberg E. Surviving repeated waves of organizational

downsizing: The recency, duration, and order effects associated with different forms of

layoff contact. Anxiety, Stress Coping. 2006;19(3):309–329.

101. Netterstrøm B, Blønd M, Nielsen M, Rugulies R, Eskelinen L. Development of

depressive symptoms and depression during organizational change – a two-year

follow-up study of civil servants. Scand J Work Environ Health [Internet].

2010;36(6):445–448. Available from:

http://www.sjweh.fi/show_abstract.php?abstract_id=3075

102. Väänänen A, Ahola K, Koskinen A, Pahkin K, Kouvonen A. Organisational merger

and psychiatric morbidity: A prospective study in a changing work organisation. J

Epidemiol Community Health. 2011;65(8):682–687.

103. Drivas S, Rachiotis G, Stamatopoulos G, Hadjichristodoulou C, Chatzis C. Company

closure and mortality in a Greek bus company. Occup Med (Chic Ill). 2013;63(3):231–

233.

104. Ferrie JE, Shipley MJ, Marmot MG, Stansfeld S, Smith GD. The Health Effects of

Major Organisational Change and Job Insecurity. Soc Sci Med. 1998;46(2):243–254.

105. Möller J, Theorell T, Faire U De, Ahlbom A, Hallqvist J. Work related stressful life

events and the risk of myocardial infarction. Case-control and case-crossover analyses

with the Stockholm heart epidemiology programme (SHEEP). J Epidemiol Community

Health. 2005;59(1):23–30.

106. Pollard TM. Changes in mental well-being, blood pressure and total cholesterol levels

during workplace reorganization: The impact of uncertainty. Work Stress.

2001;15(1):14–28.

107. Vahtera J, Kivimäki M, Pentti J, et al. Organisational downsizing, sickness absence,

and mortality: 10-town prospective cohort study. BMJ. 2004;328(7439):555.

108. Huczynski A, Fitzpatrick MJ. Managing employee absence for a competitive edge.

Pitman; 1989.

109. Rhodes SR, Steers RM. Managing Employee Absenteeism. Reading, MA: Addison-

Wesley; 1990.

110. Grinyer A, Singleton V. Sickness absence as risk-taking behaviour: A study of

organisational and cultural factors in the public sector. Health Risk Soc. 2000;2(1):7–

21.

111. Aronsson G, Gustafsson K, Dallner M. Sick but yet at work. An empirical study of

sickness presenteeism. J Epidemiol Community Health. 2000;54(7):502–509.

112. Andreeva E, Magnusson Hanson LL, Westerlund H, Theorell T, Brenner MH.

Depressive symptoms as a cause and effect of job loss in men and women: Evidence in

the context of organisational downsizing from the Swedish Longitudinal Occupational

Survey of Health. BMC Public Health. BMC Public Health; 2015;15(1):1–11.

113. Julian MW. The consequences of ignoring multilevel data structures in nonhierarchical

covariance modeling. Struct Equ Model. 2001;8(3):325–352.

114. Rosenquist JN, Fowler JH, Christakis NA. Social network determinants of depression.

Mol Psychiatry. Nature Publishing Group; 2011;16(3):273–281.

72

115. Bliese PD, Hanges PJ. Being both too liberal and too conservative: The perils of

treating grouped data as though they were independent. Organ Res Methods.

2004;7(4):400–417.

116. Diez-Roux A. Bringing Context Back into Epidemiology: Variables and Fallacies in

Multilevel Analysis. Am J Public Health. 1998;88(2):216–222.

117. Bordia P, Hunt E, Paulsen N, Tourish D, DiFonzo N. Uncertainty during organizational

change: Is it all about control? Eur J Work Organ Psychol. 2004;13(3):345–365.

118. Walker HJ, Armenakis AA, Bernerth JB. Factors influencing organizational change

efforts: An integrative investigation of change content, context, process and individual

differences. J Organ Chang Manag. 2007;20(6):761–773.

119. Hertting A, Nilsson K, Theorell T, Larsson US. Downsizing and reorganization:

Demands, challenges and ambiguity for registered nurses. J Adv Nurs. 2004;45(2):145–

154.

120. Hopkins SM, Weathington BL. The relationships between justice perceptions, trust,

and employee attitudes in a downsized organization. J Psychol Interdiscip Appl.

2006;140(5):477–498.

121. Armstrong-Stassen M, Cameron SJ, Mantler J, Horsburgh ME. The Impact of Hospital

Amalgation on the Job Attitudes of Nurses. Can J Adm Sci. 2001;18(3):149–162.

122. Ballinger GA, Cross R, Holtom BC. The right friends in the right places:

Understanding network structure as a predictor of voluntary turnover. J Appl Psychol.

2016;101(4):535–548.

123. Spreitzer GM, Mishra AK. To stay or to go: Voluntary survivor turnover following an

organizational downsizing. J Organ Behav. 2002;23(6):707–729.

124. Sung W, Woehler ML, Fagan JM, Grosser TJ, Floyd TM, Labianca G (Joe).

Employees’ responses to an organizational merger: Intraindividual change in

organizational identification, attachment, and turnover. J Appl Psychol.

2017;102(6):910–934.

125. Oshio T, Inoue A, Tsutsumi A. The Mediating and Moderating Effects of Workplace

Social Capital on the Associations between Adverse Work Characteristics and

Psychological Distress among Japanese Workers. Ind Health. 2014;52(4):313–323.

126. Baron RM, Kenny DA. The Moderator-Mediator Variable Distinction in Social The

Moderator-Mediator Variable Distinction in Social Psychological Research:

Conceptual, Strategic, and Statistical Considerations. J Pers Soc Psychol.

1986;51(6):1173–1182.

127. Patienthåndbogen. Sygemelding og sygedagpenge, lønmodtagere og ledige [Internet].

Sundhed.dk 2018 [cited 2018 Oct 9]. p. 1. Available from:

https://www.sundhed.dk/borger/patienthaandbogen/sociale-ydelser/sociale-

ydelser/sygedagpenge/sygemelding-og-sygedagpenge-loenmodtagere-og-ledige/

128. Marmot M, Feeney A, Shipley M, North F, Syme SL. Sickness absence as a measures

of health status and functioning: from the UK Whitehall II study. J Epidemiol

Community Heal. 1995;49:124–130.

129. Kivimaki M, Head J, Ferrie JE, Shipley MJ, Vahtera J, Marmot MG. Sickness absence

as a global measure of health: evidence from mortality in the Whitehall II prospective

cohort study. BMJ. 2003;327(7411):364.

130. Hjollund NH, Larsen FB, Andersen JH. Register-based follow-up of social benefits and

other transfer payments: Accuracy and degree of completeness in a Danish

interdepartmental administrative database compared with a population-based survey.

Scand J Public Health. 2007;35(5):497–502.

131. Pottegård A, Schmidt SAJ, Wallach-Kildemoes H, Sørensen HT, Hallas J, Schmidt M.

Data resource profile: The Danish national prescription registry. Int J Epidemiol.

2017;46(3):798.

73

132. Lynge E, Sandegaard JL, Rebolj M. The Danish national patient register. Scand J

Public Health. 2011;39(7):30–33.

133. Helweg-Larsen K. The Danish register of causes of death. Scand J Public Health.

2011;39(7):26–29.

134. Pejtersen JH, Kristensen TS, Borg V, Bjorner JB. The second version of the

Copenhagen Psychosocial Questionnaire. Scand J Public Health. 2010;38(3 Suppl):8–

24.

135. Cortina JM. What Is Coefficient Alpha? An Examination of Theory and Applications. J

Appl Psychol. 1993;78(1):98–104.

136. Field A. Exploratory factor analysis. In: Field A, editor. Discov Stat Using IBM SPSS

Stat. 4th editio. SAGE Publications; 2013. p. 665–719.

137. Guyatt G, Sackett D, Sinclair J, Hayward R, Cook D, Cook R. Users’ Guides to the

Medical Literature. JAMA. 1995;274(22):1800–1804.

138. Grimes DA, Schulz KF. Cohort studies: marching towards outcomes. Lancet.

2002;359:341–345.

139. Lambert D. Zero-Inflated Poisson Regression, With an Application to Defects in

Manufactoring. Technometrics. 1992;34(1):1–14.

140. Hubbard AE, Ahern J, Fleischer NL, et al. To GEE or not to GEE: Comparing

population average and mixed models for estimating the associations between

neighborhood risk factors and health. Epidemiology. 2010;21(4):467–474.

141. Brandon George. SS and IA. Survival analysis and regression models. NIH Public

Access. 2014;21(4):686–694.

142. Canette I. In the spotlight: Intraclass correlations after multilevel survival models

[Internet]. Stata News 2016 [cited 2018 Jul 31]. p. 31(2). Available from:

https://www.stata.com/stata-news/news31-2/intraclass-correlations/

143. Reeuwijk KG, Wind A De, Westerman MJ, Ybema JF, Beek AJ Van Der, Geuskens

GA. ‘All those things together made me retire’: Qualitative study on early retirement

among Dutch employees. BMC Public Health. BMC Public Health; 2013;13(1):1.

144. Moore S, Grunberg L, Greenberg E. Repeated downsizing contact: The effects of

similar and dissimilar layoff experiences on work and well-being outcomes. J Occup

Health Psychol. 2004;9(3):247–257.

145. Ferrie JE. Labour Market Status, insecurity and health. J Health Psychol. 1997;2:373–

397.

146. Johns G. Presenteeism in the workplace: A review and research agenda. J Organ

Behav. 2010;31:519–542.

147. Virtanen M, Kivimäki M, Elovainio M, Vahtera J, Ferrie JE. From insecure to secure

employment: Changes in work, health, health related behaviours, and sickness absence.

Occup Environ Med. 2003;60(12):948–953.

148. Danmarks Statistik [Internet]. Fuldtidsledige efter område, Pers pct, højeste fuldførte

Uddann alder og køn [cited 2018 Nov 24]. Available from:

http://www.statistikbanken.dk/AUL08

149. Hausknecht JP, Hiller NJ, Vance RJ. Work-unit absenteeism: Effects of satisfaction,

commitment, labor market conditions, and time. Acad Manag J. 2008;51(6):1223–

1245.

150. Gopinath C, Becker TE. Communication, procedural justice, and employee attitudes:

Relationships under conditions of divestiture. J Manage. 2000;26(1):63–83.

151. Morgan DE, Zeffane E. Employee inyolvement , organizational change and trust in

management. Int J Hum Resour Manag. 2003;14(1):55–75.

152. Sørensen OH, Hasle P, Pejtersen JH. Trust relations in management of change. Scand J

Manag. 2011;27:405–417.

153. Oksanen T, Kouvonen A, Vahtera J, Virtanen M, Kivimäki M. Prospective study of

74

workplace social capital and depression: Are vertical and horizontal components

equally important? J Epidemiol Community Health. 2010;64(8):684–689.

154. Ferrie JE, Westerlund H, Virtanen M, Vahtera J, Kivimki M. Flexible labor markets

and employee health. Scand J Work Environ Heal Suppl. 2008;(6):98–110.

155. Westerlund H, Theorell T, Alfredsson L. Organizational instability and cardiovascular

risk factors in white-collar employees. Eur J Public Health. 2004;14(1):37–42.

156. Skakon J, Nielsen K, Borg V, Guzman J. Are leaders’ well-being, behaviours and style

associated with the affective well-being of their employees? A systematic review of

three decades of research. Work Stress. 2010;24(2):107–139.

157. Kivimäki M, Vahtera J, Koskenvuo M, Uutela A, Pentti J. Response of hostile

individuals to stressful change in their working lives: Test of a psychosocial

vulnerability model. Psychol Med. 1998;28(4):903–913.

158. Fugate M, Kinicki AJ, Scheck CL. Coping with an organizational merger over four

stages. Pers Psychol. 2002;55(4):905–928.

159. Mohr GB. The changing significance of different stressors after the announcement of

bankruptcy: A longitudinal investigation with special emphasis on job insecurity. J

Organ Behav. 2000;21(3):337–359.

160. Suttorp MM, Siegerink B, Jager KJ, Zoccali C, Dekker FW. Graphical presentation of

confounding in directed acyclic graphs. Nephrol Dial Transplant. 2015;30(9):1418–

1423.

161. Schyns B, Schilling J. How bad are the effects of bad leaders? A meta-analysis of

destructive leadership and its outcomes. Leadersh Q [Internet]. Elsevier Inc.;

2013;24(1):138–158. Available from: http://dx.doi.org/10.1016/j.leaqua.2012.09.001

162. Semmer NK. Job stress intervention and the organization of work. Scand J Work

Environ Health. 2006;32(6):515–527.

163. Allen J, Jimmieson NL, Bordia P, Irmer BE. Uncertainty during Organizational

Change: Managing Perceptions through Communication. J Chang Manag.

2007;7(2):187–210.

164. Tversky A, Kahneman D. Loss Aversion in Riskless Choice: A Reference-Dependent

Model Author. Quaterly J Econ. 2018;106(4):1039–1061.

165. Lederne. Rapport om virksomheders og lederes håndtering og forebyggelse af stress

blandt medarbejdere [Internet]. 2017. Available from: https://www.lederne.dk/presse-

og-nyheder/undersoegelser/stress-og-trivsel/stress-2017/

166. Andreeva E, Brenner MH, Theorell T, Goldberg M. Risk of psychological ill health

and methods of organisational downsizing: A cross-sectional survey in four European

countries. BMC Public Health. BMC Public Health; 2017;17(1):1–12.

167. Nielsen K, Taris TW, Cox T. The future of organizational interventions: Addressing

the challenges of today’s organizations. Work Stress. 2010;24(3):219–233.

168. Nielsen K, Miraglia M. What works for whom in which circumstances? On the need to

move beyond the ‘what works?’ question in organizational intervention research. Hum

Relations. 2017;70(1):40–62.

75

Papers I-VI

Paper I

Cohort profile: the well-being in hospital employees (WHALE) study

Hvidtfeldt UA, Bjorner JB, Jensen JH, Breinegaard N, Hasle P, Bonde JP, Rod NH

Int J Epidemiol, 2017;46(6):1758-1759h. doi:10.1093/ije/dyx073

Cohort Profile

Cohort Profile: The Well-being in HospitAL

Employees (WHALE) study

Ulla Arthur Hvidtfeldt,1 Jakob Bue Bjorner,1,2,3 Johan Høy Jensen,1,4

Nina Breinegaard,4 Peter Hasle,5 Jens Peter Ellekilde Bonde1,4 and

Naja Hulvej Rod1*

1Department of Public Health, University of Copenhagen, Copenhagen, Denmark, 2National Research

Centre for the Working Environment, Copenhagen, Denmark, 3Quality Metric, Optum Patient Insights,

Lincoln, RI, USA, 4Department of Occupational and Environmental Medicine, Frederiksberg and

Bispebjerg Hospitals, Copenhagen, Denmark and 5Center for Industrial Production, Department of

Business and Management, Aalborg University, Aalborg, Denmark

*Corresponding author. Social Medicine Section, Department of Public Health, University of Copenhagen, CSS, Oster

Farimagsgade 5, DK-1014 Copenhagen, Denmark. E-mail: [email protected]

Editorial decision 10 April 2017; Accepted 14 June 2017

Why was the cohort set up?

Most health care systems face a challenge to balance effi-

ciency and quality under the pressure of limited resources

and budget cuts. Consequently, hospital employees may

face stressful working conditions, which may increase the

risk of health problems as well as poor co-operation be-

tween staff. Combined, these may increase the risk of inef-

ficiency, poor quality of care or even malpractice.1,2 The

Well-being in Hospital Employees (WHALE) study is an

ongoing prospective, observational cohort on work envir-

onment among all health care employees within the

Capital Region of Denmark. The data are collected to con-

tinuously monitor the well-being of employees, in order to

develop targets for potential intervention.

The term ‘psychosocial work environment’ denotes the

interplay of a range of psychological and social factors that

affect the employees’ well-being. The concept of job strain

was introduced with the job demand-control model,3

describing job stress from the balance between high job de-

mands and low control. Self-reported job strain has been

associated with an elevated risk of a number of adverse

health outcomes such as cardiovascular disease,4–6 type 2

diabetes,7 affective disorders8 and mortality.9 Another

aspect of psychosocial work environment is reflected in the

multidimensional concept of organizational justice.10

Here, it is argued that a poor psychosocial work environ-

ment arises from the perception of injustice at work. In

addition, other stressors such as negative interpersonal re-

lations in the workplace have received increasing research

interest, and empirical evidence indicates that bullying is

strongly associated with subsequent depression and ele-

vated risk for cardiovascular disease.11

The psychosocial work environment also encompasses

positive elements with potential beneficial effects on job

performance and individual health in occupational set-

tings. Social capital has been defined as features of a social

structure which facilitate the action of individuals within

the structure.12 Key elements within an occupational set-

ting include norms and trust between co-workers, which

facilitate coordination and co-operation.13 Over the past

decades, social capital in occupational settings has received

increasing attention, as the concept differs from other fac-

tors of the psychosocial work environment mentioned

above, by being a positive resource and by being a charac-

teristic of the workplace rather than an individual percep-

tion of the work environment. In periods of high demand,

VC The Author 2017; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association 1

International Journal of Epidemiology, 2017, 1–10

doi: 10.1093/ije/dyx073

Cohort Profile

http://www.oxfordjournals.org/

high social capital may buffer employees’ stress levels, and

the transmission of informal social norms may lead to

healthier lifestyles.12 A recent prospective study on social

capital in relation to long-term sickness absence found a

decreased risk among higher occupational grade workers

with high vs low social capital.14 In addition, several publi-

cations based on the Finnish Public Sector study have

linked workplace social capital to health outcomes such as

depression,15,16 hypertension,17 healthy lifestyle18,19 and

mortality.20

In a hospital setting, the concept of social capital is par-

ticularly relevant as the work environment, the efficiency,

and the quality of care highly depend on co-operation

across professions. However, the literature on social cap-

ital in hospital settings is sparse. A recent cross-sectional

study among Japanese health care professionals found a

positive association between unit-level social capital and

work engagement as an indicator of well-being.21 In add-

ition, a Swedish prospective study found that an increase

in social capital among health care professionals was

related to higher levels of engagement and job satisfac-

tion.22 Other previous studies related social capital to qual-

ity of care, productivity, patient satisfaction and employee

satisfaction and well-being.2,23–31 A recent large cross-

sectional study found that nurses in better work environ-

ments (as measured by managerial support for nursing,

nurse participation in hospital affairs, and doctor-nurse re-

lations) reported higher care quality and better patient

safety. Also, patients in hospitals with better work environ-

ments rated the hospital more highly.1 Thus, several inter-

national studies suggest that challenges with regard to

efficiency, quality of care and well-being of employees

within the health care sector could in part be met by

strengthening the social capital.

The majority of the above-mentioned studies are cross-

sectional in design and thus not designed to address effects

of social capital by separating cause and effect in time.

Given the collective dimension of social capital, neither

individual nor ecological approaches in isolation will cap-

ture the essence of the concept. Therefore it has been

argued that a multi-level approach to the analysis of health

effects of social capital is more appropriate, taking into ac-

count the organizational levels in which the individual re-

sponses are embedded.32,33 The data collected for the

WHALE study are a unique source to prospectively relate

social capital and other aspects of the psychosocial work

environment to various outcomes, through linkage with

registers on sickness absence, health outcomes, prescription

of medicine, socioeconomic outcomes and questionnaire

data on patient satisfaction. In addition, the structure of the

database provides detailed information on organizational

levels for every individual, which allows for the determin-

ation of effects of dimensions of the work environment at

different aggregate organizational levels. This may help to

address common methods bias which is an important

source of bias in the majority of studies in the field.34

Who is in the cohort?

The cohort includes employees at hospitals within the

Danish Capital Region at the time of the questionnaire as-

sessments in 2011 and 2014. All 35 894 ordinary em-

ployees who were actively working by 1 October 2010

throughout 12 January 2011 were invited to participate in

the survey. Data were collected in the period 12 January to

9 February 2011. Employees with a work e-mail address

filled out a web-based questionnaire, and paper versions

were handed out among remaining employees. A response

rate of 81% was obtained in the 2011 survey. Following

the same procedures, 37 720 employees were invited for a

second survey in March 2014 (response rate 84%). In

2014, the organizations included nine hospitals and institu-

tions involved with handicaps, psychiatry, pharmacy,

emergency care, and central administration (Figure 1).

Information on sex, institution, department, work unit and

professional group was obtained from the payment system.

Figure 1. Overview of the organizational structure of the Danish Capital Region in 2014.

2 International Journal of Epidemiology, 2017, Vol. 0, No. 0

The 2011 sample included 29 004 employees with any

information on the psychosocial work environment,

including 28 785 employees with information on social

capital. After exclusion of 26 persons who had responded

twice (due to being affiliated with two departments), the

total sample comprised 28 759 persons. Likewise, the 2014

sample included a total of 31 823 employees including

30 434 with information on social capital (excluding 11

duplicates). In total, 21 969 of the respondents in 2011

were employed in 2014. Of these, 19 572 responded to the

2014 survey.

The mean age of employees was 45 years [standard de-

viation (SD) ¼ 11] and women constituted 78% and 79%

in 2011 and 2014, respectively. Nurses were the largest

staff group, constituting 31% in 2011 and 34% in 2014,

whereas medical doctors represented 12% of the respond-

ents in both years. Administrative personnel comprised

21% and 19% in 2011 and 2014, respectively. In both

years, 64% were employed full-time and 92% and 93%,

respectively, were appointed on standard terms in 2011

and 2014. The percentage of employees with a seniority of

10 or more years varied from 36% in 2011 to 38% in

2014.

Socio-demographic information on non-respondents

was retrieved from the regional administration system.

enabling comparisons with participants in either of the

waves and with those participating in both waves

(Table 1). The sex distribution was somewhat skewed be-

tween participants and non-respondents, in that a higher

proportion of men was observed among non-respondents.

Also, non-respondents were slightly younger than partici-

pants in both waves, with the exception of employees who

were eligible but declined to participate in any of the sur-

veys. Most notably, medical doctors and dentists were

over-represented among non-respondents in both the 2011

and 2014 survey. Thus, these data suggest that study par-

ticipants represent a somewhat selected group, primarily

with respect to profession.

How often have they been followed up?

Figure 2 illustrates the collection of questionnaire informa-

tion, the data linkages and the sample sizes at each of the

two waves already collected. An additional wave is

planned for 2017. The questionnaire responses from 2011

and 2014 have been linked to individual-level data on sick-

ness absence from the payment system, which were col-

lected on a monthly basis according to number of hours

absent due to: general sickness absence, work injuries and

absence in relation to sick children or chronic illnesses. In

addition, the data linkage included information on stand-

ard working hours for each individual, seniority and length

of employment. The data cover the period from January

2009 to June 2016.

Table 1. Socio-demographic characteristics of participants vs non-respondents in 2011 or 2014, respectively, and participants in

both waves versus only one and neither of the waves

2011 2014 2011 and 2014

Participants Non-respondents Participants Non-respondents Participantsa Only 1 wave None

N (%) 28759 (81) 6908 (19) 30434 (84) 5934 (16) 19589 (75) 5252 (20) 1347 (5)

Sex

Women, % 78 73 79 72 78 74 64

Men, % 22 27 21 28 22 26 36

Age

Mean (SD) 45 (11) 43 (12) 45 (11) 44 (12) 45 (10)b 43 (11)b 45 (11)b

Staff group, %

Medical doctors/dentists 12 20 12 23 11 17 26

Nurses 31 32 33 31 32 35 28

SHS helpers 9 11 7 8 8 10 11

Biomedical lab technician 5 4 6 4 6 4 3

Midwives 1 2 1 2 1 2 3

Medical secretary 7 6 8 5 8 7 5

Other health staff 6 3 6 2 6 3 2

Social and pedagogical 4 2 4 3 4 3 2

Other administrative 14 6 12 9 14 7 4

Service-related 11 13 11 13 16 12 10

aAmong all employees eligible to participate in both 2011 and 2014.bAge in 2011.

International Journal of Epidemiology, 2017, Vol. 0, No. 0 3

The database is also linked to a database on patient per-

ceptions of hospital admissions and outpatient treatments

in the region. Every year a survey on treatment satisfaction

among patients in each hospital department is carried out.

A random sample of all treated patients during a specified

inclusion period (12 weeks from August to October for

admitted patients and 6 weeks from the end of August to

the beginning of October for outpatients) are invited to

participate. The sample consists of 400 patients per depart-

ment per area of specialization. In departments with less

than 400 treated patients during the inclusion period, all

patients are invited. Patients both admitted and treated in

outpatient care are only invited once.35

In addition to linkage with the above-mentioned data

sources, it is possible to link the survey data to various

registers. Through the unique personal identification num-

ber, information on vital status, cause-specific mortality,

hospital admissions and discharge diagnoses, prescription

of medicine and emigration can be obtained.36

Information on organizational changes (merging and div-

ision of work units, change in management, relocations,

cuts in staff and financial down-sizing) in the period 2009–

13 was also collected.37

What has been measured?

The survey included a broad range of questions concerning

the overall well-being of employees in which psychological

and physical work environment are key elements (Box 1).

The 2011 survey included a total of 46 questions on the

psychosocial work environment. Of these, 29 questions

were derived from the second version of the Copenhagen

Psychosocial Questionnaire (COPSOQII).38,39 The remain-

ing questions were formulated by the human resources

(HR) department, the management and employee represen-

tatives. The 2014 wave included 40 questions on the psy-

chosocial work environment, of which 37 were also

included in 2011. A complete list of items is provided in

the Appendix (available as Supplementary data at IJE on-

line). The dimensions of the physical work environment

included in 2011 and 2014 were identical and covered by

26 items in the two waves.

Social capital

The data on social capital were collected by eight items

covering elements of trust, justice and collaboration

(Figure 3). These items reflect both horizontal (relations

across employees at the same hierarchy level) as well as

vertical components (i.e. relations that span hierarchies).

The responses were re-computed into percentages and the

social capital score was given by the percentage mean.

Participants who responded to at least four of the eight

items were included in the present analyses. Person mean

imputation was performed for missing values by assigning

the mean of the remaining responses to each individual. If

the person responded to less than four of the eight items,

the social capital score was computed as missing.14 We as-

sessed social capital at the individual level (by applying

each individual assessment) and aggregated the mean so-

cial capital score within each department. Department-

Figure 2. Illustration of the collection of questionnaire information, the data linkages and the sample sizes at each of the two completed waves.


level social capital was regarded missing if based on data

from less than 50% of the eligible employees.

Other dimensions of the psychosocial work

environment

The psychosocial work environment was measured accord-

ing to dimensions of work demands (quantitative, emo-

tional as well as work pace), organization and content

(influence, possibilities for development/skill discretion),

perceived stress and burn-out, work-family imbalance,

management and collaboration (predictability, recognition,

role clarity, social support and quality of leadership) and

job satisfaction as well as exposure to sexual harassment,

violence and bullying (Box 1; and Appendix).

Physical work environment

The physical work environment concerns ergonomics, indoor

climate, noise, exposure to chemical or biological agents (e.g.

transmission risk) and work-related accidents (Box 1).

What has it found? Key findings andpublications

The overall mean level of social capital did only change

marginally between the two surveys [67.4 (SD¼ 14.9) in

2011 and 68.3 (SD¼ 15.2) in 2014]. The Cronbach’s alpha

coefficient of the social capital scale was 0.83 for the 2011

survey and 0.85 for the 2014 survey.40 In both waves, the

social capital mean was on average evaluated slightly

Box 1. Listing of individual level measures in the WHALE database

2011 2014 2011 2014

Socio-demographic Psycho-social work environment

Social capital

Age V V Trust regarding management V V

Sex V V Justice V V

Profession V V Collaboration V V

Institution/hospital V V Work demands

Department V V Emotional V

Unit V V Quantitative V V

The physical work environment

Ergonomics Stress and vitality

Lifts, movements V V Work-family imbalance V V

Potential for correct movement V V Perceived stress V V

(variation, monotony) Perceived burn-out V

Indoor climate and noise Organization and content

Temperature, air, cleaning V V Decision authority V V

Noise V V Influence on work schedule V V

Skill discretion V V

Safety/transmission risks Management and collaboration

Chemical agents exposure V V Quality of leadership V V

Biological agents exposure V V Recognition V V

Medicine exposures V V Social support V V

Skin affections V V Role clarity V

Necessary safety precautions V V Respect for differences V V

Predictability V V

Work-related accidents Job satisfaction

Sufficient focus on accidents V V Work environment V V

Specific conditions of importance Use of abilitites V V

for the risk of accidents V V Future prospects V V

Usage of precautionary equipment V V Job as a whole V V

Sickness absence Offensive behaviours

Related to work environment V V Sexual harassment V V

Specific environmental causes of Threats and violence V V

absence V V Bullying V V

Pregnancy Professional quality

Degree of necessary work-environmental precautions V V Explicit criteria for professional quality V V

Satisfaction with quality of work V V

Pride in work V V


higher among women compared with men, but it did not

vary noticeably according to age (Table 2). The highest

level of social capital was observed among medical doctors

and dentists. In both waves, social capital scores were

observed among Social and Health Service (SHS) helpers,

medical secretaries and service-related staff. In 2014,

biomedical laboratory technician and midwives also scored

below average. The social capital level did not vary

considerably between full-time vs part-time employees.

Employees on standard terms had a social capital score

considerably lower than the remaining employment

groups; especially persons who were appointed on fixed

terms (job activation, trainees, paid by the hour etc.) re-

ported high levels of social capital. However, these types of

employment constituted a very small proportion of the

total number of employees. With regard to the seniority of

employees, staff with less than 4 years of employment hadFigure 3. The eight items covering the social capital elements of trust,

justice and collaboration.

Table 2. Individually measured social capital according to socio-demographic characteristics of the cohort in 2011 and 2014

2011 2014

N Social capital mean (SD) N Social capital mean (SD)

Sex

Women 22437 68 (14) 24043 69 (15)

Men 6322 66 (16) 6391 67 (16)

Age

< 40 years 9905 68 (15) 10577 69 (15)

40–51 years 9832 67 (15) 9995 68 (15)

52þ years 9022 67 (15) 9862 68 (15)

Staff group

Medical doctors and dentists 3429 70 (15) 3776 71 (14)

Nurses 8903 68 (14) 10156 69 (14)

SHS helpers 2585 65 (15) 2167 65 (15)

Biomedical lab technician 1508 67 (14) 1705 65 (15)

Midwives 308 68 (13) 447 66 (14)

Medical secretary 2133 65 (15) 2285 66 (15)

Other health staff 1664 68 (14) 1792 69 (14)

Social and pedagogical 1173 68 (15) 1187 68 (16)

Other administrative 3960 69 (15) 3560 70 (15)

Service-related 3033 63 (17) 3205 65 (17)

Monthly work hours

Part time 10406 67 (14) 10977 68 (15)

Full time 18154 68 (15) 19457 69 (15)

Type of employment

Standard terms 26318 67 (15) 28388 68 (15)

Paid on an hourly basis 26 73 (17) 31 76 (15)

Tenured 1274 69 (15) 973 70 (16)

Paid through funding 845 72 (15) 949 73 (14)

Trainee 35 73 (14) 63 73 (16)

Job activation 54 76 (12) 21 77 (16)

Seniority

< 48 months 10454 68 (15) 8980 70 (15)

48–119 months 7793 67 (15) 9938 67 (15)

120þ months 10311 67 (15) 11516 68 (15)


higher levels of social capital compared with staff who had

been employed for 4 years or more in 2014.

The intraclass correlations for social capital within de-

partments were 0.11 in 2011 and 0.13 in 2014. In both

waves, department-level social capital was higher among

departments with fewer employees compared with larger

departments and also in departments with (on average)

younger compared with older employees (Table 3).

Likewise, departments with fewer senior staff had higher

social capital compared with departments with higher seni-

ority. The department-level score did not vary according to

sex distribution in 2011, but in 2014 departments with a

larger share of women had a higher social capital com-

pared with departments with fewer women. In 2014, the

department mean social capital was slightly higher in de-

partments with a larger share of full-time employees. The

social capital means for type of department did not show

any obvious patterns.

Previous findings

The cohort was assembled recently for research purposes,

and thus previous findings are sparse. However, the 2011

survey was used for a study examining the impact of or-

ganizational changes on psychosocial work environment

and voluntary non-disability early retirement in senior

employees (aged 60–64 years). This study found that senior

employees who had experienced organizational change, in

terms of change in management or reorganization of work

units in the 2-year period preceding follow-up, were more

likely to retire early than those who had not experienced

such changes. Also, early withdrawal from the labour mar-

ket was related to poor psychosocial work environment

measured in the 2011 survey. For instance, low scores on

factors such as organizational justice, quality of manage-

ment and social capital were associated with a higher rate

of early retirement.37

What are the main strengths andweaknesses?

A major strength of the database is the relatively large sam-

ple size, and the two rounds of questionnaire measure-

ments separated in time allow for analyses of effects of

changes over time for various dimensions of the psychoso-

cial work environment. As mentioned previously, the

structure of the database makes it possible to determine di-

mensions of the psychosocial work environment on differ-

ent aggregate organizational levels. Compared with a

measure solely based on perceptions of the work environ-

ment as measured individually, the aggregate level may

better reflect the theoretical concept of social capital. The

Table 3. Department-level social capital according to socio-demographic characteristics of the cohort in 2011 and 2014

2011 2014

N Social capital mean (SD) N Social capital mean (SD)

Department size

Small (N<20) 80 71 (9) 115 75 (10)

Medium (N¼20–99) 169 68 (6) 107 71 (7)

Large (N¼100þ) 120 67 (4) 138 68 (5)

Sex distribution, % females

< 70% 104 68 (8) 110 70 (8)

70–84% 145 69 (5) 133 70 (6)

85þ% 120 68 (5) 117 72 (9)

Department mean age

< 40 years 27 73 (6) 80 74 (8)

40–44 years 155 68 (5) 138 70 (7)

45þ years 184 67 (7) 142 70 (8)

Full-time employees, %

< 65 168 68 (5) 181 70 (7)

65þ 201 68 (7) 179 72 (9)

Average seniority

< 84 months 66 70 (7) 99 75 (7)

84–131 months 191 69 (5) 170 70 (7)

132þ months 112 67 (7) 91 70 (9)

Department type

Patients 199 69 (8) 243 71 (7)

Non-patients 137 70 (9) 117 72 (9)


possibility of linking the database to a wide range of other

registers enables analyses of the effects of social capital and

psychosocial work environment on various outcome meas-

ures such as sickness absence, specific health outcomes,

socioeconomic outcomes and mortality at the individual

level as well as, for instance, patient perceptions on an ag-

gregate level. Also, when addressing questions of associ-

ations between variables collected in the same

questionnaire, problems of common method bias may arise

in that employees who report low levels of social capital

may also tend to report low on specific outcomes of inter-

est. This creates a non-causal association between the two

parameters.34 Such bias is avoided by merging the cohort

with other data resources. The possibility of aggregating

the individual answers to the questionnaire at the depart-

ment level, and thus assigning the average value of the ex-

posure of interest to all employees in that unit, also reduces

the common method bias. Another strength is the multipli-

city of professional groups represented in the data. The

sparse literature on social capital in health care systems

suffers from under-representation of specific staff groups.

The majority of previous studies have focused on ei-

ther physician or nurse social capital.2,23–31 Yet other oc-

cupational groups such as administrative staff, social and

health service helpers and service-related personnel,

who constitute a large proportion of all hospital em-

ployees, may be just as important in terms of efficiency and

quality of care.41 Also, as mentioned previously, the prod-

uctivity and quality of care in a health care unit are

highly dependent on cooperation between staff groups,

so a focus confined to a specific profession seems

inadequate.

The included items on social capital were derived from

the validated COPSOQII.14 The questionnaire included

elements of trust, justice and collaboration and as described

previously, reflecting both horizontal and vertical compo-

nents, which are considered key in the measurement of

workplace social capital.33 The theoretically appropriate

level at which social capital is measured has been much

debated in the past decades.32 It has been argued that nei-

ther individual nor contextual measurements suffice; given

the collective dimension of social capital (i.e. beyond social

networks and support), the individual approach in isolation

would only yield effects of perceptions of social capital,

whereas a strictly ecological approach does not eliminate

the residual compositional confounding by individual char-

acteristics.33,42 Therefore, it has been argued that the ana-

lysis of health effects of social capital calls for a multi-level

methodological framework in which the individual re-

sponses and their outcomes are nested within a workplace

unit.32,33 The nature of the data collected for the WHALE

study enables analyses of this type.

Weaknesses

First of all, the cohort measurements of work environment

and well-being were self-reported secondary data that were

not initially collected for research purposes. Thus, the meas-

urements inherently suffer from some degree of misclassifi-

cation. However, in a prospective design, the

misclassification of exposures will be independent of the

outcome measurements. In addition, the secondary nature

of the data, being elaborated and collected within the HR

setting of the region, entails some important weaknesses in

that important aspects of the psychosocial work environ-

ment were omitted. Specifically, within each domain of the

psychosocial work environment examined, a lower number

of items were generally applied compared with the number

of items within the same domain in the COPSOQII.

However, regarding this as an issue of missing data, multiple

imputation procedures may be applied to give an impression

of the presumed loss of information. Such validation proced-

ures are currently being implemented into the database.43

Further, the database does include several dimensions of the

psychosocial and physical work environment and the possi-

bility of linkage between several registers, but information

on lifestyle factors has not yet been collected.

The participation rates were high in both surveys, but

the analysis comparing participants with non-respondents

showed variances according to sex, age and profession.

Most notably, medical doctors and dentists were under-

represented among respondents. These details must be

taken into account by carefully considering a link between

exposures and outcomes of interest in future studies. The

possibilities of addressing longitudinal changes in exposure

are challenged by a high rate of turnover of staff and

changes in the organization (splitting or merging of depart-

ments) over time. However, the period between the two

surveys, i.e. from 2011 to 2014, was relatively stable with

regard to re-organizations.

Can I get hold of the data? Where can I foundout more?

Anonymized data are available to other investigators

through collaborative agreements. Please contact Dr Naja

Hulvej Rod [[email protected]].

Supplementary Data

Supplementary data are available at IJE online.

Funding

This work was funded by the Danish Working Environment

Research Fund (grant number 03-2009-09).


AcknowledgementsWe thank the collaborators behind the WHALE study: the HR de-

partment at the Capital Region and the Regional Data Unit.

Conflict of interest: None declared.

References

1. Aiken LH, Sermeus W, Van den Heede K et al. Patient safety,

satisfaction, and quality of hospital care: cross sectional surveys

of nurses and patients in 12 countries in Europe and the United

States. BMJ 2012;344:e1717.

2. McHugh MD, Rochman MF, Sloane DM et al. Better nurse

staffing and nurse work environments associated with increased

survival of in-hospital cardiac arrest patients. Med Care

2016;54:74–80.

3. Karasek RA. Job demands, job decision latitude, and mental strain:

implications for job redesign. Adm Sci Q 1979;24:285–308.

4. Kivim€aki M, Nyberg ST, Batty GD et al. Job strain as a risk

factor for coronary heart disease: a collaborative meta-

analysis of individual participant data. Lancet 2012;380:

1491–97.

5. M€antyniemi A, Oksanen T, Salo P et al. Job strain and the risk of

disability pension due to musculoskeletal disorders, depression

or coronary heart disease: a prospective cohort study of 69,842

employees. Occup Environ Med 2012;69:574–81.

6. Nyberg ST, Fransson EI, Heikkil€a K et al. Job strain and cardio-

vascular disease risk factors: meta-analysis of individual-

participant data from 47,000 men and women. PLoS One

2013;8:e67323.

7. Nyberg ST, Fransson EI, Heikkil€a K et al. Job strain as a risk fac-

tor for type 2 diabetes: a pooled analysis of 124,808 men and

women. Diabetes Care 2014;37:2268–75.

8. Wieclaw J, Agerbo E, Mortensen PB, Burr H, Tuchsen F, Bonde

JP. Psychosocial working conditions and the risk of depression

and anxiety disorders in the Danish workforce. BMC Public

Health 2008;8:280.

9. Nilsen C, Andel R, Fritzell J, Kareholt I. Work-related stress in

midlife and all-cause mortality: can sense of coherence modify

this association? Eur J Public Health 2016;26:1055–61.

10. Colquitt JA, Conlon DE, Wesson MJ, Porter CO, Ng KY. Justice

at the millennium: a meta-analytic review of 25 years of organ-

izational justice research. J Appl Psychol 2001;86:425–45.

11. Kivim€aki M, Virtanen M, Vartia M, Elovainio M, Vahtera J,

Keltikangas-J€arvinen L. Workplace bullying and the risk of

cardiovascular disease and depression. Occup Environ Med

2003;60:779–83.

12. Kawachi I, Takao S, Subramanian SV. Global Perspectives on

Social Capital and Health. New York, NY: Springer New York,

2013.

13. Putnam RD. Bowling alone: america’s declining social capital.

J Democr 1995;6:65–78.

14. Rugulies R, Hasle P, Pejtersen JH, Aust B, Bjorner JB.

Workplace social capital and risk of long-term sickness absence.

Are associations modified by occupational grade? Eur J Public

Health 2016;26:328–33.

15. Oksanen T, Kouvonen A, Vahtera J, Virtanen M, Kivim€aki M.

Prospective study of workplace social capital and depression: are

vertical and horizontal components equally important?

J Epidemiol Community Health 2010;64:684–89.

16. Lofors J, Sundquist K. Low-linking social capital as a predictor

of mental disorders: a cohort study of 4.5 million Swedes. Soc Sci

Med 2007;64:21–34.

17. Oksanen T, Kawachi I, Jokela M et al. Workplace social capital

and risk of chronic and severe hypertension: a cohort study.

J Hypertens 2012;30:1129–36.

18. V€a€an€anen A, Kouvonen A, Kivim€aki M et al. Workplace social

capital and co-occurrence of lifestyle risk factors: the Finnish

Public Sector Study. Occup Environ Med 2009;66:432–37.

19. Kouvonen A, Oksanen T, Vahtera J et al. Work-place social cap-

ital and smoking cessation: the Finnish Public Sector Study.

Addiction 2008;103:1857–65.

20. Oksanen T, Kivim€aki M, Kawachi I et al. Workplace social cap-

ital and all-cause mortality: a prospective cohort study of 28,043

public-sector employees in Finland. Am J Public Health

2011;101:1742–48.

21. Fujita S, Kawakami N, Ando E et al. The association of work-

place social capital with work engagement of employees in

health care settings: a multilevel cross-sectional analysis.

J Occup Environ Med 2016;58:265–71.

Profile in a nutshell

• The WHALE study involves an ongoing prospective,

observational cohort to collect data on physical and

psychosocial work environment embedded within

the Capital Region of Denmark.

• The baseline data were collected in 2011 among all

employees in the region (N¼35 894) and a response

rate of 81% was obtained. A follow-up questionnaire

was distributed among all employees in 2014

(N¼37 720) with a response rate of 84%. Another

wave will be conducted in 2017.

• The dataset comprises a broad range of questions

concerning the overall well-being of employees, in

which psychosocial and physical work environment

are key elements. The 2011 survey included a total

of 44 questions on the psychosocial work environ-

ment and 26 on the physical work environment.

Analyses of various outcomes are possible through

linkage with a database on patient satisfaction as

well as register data on sickness absence, physical

and mental health outcomes, socioeconomic out-

comes and prescription of medicine.

• The structure of the database makes it possible to

determine dimensions of the work environment on

different aggregate organizational levels. Also, mul-

tiple professional groups are represented in the

data.

• Any researcher interested in collaborating with the

WHALE study should contact Dr Naja Hulvej Rod

[[email protected]].


22. Stromgren M, Eriksson A, Bergman D, Dellve L. Social capital

among health care professionals: A prospective study of its im-

portance for job satisfaction, work engagement and engagement

in clinical improvements. Int J Nurs Stud 2016;53:116–25.

23. Kowalski C, Driller E, Ernstmann N et al. Associations between

emotional exhaustion, social capital, workload, and latitude in

decision-making among professionals working with people with

disabilities. Res Dev Disabil 2010;31:470–79.

24. Kowalski C, Ommen O, Driller E et al. Burnout in nurses - the

relationship between social capital in hospitals and emotional

exhaustion. J Clin Nurs 2010;19:1654–63.

25. Ommen O, Driller E, Kohler T et al. The relationship between

social capital in hospitals and physician job satisfaction. BMC

Health Serv Res 2009;9:81–89.

26. Van Bogaert P, Kowalski C, Weeks SM, Van Heusden D, Clarke

SP. The relationship between nurse practice environment, nurse

work characteristics, burnout and job outcome and quality of

nursing care: a cross-sectional survey. Int J Nurs Stud

2013;50:1667–77.

27. Susanne Lehner B, Kowalski C, Wirtz M et al. [Work engage-

ment of hospital physicians: do social capital and personal

traits matter?]. Psychother Psychosom Med Psychol

2013;63:122–28.

28. Sheingold BH, Sheingold SH. Using a social capital framework

to enhance measurement of the nursing work environment.

J Nurs Manag 2013;21:790–801.

29. Ernstmann N, Ommen O, Driller E et al. Social capital and risk

management in nursing. J Nurs Care Qual 2009;24:340–47.

30. Sutinen R, Kivim M, Elovainio M, Virtanen M, Sutinen R.

Organizational fairness and psychological distress in hospital

physicians. Scand J Public Health 2002;30:209–15.

31. Driller E, Ommen O, Kowalski C, Ernstmann N, Pfaff H. The re-

lationship between social capital in hospitals and emotional ex-

haustion in clinicians: a study in four German hospitals. Int J Soc

Psychiatry 2011;57:604–09.

32. Kawachi I, Kim D, Coutts A, Subramanian SV. Commentary:

Reconciling the three accounts of social capital. Int J Epidemiol

2004;33:682–90.

33. Oksanen T, Suzuki E, Takao S, Vahtera J, Kivim€aki M. Work

place social capital and health. In: Kawachi I, Takao S,

Subramanian SV (eds). Global Perspectives on Social Capital

and Health. New York, NY: Springer New York, 2013.

34. Podsakoff PM, MacKenzie SB, Lee J-Y, Podsakoff NP. Common

method biases in behavioral research: a critical review of the lit-

erature and recommended remedies. J Appl Psychol

2003;88:879–903.

35. Enheden for Brugerundersøgelser: Baggrund og metode for den

Landsdaekkende Undersøgelse af Patientoplevelser. [Center for

Patient Experience and Evaluation: Background and methods for

the National Study of Patient Experience]. The Capital Region,

Copenhagen: 2011.

36. Pedersen CB, Gøtzsche H, Møller JØ, Mortensen PB. The

Danish Civil Registration System. A cohort of eight million per-

sons. Dan Med Bull 2006;53:441–49.

37. Breinegaard N, Jensen JH, Bonde J. Organizational change, psy-

chosocial work environment, and non-disability old-age retire-

ment: a prospective study among senior public employees. Scand

J Work Environ Health 2017, Feb 6. doi: 10.5271/sjweh.3624.

[Epub ahead of print.]

38. Pejtersen JH, Kristensen TS, Borg V, Bjorner JB. The second ver-

sion of the Copenhagen Psychosocial Questionnaire. Scand J

Public Health 2010;38:8–24.

39. Bue Bjorner J, Pejtersen JH. Evaluating construct validity of

the second version of the Copenhagen Psychosocial

Questionnaire through analysis of differential item functioning

and differential item effect. Scand J Public Health

2010;38:90–105.

40. Yu CH. An introduction to computing and interpreting

Cronbach Coefficient Alpha in SAS. In: Proceedings of the

Twenty-Sixth Annual SAS Users Group International

Conference, 22–25 April 2001. Long Beach, CA: SAS Institute,

2001.

41. Shantz A, Alfes K, Arevshatian L. HRM in health care: the role

of work engagement. Pers Rev 2016;45:274–95.

42. Subramanian S, Lochner KA, Kawachi I. Neighborhood differ-

ences in social capital: a compositional artifact or a contextual

construct? Health Place 2003;9:33–44.

43. Sterne JAC, White IR, Carlin JB et al. Multiple imputation for

missing data in epidemiological and clinical research: potential

and pitfalls. BMJ 2009;338:b2393.


Supplementary data table

Psychosocial work environment #

2011 2014

COPSOQ-II items

To what degree…

Influence 1 … do you have influence on how you do your work? x x

Possibilities for development (skill discretion)

2 … do you have the possibility of learning new things through your work? x x

Predictability 3 … are you informed well in advance concerning for example important decisions, changes, or plans for the future?

x x

4 … do you receive all the information you need in order to do your work well? x

Recognition 5 … is your work recognised and appreciated by the management? x x

Role clarity 6 … does your work have clear objectives? x x

7 … do you know exactly which areas are your responsibility? x

8 … do you know exactly what is expected of you at work? x

Social support from colleagues 9 … do you get help and support from your colleagues when needed? x x

Social support from supervisors 10 … do you get help and support from your nearest supervisor when needed? x x

Job satisfaction 11 … are you pleased with your work prospects? x x

12 … are you pleased with the physical working conditions? x x

13 … are you pleased with the way your abilities are used? x x

14 … are you pleased with your job as a whole, everything taken into consideration? x x

Stress 15 … have you been stressed during the past six months? x x

Burnout 16 … have you felt worn out during the past six months? x

Trust regarding management 17 … does the management trust the employees to do their work well? x x

18 … can you trust the information that comes from the management? x x

Justice 19 … are conflicts resolved in a fair way? x x

20 … is the work distributed fairly? x x

Quantitative demands 21 … do you have enough time for your work tasks? x x

Emotional demands 22 … does your work put you in emotionally disturbing situations? x

Commitment to workplace 23 Would you recommend a good friend to apply for a position at your workplace? x

Sexual harassment 24 Have you been exposed to undesired sexual attention at your workplace during the past 12 months?

x x

Threats of violence 25 Have you been exposed to threats of violence at your workplace during the past 12 months? x x

26 Have you been exposed to physical violence at your workplace during the past 12 months? x x

27 Have you been exposed to bullying during the past 12 months? x x

Quality of leadership To what extent would you say that your immediate superior, <NAME>…

28 … gives high priority to job satisfaction? x x

29 … is good at work planning? x x

Remaining items To what extent…

30 … do you have energy for both your work and private life? x

31 … do you have time for both your work and private life? x

32 … do you and your colleagues give space for each others differences at your workplace? (e.g. regarding sex, age, and background)

x x

33 … are your ideas and suggestions heard at your workplace? x x

34 … are you able to schedule your work time, so you can take into account private matters? x x

35 … do you have time for breaks throughout your work day? x x

36 … are you able to work without being interrupted? x x

37 … are you pleased with the quality of the work you and your colleagues do at your workplace? x x

38 … are you proud of the work you and your colleagues do at your workplace? x x

39 … do you know whom to consult if you have questions regarding your work tasks? x

40 … have you had an "employee/leader development talk" during the past 12 months? x

41 … have you benefitted from the "employee/leader development talk" in which you have participated?

x x

42 … are you and your colleagues good at coming up with suggestions for improving work procedures?

x x

43 … do you and your colleagues take responsibility for a nice atmosphere and tone of communication?

x x

44 … is your staff group respected by the other staff groups at the workplace? x x

45 … does the workplace help the employees with managing emotionally disturbing situations at work?

x

46 … does the workplace focus enough on preventing stress in the employees? x x

47 … does the workplace help employees having problems with stress? x x

48 … can you get help managing problems with bullying at your workplace? x

49 … do you get sufficient information on aims and visions of the Capital Region x

Paper II

Dual impact of organisational change on subsequent exit from work unit

and sickness absence: a longitudinal study among public healthcare

employees

Jensen JH, Flachs EM, Skakon J, Rod NH, Bonde JP

Occup Environ Med, 2018;75(7):479-485. doi:10.1136/oemed-2017-104865

479Jensen JH, et al. Occup Environ Med 2018;75:479–485. doi:10.1136/oemed-2017-104865

Original article

Dual impact of organisational change on subsequent exit from work unit and sickness absence: a longitudinal study among public healthcare employeesJohan Høy Jensen,1 esben Meulengracht Flachs,1 Janne Skakon,2 naja Hulvej rod,3 Jens Peter Bonde1

Workplace

To cite: Jensen JH, Flachs eM, Skakon J, et al. Occup Environ Med 2018;75:479–485.

► additional material is published online only. to view, please visit the journal online (http:// dx. doi. org/ 10. 1136/ oemed- 2017- 104865).

1Department of Occupational and environmental Medicine, copenhagen University Hospital, Bispebjerg Hospital, copenhagen, Denmark2Department of Psychology, University of copenhagen, copenhagen, Denmark3Section of epidemiology, Department of Public Health, University of copenhagen, copenhagen, Denmark

Correspondence toJohan Høy Jensen, Department of Occupational and environmental Medicine, copenhagen University Hospital, Bispebjerg Hospital, copenhagen DK-2400, Denmark; johan. hoey. jensen@ regionh. dk

received 31 October 2017revised 10 april 2018accepted 21 april 2018Published Online First 14 May 2018

AbsTrACTObjectives We investigated work-unit exit, total and long-term sickness absence following organisational change among public healthcare employees.Methods the study population comprised employees from the capital region of Denmark (n=14 388). Data on reorganisation at the work-unit level (merger, demerger, relocation, change of management, employee layoff or budget cut) between July and December 2013 were obtained via surveys distributed to the managers of each work unit. individual-level data on work-unit exit, total and long-term sickness absence (≥29 days) in 2014 were obtained from company registries. For exposure to any, each type or number of reorganisations (1, 2 or ≥3), the Hrs and 95% cis for subsequent work-unit exit were estimated by cox regression, and the risk for total and long-term sickness absence were estimated by zero-inflated Poisson regression.results reorganisation was associated with subsequent work-unit exit (Hr 1.10, 95% ci 1.01 to 1.19) in the year after reorganisation. this association was specifically important for exposure to ≥3 types of changes (Hr 1.52, 95% ci 1.30 to 1.79), merger (Hr 1.29, 95% ci 1.12 to 1.49), demerger (Hr 1.41, 95% ci 1.16 to 1.71) or change of management (Hr 1.24, 95% ci 1.11 to 1.38). among the employees remaining in the work unit, reorganisation was also associated with more events of long-term sickness absence (Or 1.15, 95% ci 1.00 to 1.33), which was particularly important for merger (Or 1.31, 95% ci 1.00 to 1.72) and employee layoff (Or 1.31, 95% ci 1.08 to 1.59).Conclusions Specific types of reorganisation seem to have a dual impact on subsequent work-unit exit and sickness absence in the year after change.

InTrOduCTIOnOrganisational change at the workplace is common and may be regarded as a feature of modern work life.1 2 Evidence indicates that organisational changes are associated with deleterious health and psychosocial outcomes,3–7 and consequently, subse-quent employee exit from the workplace8–11 and higher risk of sickness absence (SA).12–16Reorgani-sation may become counterproductive since work-place exit and SA are highly costly due to long-term stress-related illness, loss of productivity and costs related to replacement of employees.9 17–19

Studies of the healthcare sector have shown higher exit rates following implementation of new workflows10 and hospital mergers across occu-pational groups regardless of employee health.11 Also, higher exit rates have been found, espe-cially among senior employees, following merger of computer companies9 in line with other find-ings of higher rates of voluntary early retirement among senior employees exposed to various types of reorganisation.8

Regarding SA, epidemiological studies found major downsizing (ie, staff reduction) and work-place expansion to be associated with more SA13 and a higher risk of long-term SA.5 A study from Norway demonstrated that merger, demerger, relocation and creation or shut-down of units

Key messages

What is already known about this subject? ► Previous studies examining the impact of organisational change mainly focused on downsizing or merger at the company level to find that these types of reorganisation were related to employee exit from the workplace or a higher risk of sickness absence among the remaining employees.

► However, the potential dual impact of subsequent workplace exit and sickness absence following various types of organisational change remains to be examined at the work-unit level.

What are the new findings? ► This study demonstrates a dual impact of individual-level subsequent employee exit from the work unit and sickness absence in the year after six types of organisational change measured at the work-unit level among 14 388 healthcare employees in the Capital Region of Denmark.

How might this impact on policy or clinical practice in the foreseeable future?

► Decision and policy makers should consider the potential adverse effects of organisational change in a work unit.

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480 Jensen JH, et al. Occup Environ Med 2018;75:479–485. doi:10.1136/oemed-2017-104865

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aggregated at the hospital level was associated with total and long-term SA, but the various types of reorganisation were not examined separately.14 Another study found modest risks of long-term SA during hospital merger and 2, 3 and 4 years after merger.16 Although there is some evidence that reorganisation adversely affects SA,15 other studies found SA rates to be unaf-fected20 or even decreased in women21 following downsizing or expansion.

A limitation of previous studies of SA is that they mainly focused on employees who remained at the workplace7 without studying the potential accompanied effect of employees subsequently exiting their workplace after reorganisation. Indeed, one study has linked higher rates of workplace exit and long-term SA to self-reported negative consequences of reorganisation,22 whereas another study found no higher risk of long-term SA after privatisation of public-sector work units without major downsizing.23

We examine the impact of various types of organisational change on subsequent employee exit from the work unit, total SA and long-term SA among public healthcare employees in Denmark.

MeTHOds And MATerIAlsPopulation and study designThis longitudinal study used data from the Well-being in Hospital Employees (WHALE) cohort.24 The target population was estab-lished 13 January 2014 for distribution of a work-environment

survey to 37 720 employees nested in 2696 work units during March 2014.

The source population comprised employees each actively occupied in the same work unit of ≥3 employees with an average of ≥18.5 fixed working hours per week through 2013. To ensure that all employees worked in the same work unit through 2013 (although some work units changed their name), we applied the criteria that ≥3 employees and ≥30% of all employees from a given work unit must remain together after a change to be included. In the source population, there were 25 922 employees nested in 2322 work units (figure 1).

Organisational changeBetween April and June 2016, semi-annual binary data on organi-sational change at the work-unit level (ie, merger, demerger, relo-cation, change of management, employee layoff(s), budget cuts) from 2011 to 2013 were obtained via a survey (see online supple-mentary material 1) emailed to the work-unit managers (response rate: 59%). The measures of organisational change included expo-sure to no change (reference group), any type of change, each of the six types of change (not mutually exclusive) or the number of simultaneous changes (only 1, 2 or ≥3 types of changes) in the last six months of 2013.

Figure 1 Diagram representing the flow of participants and the study design. the study population of employees occupied in the same work unit in 2013 were potentially exposed to organisational change in the last six months of 2013 with follow-up on subsequent work-unit exit, total sickness absence or long-term sickness absence in 2014. Data on organisational change were collected between april and June 2016.

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Work-unit exit and sickness absenceMonthly work-unit affiliation and absence data for each indi-vidual employee occupied in the period from 1 January 2012 to 31 December 2014 were recorded from registries in the Capital Region of Denmark. Data on absence included total SA (ie, all types), long-term SA (ie, ≥29 consecutive days) and absence related to child’s first or second sick day. Data on background information included age, sex, occupational group, number of employees in the work units and fixed working hours. Based on the work-unit criteria stated above, we calculated subsequent monthly work-unit exit at the individual level between January and December 2014.

The two measures of SA were computed as percentages of the working hours missed in 2014 until work-unit exit due to total and long-term SA. The SA measures were calcu-lated relative to the working hours to account for sickness periods overlapping with days off work and work-unit exit in 2014. For example, if an employee was sick on Monday (one working day), but was free from work the following Tuesday through Thursday and returned to work on Friday, it would otherwise appear in the records as the employee had 4 days of SA (ie, Monday through Thursday). Moreover, if an employee exited the work unit by 28 February 2014 during follow-up, the percentages of missed working hours due to SA were calculated relative to the fixed working hours between baseline at 1 January 2014 and work-unit exit by 28 February 2014. We examined both total and long-term SA because the total measure focuses on all types of SA (eg, short-term sickness, intermittent disorder), whereas the long-term measure focuses only on severe SA.

CovariatesThe following variables were a priori considered as potential confounders for the association between organisational change and subsequent work-unit exit or total or long-term SA: age (quartiles), sex, number of employees in the work units (quar-tiles), occupation (six groups), days of SA in the year prior to reorganisation in 2012 (divided into five groups), child-related absence between 2012 and 2013 and personal gross income (quartiles) in 2012.

The study population of 14 388 employees was nested in 1245 work units. There were SA data on 14 159 employees, as 229 employees (1.59% of the study population) exited their work unit by 1 January 2014 (figure 1).

statistical analysesTo assess the a priori impact of missing data on exposure to organisational change, we estimated the differences in subse-quent work-unit exit, SA and baseline characteristics between employees with and without data on change. χ2 tests were used for categorical variables and two-way t-tests were used for continuous variables.

The employees were followed from 1 January 2014 to work-unit exit, censoring (ie, death) or end of the study by 31 December 2014, whichever came first. Using Cox proportional hazards regression analyses, we estimated work-unit exit rates in 2014 related to each measure of change compared with no change through 2013.

Since a large proportion of employees had no SA (ie, 0 percentage), we used zero-inflated Poisson regression analyses to assess the risk of total and long-term SA after organisational change. The zero-inflated Poisson regression comprises two

components in the same operation: in this study, a zero model that generates the OR and 95% CI for SA eventsi (sick: yes/no) and a Poisson model that account for the excess count of zeros and generates the rate ratio (RR) and 95% CI for a higher percentage of SA relative to the fixed working hours among the sick-listed.25 In sum, this adds up to four absence outcomes: ORs and RRs for the event and percentage, respectively, of total SA, and ORs and RRs for the event and percentage, respectively, of long-term SA.

The reference group for all Cox and zero-inflated Poisson analyses employees who did not experience any organisational change in the last six months of 2013. Exposure to any organ-isational change (yes/no) was entered in the models as one variable. Exposure to each of the six types of change was esti-mated in separate models with each single change variable (yes/no) entered in turn. To avoid potential overadjustment, we did not include any of the remaining types of changes in the model, because the relationships between each change measure and the others are unclear (eg, they could be mediators or confounders). Exposure to the number of changes performed simultaneously (1, 2, ≥3) was modelled as one variable.

Crude Cox regression analyses were controlled for age only. Adjusted Cox and all zero-inflated Poisson regression anal-yses were controlled for age, sex, number of employees within work unit, occupation, previous SA, child-related absence and personal income.

All statistical analyses were conducted using SAS V.9.4 (SAS Institute, Cary, North Carolina, USA).

resulTsTable 1 shows for the study population, the distribution and prevalence of exposure to any organisational change across covariate levels.

Table 2 shows for the employees exposed to organisational change, the prevalence and distribution of exposure to each type of change across number of simultaneous changes. This table indicates that employee layoff and budget cuts were often exclu-sively featured together or alone. Of the 8847 changes experi-enced by all employees, 5022 (57%) changes were experienced in combination with ≥1 other type of change. A correlation matrix between all types of change showed that no measures were completely overlapping (r=0.07–0.33, p<0.001; online supplementary material 2).

In total, seven employees in the study population were censored due to death during follow-up in 2014. Table 3 shows the work-unit exit rates following exposure to any, each type and number of organisational changes relative to no change. Employees exposed to change in the last six months of 2013 were more likely to exit the work unit in 2014 relative to no change in the same period.

Table 4 shows the risks of the event and higher percentages of missed working hours in 2014 due to total and long-term SA following organisational change through 2013 relative to no change. Employees exposed to reorganisation had an elevated rate of total SA percentage and were more likely to have SA periods of at least 29 days in 2014 compared with employees who underwent no change.

i For interpretation reasons, we inverted the output values to predict the OR of having sickness absence (one divided by output values).

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The Χ2 tests revealed that employees with data on change were less likely to subsequently exit their work unit (χ2(1)=22.90, p<0.001); however, they had comparable events of long-term SA (χ2(1)=0.32, p=0.57) in 2014 relative to employees without data on change. The two-way t-test showed that the employees with data on change had a significantly lower percentage of total SA in 2014 (M=4.52, SD=8.30, t(14 161)=65.02, p<0.001) compared with the employees without change data (M=4.65,

SD=9.04). This points to underestimation of the effects reported in tables 3–4. There were no noteworthy differences between employees with and without change data regarding the remaining outcomes and selected baseline characteristics (see online supplementary material 3).

In post hoc, we examined potential differential effects for men and women on subsequent work-unit exit or SA by including an interaction term between any organisational change and sex. The results did not support that sex interacted on the multipli-cative scale in the relations between exposure to any change and total SA, long-term SA or subsequent work-unit exit (results not shown).

dIsCussIOnWe show higher rates of subsequent work-unit exit among employees in the year following reorganisation, especially for exposure to ≥3 types of changes, merger, demerger, relocation or change of management. Interestingly, exposure to employee layoff or budget cut was not associated with subsequent work-unit exit. Reorganisation was also associated with a higher risk of long-term SA and elevated rates of total and long-term SA percentages after exposure to 2 or ≥3 types of changes. All findings should be interpreted in the context of a relatively low unemployment rate of 5.3% through 2014 in the Capital Region

Table 1 Distribution of the study population with complete data on all relevant variables and the prevalence of organisational change across covariate levels at baseline at 31 December 2013

study populationexposed to any change

n % of total n n % of n

Total 14 388 100 5794 40.27

Female 10 951 76.11 4375 39.95

Age group (years)

18–40 3630 25.23 1468 40.44

40–48 3605 25.06 1423 39.47

48–56 3578 24.87 1439 40.22

56–75 3575 24.85 1464 40.95

Employees in work unit

3–12 3480 24.19 1066 30.63

13–22 3636 25.27 1435 39.47

23–32 3633 25.25 1531 42.14

33–142 3639 25.29 1762 48.42

Occupational group

Nurses 6216 43.20 2564 41.25

Administrative staff 2643 18.37 1074 40.64

Social/healthcare workers 1883 13.09 667 35.42

Service/technical staff 1812 12.59 757 41.78

Medical doctors and dentists 1449 10.07 601 41.48

Pedagogical workers 385 2.68 131 34.03

Days of sickness absence 2012

0 3988 27.72 1628 40.82

1–3 3101 21.55 1242 40.05

4–6 2185 15.18 869 39.77

7–13 2742 19.05 1041 37.96

14–363 2372 16.48 1014 42.75

Sick child 2012–2013 4322 30.04 1690 39.10

Personal income (gross, Kr)

<345 000 3668 25.49 1528 41.66

345 000–400 000 3736 25.97 1492 39.94

400 000–480 000 3525 24.50 1381 39.18

>480 000 3459 24.04 1393 40.27

Table 2 Prevalence and distribution of types of organisational change across number of organisational changes performed simultaneously

study population (n=14 388) 1 type of change 2 types of changes >3 types of changes

n % of n n% of total n within subgroup n

% of total n within subgroup n

% of total n within subgroup

Total of any change 5794 40.28 3826 26.59 1212 8.42 756 5.25

Merger 1093 7.60 225 5.88 308 25.41 560 74.07

Demerger 508 3.53 119 3.11 113 9.32 276 36.51

Relocation 985 6.85 356 9.30 290 23.93 339 44.84

Change of management 2236 15.54 1177 30.76 515 42.49 544 71.96

Employee layoff 2226 15.47 1062 27.76 673 55.53 491 64.95

Budget cut 1799 12.50 887 23.18 525 43.32 387 51.19

Table 3 Rates of subsequent employee exit from the work unit in the year after organisational change

n % of n, exit

Crude, exit Adjusted, exit

Hr (95% CI) Hr (95% CI)

No change* 8594 16.65 1.00 1.00

Any change 5794 17.95 1.09 (1.01 to 1.18) 1.10 (1.01 to 1.19)

1 type of change

3826 17.12 1.03 (0.94 to 1.13) 1.04 (0.95 to 1.15)

2 types of changes

1212 17.49 1.06 (0.92 to 1.22) 1.04 (0.90 to 1.20)

≥3 types of changes

756 22.88 1.44 (1.23 to 1.69) 1.52 (1.30 to 1.79)

Merger 1093 21.32 1.33 (1.16 to 1.53) 1.29 (1.12 to 1.49)

Demerger 508 21.65 1.36 (1.12 to 1.64) 1.41 (1.16 to 1.71)

Relocation 985 19.39 1.19 (1.02 to 1.38) 1.16 (1.00 to 1.35)

Change of management

2236 19.68 1.20 (1.08 to 1.34) 1.24 (1.11 to 1.38)

Employee layoff 2226 16.58 1.00 (0.89 to 1.12) 1.03 (0.91 to 1.15)

Budget cut 1799 17.90 1.09 (0.96 to 1.23) 1.09 (0.97 to 1.24)

Crude Cox analyses controlled for age. Adjusted Cox analyses controlled for age, sex, number of employees in the work unit, occupational group, sickness absence in 2012, child-related absence and personal gross income.*Reference group.

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of Denmark,26 as unemployment rates are negatively correlated with turnover rates27 and long-term SA.28

Work-unit exitThe present findings of higher exit rates are similar to a study examining voluntary early retirement among senior employees in the Capital Region of Denmark. This study found higher retirement rates following merger, change of management and relocation at the work-unit level8 concurrent with the present findings. Among 54 787 hospital employees in Norway, Ingel-srud11 found a higher exit rate from the hospital sector only in the second year following hospital merger, whereas we found higher exit rates in the first year after the work-unit merger. Exposure to employee layoff and budget cut were not signifi-cantly associated with higher rates of subsequent work-unit exit. This may be explained by the observation that this pair of changes was commonly featured together or alone. Building on this, we found that exposure to only 1 or 2 types of changes were not significantly associated with subsequent work-unit exit, whereas exposure to ≥3 types of changes was associated with a 1.52-fold higher exit rate relative to no change.

The reasons of work-unit exit were not examined in the present study. Some employees may voluntarily exit after changes,8 whereas some changes may have the tacit or explicit purpose of ‘pushing out’ employees of the workplace. Demerger may be such latter example as it was related to a particularly large exit effect and no effect of SA. The large exit effect after demerger could also be due to co-occurring changes since about half of the employees exposed to demerger were exposed to ≥3 simultaneous changes.

We added to this literature by distinguishing and comparing the impact of various types of reorganisations, and we found that some, but not all, types of reorganisations are related to higher rates of subsequent work-unit exit.

sickness absenceIn all SA analyses, more Poisson-model estimates were statisti-cally significant than zero-model estimates. This pattern may be explained by the larger statistical power introduced by the Poisson distribution compared with the binary distribution as indicated by the 95% CIs in table 4.

The present results of higher rates of total SA following reor-ganisation correspond to previous findings after major down-sizing only in permanent employees.13 Kjekshus et al16 found elevated ORs for long-term SA of 1.05 during hospital merger and 1.04 in the second year after merger, which are smaller than the OR for long-term SA of 1.33 in the year following work-unit merger demonstrated in this study. The difference between these findings may be due to the work-unit approach applied presently ensuring that the exposed employees did de facto experience the merger. Our findings of higher risk for long-term SA of ≥29 days among various occupational groups after reorganisation are inconsistent with other findings23 of no higher risk for long-term SA of ≥91 days among hospital laboratorians and radiographers. This inconsistency may be explained by the social gradient in health between the populations studied and the conservative measure of long-term SA applied by Kokkinen et al.23

In general, those types of organisational changes associated with a higher exit rate were also associated with a higher risk of SA. This suggests that organisational change has a dual impact on subse-quent work-unit exit and SA. Interestingly, employee layoff was not associated with a higher work-unit exit rate, but it was asso-ciated with a large OR for events of long-term SA and a relatively Ta

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large RR for a higher percentage of SA among the remaining employees. This may be explained by the potentially higher job insecurity and lower job control among the remaining employees after a staff reduction,29 which may manifest as more SA. To the extent of our knowledge, only a single study has investigated potential work-environmental mediators between organisational change and SA, which found that higher job insecurity, physical demands and lower job control mediated the longitudinal associa-tion between downsizing and higher risk of long-term SA.30 Recent findings imply that supporting and redeploying employees as a part of downsizing is important for well-being of the workers.31 Thus, it is reasonable that workplace social capital may mediate adverse effects of organisational change since this concept is linked to health status32 33 and comprises aspects of organisational justice, trust and collaboration.8

We did not find differential adverse effects of organisational change between men and women in contrast to another study in the context of downsizing.34 This study showed that female employees with depression had a higher risk of exit out of employment, whereas unemployment in male employees was unaffected by their health status. Therefore, the lack of interac-tion between organisational change and sex in the present anal-yses may be ruled out by adjusting for previous SA.

In sum, the present findings of higher exit rates and SA following change seem to be related to specific types of change rather than a dose-response relation of the number of changes performed simul-taneously. More studies are needed to examine the dual effects of reorganisation on health among employees exiting and remaining on the workplace, as the literature point to poor health outcomes in both groups.35 A Swedish study found that job loss predicted new events of subsequent major depression in both sexes with a larger effect size in men,34 yet the present study did not demon-strate an interaction between any change and sex. Future research should elucidate potential mediators of the detrimental effects from organisational change as such factors may comprise targets for interventions to buffer these effects.

strengths and limitationsThis study has several strengths. First, we examined employee exit and SA simultaneously as these job withdrawal behaviours depend on each other. Second, data on organisational change were obtained from a different source than the outcomes, which hamper common method bias.36 Third, following of the participants and measurement of organisational change were conducted at the work-unit level ensuring that the participants were actually affected by the organisational change in question. Fourth, it was also a strength that we were able to distinguish between six common types of organisational change adding to the detailed nature of the study.

Potential limitations are stated in the following. First, missing data on organisational change may contribute to an underes-timation of the outcome effects, since the rate of subsequent work-unit exit and the percentage of total SA were slightly lower during 2014 among employees with data on change than employees without these data. Indeed, there was no significant difference between these employee groups regarding events of long-term SA in 2014. Second, data on reorganisation were retrieved retrospectively, which may have biased these data as organisational change itself could have affected the managers to leave their job and thus not provide reorganisation information in the online survey. However, we were able to contact managers who remained in the organisation, because their email address was not changed. Third, we were unable to examine the potential

effects of work-unit exit and SA before or during the actual reor-ganisation. Indeed, findings from post hoc analyses showed only a minor effect on total SA in the first quarter of 2014 (results not shown), suggesting that the SA effects—if any—were small before or during exposure to organisational change. Fourth, the analyses did not take into account the multilevel organisational structure of the data. For consistency reasons, we choose not to use multilevel modelling as this approach was unable to converge in a zero-inflated Poisson regression model. A post hoc Cox anal-ysis clustering employees within work units revealed an exit rate after any change of 1.12 (vs 1.10 in table 3), which suggests only a small underestimation by using a single-level approach. Fifth, a zero-inflated negative binomial Poisson model showed a supe-rior fit with long-term SA as outcome compared with the present approach, suggesting potential overdispersion in the Poisson distribution of the long-term sickness data. Indeed, the zero-in-flated negative binomial Poisson model was unable to converge with total SA as outcome. Finally, the present results cannot be attributed exclusively to each type of change as some changes are likely to be performed simultaneously and each type of change were modelled separately. Entering all six types of change vari-ables into the same model would likely result in overadjustment because some changes may mediate other changes. Tentatively, we explored the relationships between changes by mutually adjusting for the four most correlated pair of changes in the correlation matrix (see online supplementary material 2), which generally showed similar findings, although merger/demerger adjustment seemed to have a marked role in exit rates towards null (merger: HRs from 1.29 to 1.14; demerger: HRs from 1.41 to 1.00). This could be due to the observation that 92% of the 232 employees exposed to both merger and demerger were exposed to a total of ≥3 changes. Hence, overadjustment is introduced due to impu-rity of the change variables, which is supported by the finding that simultaneous changes was strongly related to subsequent exit, whereas 1 or 2 changes were not.

These findings indicate that specific types of organisational change frequently occurring in the public healthcare sector have a dual impact on subsequent employee exit from the work unit and total and long-term SA among remaining employees in the year following reorganisation. Generalisations to other public sectors should be made cautiously due to various contextual factors, including sex composition.

Acknowledgements the authors thank human-resource consultant charlotte Hyldtoft and data consultant Jesper Strøyer andersen from the capital region of Denmark for their great help and contribution to this study by providing the applied data from regional registries.

Contributors JHJ had full access to all data provided in the present study and takes responsibility for the integrity and the accuracy of the data analyses. all authors were responsible for the current study design. JHJ wrote the initial draft of the manuscript. all authors contributed to the present study and approved the final draft of the manuscript.

Funding the study was funded by the Danish Working environment research Fund (project number: 13-2015-03).

Competing interests none declared.

Patient consent not required.

ethics approval the regional ethics committees of the capital region stated that ethical approval was not required for this study.

Provenance and peer review not commissioned; externally peer reviewed.

Open access this is an Open access article distributed in accordance with the creative commons attribution non commercial (cc BY-nc 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work

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is properly cited and the use is non-commercial. See: http:// creativecommons. org/ licenses/ by- nc/ 4. 0/

© article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. all rights reserved. no commercial use is permitted unless otherwise expressly granted.

reFerenCes 1 Kompier Ma. new systems of work organization and workers’ health. Scand J Work

Environ Health 2006;32:421–30. 2 european Foundation for the improvement of living and Working conditions. ERM

annual report 2014: restructuring in the public sector. luxembourg: Publications Office of the european Union, 2015.

3 Kivimäki M, Honkonen t, Wahlbeck K, et al. Organisational downsizing and increased use of psychotropic drugs among employees who remain in employment. J Epidemiol Community Health 2007;61:154–8.

4 Bamberger Sg, Vinding al, larsen a, et al. impact of organisational change on mental health: a systematic review. Occup Environ Med 2012;69:592–8.

5 Westerlund H, theorell t, alfredsson l. Organizational instability and cardiovascular risk factors in white-collar employees. Eur J Public Health 2004;14:37–42.

6 greubel J, Kecklund g. the impact of organizational changes on work stress, sleep, recovery and health. Ind Health 2011;49:353–64.

7 de Jong t, Wiezer n, de Weerd M, et al. the impact of restructuring on employee well-being: a systematic review of longitudinal studies. Work Stress 2016;30:91–114.

8 Breinegaard n, Jensen JH, Bonde JP. Organizational change, psychosocial work environment, and non-disability early retirement: a prospective study among senior public employees. Scand J Work Environ Health 2017;43:234–40.

9 Baron Jn, Hannan Mt, Burton MD. labor Pains: change in Organizational Models and employee turnover in Young, High-tech Firms. Am J Sociol 2001;106:960–1012.

10 Sylling PW, Wong eS, liu cF, et al. Patient-centered medical home implementation and primary care provider turnover. Med Care 2014;52:1–22.

11 ingelsrud MH. Hospital Mergers in norway: employee Health and turnover to three Destinations. Nordic Journal of Working Life Studies 2017;7:107–24.

12 Westerlund H, Ferrie J, Hagberg J, et al. Workplace expansion, long-term sickness absence, and hospital admission. Lancet 2004;363:1193–7.

13 Vahtera J, Kivimäki M, Pentti J, et al. Organisational downsizing, sickness absence, and mortality: 10-town prospective cohort study. BMJ 2004;328:555.

14 ingelsrud MH. reorganization increases long-term sickness absence at all levels of hospital staff: panel data analysis of employees of norwegian public hospitals. BMC Health Serv Res 2014;14:411.

15 98110 Ferrie Je, Westerlund H, Virtanen M, et al. Flexible labor markets and employee health. Scand J Work Environ Heal Suppl 2008.

16 Kjekshus le, Bernstrøm VH, Dahl e, et al. the effect of hospital mergers on long-term sickness absence among hospital employees: a fixed effects multivariate regression analysis using panel data. BMC Health Serv Res 2014;14:50.

17 Waldman JD, Kelly F, arora S, et al. the shocking cost of turnover in health care. Health Care Manage Rev 2010;35:206–11.

18 goetzel rZ, long Sr, Ozminkowski rJ, et al. Health, absence, disability, and presenteeism cost estimates of certain physical and mental health conditions affecting U.S. employers. J Occup Environ Med 2004;46:398–412.

19 nicholson S, Pauly MV, Polsky D, et al. Measuring the effects of work loss on productivity with team production. Health Econ 2006;15:111–23.

20 Østhus S, Mastekaasa a. the impact of downsizing on remaining workers’ sickness absence. Soc Sci Med 2010;71:1455–62.

21 theorell t, Oxenstierna g, Westerlund H, et al. Downsizing of staff is associated with lowered medically certified sick leave in female employees. Occup Environ Med 2003;60:9e–9.

22 Josephson M, lindberg P, Voss M, et al. the same factors influence job turnover and long spells of sick leave--a 3-year follow-up of Swedish nurses. Eur J Public Health 2008;18:380–5.

23 Kokkinen l, Virtanen M, Pentti J, et al. Does transfer of work from a public sector organisation to a commercial enterprise without staff reductions increase risk of long-term sickness absence among the staff? a cohort study of laboratory and radiology employees: table 1. Occup Environ Med 2013;70:585–7.

24 Hvidtfeldt Ua, Bjorner JB, Jensen JH, et al; Cohort profile: the well-being in hospital employees (WHALE) study, 2017.

25 lambert D, regression Z-iP. With an application to Defects in Manufactoring. Technometrics 1992;34:1–14.

26 Denmark S. Unemployment rates in the capital region of Denmark. https://www. statistikbanken. dk/ statbank5a/ Selecttable/ Omrade0. asp? Subjectcode= 04& Planguage=0 (accessed 18 Oct 2017).

27 carsten JM, Spector Pe. Unemployment, job satisfaction, and employee turnover: a meta-analytic test of the Muchinsky model. J Appl Psychol 1987;72:374–81.

28 Knutsson a, goine H. Occupation and unemployment rates as predictors of long term sickness absence in two Swedish counties. Soc Sci Med 1998;47:25–31.

29 robinson O, griffiths a. coping With the Stress of transformational change in a government Department. J Appl Behav Sci 2005;41:204–21.

30 Kivimäki M, Vahtera J, Pentti J, et al. Factors underlying the effect of organisational downsizing on health of employees: longitudinal cohort study. BMJ 2000;320:971–5.

31 andreeva e, Brenner MH, theorell t, et al. risk of psychological ill health and methods of organisational downsizing: a cross-sectional survey in four european countries. BMC Public Health 2017;17:1–12.

32 Oksanen t, Kawachi i, Jokela M, et al. Workplace social capital and risk of chronic and severe hypertension: a cohort study. J Hypertens 2012;30:1129–36.

33 lofors J, Sundquist K. low-linking social capital as a predictor of mental disorders: a cohort study of 4.5 million Swedes. Soc Sci Med 2007;64:21–34.

34 andreeva e, Magnusson Hanson ll, Westerlund H, et al. Depressive symptoms as a cause and effect of job loss in men and women: evidence in the context of organisational downsizing from the Swedish longitudinal Occupational Survey of Health. BMC Public Health 2015;15:1–11.

35 Kivimäki M, Vahtera J, elovainio M, et al. Human costs of organizational downsizing: comparing health trends between leavers and stayers. Am J Community Psychol 2003;32:57–67.

36 Podsakoff PM, MacKenzie SB, lee JY, et al. common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol 2003;88:879–903.

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http://dx.doi.org/10.5271/sjweh.1048


http://dx.doi.org/10.1136/jech.2006.050955

http://dx.doi.org/10.1136/jech.2006.050955

http://dx.doi.org/10.1136/oemed-2011-100381

http://dx.doi.org/10.2486/indhealth.MS1211

http://dx.doi.org/10.1080/02678373.2015.1136710


http://dx.doi.org/10.1086/320296

http://dx.doi.org/10.1097/MLR.0000000000000230

http://dx.doi.org/10.18291/njwls.v7i1.81403

http://dx.doi.org/10.1016/S0140-6736(04)15949-7

http://dx.doi.org/10.1136/bmj.37972.496262.0D

http://dx.doi.org/10.1186/1472-6963-14-411

http://dx.doi.org/10.1186/1472-6963-14-411

http://dx.doi.org/10.1186/1472-6963-14-50

http://dx.doi.org/10.1097/HMR.0b013e3181e3940e

http://dx.doi.org/10.1097/HMR.0b013e3181e3940e

http://dx.doi.org/10.1097/01.jom.0000121151.40413.bd

http://dx.doi.org/10.1002/hec.1052

http://dx.doi.org/10.1016/j.socscimed.2010.06.045

http://dx.doi.org/10.1136/oem.60.9.e9

http://dx.doi.org/10.1093/eurpub/ckn009

http://dx.doi.org/10.1136/oemed-2012-101174

https://www.statistikbanken.dk/statbank5a/SelectTable/Omrade0.asp?SubjectCode=04&Planguage=0



http://dx.doi.org/10.1037/0021-9010.72.3.374

http://dx.doi.org/10.1016/S0277-9536(97)10139-3

http://dx.doi.org/10.1177/0021886304270336

http://dx.doi.org/10.1136/bmj.320.7240.971

http://dx.doi.org/10.1186/s12889-017-4789-3

http://dx.doi.org/10.1097/HJH.0b013e32835377ed

http://dx.doi.org/10.1016/j.socscimed.2006.08.024

http://dx.doi.org/10.1186/s12889-015-2377-y

http://dx.doi.org/10.1023/A:1025642806557

http://dx.doi.org/10.1037/0021-9010.88.5.879

http://oem.bmj.com/

Supplementary material 1. Between April and June 2016, semiannual binary data on organizational change at the work-unit level from

2011 to 2013 were obtained via the following online survey emailed to the managers of all work units.

This questionnaire relates to the work unit "<name of work unit>" in <name of hospital>.

Yes No

Were you a manger in the above-stated work unit during the TrivselOP in March 2014?

□ □

In the work unit you manage/managed, have there been following organizational changes in the period 1.1.2011-31.12.2013?

If yes, please specify when:

1. half-

year 2011 2. half-

year 2011 1. half-

year 2012 2. half-

year 2012 1. half-

year 2013 2. half-

year 2013 No

Do not know

Merger with another work unit □ □ □ □ □ □ □ □

Division into other work units □ □ □ □ □ □ □ □

Change of management in the work unit □ □ □ □ □ □ □ □

Physical relocation of the work unit □ □ □ □ □ □ □ □

Layoff of employee(s) in the work unit □ □ □ □ □ □ □ □

Selective budget cuts in the work unit □ □ □ □ □ □ □ □

Supplementary material 2. Spearman’s r-correlation coefficients between the six types of

organizational change.

Merger Demerger Relocation

Change of

management

Employee

layoff Budget cuts

Merger

0.28 0.33 0.23 0.15 0.14

Demerger 0.28

0.18 0.17 0.09 *-0.01

Relocation 0.33 0.18

0.14 0.07 *0.01

Change management 0.23 0.17 0.14

0.09 0.09

Employee layoff 0.15 0.09 0.07 0.09

0.25

Budget cuts 0.14 *-0.01 *0.01 0.09 0.25

p<0.001, * p>0.21

Supplementary material 3. Differences between employees with data on organizational change (i.e.,

study population) and employees without change data from the source population regarding work-

unit exit, sickness absence, covariate levels, and number of work units.

Source population* (N=25,922)

With change data (study

population), n (%)

Without change

data, n (%) p

Total number of employees 14,388 (100) 11,534 (100)

Subsequent work-unit exit 2014 2471 (17.17) 2247 (19.48) <0.001

Total SA 2014

With SA event 11,177 (78.94) 8910 (78.52) 0.42

Mean (SD) 4.52 (8.30) 4.65 (9.04) <0.001

Long-term SA 2014

With SA event 836 (5.90) 689 (6.07) 0.57

Mean (SD) 1.38 (7.47) 1.53 (8.20) <0.001

Female, n (%) 10,951 (76.11) 8876 (76.96) 0.11

Age group

0.69

18-40 3630 (25.23) 2842 (24.64)

40-48 3605 (25.06) 2886 (25.02)

48-56 3578 (24.87) 2889 (25.05)

56-75 3575 (24.85) 2917 (25.29)

Number of small-large work units, employees 1245 (100) 1077 (100) 0.20

3-12 645 (51.81) 593 (55.06)

13-22 294 (23.86) 260 (24.14)

23-32 187 (15.02) 133 (12.35)

33-142 116 (9.32) 91 (8.45)

Employees in work unit

<0.001

3-12 3480 (24.19) 3151 (27.32)

13-22 3636 (25.27) 3092 (26.81)

23-32 3633 (25.25) 2505 (21.72)

33-142 3639 (25.29) 2786 (24.15)

Occupational group

<0.001

Nurses 6216 (43.20) 4967 (43.06)

Administrative staff 2643 (18.37) 2199 (19.07)

Social/healthcare workers 1883 (13.09) 1369 (11.87)

Service/technical staff 1812 (12.59) 1280 (11.10)

Medical doctors and dentists 1449 (10.07) 1343 (11.64)

Pedagogical workers 385 (2.68) 376 (3.26)

Days of sickness absence 2012

0.99

0 3988 (27.72) 3226 (27.97)

1-3 3101 (21.55) 2487 (21.56)

4-6 2185 (15.19) 1748 (15.16)

7-13 2742 (19.06) 2189 (18.98)

14-363 2372 (16.49) 1884 (16.33)

χ2 or t-test applied as appropriate. Total and long-term sickness absence were calculated as the

percentage of missed working hours due to all sickness absence or spells of >29 days, respectively.

* No missing data except on organizational change.

Abbreviations: SA = sickness absence, SD = standard deviation.

Paper III

Longitudinal associations between organizational change, work-unit social

capital, and employee exit from the work unit among public healthcare

workers: a mediation analysis

Jensen JH, Flachs EM, Skakon J, Rod NH, Bonde JP

Scand J Work Environ Health, 2019;45(1):53-62, online first [21-Aug-2018].

doi:10.5271/sjweh.3766

Scand J Work Environ Health 2019, vol 45, no 1 53

Original articleScand J Work Environ Health. 2019;45(1):53–62 doi:10.5271/sjweh.3766

Longitudinal associations between organizational change, work-unit social capital, and employee exit from the work unit among public healthcare workers: a mediation analysisby Johan Høy Jensen, MSc,1 Esben Meulengracht Flachs, PhD,1 Janne Skakon, PhD,2 Naja Hulvej Rod, Professor,3 Jens Peter Bonde, Professor 1

Jensen JH, Flachs EM, Skakon J, Rod NH, Bonde JP. Longitudinal associations between organizational change, work-unit social capital, and employee exit from the work unit among public healthcare workers: a mediation analysis. Scand J Work Environ Health. 2019;45(1):53–62. doi:10.5271/sjweh.3766

Objectives Organizational changes are associated with higher rates of subsequent employee exit from the work-place, but the mediating role of social capital is unknown. We examined the associations between organizational changes and subsequent employee exit from the work unit and mediation through social capital.Methods Throughout 2013, 14 059 healthcare employees worked in the Capital Region of Denmark. Data on work-unit changes (yes/no) from July‒December 2013 were collected via a survey distributed to all managers (merger, split-up, relocation, change of management, employee layoff, budget cuts). Eight employee-reported items assessing social capital were aggregated into work-unit measures (quartiles: low-high). Data on employee exit from the work unit in 2014 were obtained from company registries.Results We found a somewhat higher rate of employee exit from the work unit after changes versus no changes [hazard ratio (HR) 1.10, 95% confidence interval (CI) 1.01–1.19] and an inverse dose‒response relationship between social capital and employee-exit rates (low versus high: HR 1.65, 95% CI 1.46–1.86). We also showed a higher risk of low social capital in work units exposed to changes [low versus high: odds ratio (OR) 2.04, 95% CI 1.86–2.23]. Accounting for potential mediation through social capital seemed slightly to reduce the association between changes and employee-exit rates (HR 1.07, 95% CI 0.98–1.16 versus HR 1.10).Conclusions Work-unit organizational changes prospectively predict lower work-unit social capital, and lower social capital is associated with higher employee-exit rates. Detection of weak indications of mediation through social capital, if any, were limited by inconsistent associations between changes and employee exit from the work unit.

Key terms downsizing; health care; longitudinal study; mediator; merger; organisational change; psychosocial work environment; public sector; reorganisation; reorganization; restructuring; turnover

1 Department of Occupational and Environmental Medicine, Copenhagen University Hospital, Bispebjerg Hospital, Denmark.2 Department of Psychology, University of Copenhagen, Denmark.3 Section of Epidemiology, Department of Public Health, University of Copenhagen, Denmark.

Correspondence to: Johan Høy Jensen, Department of Occupational and Environmental Medicine, Bispebjerg University Hospital, Bispebjerg Bakke 23F, DK-2400, Copenhagen NV, Denmark. [E-mail: [email protected]]

Restructuring of workplaces is widely performed to keep up with increasing demands for productivity and cost-efficiency. However, there seems to be a downside to organizational changes in terms of poor employee health and well-being (1–6). Elevated rates of employee exit (ie, turnover) from the workplace following reorga-nization have been reported consistently in the literature (7–13), and studies suggest that organizational changes may have a dual impact on employee exit and health (11, 14). Specifically, quarterly employee-exit rates increased from 3.1% to 3.4% after implementation of

new healthcare workflows (9), and ‒ relative to no change ‒ excess employee-exit rates of 15–50% have been demonstrated in the years following merger, split-up, relocation, change of management, and >3 changes performed simultaneously in the healthcare sector (11, 12). Such higher employee-exit rates have been associ-ated with adverse psychosocial outcomes among the remaining employees as well as high replacement costs and loss of productivity (15).

Social capital refers to the “resources that are accessed by individuals as a result of their membership of a net-

54 Scand J Work Environ Health 2019, vol 45, no 1

From restructuring to job exit via social capital?

work or a group” (16) and manifests as trust, reciprocity and social cohesion within a group of co-workers (16). The literature on workplace social capital in the context of reorganization is limited. However, since the work-place can be seen as having social dimensions among coworkers, it is reasonable to assume that reorganizations disrupt work-related social networks and friendship ties in a work unit. Employees can perceive such processes as being unfair, lowering their attachment to the workplace (17–20). This is supported by findings of a 4% decrease in trust of management after reorganization involving change of top management (21) as well as distributive justice partially mediating the association between trust and intention to quit in the context of downsizing (20).

Low social capital has been linked to a higher risk of mental-health problems (22, 23), sickness absence (24–26), early retirement (12), and poor self-rated health (27). A study found that self-reported poor health was associated with a 2.3-fold higher “risk” of intention to quit, whereas good collaboration among colleagues as well as trustworthiness and support from managers were associated with 60–80% lower chance of intention to quit (28). Indeed, the associations of workplace social capital on the pathway between organizational changes and employee exit from the workplace remain unclear.

We aimed to investigate the hypothesized (objec-tive a) prospective associations between organizational changes and low work-unit social capital, (objective b) the association between low social capital and higher rates of employee exit from the work unit (EFW), and (objective c) work-unit social capital as a mediator on the associations between organizational changes and higher rates of subsequent EFW (figure 1). In this study, EFW refers to an employee terminating employment in a work unit regardless of the reason. A mediator refers to a factor that explains the impact of an exposure on a given outcome (29). Such mediation may highlight social capi-tal as a target of intervention to prevent adverse effects of organizational changes.

Methods

Study design and data collection

This longitudinal study was based on the Well-being in Hospital Employees (WHALE) cohort (30) and exam-ined the associations between work-unit organizational change in the last six months of 2013, work-unit social capital in March 2014, and employee EFW during 2014.

The source population comprised 37 720 employees from the Capital Region of Denmark who were invited to complete a work-environment questionnaire in March 2014 (response rate: 84%). From April through June

2016, we distributed a survey to the managers of all 2696 work units to collect data on six types of orga-nizational changes occurring in the last six months of 2013 (response rate: 59%). Sociodemographic and occu-pational background information for every employee holding a paid position between January 2012 through December 2014 was recorded from company registers, and information on income during 2013 were extracted via linkage to national registers. These data were applied to estimate monthly employee EFW in 2014 as well as employee- and work-unit-level covariates at baseline (31 December 2013).

Study population

At baseline, 25 926 eligible employees had at least one year of seniority in the current work unit (or one of its associated unit[s] if merger and/or split-up had occurred) and a minimum of 18.5 weekly fixed working hours in average (ie, part-time working hours) during 2013. We excluded 279 work units with fewer than three employ-ees. Some work units changed their name during 2013. Thus, to ensure that the employees had at least one year of seniority in the current work unit at baseline (31 December 2013), we included employees in the study population if they were affiliated to a work unit where a significant proportion of the staff (ie, ≥30% and ≥3 employees) remained in the new-named work unit. For instance, if work unit A with six employees split-up into work unit B with two employees and work unit C with four employees, only the four employees in work unit C were included in the study population.

The study population comprised 14 059 employees nested in 1216 work units with complete data on work-unit organizational change in the last six months of 2013, work-unit social capital in March 2014, employee EFW from January through December 2014, and covari-ates (figure 2).

Employee exit from the work unit

We estimated monthly EFW from January through December 2014 at the employee level. This was defined

Association between organizational change and work-unit social capital (a). Associationbetween work-unit social capital and employee exit from the work unit (b). Work-unit socialcapital mediates the association between organizational change and subsequent employee exitfrom the work unit (c).

Figure 1. Diagram of the associations examined in the present study. (a) Association between organizational change and work-unit social capital. (b) Association between work-unit social capital and employee exit from the work unit. (c) Work-unit social capital mediates the association between organizational change and subsequent employee exit from the work unit.


Jensen et al

as an employee’s loss of affiliation to the current work unit at baseline. Because we presumed that some work units also changed name during 2014, we did not regard it as an EFW if an employee was affiliated to a work unit where a significant proportion of the staff (ie, >30% and >3 employees) worked in a renamed work unit.

Work-unit organizational change

In the survey on organizational changes, the managers were invited to provide semi-annual information on six types of changes (yes/no) at the work-unit level occurring from January 2011 to December 2013 (Q “In the work unit you manage/managed, have there been the following organizational changes in the period 1 January 2011 and 31 December 2013?”): (A1) merger, (A2) demerger, (A3) relocation of a work unit, (A4) change of management, (A5) employee layoff or (A6) budget cuts. This study used measures of organizational changes in the last six months of 2013, including no change (reference category), change (any/no change), number of changes performed simulta-neously (1, 2 or >3 changes/no change), and each type of change (yes/no change). Exposure to each of these six types of change were modelled separately.

Work-unit social capital

The social capital scale ranging 0‒100 was based on eight employee-reported items from the work-

environment survey in March 2014 assessing col-laboration (“To what degree…:” Q1“…are you and your colleagues good at coming up with suggestions for improving work procedures?”, Q2 “…do you get help and support from your colleagues when needed?”, Q3 “…do you and your colleagues take responsibility for a nice atmosphere and tone of communication?”) and trust/organizational justice (“To what degree…:” Q4 “…does the management trust the employees to do their work well?”, Q5 “…can you trust the infor-mation that comes from the management?”, Q6 “…are conflicts resolved in a fair way?”, Q7 “…is the work distributed fairly?”, Q8 “…is your staff group respected by the other staff groups at the workplace?”). Five of these items originated from the Copenhagen Psychosocial Questionnaire (31), whereas the remain-ing three items were selected by four specialists in occupational medicine. Responses on <50% of the social-capital items were set to missing. Cronbach’s alpha was 0.85 and correlation coefficients between all items ranged 0.24–0.74 (P-values<0.001). The work-unit-level social capital measure was computed by averaging the employee-level social capital scores in work units with ≤50% missing data. The work-unit social capital measure was categorized into quartiles (level I‒IV: low‒high) and assigned to each individual employee in a given work unit. This approach was consistent with previous studies using WHALE cohort data (12, 26, 30).

1

* Data collected in March 2014.

** Data collected from April through June 2016.

Figure 2. Diagram showing the study design and the flow of employees and work units. * Data collected in March 2014. ** Data collected from April‒June 2016.



Employee- and work-unit-level covariates at baseline

We used the following a priori confounder variables at the employee level: age (quartiles), sex, occupational groups, previous absence related to sick child between 2012–2013 (yes/no), previous number of sickness-absence days in 2012 (quartiles), and personal gross income in 2013 (quartiles). Absence due to sick child was a proxy variable for having a child. Number of previous sickness-absence days was a proxy variable for health status. Employees with no observed sickness absence were allocated to the lower-quartile category. Personal gross income in Danish kroner were divided by 7.5 to express these values in euros (€).

We used the following a priori confounder variables aggregated at the work-unit level: number of employees within work unit (quartiles), mean of employee age (con-tinuous), mean of personal gross income in 2013 (continu-ous), mean of sickness-absence days in 2012 (continuous), proportion of females within work unit (continuous), pro-portion of employees with child-related absence between 2012‒2013 within work unit (continuous), and proportion of each occupation group within work unit (continuous).

Statistical main analysis

Work-unit organizational changes and work-unit social capital. Logistic regression models were used to estimate the risk of low social capital in March 2014 according to each measure of organizational changes in the last six months of 2013 (objective a). Analyses were weighted by the number of employees within each work unit (con-tinuous variable). We adjusted for all work-unit-level confounders (except the categorical variable for number of employees within work unit) because exposure and outcome were both measured at the work-unit level.

Work-unit social capital and employee exit from the work unit. Marginal Cox models were used to assess the rate of EFW during 2014 associated with each lower level of social capital in March 2014 relative to the highest level (objec-tive b). The employees were followed on the month-scale from 1 January 2014 until EFW, censoring by death, or end of study (31 December 2014), whichever came first. We adjusted for all employee-level covariates and the number of employees at the work-unit level. Since the variables in the marginal Cox models were measured at multiple levels, we used the COVSANDWICH option on the work-unit level to obtain robust 95% confidence intervals (CI). We fitted marginal models with no distri-butional assumptions instead of mixed-effects models because the latter require assumptions about the joint distribution and the random effects, which are unclear (eg, due to new changes and seasonal variances in EFW during follow-up) (32).

Mediation through work-unit social capital. Marginal Cox models were also used to assess the rate of EFW during 2014 after organizational changes in the last six months of 2013 relative to no change. We used the same covari-ates and criteria during follow-up on EFW as those described above for the marginal Cox models addressing objective b. To establish mediation (objective c), the mediator variable (social capital) must be associated with both the exposure (organizational changes) and the outcome (EFW). We interpreted a reduction in the EFW rate when including the social-capital variable in model as evidence of mediation (29).

Sensitivity analyses

We conducted four sensitivity analyses using the same methods as above unless otherwise stated.

First, because social capital was measured in March 2014 and follow-up on EFW started on January 2014, we assessed potential reverse causation by splitting the follow-up into two periods: one period from January through March 2014, and a second period from April through December 2014 (excluding employees EFW in the first period). Two analyses assessed the associa-tion between social capital and EFW in each follow-up period (relating to objective b). Four analyses assessed the associations between organizational changes and EFW in each period with and without social capital included in the model (relating to objective c).

Second, we explored if work-unit collaboration and trust/organizational justice (comprising social capital) separately mediated the association between organiza-tional changes and EFW during 2014. This was assessed with two analyses for the association between changes and EFW including work-unit-aggregated collaboration and trust/organizational justice, respectively, in compari-son to a model without any mediator.

Third, we analyzed the association between orga-nizational changes and subsequent employee exit from the company instead of EFW. We calculated employee exit from the company as months to loss of affiliation to the Capital Region of Denmark from January through December 2014.

Fourth, to assess the impact of missing data on orga-nizational changes, we used a two-way t-test and a χ2-test to analyze if work-unit social capital and employee EFW rates differed among work units and employees, respec-tively, with and without data on changes.

All statistical analyses were performed using SAS Software 9.4 (SAS Institute Inc, Cary, NC, USA).


Jensen et al

Results

Table 1 shows the distribution of the study population on exposure to organizational changes and low/high social capital across covariate levels. Exposure to orga-nizational changes was more prevalent in work units

with low social capital and those with more employees. Male employees, work units with more employees, and employees with a lower income were mostly repre-sented in work units with low social capital. In contrast, female employees, work units with fewer employees, and employees with a higher income were mostly rep-resented in work units with high social capital.

Table 1. Distribution of the study population and the prevalence of organizational changes, work-unit social capital level I (low) and level IV (high), exit from the work unit (EFW), and covariate levels. [WSC=work-unit social capital.]

Study population

Employees exposed to change

WSC level I (lowest)

WSC level IV (highest)

N % N % N % N %Employee levelTotal employees 14 059 100 5649 40 3406 24 3715 26EFW 2383 17 999 18 680 20 504 14Female 10 727 76 4258 75 2278 67 2948 79Male 3332 24 1391 25 1128 33 767 21Age group (years)

18–40 3469 25 1378 24 908 27 792 2140–48 3550 25 1400 25 837 25 1010 2748–56 3530 25 1424 25 825 24 986 2756–75 3510 25 1447 26 836 25 927 25

Occupational groupNurses 6038 43 2444 43 1195 35 1769 48Administrative staff 2615 19 1060 19 581 17 710 19Social/healthcare workers 1865 13 665 12 593 17 369 10Service/technical staff 1777 13 751 13 789 23 280 8Medical doctors and dentists 1379 10 598 11 137 4 451 12Pedagogical workers 385 3 131 2 111 3 136 4

Days of sickness absence during 20120–3 6897 49 2787 49 1440 42 2102 574–6 2141 15 851 15 504 15 576 167–13 2687 19 1015 18 722 21 607 1614–363 2334 17 996 18 740 22 430 12

Child–related absence during 2012 and 2013 (yes) 4222 30 1645 29 1026 30 1134 31Personal gross income (€)

<46 000 3602 26 1501 27 1039 31 727 2046 000–53 333 3630 26 1427 25 929 27 817 2253 333–64 000 3455 25 1346 24 861 25 952 26>64 000 3372 24 1377 24 577 17 1219 33

Work-unit levelTotal work units 1216 100 430 35 238 20 434 36No organizational change 786 65 . . 139 58 303 70Organizational change 430 35 . . 99 42 131 301 type of change 272 22 . . 61 26 82 192 types of changes 99 8 . . 26 11 31 7≥3 types of changes 59 5 . . 12 5 18 4Merger 88 10 . . 23 14 22 7Split–up 44 5 . . 11 7 10 3Relocation 89 10 . . 21 13 35 10Change of management 166 17 . . 41 23 45 13Employee layoff 161 17 . . 33 19 51 14Budget cuts 126 14 . . 28 17 42 12

Number of employees in work unit3–12 634 52 186 43 98 41 300 6913–22 289 24 113 26 62 26 72 1723–32 182 15 81 19 46 19 44 1033–142 111 9 50 12 32 13 18 4

Mean SD Mean SD Mean SD Mean SD

Employee age (years) 48 6 48 6 47 5 48 6Proportion of females 74 30 73 29 66 35 77 28Personal gross income (€) 61 946 23 127 63 548 25 519 57 182 19 038 65 495 24 562Proportion with child–related absence 30 22 28 19 29 21 30 25Days of sickness absence during 2012 8 8 9 10 10 9 6 9Proportion of nurses 34 42 36 43 28 40 36 41Proportion of administrative staff 24 36 25 37 23 37 25 35Proportion of social/healthcare/pedagogical workers 19 33 14 29 21 36 19 32Proportion of service/technical staff 13 31 23 31 20 38 9 26Proportion of medical doctors and dentists 11 28 12 29 7 23 12 27



Work-unit organizational change and social capital

Table 2 shows that work units had an excess risk of lower social-capital levels relative to high social capital after organizational changes. However, this pattern was not observed for exposure to relocation.

Work-unit social capital was slightly lower in work units without data on changes [mean 68, standard devia-tion (SD) 10] than work units with data on changes [mean 69, SD 10; t(2242) = -3.6, P<0.001], indicating some underestimation.

Work-unit social capital and employee exit from the work unit

Table 3 shows an inverse dose‒response relationship between social capital and EFW through 2014. In total, 7 employees were censored from the analyses due to death in 2014. Of the 2471 employees who exited their work unit in 2014, 785 employees (32%) exited before the assessment of social capital in March 2014. Only 35 of these 785 employees (4%) had missing data on work-unit social capital. Splitting the follow-up on EFW during 2014 into January‒March and April‒December yielded similar inverse dose‒response relationships between social capital and EFW. However, the associa-tions were slightly stronger in the period after assess-ment of social capital (supplementary table S1, www.sjweh.fi/show_abstract.php?abstract_id=3766).

Mediation through work-unit social capital

Table 4 shows that only some change indicators were associated with a higher rate of subsequent EFW, spe-cifically >3 types of simultaneous changes, merger, split-up, relocation, and change of management. Includ-ing social capital in the models reduced the EFW rates only slightly, suggesting no convincing indications of mediation through social capital on the inconsistent

association between changes and EFW.The EFW rate after changes were higher January‒

March than April‒December 2014, but social capital did not consistently mediate the excess EFW rates in either of period (supplementary table S2, www.sjweh.fi/show_abstract.php?abstract_id=3766). Similar inconsistent indications of mediation were observed for collaboration and trust/organizational justice (supplementary table S3, www.sjweh.fi/show_abstract.php?abstract_id=3766). There was a ≈1.5-fold higher company-exit rate after >3 types of changes, merger or relocation relative to no change (supplementary table S4, www.sjweh.fi/show_abstract.php?abstract_id=3766), indicating the sensitivity of the EFW measure. The rate of EFW during 2014 was higher among eligible employees without data on changes (19%) than employees with data on changes (17%; χ2=22.22 (1), P<0.001), pointing to some under-estimation of the EFW rates.

Discussion

We found that work units had an excess risk of low social capital after organizational changes relative to no change. There was an inverse dose‒response relation-ship between social capital and EFW regardless of the reason. Some change measures were associated with a higher rate of employee EFW, but there were no con-vincing indications of mediation via social capital on these inconsistent associations.

Work-unit organizational change and social capital

Previous findings showed significant declines on a 3-point trust scale at the employee level associated with reorganization of divisions/sections (β=-0.075) and change of management (β=-0.085) (33) pointing to the

Table 2. Odds ratios (OR) of lower work-unit social capital (level I, II or III) than the highest level of work-unit social capital (level IV) as reference after exposure to organizational change. Logistic regression analyses were adjusted for work-unit level mean of employee age, proportion of females, mean personal gross income, proportion of employees with previous child-related absence, mean of sickness absence days in 2012, and proportion of each occupational group within work unit. [WSC=work-unit social capital; CI=confidence interval]

Organizational change N WSC level I WSC level II WSC level III% OR 95% CI % OR 95% CI % OR 95% CI

No change 786 18 20 24Change 430 23 2.04 1.86–2.23 22 1.51 1.39–1.64 24 1.51 1.39–1.65

1 change 272 22 2.05 1.85–2.27 22 1.60 1.45–1.76 25 1.58 1.44–1.752 changes 99 26 1.85 1.58–2.16 21 0.92 0.78–1.08 21 1.23 1.06–1.42>3 changes 59 20 2.30 1.87–2.82 24 2.30 1.91–2.76 25 1.70 1.41–2.06

Merger 88 26 2.24 1.88–2.66 27 1.89 1.60–2.22 22 1.52 1.28–1.79Split-up 44 25 3.66 2.85–4.70 32 3.33 2.62–4.22 20 1.50 1.16–1.95Relocation 89 24 1.13 0.96–1.33 19 1.10 0.95–1.28 18 0.67 0.57–0.79Change of management 166 25 2.58 2.28–2.93 25 1.78 1.57–2.01 23 1.72 1.52–1.94Employee layoff 161 21 1.86 1.63–2.11 22 1.67 1.48–1.89 26 1.72 1.52–1.94Budget cuts 126 22 1.92 1.68–2.15 15 0.87 0.75–1.01 29 1.90 1.68–2.15


Jensen et al

same direction as the present findings of 1.5–3.7-fold excess risk of lower social capital after merger, split-up or change of management versus no change. Work units with high social capital may have difficulties including outsiders (16), which could decrease social cohesion and trust, for instance, in the context of a merger. However, relocation did not predict lower social capital, which could be explained by the fewer social ties being dis-rupted in relation to this type of change.

One interpretation of these associations is that orga-nizational changes adversely impact the work-unit social capital, which is consistent with conclusions of a review on other psychosocial factors (1). An alternative interpre-tation of lower social capital after organizational changes may be due to reverse causality. Lower social capital has been linked to lower quality of patient care (34) and productivity (35), which may encourage reorganization. However, changing a work unit with low social capital may arguably have some positive influence on the psy-chosocial work environment (eg, change of a distrusted management), which is in contrast to our consistent dem-onstrations of low social capital after changes.

Work-unit social capital and employee exit from the work unit

We found an inverse dose‒response relationship between social capital and EFW concurrent with a meta-analysis on 190 studies concluding strong signifi-cant inverse correlations between procedural/distributive justice and intention to quit (weighted r-values = -0.40) (36). Our findings also corroborate demonstrations of a 1.3 times higher rate of early retirement associated with a 20-point decrease on a 100-point social-capital scale (12) and an inverse dose‒response relationship between social capital and long-term sickness absence (26). Previous findings show that good collaboration among employees and trust in managers were associated with a 60–80% lower chance of intention to quit (28).

Collaboration and trust may be prerequisites for a well-functioning workplace and a decline in these factors could lower job satisfaction and lead to EFW.

Although 785 employees exited their work unit before/during assessment of social capital in March 2014, only 4% of these employees had missing data on work-unit social capital since this score was assigned to each employee regardless of survey participation. Sen-sitivity analyses showed comparable EFW rates before/during and after assessment of social capital. Indeed, employees exiting before assessment of social capital due to changes would likely respond more critically to the social-capital items than their participating col-leagues, and thus the time gap between organizational changes and assessment of social capital may contribute to some underestimation of the association.

Work-unit social capital as a potential mediator

There were no convincing indications of mediation through social capital (objective c) on the rather incon-sistent association between changes and EFW demon-strated in this study. Although the relative reduction in the EFW rate for change versus no change comprised ≈30% when including social capital in the model, media-tion should also be interpreted in keeping with the absolute reduction (HR 1.10 versus 1.07). It is likely that the inconsistent association between changes and EFW limited the statistical power to detect a potential mediation through social capital. Indeed, a sensitivity analysis on a stronger association between changes and EFW in the first three months of follow-up neither showed convincing indications of mediation through social capital (15%; HR 1.27 versus HR 1.23). These indications are somewhat comparable to other findings

Table 3. Adjusted hazard ratios (HR) and robust 95% confidence inter-vals (CI) of employee exit from the work unit through 2014 associated with levels of work-unit social capital (level IV‒I: high‒low) compared to high work-unit social capital as reference. Marginal Cox regres-sion analyses were adjusted for employee-level age, sex, occupational group, previous sickness absence, child-related absence and personal gross income, and work-unit level number of employees. [WSC=work-unit social capital.]

WSC level

Study population (N=14 059) Source population (N=25 296) a

N Exited (%) HR 95% CI N Exited (%) HR 95% CI

IV 3715 14 1.00 6323 15 1.00III 3566 17 1.29 1.15–1.45 6277 17 1.16 1.06–1.26II 3372 17 1.34 1.18–1.51 6349 18 1.26 1.15–1.37I 3406 20 1.65 1.46–1.86 6347 21 1.60 1.47–1.74a Including participants with and without missing data on exposure to organi-

zational change.

Table 4. Adjusted hazard ratios (HR) and 95% confidence intervals (CI) of employee exit from the work unit after organizational change relative to no change. Main model additionally adjusted for potentially mediated effects via work-unit social capital. Marginal Cox regression analyses were adjusted for employee-level age, sex, occupational group, previous sickness absence, child-related absence and personal gross income, and work-unit level number of employees. [WSC=work-unit social capital.]

Organizational change

N % Main model Adjusted for WSCHR 95% CI HR 95% CI

No change (reference) 8410 17 1.00 1.00Change 5649 18 1.10 1.01–1.19 1.07 0.98–1.16

1 change 3728 17 1.04 0.95–1.15 1.01 0.92–1.112 changes 1170 17 1.03 0.89–1.20 0.99 0.85–1.15>3 changes 751 23 1.53 1.30–1.80 1.48 1.26–1.73

Merger 1085 21 1.29 1.12–1.49 1.24 1.08–1.43Split-up 508 22 1.41 1.16–1.72 1.33 1.09–1.62Relocation 978 19 1.17 1.00–1.36 1.16 0.99–1.35Change of management 2149 19 1.23 1.10–1.38 1.17 1.05–1.31Employee layoff 2163 16 1.03 0.91–1.16 1.00 0.89–1.13Budget cuts 1757 18 1.10 0.97–1.25 1.08 0.96–1.23



showing no mediation by employee-level social conflict between downsizing and self-rated health (37).

A previous study found that trust partially medi-ated the association between lower distributive justice and intention to quit among employees remaining at the workplace after downsizing (from r = -0.64 to r = -0.50) (20). However, we showed a higher rate of EFW only in the first three months after employee layoff, which seemed not to be mediated convincingly by social capital. Another study (38) found that about half of the association between major staff reduction and long-term sickness absence were reduced when adjusting for mediation through job control, job insecurity, and physical demands. These factors may also be mediators on EFW, but this remains to be investigated.

Social capital may as well buffer the adverse effects of organizational change as such properties were found between high job strain and smoking status (39). How-ever, since social capital was measured after the organi-zational changes occurred, we refrained from examining the potential modifying effects of social capital between changes and EFW.

EFW may be considered as a less problematic out-come than exit out of the healthcare sector: the latter would more likely predict severe illness, long-term unemployment, disability retirement etc. Although job rotation within the healthcare sector may comprise a healthy work life, the literature on organizational change mainly show adverse impacts on employees. Thus, employee EFW to another work unit may likely be motivated by deteriorated well-being and/or health among some employees. In addition, high EFW rates seem also to adversely affect those who remain in the work unit in terms of mental health problems, lower job satisfaction, and excess risk of medical errors (40).

Strengths and limitations

It was a strength of this longitudinal study that we tracked the work-unit affiliation of all employees (despite some work-unit names being changed) reduc-ing loss to follow-up mainly among employees exposed to organizational changes. Also, data on exposure, out-come and mediation were obtained from independent data sources, which reduces common-method bias in the associations examined (41). By collecting data on changes from the work-unit managers and assigning these to each employee, we obtained valid information on organizational changes since managers may recall the organizational history more accurately than the employ-ees. Using data from independent sources is particularly important in mediation analysis, and therefore a major strength of this study, because mediated effects found in data from the same source could be due to the com-mon method applied (41). Additionally, we included

employees regardless of survey participation as social capital was aggregated at the work-unit level, which also makes the findings less influenced by individual factors (eg, lifestyle).

This study has some potential limitations. We assessed the sensitivity of EFW by analyzing associa-tions between changes and company exit. These associa-tions attenuated compared to results in table 4, but some change measures, including merger, remained signifi-cantly associated with company exit, which is contrary to previous findings (10). Not examining EFW during or before the organizational changes occurred could have underestimated the results. It has been demonstrated that the adverse effects of reorganization can be observed shortly after a merger is announced (42). Although data on EFW were available during occurrence of the changes, we did not use these because it was unclear when the changes were announced. Moreover, we were unable to adjust for effects of organizational change during the follow-up on EFW through 2014 due to lack of data. This may have underestimated the results as work units not changed during 2013 may more likely be changed in the following year. Assessment of mediation through social could be limited by focusing on a 2-year period, since changes in social capital may occur over a longer period. However, choosing this narrow time frame was pivotal to capture the immediate prospective associations on EFW soon after organizational changes. Finally, the differences in EFW rates and social capital among employees and work units with/without data on changes suggest that these missing data may somewhat contribute to some underestimate the findings.

In conclusion, we demonstrated a higher risk of low work-unit social capital after organizational change relative to no change and an inverse dose‒response rela-tionship between work-unit social capital and EFW. We found no convincing indications of mediation through social capital between organizational change and subse-quent EFW. The inconsistent effects of change on EFW may have limited the statistical power to detect such – if any – mediation.

Acknowledgements

The Danish Working Environment Research Fund is acknowledged for its financial contribution to the study (13-2015-03). The Capital Region of Denmark is acknowledged for providing data on background infor-mation, work-unit exit, and social capital. The sponsors had no role in planning or conducting the current study, interpretation of the results or no decision in submitting the paper for publication.

The authors declare no conflicts of interest.


Jensen et al

References

1. de Jong T, Wiezer N, de Weerd M, Nielsen K, Mattila-Holappa P, Mockałło Z. The impact of restructuring on employee well-being: a systematic review of longitudinal studies. Work Stress 2016;30(1):91–114. http://dx.doi.org/10.1080/02678373.2015.1136710.

2. Vahtera J, Kivimäki M, Pentti J, Linna A, Virtanen M, Virtanen P et al. Organisational downsizing, sickness absence, and mortality: 10-town prospective cohort study. BMJ 2004 Mar;328(7439):555. http://dx.doi.org/10.1136/bmj.37972.496262.0D.

3. Kivimäki M, Honkonen T, Wahlbeck K, Elovainio M, Pentti J, Klaukka T et al. Organisational downsizing and increased use of psychotropic drugs among employees who remain in employment. J Epidemiol Community Health 2007 Feb;61(2):154–8. http://dx.doi.org/10.1136/jech.2006.050955.

4. Ingelsrud MH. Reorganization increases long-term sickness absence at all levels of hospital staff: panel data analysis of employees of Norwegian public hospitals. BMC Health Serv Res 2014 Sep;14:411. http://dx.doi.org/10.1186/1472-6963-14-411.

5. Dahl MS. Organizational Change and Employee Stress. 2011;57(2):240–56.

6. Greubel J, Kecklund G. The impact of organizational changes on work stress, sleep, recovery and health. Ind Health 2011;49(3):353–64. http://dx.doi.org/10.2486/indhealth.MS1211.

7. Baron JN, Hannan MT, Burton MD. Labor Pains: Change in Organizational Models and Employee Turnover in Young, High-Tech Firms. Am J Sociol 2001;106(4):960–1012. http://dx.doi.org/10.1086/320296.

8. Cameron K, Kim M, Whetten D. Organizational Effects of Decline and Turbulence. Adm Sci Quaterly 1987;32(2):222–40. http://dx.doi.org/10.2307/2393127.

9. Sylling PW, Wong ES, Liu CF, Hernandez SE, Batten AJ, Helfrich CD et al. Patient-centered medical home implementation and primary care provider turnover. Med Care 2014 Dec;52(12):1017–22.

10. Ingelsrud MH. Hospital Mergers in Norway: Employee Health and Turnover to Three Destinations. Nord J Work Life Stud 2017;7(1):107–24.

11. Jensen JH, Flachs EM, Skakon J, Rod NH, Bonde JP. Dual impact of organisational change on subsequent exit from work unit and sickness absence: a longitudinal study among public healthcare employees. Occup Environ Med 2018 Jul;75(7):479–85. http://dx.doi.org/10.1136/oemed-2017-104865.

12. Breinegaard N, Jensen JH, Bonde JP. Organizational change, psychosocial work environment, and non-disability early retirement: a prospective study among senior public employees. Scand J Work Environ Health 2017 May;43(3):234–40. http://dx.doi.org/10.5271/sjweh.3624.

13. Bauer TK, Bender S. Technological change, organizational change, and job turnover. Labour Econ 2004;11(3):265–91. http://dx.doi.org/10.1016/j.labeco.2003.09.004.

14. Kivimäki M, Vahtera J, Elovainio M, Pentti J, Virtanen M. Human costs of organizational downsizing: comparing health trends between leavers and stayers. Am J Community Psychol 2003 Sep;32(1-2):57–67. http://dx.doi.org/10.1023/A:1025642806557.

15. Hayes LJ, O’Brien-Pallas L, Duffield C, Shamian J, Buchan J, Hughes F, et al. Nurse turnover: A literature review - An update. Int J Nurs Stud . 2012;49(7):887–905. https://doi.org/10.1016/j.ijnurstu.2011.10.001.

16. Kawachi I, Berkman L. Social Capital, Social Cohesion, and Health. In: Berkman L, Kawachi I, Glymour M, editors. Soc Epidemiol 2nd ed. 2014. p. 290–319.

17. Allen TD, Freeman DM, Russell JE, Reizenstein RC, Rentz JO. Survivor reactions to organizational downsizing: does time ease the pain? J Occup Organ Psychol 2001;74(2):145–64. http://dx.doi.org/10.1348/096317901167299.

18. Armstrong-Stassen M, Cameron SJ, Mantler J, Horsburgh ME. The Impact of Hospital Amalgation on the Job Attitudes of Nurses. Can J Adm Sci 2001;18(3):149–62. http://dx.doi.org/10.1111/j.1936-4490.2001.tb00252.x.

19. Spreitzer GM, Mishra AK. To stay or to go: voluntary survivor turnover following an organizational downsizing. J Organ Behav 2002;23(6):707–29. http://dx.doi.org/10.1002/job.166.

20. Hopkins SM, Weathington BL. The relationships between justice perceptions, trust, and employee attitudes in a downsized organization. J Psychol 2006 Sep;140(5):477–98. http://dx.doi.org/10.3200/JRLP.140.5.477-498.

21. Sørensen OH, Hasle P, Pejtersen JH. Trust relations in management of change. Scand J Manage 2011;27:405–17. http://dx.doi.org/10.1016/j.scaman.2011.08.003.

22. Oksanen T, Kouvonen A, Vahtera J, Virtanen M, Kivimäki M. Prospective study of workplace social capital and depression: are vertical and horizontal components equally important? J Epidemiol Community Health 2010 Aug;64(8):684–9. http://dx.doi.org/10.1136/jech.2008.086074.

23. Tsuboya T, Tsutsumi A, Kawachi I. Change in psychological distress following change in workplace social capital: results from the panel surveys of the J-HOPE study. Occup Environ Med 2015 Mar;72(3):188–94. http://dx.doi.org/10.1136/oemed-2014-102427.

24. Rugulies R, Hasle P, Pejtersen JH, Aust B, Bjorner JB. Workplace social capital and risk of long-term sickness absence. Are associations modified by occupational grade? Eur J Public Health 2016 Apr;26(2):328–33. http://dx.doi.org/10.1093/eurpub/ckv244.

25. De Clercq B, Clays E, Janssens H, De Bacquer D, Casini A, Kittel F et al. Health behaviours as a mechanism in the prospective relation between workplace reciprocity and absenteeism: A bridge too far? PLoS One 2015 Nov;10(11):e0141608. http://dx.doi.org/10.1371/journal.pone.0141608.



26. Török E, Clark AJ, Jensen JH, Lange T, Bonde JP, Bjorner JB et al. Work-unit social capital and long-term sickness absence: A prospective cohort study of 32 053 hospital employees. Occup Environ Med [Epub ahead of print]. 2018; Available from: http://doi.org/10.0.4.112/oemed-2017-104954.

27. Oksanen T, Kouvonen A, Kivimäki M, Pentti J, Virtanen M, Linna A et al. Social capital at work as a predictor of employee health: multilevel evidence from work units in Finland. Soc Sci Med 2008 Feb;66(3):637–49. http://dx.doi.org/10.1016/j.socscimed.2007.10.013.

28. Suadicani P, Bonde JP, Olesen K, Gyntelberg F. Job satisfaction and intention to quit the job. Occup Med (Lond) 2013 Mar;63(2):96–102. http://dx.doi.org/10.1093/occmed/kqs233.

29. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 1986 Dec;51(6):1173–82. http://dx.doi.org/10.1037/0022-3514.51.6.1173.

30. Hvidtfeldt UA, Bjorner JB, Jensen JH, Breinegaard N, Hasle P, Bonde JP et al. Cohort Profile: The Well-being in HospitAL Employees (WHALE) study. Int J Epidemiol 2017 Dec;46(6):1758–1759h. http://dx.doi.org/10.1093/ije/dyx073.

31. Pejtersen JH, Kristensen TS, Borg V, Bjorner JB. The second version of the Copenhagen Psychosocial Questionnaire. Scand J Public Health 2010 Feb;38(3_Suppl):8–24. http://dx.doi.org/10.1177/1403494809349858.

32. Hubbard AE, Ahern J, Fleischer NL, Van der Laan M, Lippman SA, Jewell N et al. To GEE or not to GEE: comparing population average and mixed models for estimating the associations between neighborhood risk factors and health. Epidemiology 2010 Jul;21(4):467–74. http://dx.doi.org/10.1097/EDE.0b013e3181caeb90.

33. Morgan DE , Zeffane E . Employee inyolvement, organizational change and trust in management. Int J Hum Resour Manage 2003;14(1):55–75. http://dx.doi.org/10.1080/09585190210158510.

34. Van Bogaert P, Kowalski C, Weeks SM, Van heusden D, Clarke SP. The relationship between nurse practice environment, nurse work characteristics, burnout and job outcome and quality of nursing care: A cross-sectional survey. Int J Nurs Stud . 2013;50(12):1667–77. https://doi.org/10.1016/j.ijnurstu.2013.05.010.

35. Sabatini F. Does Social Capital Improve Labour Productivity in Small and Medium Enterprises? Int J Manag Decis Mak 2008;9:454–80.

36. Cohen-Charash Y, Spector PE. The role of justice in organizations: A meta-analysis. Organ Behav Hum Decis Process 2001;86(2):278–321. http://dx.doi.org/10.1006/obhd.2001.2958.

37. Kivimäki M, Vahtera J, Pentti J, Thomson L, Griffiths A, Cox T. Downsizing, changes in work, and self-rated health of employees: A 7-year 3-wave panel study. Anxiety Stress Coping 2001;14(1):59–73. http://dx.doi.org/10.1080/10615800108248348.

38. Kivimäki M, Vahtera J, Pentti J, Ferrie JE. Factors underlying the effect of organisational downsizing on health of employees: longitudinal cohort study. BMJ 2000 Apr;320(7240):971–5. http://dx.doi.org/10.1136/bmj.320.7240.971.

39. Sapp AL, Kawachi I, Sorensen G, LaMontagne AD, Subramanian SV. Does workplace social capital buffer the effects of job stress? A cross-sectional, multilevel analysis of cigarette smoking among U.S. manufacturing workers. J Occup Environ Med 2010 Jul;52(7):740–50. http://dx.doi.org/10.1097/JOM.0b013e3181e80842.

40. O’Brien-Pallas L, Murphy GT, Shamian J, Li X, Hayes LJ. Impact and determinants of nurse turnover: a pan-Canadian study. J Nurs Manag 2010 Nov;18(8):1073–86. http://dx.doi.org/10.1111/j.1365-2834.2010.01167.x.

41. Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol 2003 Oct;88(5):879–903. http://dx.doi.org/10.1037/0021-9010.88.5.879.

42. Schweiger DM, Denisi AS. Communication with employees following a merger: A longitudinal field experiment. Acad Manage J 1991;34(1):110–35.

Received for publication: 31 January 2018

Longitudinalassociationsbetweenorganizationalchange,work-unitsocialcapital,andemployeeexitfromtheworkunitamongpublichealthcareworkers:amediationanalysis1byJohanHøyJensen,MSc,2EsbenMeulengrachtFlachs,PhD,JanneSkakon,PhD,NajaHulvejRod,Professor,JensPeterBonde,Professor

1. Supplementarytables2. Correspondenceto:JohanHøyJensen,DepartmentofOccupationalandEnvironmentalMedicine,

BispebjergUniversityHospital,BispebjergBakke23F,DK-2400,CopenhagenNV,Denmark.[E-mail:[email protected]]

Supplementary Table S1. Adjusted hazard ratios and robust 95% confidence intervals of employee exit from the work unit (EFW) in 2014 associated with levels work-unit social capital (level IV-I: high-low) measured through March 2014. Follow-up is split-up into two periods: one period before/during measurement of social capital (i.e., January-March 2014) and one period after measurement of social capital (i.e., April-December).

EFW, follow-up January-March 2014 EFW, follow-up April-December 2014 WSC level N % of N HR 95% CI N % of N HR 95% CI

IV 3715 26.4 1.00 3560 26.8 1.00

III 3566 25.4 1.24 1.00-1.53 3380 25.4 1.32 1.14-1.52 II 3372 24.0 1.26 1.01-1.56 3191 24.0 1.38 1.19-1.60 I 3406 24.2 1.59 1.28-1.97 3178 23.9 1.68 1.44-1.94

Cox regression analyses were adjusted for age, sex, number of employees in the work unit, occupational group, previous sickness absence, child-related absence, and personal gross income. Abbreviations: EFW = exit from the work unit, WSC = work-unit social capital.

Supplementary Table S2. Adjusted hazard ratios and robust 95% confidence intervals of employee exit from the work unit (EFW) in 2014 associated with organizational changes relative to no change in the last six months of 2013. Follow-up on work-unit exit was split-up into two periods: one period before/during measurement of social capital (i.e., January-March 2014) and one period after measurement of social capital (i.e., April-December). Main models additionally adjusted for mediating effects of social capital.

EFW, follow-up January-March 2014 EFW, follow-up April-December 2014 Main model Adjusted for

social capital Main model Adjusted for social capital

N % of N HR 95% CI HR 95% CI N % of N HR 95% CI HR 95% CI No change (reference) 8410 4.9 1.00 1.00 8000 12.2 1.00 1.00

Change 5649 6.0 1.27 1.10-1.47 1.23 1.07-1.43 5309 12.4 1.03 0.93-1.14 0.99 0.90-1.10 1 type of change 3728 5.4 1.13 0.95-1.34 1.10 0.93-1.31 3526 12.1 1.01 0.90-1.13 0.98 0.87-1.10 2 types of changes 1170 5.6 1.18 0.91-1.52 1.14 0.88-1.47 1104 12.0 0.96 0.80-1.15 0.92 0.77-1.11 >3 types of changes 751 9.6 2.18 1.69-2.81 2.11 1.64-2.72 679 14.6 1.25 1.02-1.54 1.21 0.98-1.48 Merger 1085 8.5 1.79 1.42-2.26 1.74 1.38-2.20 993 13.7 1.08 0.90-1.29 1.03 0.86-1.24 Split-up 508 8.5 1.79 1.30-2.45 1.72 1.25-2.38 465 14.4 1.25 0.97-1.59 1.17 0.91-1.49 Relocation 978 6.3 1.39 1.06-1.81 1.37 1.05-1.79 916 13.8 1.08 0.90-1.30 1.07 0.89-1.29 Change of management 2149 6.0 1.30 1.07-1.59 1.26 1.03-1.53 2020 14.0 1.20 1.05-1.37 1.13 0.99-1.30 Employee layoff 2163 6.1 1.23 1.01-1.51 1.21 0.99-1.48 2031 10.9 0.93 0.81-1.08 0.91 0.79-1.05 Budget cut 1757 6.2 1.33 1.08-1.65 1.30 1.05-1.61 201 12.2 1.00 0.85-1.16 0.98 0.84-1.15

Cox regression analyses were adjusted for age, sex, number of employees in the work unit, occupational group, previous sickness absence, child-related absence, and personal gross income. Abbreviations: EFW = exit from the work unit Supplementary Table S3. Adjusted hazard ratios and 95% confidence intervals of employee exit from the work unit (EFW) throughout 2014 after organizational change relative to no change. Main models additionally adjusted in turn for potentially mediated effects via dimensions of trust/organizational justice and collaboration (comprising social capital).

EFW, main model EFW, adjusted for trust/ organizational justice

EFW, adjusted for collaboration

N % of N HR 95% CI HR 95% CI HR 95% CI No change (reference) 8410 17 1.00 1.00 1.00 Change 5649 18 1.10 1.01-1.19 1.07 0.99-1.16 1.08 1.10-1.17

1 type of change 3728 17 1.04 0.95-1.15 1.02 0.93-1.13 1.03 0.93-1.13 2 types of changes 1170 17 1.03 0.89-1.20 0.99 0.85-1.15 1.01 0.86-1.16 >3 types of changes 751 23 1.53 1.30-1.80 1.46 1.25-1.72 1.52 1.29-1.78 Merger 1085 21 1.29 1.12-1.49 1.25 1.07-1.42 1.27 1.10-1.46 Split-up 508 22 1.41 1.16-1.72 1.35 1.11-1.64 1.39 1.14-1.70 Relocation 978 19 1.17 1.00-1.36 1.16 1.04-1.30 1.17 1.10-1.36 Change of management 2149 19 1.23 1.10-1.38 1.15 1.03-1.29 1.20 1.08-1.35 Employee layoff 2163 16 1.03 0.91-1.16 1.01 0.90-1.14 1.02 0.90-1.15 Budget cut 1757 18 1.10 0.97-1.25 1.09 0.96-1.23 1.09 0.96-1.23

Cox regression analyses were adjusted for age, sex, number of employees in the work unit, occupational group, previous sickness absence, child-related absence, and personal gross income. Abbreviations: EFW = exit from the work unit

Supplementary Table S4. Adjusted hazard ratios and 95% CI robust confidence intervals of subsequent employee exit from the company (i.e., the Capital Region of Denmark) throughout 2014 associated with organizational changes relative to no change in the last six months of 2013.

Company exit N % of N HR 95% CI No change (reference) 8410 9.8 1.00

Change 5649 10.1 0.99 0.89-1.10 1 type of change 3728 9.6 0.90 0.79-1.02 2 types of changes 1170 9.2 0.94 0.77-1.15 >3 types of changes 751 14.1 1.59 1.30-1.93 Merger 1085 13.0 1.40 1.19-1.68 Split-up 508 10.0 1.05 0.79-1.40 Relocation 978 16.6 1.55 1.32-1.82 Change of management 2149 9.9 1.04 0.90-1.21 Employee layoff 2163 10.4 1.06 0.91-1.23 Budget cut 1757 7.9 0.76 0.63-0.91

Cox regression analyses were adjusted for age, sex, number of employees in the work unit, occupational group, previous sickness absence, child-related absence, and personal gross income.

Paper IV

Organizational change, psychosocial work environment, and non-disability

early retirement: a prospective study among senior public employees

Breinegaard N, Jensen JH, Bonde JP

Scand J Work Environ Health, 2017;43(3):234-240. doi:10.5271/sjweh.3624

Downloaded from www.sjweh.fi on November 27, 2018

This work is licensed under a Creative Commons Attribution 4.0 International License.

Print ISSN: 0355-3140 Electronic ISSN: 1795-990X Copyright (c) Scandinavian Journal of Work, Environment & Health

Original articleScand J Work Environ Health 2017;43(3):234-240

doi:10.5271/sjweh.3624

Organizational change, psychosocial work environment, andnon-disability early retirement: a prospective study amongsenior public employeesby Breinegaard N, Jensen JH, Bonde JP

To date, this is the most exhaustive study to examine voluntary earlyretirement behavior among senior public service employees exposedto organizational change and subsequent assessment of thepsychosocial work environment on the work-unit level.Decision-makers should consider the impact of organizational changeand the psychosocial work environment in strategies to maintainsenior public employees in the labor market.

Affiliation: Department of Occupational & Environmental Medicine,Frederiksberg and Bispebjerg Hospitals, Bispebjerg Bakke 23,DK-2400, Copenhagen NV, Denmark. [email protected]

Refers to the following text of the Journal: 2014;40(2):105-209

The following articles refer to this text: 0;0 Special issue:0; 0;0 Specialissue:0

Key terms: ageing; Denmark; early retirement; older worker;organizational change; organizational restructuring; prospective study;psychosocial; psychosocial work environment; public employee; publicsector; retirement; retirement behavior; work environment

This article in PubMed: www.ncbi.nlm.nih.gov/pubmed/28166362

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Original articleScand J Work Environ Health. 2017;43(3):234–240. doi:10.5271/sjweh.3624

Organizational change, psychosocial work environment, and non-disability early retirement: a prospective study among senior public employeesby Nina Breinegaard, PhD,1 Johan Høy Jensen, MSc,1, 2 Jens Peter Bonde, PhD 1, 2

Breinegaard N, Jensen JH, Bonde JP. Organizational change, psychosocial work environment, and non-disability early retirement: a prospective study among senior public employees. Scand J Work Environ Health. 2017;43(3):234–240. doi:10.5271/sjweh.3624

Objective This study examines the impact of organizational change and psychosocial work environment on non-disability early retirement among senior public service employees.Methods In January and February 2011, Danish senior public service employees aged 58–64 years (N=3254) from the Capital Region of Denmark responded to a survey assessing psychosocial work environment (ie, social capital, organizational justice, and quality of management). Work-unit organizational changes (ie, change of management, merging, demerging, and relocation) were recorded from January 2009 to March 2011. Weekly data on non-disability early retirement transfer were obtained from the DREAM register database, which holds weekly information about all public benefit payments in Denmark. Hazard ratios (HR) for early retirement following employees’ 60th birthday were estimated with Cox regression adjusted for age, gender, and socioeconomic status.Results Exposure to change of management [HR 1.37, 95% confidence interval (95% CI) 1.13–1.66], mergers (HR 1.23, 95% CI 1.02–1.48), and relocation of work unit (HR 1.24, 95% CI 1.01–1.54) increased rate of non-disability early retirement, while demerging of work unit did not (HR 1.03, 95% CI 0.79–1.33). Work units with lower levels of social capital (HR 1.22, 95% CI 1.05–1.41), organizational justice, (HR 1.18, 95% CI 1.04–1.32), and quality of management (HR 1.14, 95% CI 1.02–1.25) increased rate of early retirement.Conclusion Organizational change and poor psychosocial work environment contribute to non-disability early retirement among senior public service employees, measured at work-unit level.

Key terms ageing; Denmark; organizational restructuring; older worker; public sector; retirement behavior.

1 Department of Occupational & Environmental Medicine, Frederiksberg and Bispebjerg Hospitals, University of Copenhagen, Copenhagen, Denmark.2 Department of Public Health, University of Copenhagen, Copenhagen, Denmark.

Correspondence to: Nina Breinegaard, Department of Occupational & Environmental Medicine, Frederiksberg and Bispebjerg Hospitals, Bispebjerg Bakke 23, DK-2400, Copenhagen NV, Denmark. [E-mail: [email protected]]

Life expectancy is increasing and at the same time, birth rates are stagnating and people enter the labor market at an older age as a larger proportion of the population pursues higher education. As a consequence, the old-age dependency ratio is growing, and a potential policy to address the resulting pensions crisis is to motivate senior employees to remain economically active longer and reduce voluntary early retirement (1).

Previous research has identified a number of factors affecting individual retirement decisions. Economic fac-tors have been found to play a major role (2) and empiri-cal evidence suggests that poor health causes workers to retire earlier (3–5). However, a recent systematic review of 29 studies conclude that self-reported health, chronic disease and mental health plays a marginal role for early retirement when not granted on health grounds

(6). Qualitative studies have indicated that retirement decisions are affected by major life events like illness or death in the immediate social circle (7) and other social circumstances, especially a retiring spouse (8, 9).

The work environment is also assumed to affect indi-vidual retirement decisions. A demanding physical work environment can cause employees to retire earlier, but several studies have also linked the psychosocial work environment to early retirement (7, 10). Organizational changes causing job insecurity, like down-sizing, have been associated with adverse health outcomes and disabil-ity pension (11–15), but when focus is on voluntary retire-ment, even minor organizational changes may play a role. Limited attention has been devoted to the potential effects of the psychosocial work environment and organizational change in retaining healthy senior employees (16–18).


Breinegaard et al

In the period 2011–2012, the nominal pension age in Denmark was 65 years when the government-paid old-age pension could be claimed and, before the age of 65, retirement was possible via disability or early retirement benefit. Disability benefit could be received in the age range 18–64 years, mainly on health grounds (mental as well as physical). Eligibility for early retirement benefit is unrelated to health and could be claimed from the age of 60–64 by anyone insured for a long time in an unemployment insurance fund. At the end of 2012, 34% of women and 27% of men aged 60–64 years received early retirement benefit.

The primary aim of this study was to investigate how organizational change is related to subsequent transi-tion to early retirement benefit. A secondary aim was to investigate how the psychosocial work environment affects early retirement and the association between organizational change and early retirement.

Methods

Study sample and design

This study was a prospective cohort study of employees in the Capital Region of Denmark, and the study end-point was retirement through the early retirement benefit program. The source population was all employees in the Capital Region of Denmark who were invited to par-ticipate in a questionnaire-based survey of well-being and work environment from 12 January 2011 to 9 February 2011. Among a total of 35 560 employees, 28 820 (81%) responded to the survey. Employees were organized in 2761 work units, where a work unit is defined as a group of employees with reference to a specified manager or head of unit. Data on organizational change from 1 January 2009 to 31 March 2011 were collected via an internet-based survey, in which all heads of work units were contacted (response rate 68.5%). The final study population included employees eligible for early retirement benefit, ie, aged 60–64 years for at least one week during the follow-up period from 4 April 2011 to 31 December 2012.

The study sample comprised 3254 employees after excluding those who were already retired or had died before start of follow-up, were employed in a flexible job (for people with reduced ability to work), or had missing values on the baseline covariates,

Organizational change and the psychosocial work environment

Data on organizational change included information about change of management, merging, demerging, and relocation. Data were recorded at the work-unit level

and linked to each employee in the work unit.In the well-being and work environment survey, 44

items were related to the psychosocial work environment of the work unit. Altogether, 35 items were retrieved from the Copenhagen Psychosocial Questionnaire, second ver-sion (19), and the regional human resource departments, management, and employee representatives edited the remaining convenience questions. Since psychosocial work environment was not measured using established and validated scales, a research group including three specialists in occupational medicine categorized selected items into three composite scales for purposes of the present analyses. Survey items about the psychosocial work environment were all ordinal with 5 or 7 response categories. Three composite scales of psychosocial work environment were constructed: (i) organizational justice (6 items, Cronbach’s α 0.89), (ii) quality of management (4 items, Cronbach’s α 0.86), and (iii) social capital (8 items, Cronbach’s α 0.84) (see appendix for construc-tion of scales, www.sjweh.fi/index.php?page=data-repository). The scales ranged from 0–100 with higher values representing a more positive evaluation of the psychosocial work environment.

In the present study, we focused on an aggregated measure of psychosocial work environment within work units, where employees were expected to have similar psychosocial working conditions rather than individual perceptions. Individual-level scores were computed as the mean of non-missing item responses and rescaled to 0–100 and work-unit-level scores were subsequently computed as the mean score for each work-unit and assigned to all employees within that unit, including non-respondents. The work-unit-level scores were based on responses from all employees of all ages and recorded as missing if data were available for <50% of items (individual level) or employees (work-unit level). Table 1 shows the distribution of work units and employees by work unit size.

Covariates

Age, gender, and socioeconomic factors were potential confounders for the association between the psychosocial work environment and early retirement. From the region’s registers we obtained information on work-unit affiliation,

Table 1. Number of employees distributed by work-unit size.

Number of employees in unit1–5 6–15 16–30 >30 Total

Employees all ages 2575 13 102 10 674 9209 35 560Respondents 2256 10 473 9238 6853 28 820Employees 60–64 years 360 1549 1460 865 4234Employees in study sample 241 1241 1066 706 3254

http://www.sjweh.fi/index.php?page=data-repository

http://www.sjweh.fi/index.php?page=data-repository


Early retirement in senior public employees

occupational group, and gender for all participants. This information was linked to national registers containing information on age (continuous), gross personal income in 2010, gross household income in 2010, hospitalization in 2010 (yes/no), and civil status (single/married). Income categories were formed by dividing participants into four groups of approximately equal size.

Non-disability early retirement

Data on non-disability early retirement were obtained from the DREAM database, which holds weekly infor-mation about all public benefit payments in Denmark. For each participant, we recorded the first week receiv-ing early retirement benefit. Participants who turned 65 years, died, emigrated, were on long-term sick-leave or started a flexi-job were censored.

Statistical analysis

Participants were followed from 4 April 2011 or their 60th birthday, whichever came last, to first payment of early retirement benefit, censoring or end of study, whichever came first. We estimated hazard ratios (HR) and 95% confidence intervals (95% CI) using Cox proportional hazards regression to study the association between early retirement and (i) organizational change, and (ii) psycho-social work environment using work-unit mean values.

The association between each of the four binary (organizational change) and three continuous (psycho-social work environment) explanatory variables and the outcome was evaluated in separate Cox regression models. All analyses were adjusted for age by setting the time scale in the Cox regression to weeks since 60th birthday of the participant. We performed crude Cox regression analyses (adjusted for age only) and adjusted Cox regression analyses (adjusted for age, gender, and socioeconomic status). Finally, the associations between each type of organizational change and early retirement were adjusted for all psychosocial work environment scales in addition to baseline covariates, and vice versa.

Results

Table 2 shows the distribution of the study population on levels of covariates and occurrence of organizational change or early retirement for corresponding groups of employees. Among the 2206 employees where we had information about all types of organizational changes, 65.1% experienced one or more organizational changes in the two-year follow-up period. Organizational change was most frequent among social and healthcare workers (74.9%) and least frequent among laboratory technicians

(46.0%) and technical/service staff (54.1%). During the follow-up period, 525 women (21%) and 117 men (14%) retired early. Retirement was frequent (17–26%) among all occupational groups except medical doctors and den-tists (2%).

In crude Cox regression analyses (table 3), we found that the rate of early retirement was significantly higher among employees who experienced change of man-agement, namely a 32% increase (HR 1.32, 95% CI 1.09–1.59). The same was true for employees who expe-rienced merging of units with a 23% increased rate (HR 1.23, 95% CI 1.02–1.48). In adjusted analyses, change of management increased the rate of early retirement by 37% (HR 1.37, 95% CI 1.13–1.66), while the effect of merging work units remained unchanged compared to crude analyses (HR 1.23, 95% CI 1.01–1.49). Employ-ees who experienced relocation of work-unit retired at a 10% higher rate when adjusted only for age and this was not significant (HR 1.10, 95% CI 0.89–1.35) but the rate was 25% higher in the adjusted model (HR 1.25, 95% CI 1.01–1.54). In adjusted models that also included the

Table 2. Distribution of the study sample and the prevalence of organizational change and early retirement across covariate levels.

Total Experienced change

Retired

N % N % N %GenderFemale 2446 75.2 1078 65.8 525 21.5Male 808 24.8 366 64.4 117 14.5

Age at 4 April 2011 (years)58–59 1181 36.3 525 67.2 204 17.360–61 1468 45.1 658 64.6 386 26.362–63 329 10.1 130 60.7 38 11.664 276 8.5 131 68.2 14 5.1

Medical diagnosis in 2010 Yes 1396 42.9 617 64.7 276 19.8No 1858 57.1 827 66.0 366 19.7

Civil statusSingle 1079 33.2 478 66.3 168 15.6Married/cohabiting 2175 66.8 966 65.1 474 21.8

Occupational groupNurses 654 20.1 316 70.9 138 21.1Medical doctors & dentists 344 10.6 153 64.8 7 2.0Social & healthcare workers 463 14.2 236 74.9 129 27.9Other healthcare workers 348 10.7 172 70.8 60 17.2Laboratory technicians 242 7.4 74 46.0 52 21.5Administrative staff 755 23.2 314 66.2 136 18.0Technical/service staff 448 13.8 152 54.1 120 26.8

Personal income 2010 (gross, dkr.)<325 000 828 25.4 357 62.9 274 33.1325 000–375 000 817 25.1 360 64.4 181 22.2375 000–450 000 827 25.4 377 67.3 142 17.2>450 000 782 24.0 350 67.4 45 5.8

Household income 2010 (gross, dkr.)<450 000 943 29.0 402 64.9 188 19.9450 000–700 000 897 27.6 402 65.8 212 23.6700 000–950 000 793 24.4 344 63.1 181 22.8>950 000 621 19.1 296 68.7 61 9.8

All respondents 3254 100 1444 65.5 642 19.7


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psychosocial work environment scales, the effect of any type of organizational change on subsequent early retire-ment was attenuated. The HR associated with change of management dropped to 1.27 but remained significant (HR 1.27, 95% CI 1.03–1.57). The HR dropped to 1.11 (95% CI 0.90–1.38) for merging, to 0.98 (95% CI 0.73–1.30) for demerging and to 1.10 (95% CI 0.87–1.40) for relocation of work unit.

Table 4 shows HR for a negative 20-point difference (approximately equal to the interquartile ranges) in the psychosocial work environment scales. In crude analy-ses, this difference in score on the dimension of social capital was associated with a 19% increase in the rate of early retirement (HR 1.19, 95% CI 1.03–1.37). This increased to 22% in adjusted analyses (HR 1.22, 95% CI 1.05–1.41). Employees in work units with lower levels of organizational justice also had significantly higher rate of early retirement; 12% in the crude analyses (HR 1.12, 95% CI 1.00–1.27) and 18% in the adjusted analy-ses (HR 1.18, 95% CI 1.04–1.32). Quality of manage-ment showed no significant effect on the rate of early retirement when adjusted only for age (HR 1.05, 95% CI 0.95–1.16) but when adjusted for all covariates, the association became significant (HR 1.14, 95% CI 1.02–1.25). When adjusted for the four organizational change indicators as well, a moderate increase was seen in the estimated effect size for all three scales of the individual appraisal of the psychosocial work environment.

Discussion

The adjusted analyses of organizational change pro-vided in this paper showed that Danish senior public employees who experienced change of management, or merging or relocation of work units had a higher rate of early retirement. Demerging of work unit was not related to early retirement. Adjusted analyses of psychosocial work environment showed that poorer social capital and organizational justice and lower quality of management increased the rate of early retirement. After adjusting for these psychosocial factors, the rate of early retirement

was still significantly higher among employees who experienced change of management, but the remaining types of organizational change had no significant effect. Adjusting for organizational changes increased the effect of psychosocial work environment on retirement.

Other findings

To the best of our knowledge, the association between workplace organizational change and early retirement not granted by disability or poor health has only been systematically examined in one previous epidemiologi-cal study: de Wind et al (8) examined determinants of early retirement in a longitudinal study with one-year follow-up of 2317 Dutch employees aged 59–63 years. While financial possibility and spouse expectations were strong determinants, this study did not reveal increased rate of early retirement in relation to organizational change, which 34% of the participants reported. On the contrary, organizational change associated with com-pulsory redundancies was related to a decreased rate of early retirement (OR 0.75, 95% CI 0.48–1.17). This finding based upon self-reports seems contrary to results obtained in our study, but the type(s) of organizational change(s) was not specified and the study population differed in terms of job types and gender distribution, limiting comparison.

Mechanisms

A study including qualitative interview data on 30 Dutch employees aged 60–64 who retired early found that orga-nizational change was frequently reported as reasons for early retirement (7). Employees in this study highlighted loss of motivation to continue working due to job rou-tines undergoing continuous changes not perceived as necessary. Organizational change has been found to have negative effects on employee outcomes, such as decreased organizational commitment, job insecurity, and job withdrawal in all ages (20, 21). Moreover, senior employees tend to perceive organizational change as more stressful compared to their younger counterparts (16), which may accelerate the decision to retire early

Table 3. Crude and adjusted hazard ratios (HR) for early retirement associated with each type of organizational change at the work-unit level. [95% CI=95% confidence interval.]

Type of organizational change N % Crude a Adjusted b Adjusted c

HR 95% CI HR 95% CI HR 95% CIChange of management 937 42.9 1.32 1.09–1.59 1.37 1.13–1.66 1.27 1.03–1.57Merging units 901 41.4 1.23 1.02–1.49 1.23 1.01–1.49 1.11 0.90–1.38Demerging unit 344 15.8 1.01 0.78–1.31 1.03 0.79–1.33 0.98 0.73–1.30Relocation of unit 609 27.8 1.10 0.89–1.35 1.25 1.01–1.54 1.10 0.87–1.40a Adjusted for age.b Adjusted for age, gender, medical diagnosis, civil status, occupation, personal income, and household income. c Adjusted for all variables above + psychosocial work environment.



(22). In addition, a previous study from our research group (2) investigated factors associated with the “risk” of intending to quit the job if economically possible in a cohort of hospital employees of all ages. Suadicani et al (23) found that employees with intention to quit their job rated the quality of management lower than employees with no intention to quit, which is in line with findings of the present study. However, inclination to quit does not necessarily lead to de facto retirement. Social pres-sures in the workplace are suggested to be a key factor in limiting employees’ decision to exit the labor market (17). Interestingly, demerging of the work unit was not associated with increased risk of early retirement. This could be explained by this type of organizational change being less extensive, since Vahtera et al (24) found that minor organizational changes have fewer adverse health effects than major changes.

Furthermore, organizational changes have been dem-onstrated to affect the employees’ perceived psycho-social work environment (12). In this study, we found that adjusting for psychosocial work environment scales diminished the effect of organizational changes on early retirement. This indicates that part of this effect is due to perceived changes in the psychosocial work environment.


This prospective study employed independent measures of exposure and outcome. Thus we obtained information on organizational change by few specific questions to work-unit managers while information on retirement was retrieved from highly reliable national registers. Selection bias is not an issue since employees were included regardless of their participation in the ques-tionnaire survey and the registers provide complete data. We also consider it a strength that assessment of psychosocial work environment factors such as social capital, organizational justice, and quality of manage-ment were based on a work-unit-level approach. Using work-unit means of psychosocial work environment provides aggregated measures that are less affected by individual perceptions, and this can limit reporting bias

and is more relevant in identifying risk factors relating to the psychosocial working environment (25, 26). In this particular study, this approach also reduced miss-ingness since non-respondents were assigned the work-unit mean score. Analyses using individual scores on psychosocial work-environment scales showed similar associations, but all estimates were inflated compared to the analyses using work-unit mean scores (results not shown).

Some limitations also need to be acknowledged. First, the variation in the three psychosocial work envi-ronment scales was large within work units compared to between units (intra-class correlations 0.17–0.21). Still, the contrast between work units was considerable with an interquartile range spanning some 20–25% and even if disregarding within-unit variation leads to measurement error, risk estimates were not necessarily attenuated (27).

Second, missingness could be a source of bias if non-respondents differed from our study population with respect to work environment or organizational changes as well as the tendency to retire early. We found that the frequency of early retirement did not differ significantly between respondents and non-respondents with respect to organizational changes but that retirement was more frequent with a missing response on all three work-environment scales. If non-respondents would have had a lower score on the scales, this missingness could lead to underestimating the effect of the psychosocial work environment.

Third, ad hoc scales modified from the second ver-sion of the Copenhagen Psychosocial Questionnaire (19) were applied to measure the perceived psychosocial work environment, but high alpha-values indicate reli-ability of the scales.

Fourth, the present study focused on push (or nega-tive) factors related to early retirement, such as poor social capital, organizational injustice, and low quality of management. In contrast to push factors, pull factors are positive considerations increasing the motivation to retire early, such as wish to spend more time with significant others or hobbies (10, 18). The current study would probably have benefitted from data on pull fac-

Table 4. Association between work-unit mean score on psychosocial work environment scale and early retirement. Adjusted hazard ratio (HR) associated with a 20-point decrease on the scale. [IQR=interquartile range; 95% CI=95% confidence interval.]

Scale N Mean IQR Crude a Adjusted b Adjusted c

HR 95% CI HR 95% CI HR 95% CI

Social capital 2912 67 59–74 1.19 1.03–1.37 1.22 1.05–1.41 1.30 1.09–1.55Organizational justice 2893 62 53–72 1.12 1.00–1.27 1.18 1.04–1.32 1.27 1.10–1.47Quality of management 2837 64 54–75 1.05 0.95–1.16 1.14 1.02–1.25 1.21 1.07–1.38a Adjusted for age.b Adjusted for age, gender, medical diagnosis, civil status, occupation, personal income, and household income. c Adjusted for all variables above + organizational change indicators.


Breinegaard et al

tors and financial possibilities facilitating early exit from the labor market. Such factors are strong predictors for retirement (8, 9), but since there is no obvious reason to believe that these variables are correlated with the explanatory variables of interest, organizational changes or psychosocial work environment, we argue that this did not cause bias.

Fifth, the study population were predominantly female. This is a characteristic of healthcare employees and generalizations of the results to other parts of the public sector should be made with caution.

In conclusion, the findings of this study indicate that organizational changes in the public sector have potentially strong impact on early retirement among employees older than 60 years of age, independent of disability or poor health, and that efforts to improve the psychosocial work environment during restructuring is important.

Acknowledgements

We would like to thank specialists in occupational medicine Kasper Olesen, PhD, Marianne Borritz, MD, PhD and Nanna Eller, MD, PhD (along with Jens Peter Bonde) for their time and great effort in categorization of items in composite psychosocial work environment scales. Also, we thank data analyst Johan Reventlow for collecting data on organizational change. Finally, we wish to thank employees in the Capital Region of Denmark for their participation in the psychosocial work environment survey. The Capital Region of Denmark and the Danish Working Environment Research Fund are acknowledged for their financial contribution to the study. The sponsors had no role in the planning of the study or the interpretation of the results. All authors state that they have no conflicts of interest.

References:

1. OECD social indicators. Age-Dependency Ratios. Soc a Glance 2006. 2007:110–11.

2. Suadicani P, Bonde JP, Olesen K, Gyntelberg F. Job satisfaction and intention to quit the job. Occup Med (Chic Ill). 2013;63(2):96–102. https://doi.org/10.1093/occmed/kqs233.

3. Bazzoli GJ. An early retirement decision: New empirical evidence on the influence of health. J Hum Resour. 1985;20(2):214–34. https://doi.org/10.2307/146009.

4. Dwyer DS, Mitchell OS. Health problems as determinants of retirement: Are self-rated measures endogenous? J Health Econ. 1999;18(2):173–93. https://doi.org/10.1016/S0167-6296(98)00034-4.

5. Anderson KH, Burkhauser RV. The retirement-health nexus: A new measure of an old puzzle. J Hum Resour. 1985;20(3):315–30. https://doi.org/10.2307/145884.

6. van Rijn RM, Robroek SJW, Brouwer S, Burdorf A. Influence of poor health on exit from paid employment: a systematic review. Occup Environ Med. 2014;71(4):295–301. https://doi.org/10.1136/oemed-2013-101591.

7. Reeuwijk KG, de Wind A, Westerman MJ, Ybema JF, van der Beek AJ, Geuskens GA “All those things together made me retire”: qualitative study on early retirement among Dutch employees. BMC Public Health. 2013;13(1):516. https://doi.org/10.1186/1471-2458-13-516.

8. de Wind A, Geuskens GA, Ybema JF, Blatter BM, Burdorf A, Bongers PM, van der Beek AJ. Health, job characteristics, skills, and social and financial factors in relation to early retirement--results from a longitudinal study in the Netherlands. Scand J Work Environ Health. 2014;40(2):186–94. https://doi.org/10.5271/sjweh.3393.

9. Lund T, Iversen L, Poulsen KB. Work environment factors, health, lifestyle and marital status as predictors of job change and early retirement in physically heavy occupations. Am J Ind Med. 2001;40(2):161–9. https://doi.org/10.1002/ajim.1084.

10. van den Berg TIJ, Elders LAM, Burdorf A. Influence of health and work on early retirement. J Occup Environ Med. 2010;52(6):576–83. https://doi.org/10.1097/JOM.0b013e3181de8133.

11. Greubel J, Kecklund G. The impact of organizational changes on work stress, sleep, recovery and health. Ind Health. 2011;49(3):353–64. https://doi.org/10.2486/indhealth.MS1211.

12. Ferrie JE, Westerlund H, Oxenstierna G, Theorell T. The impact of moderate and major workplace expansion and downsizing on the psychosocial and physical work environment and income in Sweden. Scand J Public Health. 2007;35(1):62–9. https://doi.org/10.1080/14034940600813073.

13. Vahtera J, Kivimäki M, Forma P, Wikstrom, J, Halmeenmaki, T, Linna,J et al. Organisational downsizing as a predictor of disability pension: the 10-town prospective cohort study. J Epidemiol Community Health. 2005;59(3):238–42. https://doi.org/10.1136/jech.2004.021824.

14. Westerlund H, Theorell T, Alfredsson L. Organizational instability and cardiovascular risk factors in white-collar employees. Eur J Public Health. 2004;14(1):37–42. https://doi.org/10.1093/eurpub/14.1.37.

15. Vahtera J, Kivimäki M, Pentti J. Effect of organisational downs iz ing on hea l th o f employees . Lance t . 1997;350(9085):1124–8. https://doi.org/10.1016/S0140-6736(97)03216-9.

16. Vedina R, Dolan SL. Elder Employees’ Well-Being Following Organizational Restructuring: Testing the Direct and the Moderating Effects Among Spanish Workers. J Workplace Behav Health. 2014;29(2):143–74. https://doi.org/10.1080/15555240.2014.897159.

17. Van Solinge H, Henkens K. Living longer, working longer? The impact of subjective life expectancy on retirement

https://doi.org/10.1093/occmed/kqs233

https://doi.org/10.2307/146009

https://doi.org/10.1016/S0167-6296(98)00034-4

https://doi.org/10.1016/S0167-6296(98)00034-4

https://doi.org/10.2307/145884

https://doi.org/10.1136/oemed-2013-101591


https://doi.org/10.1186/1471-2458-13-516

https://doi.org/10.1186/1471-2458-13-516

https://doi.org/10.5271/sjweh.3393

https://doi.org/10.1002/ajim.1084

https://doi.org/10.1097/JOM.0b013e3181de8133

https://doi.org/10.1097/JOM.0b013e3181de8133

https://doi.org/10.2486/indhealth.MS1211

https://doi.org/10.2486/indhealth.MS1211

https://doi.org/10.1080/14034940600813073

https://doi.org/10.1136/jech.2004.021824

https://doi.org/10.1136/jech.2004.021824

https://doi.org/10.1093/eurpub/14.1.37

https://doi.org/10.1093/eurpub/14.1.37

https://doi.org/10.1016/S0140-6736(97)03216-9

https://doi.org/10.1016/S0140-6736(97)03216-9

https://doi.org/10.1080/15555240.2014.897159

https://doi.org/10.1080/15555240.2014.897159



intentions and behaviour. Eur J Public Health. 2010;20(1):47–51. https://doi.org/10.1093/eurpub/ckp118.

18. Shultz KS, Morton KR, Weckerle JR. The influence of push and pull factors on voluntary and involuntary early retirees’ retirement decision and adjustment. J Vocat Behav. 1998;53(1):45–57. https://doi.org/10.1006/jvbe.1997.1610.

19. Pejtersen JH, Kristensen TS, Borg V, Bjorner JB. The second version of the Copenhagen Psychosocial Questionnaire. Scand J Public Health. 2010;38(3 suppl):8–24. https://doi.org/10.1177/1403494809349858.

20. Probst TM. Explor ing Employee Outcomes of Organizational Restructuring: A Solomon Four-Group Study. Gr Organ Manag. 2003;28(3):416–39. https://doi.org/10.1177/1059601102250825.

21. de Jong T, Wiezer N, de Weerd M, Nielsen K, Mattila-Holappa P, Mockałło Z. The impact of restructuring on employee well-being: a systematic review of longitudinal studies. Work Stress. 2016;8373(February):1–24. https://doi.org/10.1080/02678373.2015.1136710.

22. Henkens K, Tazelaar F. Early retirement of civil servants in the netherlands. J Appl Soc Psychol. 1994;24(21):1927–43. https://doi.org/10.1111/j.1559-1816.1994.tb00568.x.

23. Suadicani P, Bonde JP, Olesen K, Gyntelberg F. Job satisfaction and intention to quit the job. Occup Med (Chic Ill). 2013;63(2):96–102. https://doi.org/10.1093/occmed/kqs233.

24. Vahtera J, Kivimäki M, Pentti J. Effect of organisational downs iz ing on hea l th o f employees . Lance t . 1997;350(9085):1124–8. https://doi.org/10.1016/S0140-6736(97)03216-9.

25. Kolstad HA, Hansen ÅM, Kærgaard A, Thomsen JF, Kaerlev L, Mikkelsen S, et al. Job strain and the risk of depression: Is reporting biased? Am J Epidemiol. 2011;173(1):94–102. https://doi.org/10.1093/aje/kwq318.

26. Grynderup MB, Mors O, Hansen ÅM, Andersen JM, Bonde JP, Kaergaard A, et al. Work-unit measures of organisational justice and risk of depression--a 2-year cohort study. Occup Environ Med. 2013;70(6):380–5. https://doi.org/10.1136/oemed-2012-101000.

27. Armstrong BG. Effect of measurement error on epidemiological studies of environmental and occupational exposures. Occup Environ Med. 1998;55(10):651–6. https://doi.org/10.1136/oem.55.10.651.

Received for publication: 20 May 2016

https://doi.org/10.1093/eurpub/ckp118

https://doi.org/10.1006/jvbe.1997.1610

https://doi.org/10.1177/1403494809349858

https://doi.org/10.1177/1403494809349858

https://doi.org/10.1177/1059601102250825

https://doi.org/10.1177/1059601102250825

https://doi.org/10.1080/02678373.2015.1136710

https://doi.org/10.1080/02678373.2015.1136710

https://doi.org/10.1111/j.1559-1816.1994.tb00568.x

https://doi.org/10.1093/occmed/kqs233

https://doi.org/10.1016/S0140-6736(97)03216-9

https://doi.org/10.1016/S0140-6736(97)03216-9

https://doi.org/10.1093/aje/kwq318



https://doi.org/10.1136/oem.55.10.651

https://doi.org/10.1136/oem.55.10.651

Original article. Scand J Work Environ Health. 2017;43(3):234–240.

doi:10.5271/sjweh.3624

Organizational change, psychosocial work environment, and non-disability early

retirement: a prospective study among senior public employees 1

by Nina Breinegaard, PhD,2 Johan Høy Jensen, MSc, Jens Peter Bonde, PhD

1 Appendix of Psychosocial Work Environment Scales

2 Correspondence to: Nina Breinegaard, Department of Occupational & Environmental

Medicine, Frederiksberg and Bispebjerg Hospitals, Bispebjerg Bakke 23, DK-2400, Copenhagen NV,

Denmark. [E-mail: [email protected]]

Psychosocial work environment scales

Items forming the scales with number of response categories in parentheses.

Quality of leadership:

To what extent would you say that:

– the management does enough to help employees cope with emotionally demanding situations at work? (5)

– your immediate superior gives high priority to job satisfaction? (5) – your immediate superior is good at work planning? (5) – you get help and support from your immediate superior when needed? (5)

Organizational justice:


– you are informed well in advance concerning for example important decisions, changes, or plans for the future? (5)

– you receive all information you need in order to do your job well? (5) – you can trust information coming from the management? (7) – the management trusts employees to do their job well? (7) – conflicts are resolved in a fair way? (7) – work is distributed fairly? (7)

Social capital:


– your occupational group is respected by others at your workplace? (5) – you and your colleagues are good at suggesting improvements in work routines? (5) – you and your colleagues take responsibility for a good atmosphere and tone at your

workplace? (5) – you get help and support from your colleagues? (5) – you can trust information coming from the management? (7) – the management trusts employees to do their job well? (7)

– conflicts are resolved in a fair way? (7) – work is distributed fairly? (7)

Paper V

Work-unit organizational changes and subsequent prescriptions for

psychotropic medication: a longitudinal study among public healthcare

employees

Jensen JH, Bonde JP, Flachs EM, Skakon J, Rod NH, Kawachi I

Occup Environ Med, accepted [28-Nov-2018]. doi:10.1136/oemed-2018-105442

1

Title: Work-unit organizational changes and subsequent prescriptions for psychotropic medication: a

longitudinal study among public healthcare employees

Corresponding author: Johan Høy Jensen, Department of Occupational and Environmental Medicine,

Bispebjerg Bakke 23F, Entrance 20F, DK-2400 Copenhagen, Denmark, e-mail:

[email protected], telephone: +45 2118 3355

Authors: Johan Høy Jensen 1,2, Jens Peter Bonde 1, Esben Meulengracht Flachs 1, Janne Skakon 3, Naja

Hulvej Rod 4, Ichiro Kawachi 2

Affiliations: 1Department of Occupational and Environmental Medicine, Copenhagen University

Hospital, Bispebjerg Hospital, Copenhagen, Denmark. 2Department of Social and Behavioral Sciences,

Harvard T.H. Chan School of Public Health, Boston, MA, USA. 3Department of Psychology,

University of Copenhagen, Copenhagen, Denmark. 4Section of Epidemiology, Department of Public

Health, University of Copenhagen, Copenhagen, Denmark.

Word count, excluding title page, abstract, references, figures and tables: 3499

Number of tables/illustrations: 5

mailto:[email protected]

2

Abstract

Objectives: We examined exposure to different types of organizational changes at work as risk factors

for subsequent prescription for psychotropic medication among employees.

Methods: The study population included 15,038 public healthcare employees nested within 1284 work

units in the Capital Region of Denmark. Multilevel mixed-effects parametric survival models were

developed to examine time to prescription for psychotropic medications

(anxiolytics/hypnotics/sedatives/antidepressants) during the 12-month interval following exposure to

organizational changes relative to no change from January through December 2013. Data on work-unit-

level organizational changes (including mergers, split-ups, relocation, change in management,

employee layoffs, and budget cuts) were collected from work-unit managers (59% response).

Results: Any organizational change vs no change was associated with a higher risk of psychotropic

prescription (HR: 1.14, 95% CI: 1.02-1.26), especially change in management (HR: 1.23, 95% CI:

1.07-1.41). Splitting the 12-month follow-up period into two halves yielded particularly high rates of

psychotropic prescription in the latter half of the follow-up, e.g., any change (HR: 1.25, 95% CI: 1.11-

1.41), change in management (HR: 1.42, 95% CI: 1.22-1.65), mergers (HR: 1.26, 95% CI: 1.06-1.50),

employee layoff (HR: 1.23, 95% CI: 1.03-1.46), and budget cuts (HR: 1.13, 95% CI: 1.00-1.41). The

associations did not vary by sex.

Conclusions: Organizational changes in the workplace, especially change in management, may be

associated with increased risk of psychotropic prescription among employees regardless of sex.

3

What this paper adds

What is already known about this subject?

• Different types of organizational changes at work may have different negative effects on stress-related

prescriptions for psychotropic medication among male and female employees.

What are the new findings?

• Organizational changes in the workplace were associated with a higher rate of prescription for

psychotropic medication in the following year relative to no change in the workplace. This association

was particularly strong for exposure to change in management and prescriptions for antidepressants,

but results did not vary by sex or previous history of psychotropic medication prescription.

• Splitting the 12-month follow-up period into two halves yielded the strongest effects during the latter

period after different types of change, suggesting a latency period before excess use of psychotropic

medication.

• Factors at the work-unit level accounted for 6% of the total variance in prescriptions for psychotropic

medication, indicating that this level is an important contributor to use of psychotropic medication

among employees.


• Decision and policy makers should consider that different types of organizational change in the

workplace may pose risks for employee mental health.

4

Introduction

Depressive and anxiety disorders are estimated to be the third and ninth leading causes of disability

globally.[1] Most people spend many of their waking hours in an occupational setting and workplace

conditions thus play a key role for employee well-being. Organizational changes in the workplace (e.g.,

downsizing or mergers) are often implemented as a strategy to maximize competitiveness in a

globalized economy.[2,3]

Organizational changes at work seem to have adverse impacts on employee health and well-being.[4–8]

Although evidence supports an association between individual reporting of higher psychosocial stress

and excess risk of depression,[9] anxiety,[10,11] and disturbed sleep,[12] longitudinal associations

between organizational changes and stress-related clinical psychiatric disorders remain unclear.[13]

Some studies found excess self-reported psychiatric symptoms following changes including

downsizing and mergers,[14–17] but inconsistent findings have also been reported.[13,18] Negative

appraisals of mergers has been linked to 1.60-fold higher risk of a psychiatric event;[19] however, this

association may likely be inflated due to underlying negative affectivity influencing both exposure and

outcome.[20] Another study found 1.03-1.16-fold higher relative risk of total sickness absence for ≥2

simultaneous change types, mergers, change in management, and budget cuts,[21] suggesting that

employees react differently to different change types.[22]

Studies on organizational changes and prescriptions for psychotropic medication[23–26] mainly

focused on major company downsizing (≥18% staff reduction) to find associations with higher relative

risk of psychotropic prescriptions.[23–25] Associations stratified by sex showed similar patterns of

prescriptions in Swedish studies,[24,25] with higher relative risk for prescriptions among male

employees (RR 1.49, 95% CI 1.10-2.02) compared to female employees (RR 1.12, 95% CI 1.27) in the

Finnish study.[23] This sex difference could be due to greater job demands and lower social support

among men.[27]

A Danish study on company changes targeting specific dimensions showed that changes regarding

cooperation/coordination and, especially, those targeting multiple dimensions were associated with

higher prescription rates,[26] suggesting a cumulative effect of multiple changes. These prescription

effects were stronger in the first year than the two-year period after changes. No associations were

found for changes targeting effectiveness, adaption/turbulence or skill/knowledge enhancement.[26]

5

Indeed, these prior studies of organizational changes and psychotropic prescriptions may be subject to

exposure misclassification (i.e., employees not experiencing the changes personally) since change data

were defined at the company level. Also, none of the studies used multilevel modeling to account for

potential clustering of psychotropic prescriptions within the organizational structure of workplaces,[28]

which may elevate risk of Type-I error.[29]

The literature highlights organizational changes as a heterogenous short-term risk factor for clinical

mental health problems among employees since specific and multiple simultaneous changes seem to be

associated with higher risk of psychotropic prescriptions in the years closer to the change event. Sex

differences in this association remain unclear although some evidence suggest more adverse effects

among men. To better understand and potentially mitigate development of negative employee effects of

organizational changes, there is a need to examine the short-term associations between objective

measures of specific change types and prescriptions for psychotropic medication accounting for

multilevel clustering.

This study contributes to the literature by using multilevel modeling to investigate the putative

associations between specific types of work-unit organizational changes and excess rates of

prescription for psychotropic medication among employees during the subsequent year. We

hypothesized excess prescription rates to vary immediately after specific types of organizational

change. Stronger cumulative prescription effects were expected after multiple simultaneous changes.

Also, we hypothesized more adverse change effects on psychotropic prescriptions among males

relative to females. We examined these potential sex differences in terms of both additive (i.e., absolute

risk) and multiplicative interaction between changes and males (i.e., relative risk) since these two types

of interaction may be observed independently.[30]

Methods

Study design, data sources and population

We examined the prospective association between exposure to work-unit organizational changes during

the observation period through 2013 and prescription of psychotropic medication during the ensuing

12-month follow-up period among employees (between 1 January 2014 [baseline] and 31 December

2014). All participants were part of the ongoing Well-being in Hospital Employees (WHALE) cohort

6

study.[31] The source population included all 37,720 healthcare employees nested in 2696 work units

from 14 institutions comprising the Capital Region of Denmark. A work unit was defined as a group of

employees referring to same immediate manager. Data on organizational changes were gathered April-

June 2016 via a survey distributed to all managers of these work units (59% response). Complete data

on occupational/sociodemographic information at baseline and prescription of psychotropic medication

from 2011 through 2014 were extracted via linkage to company and national registers, respectively.

Eligible employees (n=25,897) nested in 2318 work units at baseline were identified based on the

following inclusion criteria: working in work units with ≥3 employees, ≥1 year of seniority in the same

work unit, ≥18.5 weekly working hours throughout 2013, not working multiple positions, not working

in Denmark or aged ≥18. We allowed for >1 year of seniority in a work unit changing name during the

observation period of organizational changes by including employees if >3 co-workers and >30% of

the work-unit staff remained in the work unit after the name change. These criteria were applied in

keeping with a previous approach.[5,32] We excluded 10,859 eligible employees with missing data on

organizational changes. The study population comprised 15,038 employees nested in 1284 work units

(Figure 1).

7

Figure 1. Flow of the study population and study design. Employees could fulfill multiple exclusion

criteria.

There were no significant differences between the source population, eligible employees, and the study

population regarding sex composition (p=0.15) or prescriptions for psychotropic medication (p=0.62)

as indicated by χ²-tests. Nurses were slightly overrepresented in the groups of eligible and studied

employees (43%) relative to the source population (41%), whereas medical doctors/dentists were

somewhat underrepresented among eligible (11%) and studied employees (10%) compared to the

source population (14%) (p<0.001).

Work-unit-level organizational changes

We collected data on exposure to different types of organizational changes at the work-unit level by

administering a questionnaire via working email to all work-unit managers from April through June

2016. The managers provided information on occurrence of the following specific types of

organizational changes in their work unit for each semester during 2013, viz., mergers, split-ups,

relocation, change in management, employee layoff or budget cuts. Responses for 2013 were collapsed

8

because we did not collect information on when the changes were announced or initiated within the

company. We created seven change-indicator variables at the work-unit level: six variables for each of

the types of organizational changes and one variable for “any changes” (yes/no change). None of the

individual types of changes were completely overlapping as co-occurrence rates were ≤56%

(Supplementary material 1).

Employee-level prescriptions for psychotropic medication

For outcome purposes, we used information from 1 January through 31 December 2014 on the date of

psychotropic prescriptions including anxiolytics (WHO Anatomical Therapeutic Chemical [ATC]

code: N05B), hypnotics/sedatives (N05C), and antidepressants (N06A). These data were used

regardless of the prescribed daily dose or period for intended use. Although follow-up data on

psychotropic prescriptions in 2015 were available, we did not use these because we presumed that

psychotropic prescriptions in 2015 would be highly impacted by organizational changes in 2014, on

which we had no information.

Employee- and work-unit-level covariates

We used the following employee-level variables as a priori covariates: age, sex, occupational group,

manager, personal gross income, fixed weekly working hours, contractual employment, years of

seniority, and days of sickness absence in 2012. We did not consider prior psychotropic prescriptions to

be a potential confounder because we presumed no causal impact of employee-level psychotropic

prescriptions on subsequent work-unit organizational changes.

As work-unit-level a priori covariates, we used the number of employees within the work unit and

selected types of organizational changes confounding other types of changes (Supplementary material

2). For example, we regarded work-unit mergers and split-ups as confounders for the relation between

change in management and psychotropic prescriptions. All covariates were categorical variables (Table

1).

Statistical analysis

We estimated hazard ratios (HR) and 95% confidence intervals (CI) using multilevel mixed-effects

parametric survival models to study the association between organizational changes in 2013 and

9

psychotropic prescriptions in 2014. Employees (level 1) were nested within work units (level 2).

Analyses were unable to converge in three-level models nesting work units within institutions.

Employees were followed from 1 January 2014 to the first psychotropic prescription (event), death

(censoring) or end of study by 31 December 2014, whichever came first. The relative impact of each

change-indicator variable (adjusted for other changes as appropriately) were evaluated in separate

models.

Parametric survival models follow a specified distribution from which residual variance at multiple

levels is estimated. In a null model, residual variance at the work-unit level reflects the relative

importance of any work-unit factors for psychotropic prescriptions among employees. We fitted a

Weibull distribution to the survival models as we expected the hazard of psychotropic prescriptions to

either increase or decrease during follow-up[33] since the magnitude and exact date of the change

announcements were unclear. We applied Acceleration Failure Time parametrization for the Weibull

models, which allows for estimation of the Intraclass Correlation Coefficient (ICC) for work units.[34]

We interpreted 𝐼𝐶𝐶 ∗ 100 as the proportion of any work-unit factors – observed as well as unobserved

– explaining the total variance in psychotropic prescriptions among employees.

A four-step sequential modeling strategy with incremental adjustment for covariates was used to assess

confounding and variation in prescriptions explained by the work-unit level (Supplementary material

3).

We analyzed the association between any changes and each subgroup of psychotropic medication (i.e.,

anxiolytics, hypnotics/sedatives and antidepressants) firstly prescribed in 2014 to assess their relative

importance. Sex differences in change effects on psychotropic prescriptions were evaluated in additive

interaction analysis (i.e., combined effect) by calculating the synergy index (S)[35] and 95% CI[36] as

well as in multiplicative interaction analysis by including an interaction term between indicator

variables of any change and male adjusted for the separate main effects of change and sex. We

estimated additive interaction between any changes and females since we were unable to calculate 95%

CI to S for any changes and males (Supplementary material 4).


We reran the analysis for any changes and psychotropic prescriptions during 2014 additionally

adjusting for potential confounding by prior psychotropic prescriptions between 2011-2012 (i.e.,

10

preceding changes that occurred in 2013) to evaluate confounding by prior psychotropic prescriptions.

To assess if the association between change in management and psychotropic prescriptions was driven

by (e.g., laid-off) managers, we reran this analysis excluding all managers (n=14,040) for comparison

with the analysis on the total study population. Associations with prescriptions through 2014 were

analyzed according to any changes in each semester of 2013 to evaluate possible temporality in change

exposure. Finally, we created an indicator variable of exposure to 1, 2, or 3≤ types of simultaneous

changes compared to no changes to explore the potential cumulative effect on psychotropic

prescriptions.

We used a significance level of 0.05 throughout. All statistical analyses were conducted using STATA

version 14.2 software (Stata Corp., College Station, TX, USA).

Results

Table 1 presents the hierarchical data structure and distribution of employee- and work-unit-level

variables for the study population (N=15,038) and among employees/work units exposed to any

organizational changes (n=8242). The study population predominantly comprised female employees,

nurses, employees with ≥37 weekly working hours (i.e., full-time employment). Employees with prior

psychotropic prescriptions between 2011-2012 were similarly distributed in the study population (14%,

n=2,049) and among employees exposed to any changes (14%, n=1173), indicating no confounding by

prior psychotropic prescriptions. Among the study population, 1,616 employees (11%) were prescribed

psychotropic medication in 2014. More antidepressants (52%, n=833) and hypnotics/sedatives (38%,

n=614) were prescribed than anxiolytics (13%, n=202). During the follow-up in 2014, eight employees

died, and four of these deaths occurred before a psychotropic prescriptions (censoring).

11

Table 1. The two-level data structure and distribution of variables for the study population and

employees/work units exposed to any organizational changes.

Categories Study population, n (% of N) Exposed to any changes, n (% of N)

Level 1: Employees, N 15,038 (100) 8242 (55)

Prescription for psychotropic

medication in 2014 1,616 (11) 931 (11)

Days to first prescription, M (SD) 107 (101) 109 (103)

Age group 19-40* 3821 (25) 2093 (25)

40-48 3780 (25) 2056 (25)

48-56 3728 (25) 2027 (25)

56-75 3709 (25) 2066 (25)

Sex Female* 11,507 (77) 6299 (76)

Male 3531 (23) 1943 (24)

Occupational group Nurses* 6534 (43) 3682 (45)

Medical doctors/dentists 1464 (10) 758 (9)

Social/healthcare workers 1966 (13) 1055 (13)

Pedagogical workers 401 (3) 217 (3)

Service/technical workers 1864 (12) 975 (12)

Administration workers 2809 (19) 1555 (19)

Seniority, years 1-4* 3125 (21) 173 (21)

5-10 3818 (25) 2076 (25)

11-20 4097 (27) 2239 (27)

21≤ 3998 (27) 2197 (27)

Manager No* 14,040 (93) 7591 (92)

Yes 998 (7) 651 (8)

Weekly working hours 18.5-32* 2662 (18) 1511 (18)

32-37 3643 (24) 2023 (25)

37≤ 8733 (58) 4708 (57)

Contractual employment No* 1066 (7) 487 (6)

Yes 13,972 (93) 7755 (94)

Personal gross income, DKK ≤345,000* 4427 (29) 2458 (30)

345,000-400,000 3862 (26) 2124 (26)

400,000-480,000 3471 (23) 1852 (22)

480,000≤ 3278 (22) 1808 (22)

Sickness absence in 2012, days No days* 4132 (27) 2274 (28)

1-3 3242 (22) 1760 (21)

4-6 2292 (15) 1271 (15)

7-13 2877 (19) 1517 (18)

14≤ 2495 (17) 1420 (17)

Level 2: Work units, N 1284 (100)

Organizational changes No changes* 642 (50)

Any changes 642 (50)

Mergers 195 (15)

Split-ups 75 (6)

Relocation 157 (12)

Change in management 294 (23)

Employee layoff 245 (19)

Budget cuts 191 (15)

Number of employees within work unit 3-12* 654 (51) 283 (44)

13-22 306 (24) 164 (26)

23-32 198 (15) 116 (18)

33-142 126 (10) 79 (12)

12

* Reference category. Abbreviations: DKK = Danish Krone, M = Mean, SD = Standard Deviation.

Table 2 shows that any organizational changes in 2013 was associated with a HR of 1.14 (95% CI:

1.02-1.26) for psychotropic prescriptions in 2014 relative to no changes. Medical doctors/dentists and

employees aged 56-75 had a particularly high risk of psychotropic prescriptions. As reflected by the

ICC, the correlation of psychotropic prescriptions between work units explained 6% of the total

variation in psychotropic prescriptions. This indicates that work units are an important contributor to

prescriptions for psychotropic medication among employees. Adjustment for employee and work-unit-

level covariates led to slightly higher HR of psychotropic prescriptions after any changes. Additional

adjustment for potential confounding by prior psychotropic prescriptions in this model attenuated the

association (HR: 1.08, 95% CI: 0.97-1.20).

Following any organizational changes there was a higher rate ratio for prescription of antidepressants

(HR: 1.21, 95% CI: 1.05-1.40) and indications of a higher rate ratio for prescription of anxiolytics (HR:

1.25, 95% CI: 0.93-1.69), but no association for prescription of hypnotics/sedatives (HR: 1.00, 95% CI:

0.85-1.19).

We only found weak indications of an additive interaction between any changes and females (S: 1.36,

95% CI: 0.32-5.84; Supplementary material 5) and no multiplicative interactions (p=0.69). This

indicates no differential effects of organizational changes regarding sex.

13

Table 2. Hazard ratios (HR) of prescription for psychotropic medication in 2014 (n=1,616) among the

study population (N=15,038).

Prescription, follow-up 1 January through 31 December 2014

Model 1 Model 2 Model 3

Fixed part HR 95% CI HR 95% CI

Work-unit level variables (level 2)

Any organizational changesa 1.13 1.02-1.26 1.14 1.02-1.26

Number of employees within work unitb

13-22 1.00 0.86-1.15

23-32 0.95 0.81-1.10

33-142 0.82 0.70-0.96

Individual-level variables (level 1)

Age groupc

40-48 1.41 1.19-1.69

48-56 1.68 1.40-2.00

56-75 2.09 1.73-2.51

Maled 0.88 0.77-1.02

Occupational groupe

Medical doctors/dentists 2.33 1.85-2.88

Social/healthcare workers 0.95 0.80-1.13

Pedagogical workers 0.89 0.63-1.25

Service/technical workers 0.94 0.77-1.16

Administrative workers 1.17 1.00-1.35

Seniority, yearsf

5-10 1.10 0.94-1.28

11-20 1.00 0.85-1.19

21≤ 0.99 0.82-1.19

Manager, yesg 0.97 0.79-1.21

Weekly working hoursh

32-37 1.00 0.85-1.17

37≤ 0.77 0.68-0.88

Contractual employment, yesi 1.00 0.82-1.22

Personal gross income, DKKj

345,000-400,000 0.90 0.79-1.04

400,001-480,000 0.85 0.72-0.98

480,001≤ 0.86 0.71-1.03

Sickness absence in 2012, daysk

1-3 0.91 0.77-1.07

4-6 1.20 1.01-1.42

7-13 1.52 1.30-1.78

14≤ 2.33 2.01-2.70

Random part

ICC (p-value), work-unit level (level 2) 0.06 (<0.01) 0.05 (<0.01) 0.02 (0.13)

Reference categories: a no change, b 3-12 employees within the work unit, c age group 19-40, d female, e

nurses, f 1-4 seniority years, g not manager, h 18.5-32 weekly working hours, i no contractual

employment, j personal gross income ≤345,000 DKK, k No days of sickness absence in 2012.

Model 1: Null model with a random intercept for the work-unit level. Model 2: As model 1, but the

“Any changes”-variable in the fixed part. Model 3: As model 2, but effects of the “Any changes”-

variable were fully adjusted for all employee-level covariates and number of employees within work

unit.

14

Abbreviations: DKK = Danish Krone, ICC = Intraclass Correlation Coefficient.

In Table 3, the fully adjusted model 4 shows that change in management in 2013 was associated with a

HR of 1.23 (95% CI: 1.07-1.41) for psychotropic prescriptions in 2014 relative to no changes. This

excess rate of psychotropic prescriptions remained in a sample excluding all managers (n=14,040; HR:

1.24, 95% CI: 1.07-1.42), indicating that the effect was not attributable to managers laid off. There

were indications of a higher rate after mergers, employee layoff or budgets cuts, but these findings

were not statistically significant. Indeed, employee layoff and budget cuts were statistically

significantly associated with a higher rate of psychotropic prescriptions in model 3 adjusted for age,

sex, and socio-occupational factors. However, these effects attenuated in model 4 when additionally

adjusting for mergers, change in management, and budget cuts as confounders on the association

between employee layoffs and psychotropic prescriptions.

Table 3. Hazard ratios (HR) of prescription of psychotropic medication in 2014 (n=1,616) according to

each type of organizational changes in 2013 among the study population (N=15,038).

Prescription, follow-up 1 January through 31 December 2014

Model 3 Model 4 N (% prescriptions) HR 95% CI HR 95% CI

No changes 6796 (10.1) 1.00 1.00

Mergers 2560 (11.4) 1.11 0.95-1.28 1.14ᵃ 0.97-1.34

Split-ups 956 (10.2) 0.98 0.79-1.23 0.98ᵇ 0.78-1.23

Relocation 1872 (10.3) 1.00 0.85-1.19 1.02ᶜ 0.84-1.24

Change in management 3781 (12.1) 1.19 1.05-1.35 1.23ᶜ 1.07-1.41

Employee layoff 3204 (11.8) 1.20 1.05-1.38 1.15ᵈ 0.98-1.35

Budget cuts 2401 (11.6) 1.15 1.00-1.34 1.12ᵉ 0.95-1.31

Results for covariates omitted as no noteworthy changes in estimates were observed relative to Table 2.

Model 3: Each type of change indicator adjusted for all employee-level covariates and number of work-

unit employee and in the fixed part and a random intercept for the work-unit level. Model 4: As model

3, but each type of organizational changes additionally adjusted for other changes as potential

confounders on the association with psychotropic prescriptions: ᵃSplit-ups and Budget cuts; ᵇBudget

cuts; ᶜMergers and Split-ups; ᵈMergers, Change in management, and Budget cuts; ᵉChange in

management.

15

Table 4 presents the rates of psychotropic prescriptions splitting the 12-month follow-up period into

two halves. In the former follow-up period, only change in management was associated with a higher

rate of psychotropic prescriptions. In the second period, exposure to any changes, mergers, change in

management, employee layoff or budget cuts were statistically significantly associated with a 1.19-1.42

times higher rate of psychotropic prescriptions relative to no change. Any changes occurring in the

former and latter semester of 2013 were similarly associated with excess rates of psychotropic

prescriptions through 2014 (HR: 1.11, 95% CI: 0.99-1.24 and HR: 1.16, 95% CI: 1.04-1.29,

respectively), suggesting a comparable effect of the exposure on outcome over time. There were no

meaningful differences between prescription rates following exposure to 1, 2 or 3≤ types of

simultaneous changes relative to no change in either half follow-up period, indicating no cumulative

effects by multiple changes (data not shown).

Table 4. Hazard ratios (HR) of prescription for psychotropic medication in the former period from

January through June 2014 or in the latter period from July through December 2014 according to

organizational changes in 2013 among the study population (N=15,038).

Prescription, follow-up

1 January through 30 June 2014

Prescription, follow-up

1 July through 31 December 2014

Fully adjusted

model

Fully adjusted

model

N Prescriptions

(n=1257), % of N HR 95% CI Prescriptions

(n=1268), % of N HR 95% CI

No changes 6796 8.0 1.00 7.5 1.00 Any changes 8242 8.6 1.09 0.97-1.22 9.3 1.25 1.11-1.41

Mergers 2560 8.2 1.05ᵃ 0.88-1.26 9.3 1.26ᵃ 1.06-1.50

Split-ups 956 7.2 0.87ᵇ 0.67-1.14 7.9 1.02ᵇ 0.79-1.31

Relocation 1872 7.8 1.02ᶜ 0.82-1.28 8.6 1.16ᶜ 0.93-1.44

Change in management 3781 9.2 1.20ᶜ 1.03-1.41 10.3 1.42ᶜ 1.22-1.65

Employee layoff 3204 9.0 1.16ᵈ 0.97-1.39 9.6 1.23ᵈ 1.03-1.46

Budget cuts 2401 8.5 1.04ᵉ 0.87-1.24 9.5 1.19ᵉ 1.00-1.41

Results for covariates omitted as no noteworthy change in estimates were observed relative to Table 2.

Fully adjusted model: “Any changes” adjusted for all employee-level covariates and number of work-

unit employee and in the fixed part and a random intercept for the work-unit level (model 3). Each type

of organizational changes additionally adjusted for other changes as potential confounders on the

association with psychotropic prescriptions: ᵃSplit-ups and Budget cuts; ᵇBudget cuts; ᶜMergers and

Split-ups; ᵈMergers, Change in management, and Budget cuts; ᵉChange in management (model 4).

16

Discussion

More than half of the studied employees were exposed to organizational changes. We found a higher

rate of prescriptions for psychotropic medication among employees in the year after they remained in

the work unit during any organizational changes compared to no changes. This association was

particularly strong following change in management and in relation to prescription of antidepressants.

Splitting the follow-up period into two halves yielded a stronger association in the latter half of the 12-

month follow-up compared to the former half, indicating a latency period before an increase in

prescriptions. The observed association did not vary according to sex.

Our findings of a 1.09 and 1.25-fold higher rate ratio of psychotropic prescriptions in the former and

the latter half of the year after any changes, respectively, corroborate prior findings from another

Danish study of a 1.09-fold higher rate of psychotropic prescriptions, which was strongest in the year

immediately after the changes.[26] In our study, this association was particularly strong for change in

management, which, to our knowledge, has not been reported before. Among both men and women, we

found a roughly 1.15-fold higher rate over the 12-month follow-up, which is comparable to the Finnish

10-Town study estimate of a 1.12 times higher rate of psychotropic drug prescription after major

downsizing among women. In that same study the prescription rates were notably higher among male

employees (1.49),[23] whereas in our study the interaction analyses yielded no sex differences in line

with other studies.[24,25] In fact, sex did not predict psychotropic prescriptions significantly in the

present study. This could be due to limited statistical power since only 357 (2%) male employees were

prescribed psychotropic medications during follow-up. Also, we found no cumulative effects of

exposure to multiple changes (1, 2 or 3≤), which contradicts previous findings of comprehensive

changes being particularly associated with excess risk of psychotropic prescriptions.[26] This may be

explained by excess employee turnover rates during a greater number simultaneous changes as

indicated by a prior study.[21]

Adjusting the association between changes and psychotropic prescriptions during 2014 on prior

psychotropic prescriptions between 2011-2012 attenuated the HR from 1.14 to 1.08; however, this

reduction toward the null may be explained by the introduction of an index event bias[37] as episodes

of mental disorders are highly recurrent in a workplace context.[38] Index event bias refers to the

17

selection of participants based on index events (prior prescriptions) for whom the putative risk factor

(changes) is associated with an observed lower probability of new events (new prescriptions).

Conditioning on prior psychotropic prescriptions could actually “protect” against new psychotropic

prescriptions as associated with organizational changes because employees with mental illness would

likely adapt their working life according to their limited occupational capacity (e.g., not working full-

time), which thus induces dependence between otherwise independent confounder variables. In support

to this perspective, the descriptive statistics indicated no confounding by prior psychotropic

prescriptions, and the proportion of full-time employees was in fact smaller among those with prior

psychotropic prescriptions (53% of n=2049) than employees without such prior prescriptions (59% of

n=12,989).

It has previously been highlighted that adverse effects of organizational changes at the workplace are

primarily driven by changes in job insecurity among employees.[39] This is in line with findings of

excess psychotropic-prescription rates after change in management or employee layoff as such changes

may elevate uncertainty about future employment and new downsizing waves in the work unit. In

addition, mergers were also associated with an excess rate of psychotropic prescriptions in the latter

half of the 12-month follow-up period, which could be hypothetically explained by subsequent

reduction in redundant staff following mergers. We had, however, no data on changes during follow-up

to test this. Adverse effects of organizational changes are also previously found to be mediated by

changes in job strain.[40] Hence, long-term changes in job strain may explain why the excess rates of

psychotropic prescriptions were observed to be stronger in the latter period of follow-up relative to the

earlier period immediately following organizational changes. Managers have a key role in organizing

work and a change in management may follow increased demands in work procedures (e.g., excess

work documentation) inducing further psychosocial repercussions among employees. In addition,

demands of work-unit productivity may not be adjusted to the staff composition after employee layoffs,

which could lead to workload intensification among the remaining employees.


The associations found in the present study may be underestimated because we were unable to adjust

for organizational changes occurring during the 12-month follow-up on psychotropic prescriptions.

Neither did we assess the effects on psychotropic prescriptions during or before the observation of

18

organizational changes in 2013. It is, however, reasonable to assume that the majority of employees

prescribed psychotropic medication in 2013 would extend their medical treatment into the follow-up

period in 2014. Indeed, if an employee exited the work unit during 2013 (e.g., due to common mental

disorder), the participant would not be included in the study population. Data on changes in 2013

obtained from managers three years later may be influenced by recall bias; however, since the

managers most likely executed the changes, this bias is considered minor. Finally, using composite

change measures for 2013 limits conclusions on the duration of the latency period.

This study benefitted from assessing the relative impact of various and frequently occurring types of

organizational changes. These changes were measured at the work-unit level among employees who

remained in the work unit during the observation of the changes. This approach ensured that the

employees personally experienced the changes. Assessing various types of changes also allowed us to

create a purer reference group not exposed to any of these changes as opposed to previous studies

focusing on a single type of change. In addition, the nesting of employees within work units enabled us

to adjust for clustering within work units. It was also a strength of the study that we used independent

data sources hampering bias due to common variance regarding the exposure and outcome variables.

Finally, data on psychotropic prescriptions were extracted from highly reliable national registries

adding to the validity of the findings.

More research and practitioner attention should be devoted to the temporality in adverse effects of

specific change types since mental health effects may develop over an extended period following the

change event. The present study highlighted change in management as a particular risk factor for

employee mental health and elucidating the underlying psychosocial mechanisms on this longitudinal

association is an objective for future studies.

Acknowledgements

JHJ thanks University of Copenhagen, Julie Von Müllens Fond, Else & Mogens Wedell-Wedellsborgs

Fond, and the Graduate School of Public Health (at University of Copenhagen) for their financial

contribution to his stay as a visiting researcher in Department of Social and Behavioral Sciences at

Harvard T.H. Chan School of Public Health during this study.

19

Contributorship

JHJ had full access to all data provided in the present study, and JHJ takes responsibility for the

integrity and the accuracy of the data analyses. All authors were responsible for the current study

design. JHJ wrote the initial draft of the manuscript. All authors contributed to the present study and

approved the final draft of the manuscript.

Competing interests

None declared.

Funding

This work was funded by the Danish Working Environment Research Fund (13-2015-03).

20

References

1 Vos T, Allen C, Arora M, et al. Global, regional, and national incidence, prevalence, and years

lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the

Global Burden of Disease Study 2015. Lancet 2016;388:1545–602. doi:10.1016/S0140-

6736(16)31678-6

2 Ostry AS, Spiegel JM. Labor markets and employment insecurity: Impacts of globalization on

service and healthcare-sector workforces. Int J Occup Environ Health 2004;10:368–74.

doi:10.1179/oeh.2004.10.4.368

3 Gaynor M, Laudicella M, Propper C. Can governments do it better? Merger mania and hospital

outcomes in the English NHS. J Health Econ 2012;31:528–43.

doi:10.1016/j.jhealeco.2012.03.006

4 de Jong T, Wiezer N, de Weerd M, et al. The impact of restructuring on employee well-being: a

systematic review of longitudinal studies. Work Stress 2016;30:91–114.

doi:10.1080/02678373.2015.1136710

5 Jensen JH, Flachs EM, Skakon J, et al. Dual impact of organisational change on subsequent exit

from work unit and sickness absence: a longitudinal study among public healthcare employees.

Occup Environ Med 2018;75:479–85. doi:10.1136/oemed-2017-104865

6 Westerlund H, Ferrie J, Hagberg J, et al. Workplace expansion, long-term sickness absence and

hospital admission. Lancet 2004;363:1193–7.

7 Kivimäki M, Vahtera J, Pentti J, et al. Downsizing, changes in work, and self-rated health of

employees: A 7-year 3-wave panel study. Anxiety, Stress Coping 2001;14:59–73.

doi:10.1080/10615800108248348

8 Bordia P, Hobman E, Jones E, et al. Uncertainty during organizational change: Types,

consequences, and management strategies. J Bus Psychol 2004;18:507–32.

doi:10.1023/B:JOBU.0000028449.99127.f7

9 Bonde JPE. Psychosocial factors at work and risk of depression: A systematic review of the

epidemiological evidence. Occup Environ Med 2008;65:438–45. doi:10.1136/oem.2007.038430

10 Jensen HK, Wieclaw J, Munch-Hansen T, et al. Does dissatisfaction with psychosocial work

climate predict depressive, anxiety and substance abuse disorders? A prospective study of

Danish public service employees. J Epidemiol Community Health 2010;64:796–801.

21

doi:10.1136/jech.2008.083980

11 Wieclaw J, Agerbo E, Bo Mortensen P, et al. Psychosocial working conditions and the risk of

depression and anxiety disorders in the Danish workforce. BMC Public Health 2008;8:1–9.

doi:10.1186/1471-2458-8-280

12 Van Laethem M, Beckers DG, Kompier MA, et al. Psychosocial work characteristics and sleep

quality: a systematic review of longitudinal and intervention research. Scand J Work Environ

Health 2013;39:535–49. doi:10.5271/sjweh.3376

13 Bamberger SG, Vinding AL, Larsen A, et al. Impact of organisational change on mental health:

a systematic review. Occup Environ Med 2012;69:592–8. doi:10.1136/oemed-2011-100381

14 Loretto W, Platt S, Popham F. Workplace change and employee mental health: results from a

longitudinal study. Br J Manag 2010;21:526–40. doi:10.1111/j.1467-8551.2009.00658.x

15 Greubel J, Kecklund G. The impact of organizational changes on work stress, sleep, recovery

and health. Ind Health 2011;49:353–64. doi:10.2486/indhealth.MS1211

16 Ferrie JE, Shipley MJ, Marmot MG, et al. the Health Effects of Major Organisational Change

and Job Insecurity. Soc Sci Med 1998;46:243–54. doi:10.1016/S0277-9536(97)00158-5

17 Falkenberg H, Fransson EI, Westerlund H, et al. Short- and long-term effects of major

organisational change on minor psychiatric disorder and self-rated health: Results from the

Whitehall II study. Occup Environ Med 2013;70:688–96. doi:10.1136/oemed-2013-101385

18 Netterstrøm B, Blønd M, Nielsen M, et al. Development of depressive symptoms and depression

during organizational change – a two-year follow-up study of civil servants. Scand J Work

Environ Health 2010;36:445–8. doi:10.5271/sjweh.3075

19 Väänänen A, Ahola K, Koskinen A, et al. Organisational merger and psychiatric morbidity: A

prospective study in a changing work organisation. J Epidemiol Community Health

2011;65:682–7. doi:10.1136/jech.2010.126482

20 Podsakoff PM, MacKenzie SB, Lee JY, et al. Common method biases in behavioral research: a

critical review of the literature and recommended remedies. J Appl Psychol 2003;88:879–903.

doi:10.1037/0021-9010.88.5.879

21 Jensen JH, Flachs EM, Skakon J, et al. Dual impact of organisational change on subsequent exit

from work unit and sickness absence: A longitudinal study among public healthcare employees.

Occup Environ Med Published Online First: 2018. doi:10.1136/oemed-2017-104865

22

22 Oreg S, Vakola M, Armenakis A. Change recipients’ reactions to organizational change: A 60-

year review of quantitative studies. J Appl Behav Sci 2011;47:461–524.

doi:10.1177/0021886310396550

23 Kivimäki M, Honkonen T, Wahlbeck K, et al. Organisational downsizing and increased use of

psychotropic drugs among employees who remain in employment. J Epidemiol Community Heal

2007;61:154–8. doi:10.1136/jech.2006.050955

24 Blomqvist S, Alexanderson K, Vahtera J, et al. Downsizing and purchases of psychotropic

drugs: A longitudinal study of stayers, changers and unemployed. PLoS One 2018;13:e0203433.

doi:https://doi.org/10.1371/journal.pone.0203433

25 Hanson LLM, Westerlund H, Chungkham HS, et al. Purchases of prescription antidepressants in

the Swedish population in relation to major workplace downsizing. Epidemiology 2016;27:257–

64. doi:10.1097/EDE.0000000000000414

26 Dahl MS. Organizational Change and Employee Stress. 2011;57:240–56.

doi:10.1287/mnsc.1100.1273

27 Stansfeld S, Candy B. Psychosocial work environment and mental health - A meta-analytic

review. Scand J Work Environ Heal 2006;32:443–62. doi:10.5271/sjweh.1050

28 Rosenquist JN, Fowler JH, Christakis NA. Social network determinants of depression. Mol

Psychiatry 2011;16:273–81. doi:10.1038/mp.2010.13

29 Bliese PD, Hanges PJ. Being both too liberal and too conservative: The perils of treating

grouped data as though they were independent. Organ Res Methods 2004;7:400–17.

doi:10.1177/1094428104268542

30 Vandenbroucke JP, Von Elm E, Altman DG, et al. Strengthening the Reporting of Observational

Studies in Epidemiology (STROBE): Explanation and elaboration. Epidemiology 2007;18:805–

35. doi:10.1097/EDE.0b013e3181577511

31 Hvidtfeldt UA, Bjorner JB, Jensen JH, et al. Cohort Profile: The Well-being in Hospital

Employees (WHALE) study. Int J Epidemiol 2017;46:1758–1759h.

doi:http://doi.org/10.1093/ije/dyx073

32 Jensen JH, Flachs EM, Skakon J, et al. Longitudinal associations between organizational

change, work-unit social capital, and employee exit from the work unit among public healthcare

workers: a mediation analysis. Scand J Work Environ Health 2018;:0–10.

23

doi:10.5271/sjweh.3766

33 Brandon George. SS and IA. Survival analysis and regression models. NIH Public Access

2014;21:686–94. doi:10.1007/s12350-014-9908-2.Survival

34 Canette I. In the spotlight: Intraclass correlations after multilevel survival models. Stata News.

2016;:31(2).https://www.stata.com/stata-news/news31-2/intraclass-correlations/ (accessed 31 Jul

2018).

35 De Mutsert R, Jager KJ, Zoccali C, et al. The effect of joint exposures: Examining the presence

of interaction. Kidney Int 2009;75:677–81. doi:10.1038/ki.2008.645

36 Andersson T, Alfredsson L, Källberg H, et al. Calculating Measures of Biological Interaction.

Eur J Epidemiol 2005;20:575–9. doi:10.1007/sl0654-005-7835-x

37 Kent DM. Index Event Bias as an Explanation for the Paradoxes of Recurrence Risk Research.

2013;02111:25–6.

38 Koopmans PC, Bültmann U, Roelen CAM, et al. Recurrence of sickness absence due to

common mental disorders. Int Arch Occup Environ Health 2011;84:193–201.

doi:10.1007/s00420-010-0540-4

39 Bordia P, Hunt E, Paulsen N, et al. Uncertainty during organizational change: Is it all about

control? Eur J Work Organ Psychol 2004;13:345–65. doi:10.1080/13594320444000128

40 Kivimäki M, Vahtera J, Pentti J, et al. Factors underlying the effect of organisational downsizing

on health of employees: longitudinal cohort study. BMJ 2000;320:971–5.

Supplementary material 1. Co-occurring types of organizational changes at the employee-level

among the study population (N=15,038).

Employees, N Mergers, % Split-ups, % Relocation, % Change in

management, %

Employee

layoff, % Budget cuts, %

Any changes 8,242 31 12 22 46 39 29

Mergers 2,560 20 41 53 28 25

Split-ups 956 54

46 55 31 21

Relocation 1,872 56 23 46 27 17

Change in management 3,781 36 14 23

28 22

Employee layoff 3,204 22 9 16 33 45

Budget cuts 2,401 27 8 13 35 45

Table should be read horizontally.

Supplementary material 2. Association between work-unit organizational changes and prescriptions

for psychotropic medication confounded by other types of changes.

Supplementary material 3. Four-step sequential modeling strategy used to assess confounding and

variation in psychotropic prescriptions explained by the work-unit level.

Model 1 (null): A model with a random-part intercept for the work-unit level to assess the variation

in psychotropic prescriptions explained by any work-unit-level factors.

Model 2 (crude): As model 1, but entering an indicator of work-unit organizational changes in the

fixed part to assess the crude association with psychotropic prescriptions for later comparison.

Model 3 (adjusted): As model 2, but adjusting for all employee-level covariates and number of

employees within each work unit to assess confounding and the association between any

organizational changes and psychotropic prescriptions conditioned on these covariates.

Model 4 (additionally adjusted for other changes): As model 3, but entering other relevant work-

unit changes as covariates to assess the fully adjusted association between each type of change and

psychotropic prescriptions relative to no change.

Supplementary material 4. Additive and multiplicative interaction analyses.

Differential effects of any changes on psychotropic prescriptions due to gender were evaluated with

additive (i.e., combined effects) and multiplicative interaction analyses in terms of absolute and

relative risk, respectively.

For additive interaction analysis, a new composite variable with three categories (a-b+, a+b-, and

a+b+) were created for any change (a; no: -, yes: +) and gender (b; male: -, female: +). As

recommended for survival models, we calculated the synergy index (S) for the combined effect of

any changes and female using the following formula:

𝑆 =𝐻𝑅(𝑎+𝑏+)−1

(𝐻𝑅(𝑎+𝑏−)−1)+(𝐻𝑅(𝑎−𝑏+)−1).

We estimated S for any changes and females since we were unable to calculate 95% CI to S for any

changes and males. Given S≠1, S reflects the presence of additive interaction of both risk factors

(any change and female) relative to both exposures without their additive interaction (de Mutsert et

al., Kidney Int, 2009). We calculated 95% CIs as proposed by Andersson and colleagues (Eur J

Epidemiol, 2005).

For multiplicative interaction analysis, differential effects of any changes on prescriptions for

gender were evaluated by including an interaction term between indicator variables of any change

and male in the regression model adjusted for the separate main effects of change and gender.

Supplementary material 5. Hazard ratio (HR) and 95% confidence intervals (CI) for prescription of

psychotropic medication in 2014 for females and males according to exposure to any

organizational changes through 2013.

Male employees Female employees

n HR 95% CI n HR 95% CI

No changes 1,588 1.00 5,208 1.10 0.90-1.34

Any changes 1,943 1.10 0.88-1.36 6,299 1.26 1.01-1.48

Paper VI

Work-unit organizational changes and risk of ischemic heart disease: a

prospective study of public healthcare employees in Denmark

Jensen JH, Flachs EM, Skakon J, Rod NH, Bonde JP, Kawachi I

BMJ Open (submitted)

1

Title: Work-unit organizational changes and risk of ischemic heart disease: a prospective

study of public healthcare employees in Denmark

Corresponding author: Johan Høy Jensen, Department of Occupational and Environmental

Medicine, Bispebjerg Bakke 23F, Entrance 20F, DK-2400 Copenhagen, Denmark, e-mail:

[email protected], telephone: +45 2118 3355

Authors: Johan Høy Jensen1, 2, Esben Meulengracht Flachs1, Janne Skakon3, Naja Hulvej

Rod4, Jens Peter Bonde1, Ichiro Kawachi2

Affiliations: 1Department of Occupational and Environmental Medicine, Copenhagen

University Hospital, Bispebjerg Hospital, Copenhagen, Denmark. 2Department of Social and

Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

3Department of Psychology, University of Copenhagen, Copenhagen, Denmark. 4Section of

Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen,

Denmark.

Word count, excluding title page, abstract, references, figures and tables: 3215

Number of tables/illustrations: 5

mailto:[email protected]

2

ABSTRACT

Objectives: We prospectively examined the associations between different types of work-unit

organizational changes and risk of ischemic heart disease (IHD) among public employees

from the Capital Region of Denmark.

Methods: We used multilevel mixed-effects parametric survival models to assess the risk of

incident IHD (hospital admission) during 2014 according to organizational changes in 2013

among 14,842 employees working in the same work unit from January through December

2013. We excluded employees with pre-existing IHD. Data on organizational changes defined

as mergers, split-ups, relocations, change in management, employee layoffs, and budget cuts

were obtained from work-unit managers (59% response).

Results: Specific indicators of organizational changes were associated with excess risk of

IHD relative to no change, viz, relocation (HR 2.91, 95% CI: 1.07-7.90), employee layoff

(HR 2.90, 95% CI: 1.36-6.16) and change in management (HR 2.18, 95% CI: 1.02-4.68).

Including perceived stress as mediator in the regression models attenuated the relative risk

only slightly (HR 2.81, 95% CI: 1.06-8.03, HR 2.78, 95% CI: 1.29-5.96, and HR 2.10, 95%

CI: 0.97-4.54, respectively). No association with IHD was found for any changes (HR 1.50,

95% CI: 0.81-2.75), mergers (HR 0.75, 95% CI: 0.24-2.37), split-ups (HR 0.90, 95% CI:

0.20-4.07), or budget cuts (HR 0.93, 95% CI: 0.35-2.50).

Conclusions: Relocation, change in management or employee layoff at the work-unit level

were associated with excess risk of incident IHD among the remaining employees relative to

no change. Other types of organizational changes presently examined were not associated

with excess relative risk of IHD.

Keywords: cardiovascular; downsizing; hospital employees; layoff; multilevel;

organisational changes; reorganisation; reorganization; restructuring

3

WHAT THIS PAPER ADDS

What is already known about this subject?

• The potential adverse impacts of organizational changes on employee health are

unclear.

What are the new findings?

• Exposure to relocation, change in management or employee layoff in the work unit

were associated with higher risk of ischemic heart disease among employees from the

same work unit relative to no work-unit organizational changes.

• Higher stress perceived by the individual employees did not appear to mediate these

associations.

• Factors at the work-unit level accounted for 40% of the association between

organizational changes and ischemic heart disease.


• Detrimental effects of organizational changes are not only a burden to the individual,

but also to society.

4

INTRODUCTION

Organizational changes in workplaces have become a part of many employees’ lives. Such

changes seem to be motivated by a combination of rapid technological developments (e.g.,

digitalization of workflows) as well as globalization processes (e.g., flexibility of labor) and

increasing concentration of capital.[1–3] Public-sector workplaces are no exception against

these forces. During the last two decades, all public-sector hospitals in Denmark were

required by the government to increase annual treatment rates by 1.5-2.0% without parallel

budget adjustments.[4] This has led to numerous organizational changes (e.g., mergers,

downsizing, implementation of new technology etc.) attempting to maximize efficiency.

Increasingly, there are indications that organizational changes are extracting a cost in terms of

employee health and psychological well-being.[5–8] The existing epidemiological literature

on organizational changes and health is mainly based in Nordic studies and focuses on single

types of changes (e.g., downsizing).[6] The majority of these studies show deleterious health

effects among employees remaining after the changes,[5,6,9,10] although inconsistent

evidence exists.[11,12] The Finnish 10-town study demonstrated a doubled risk of

cardiovascular mortality among permanent employees related to major downsizing (i.e.,

>18% staff reduction). Interestingly, this excess risk of cardiovascular mortality was observed

soon after downsizing, indicating a triggering effect.[13] No studies have yet focused on

cardiovascular outcomes following other types of organizational changes, but there are reports

of higher long-term sickness-absence rates following mergers, split-ups, reallocation of

employees, and the establishment or shutting down of work units.[8,14]

Researchers have argued in favor of a causal relation between perceived stress and

cardiovascular diseases,[15] and a meta-analysis found a 1.3-fold increased risk of coronary

heart disease related to high perceived stress in the general population.[16] Also, there is

evidence of increased use of medications for stress-related disorders following various types

of organizational changes,[7] including downsizing.[17] One study found common stressful

work-related events (e.g., pressure of deadlines, perceived competition) to trigger heart

attacks, whereas no higher risks due to self-reported events of being laid-off/quitting.[18]

Moreover, levels of blood pressure and mental distress have been found to be elevated shortly

before and after reorganization involving change in management with strongest effects among

employees reporting most future job uncertainty.[19]

In sum, organizational changes may be associated with a higher risk of cardiovascular

diseases that is potentially mediated through work stress. Yet, there is a need for studies

5

examining these complex associations and distinguishing between different types of


We sought to investigate the prospective relations between work-unit organizational changes

and ischemic heart disease (IHD) among public healthcare employees in the Capital Region

of Denmark.

METHODS AND MATERIALS

Data sources and population

This study used data from the Well-being in Hospital Employees (WHALE) cohort[20] to

examine work-unit organizational change observed from 1 January through 31 December

2013 with follow-up on IHD among employees from baseline at 1 January through 31

December 2014. The source population was established when all 37,720 employees (nested in

2,696 work units nested in 14 institutions) in the Capital Region of Denmark were invited to

take part in a work-environment survey in March 2014 (84% response). The vast majority of

the surveys were administered by working email, and paper versions were distributed to

employees with no working email (e.g., cleaning staff). The employees received up to 3

reminders on completing the survey.

We extracted complete sociodemographic and occupational information at baseline from

company registers. Complete data on cause of death, date of hospital admission for IHD

(ICD-10 codes: I20-I25), and personal gross income were obtained via linkage to national

registers.

We included employees aged >18 years with >18.5 weekly working hours in the same work

unit (or its derived unit if changes had occurred). We included employees from a work unit if

>3 employees and >30% of the staff remained in the same unit throughout the period of

observation on organizational changes. For example, if work-units A and B (each with 3

employees) merged into work-unit C, we included all 6 employees in the study population.

We excluded smaller work units (fewer than 3 employees) as well as individuals with a

personal history of IHD between 2008-2012 and employees working in a department in Spain.

The final study population with complete data on work-unit organizational changes, event of

IHD, and covariates included 14,842 employees nested in 1,283 work units nested in 13

institutions (Figure 1 and Supplementary material 1).

6

Figure 1. Study flow and design. Employees could have multiple causes of exclusion.

* Of these 5,442 employees did not work in the Capital Region of Denmark by 1 January

2013.

Work-unit organizational changes

From April through June 2016, we collected data on work-unit-level organizational changes

by distributing an email survey to every manager in the source population. In this survey,

each manager was asked to provide semi-annual information (yes/no) on the work unit that

they managed regarding the occurrence of mergers, split-ups, relocation, change in

management, employee layoff(s), and budget cuts in 2013 (59% response). At the work-unit

level (level 2), we created an indicator variable (yes/no) for each of the six types of

organizational changes occurring throughout 2013. Also, we created an indicator variable for

any of these changes in the same period.


Employees were followed from baseline at 1 January 2014 to first-time hospital admission or

death due to IHD (i.e., event), death not due to IHD (i.e., censoring) or end of study by 31

7

December 2014, whichever came first. Although data on IHD during 2015 were available, we

did not use these because IHD-events in 2015 would likely be confounded by organizational

changes occurring in 2014, which we had no data on.

Covariates

The following employee-level variables were included as potential confounders of the relation

between work-unit organizational changes and IHD: age, sex, occupational group, seniority,

full-time employment, manager status, contractual employment, personal gross income, and

days of sickness absence in 2012. We also included number of employees within work units

as a potential work-unit-level confounder. Since different types of organizational changes

were partially overlapping, we included a priori selected work-unit level variables as potential

confounders (Supplementary materials 2-4). For example, confounders for employee layoff

and IHD included mergers, change in management, and budget cuts.

Employee perceived stress was measured with the item “To what degree have you been

stressed for the last six months?” using a 5-point scale ranging 1=”Not at all” to 5=”Very

high degree”. Non-respondents in the study population (15%) were included in the analyses.

Statistical analyses

Hazard ratios (HR) and 95% confidence intervals (CI) from multilevel mixed-effects

parametric survival models were used to assess the relations between work-unit

organizational changes in 2013 and days to IHD through 2014. Employees (level 1) were

nested within work units (level 2), which again were nested within institutions (level 3) to

account for clustering in the hierarchical structure of the data.

We fitted models with a Weibull distribution because we expected the effect of organizational

changes on subsequent IHD to decrease monotonically during follow-up.[21] Employees that

experienced organizational change would likely establish stressful new workflows as standard

during the following year. We assessed the proportion of variance explained by the

organizational higher levels as this could be a target of intervention. This was done by

rerunning the Weibull model but with Accelerated-Failure Time (AFT) parametrization to

calculate the Intraclass Correlation Coefficient (ICC) using the following formula for work

unit j and institution k:

𝐼𝐶𝐶𝑗,𝑘 =𝜎𝑗

2+𝜎𝑘2

𝜎𝑖2+𝜎𝑗

2+𝜎𝑘2 and 𝐼𝐶𝐶𝑘 =

𝜎𝑘2

𝜎𝑖2+𝜎𝑗

2+𝜎𝑘2 , respectively, where 𝜎𝑖

2 =𝜋2

6 ∗ exp(𝜌2)

8

and ρ is the ancillary parameter from the Weibull model.[22] Using AFT parametrization does

not change the fitted Weibull model – only the interpretation of the output[21]. The 𝐼𝐶𝐶 ∗

100 can be interpreted as the percentage of total variance in IHD-event explained by each

higher organizational level.[23] This could be any factor differing between work units (e.g.,

organizational changes, medical specialties) and between institutions (e.g., local policies on

working environment).

We used a six-step sequential modeling strategy as follows:

Model 1: A null model with a random intercept for the work-unit level. Assesses the

proportion of IHD-variation explained by factors at the work-unit level.

Model 2: As model 1, but nesting the work-unit level within the institutional level (null

multilevel model with three levels). Assesses the proportion of IHD-variance explained by

factors at the work-unit (nested within institutions) and the institutional level.

Model 3: As model 1, but entering only the indicator variable for any work-unit

organizational change (random-intercepts model with two levels). Assess the crude

association between any organizational change and risk of IHD for future comparison.

Model 4: As model 3, but entering work-unit-level organizational change variables in the

fixed part (random-intercepts model with two levels). Assesses the risk of IHD explained by

the organizational-change indicators conditioned on employee-level confounders and latent

work-unit-level factors.

Model 5: As model 4, but nesting the work-unit level within the institutional level (random-

intercepts model with three levels). Risk estimates of IHD additionally conditioned on latent

institutional-level factors.

Model 6: As model 5, but entering work-unit level confounder(s) when modeling each type of

change (random-intercepts model with three levels). Allows interpretation of the relative

IHD-risk associated with each change conditioned on employee-level factors, confounding

work-unit-level changes, and latent work-unit and institutional-level factors.

The mediating roles of perceived stress were assessed by comparing the risk estimates from

each model with and without the perceived-stress variable. A reduced risk estimate when

included was taken as evidence of mediation.[24]

A significance level of 0.05 was used throughout. The statistical analyses were performed in

STATA version 14.2 software (Stata Corporation, College Station, TX, USA).

9

RESULTS

The descriptive statistics and data structure of the study population are shown in Table 1 and

Supplementary material 1. The study sample predominantly comprised females, nursing-care

workers, and employees with permanent employment, where about half of the work units (and

employees) were exposed to any organizational changes. All 49 IHD-events in 2014 were due

to hospital admission.

Table 1. Data structure and variables for the study population.

Categories Study population,

n (% of N)

Exposed to any

changes, n (% of N)

Level 1: Employees, N

14,842 (100) 8130 (100)

Hospital admission for ischemic heart disease, no / yes

14,793 / 49 8099 / 31

Days to event, M (SD)

200 (105) 203 (107)

Years of age, M (SD)

47 (10.6) 47 (10.7)

Sex Females* 11,392 (77) 6226 (77)

Males 3450 (23) 1904 (23)

Occupational group Medical doctors/dentists* 1441 (10) 742 (9)

Nursing-care workers 6472 (44) 3649 (45)

Social/healthcare workers 2336 (16) 1255 (15)

Service/technical workers 1820 (12) 955 (12)

Administration workers 2773 (19) 1529 (19)

Seniority, years 1-4* 3097 (21) 1709 (21)

4-10 3789 (26) 2057 (25)

10-20 4048 (27) 2212 (27)

20≤ 3908 (26) 2152 (26)

Full-time employment No* 5362 (36) 2964 (36)

Yes 9480 (64) 5166 (64)

Manager No* 13,862 (93) 7490 (92)

Yes 980 (7) 640 (8)

Contractual employment No* 1037 (7) 476 (6)

Yes 13,805 (93) 7654 (94)

Personal gross income, DKK <345,000* 4384 (30) 2430 (30)

345,000-400,000 3805 (26) 2093 (26)

400,000-480,000 3423 (23) 1829 (23)

480,000< 3230 (22) 1778 (22)

Sickness absence in 2012, days No days* 4095 (28) 2250 (28)

1-3 3208 (22) 1739 (21)

4-6 2269 (15) 1261 (16)

7-13 2841 (19) 1499 (18)

14≤ 2429 (16) 1381 (17)

Perceived stress Not at all* 3341 (23) 1716 (21)

Lesser degree 4724 (32) 2503 (31)

Some degree 1420 (10) 794 (10)

High degree 2284 (15) 1316 (16)

Very high degree 937 (6) 585 (7)

Non-respondents 2136 (14) 1216 (15)

Level 2: Work units, N

1283 (100) 642 (100)

Organizational changes No changes* 641 (50)

Any changes 642 (50) 642 (100)

10

Categories Study population,

n (% of N)

Exposed to any

changes, n (% of N)

Mergers 195 (15) 195 (30)

Split-ups 75 (6) 75 (12)

Relocation 157 (12) 157 (24)

Change in management 294 (23) 294 (46)

Employee layoff 245 (19) 245 (38)

Budget cuts 191 (15) 191 (30)

Number of employees within work unit 3-12* 653 (51) 283 (44)

13-22 306 (24) 164 (26)

23-32 198 (15) 116 (18)

33-142 126 (10) 79 (12)

Level 3: Institutions, N 13 (100) 13 (100)

* Reference group for categorical variables. DKK = Danish Kroner.

During follow-up through 2014, seven employees died due to other reasons than IHD and

were thus censored. Table 2 shows the risk of IHD related to all employee-level confounders,

exposure to any work-unit organizational change, and perceived stress. Models 1-2 indicate

that the work-unit-level and the institutional-level accounted for ≈40% and ≈5%, respectively,

of the total variance. Models 3-5 show that the HR estimates for any organizational change

relative to no change increased slightly from 1.46 (95% CI: 0.79-2.69) to 1.50 (95% CI: 0.81-

2.75) when including all employee-level variables in the fixed part and the institutional level

in the random part. Models 4-5 show that the HR of IHD associated with any organizational

change somewhat attenuated from 1.50 (95% CI: 0.81-2.75) to 1.45 (95% CI: 0.78-2.69)

when accounting for perceived stress in the regression model. Despite weak statistical

evidence, the direction of the HR-point estimate indicated a higher risk of IHD among

employees reporting a very high degree of perceived stress relative to those reporting no

stress at all.

11

Table 2. Hazard ratios (HR) and 95% confidence intervals (CI) for incident ischemic heart disease in 2014 (n=49) among the study population

(N=14,842).

Model 1 (null) Model 2 (null) Model 3 Model 4 Model 5 (main model) Model 5 + perceived stress

Fixed part HR 95% CI HR 95% CI HR 95% CI HR 95% CI

Work-unit-level variables (L 2)

Any work-unit changes in 2013a

1.46 0.79-2.69 1.50 0.81-2.75 1.50 0.81-2.77 1.45 0.78-2.69

Number of employees in work unitb

13-22

1.32 0.61-2.91 1.32 0.60-2.89 1.31 0.59-2.88

23-32

0.77 0.30-2.02 0.78 0.30-2.03 0.76 0.29-2.00

33-142

1.06 0.43-2.58 1.07 0.44-2.63 1.04 0.42-2.58

Individual-level variables (L 1)

Age

1.09 1.04-1.13 1.09 1.05-1.13 1.09 1.05-1.14

Malec

2.59 1.25-5.34 2.59 1.25-5.34 2.65 1.27-5.50

Occupational groupd

Nursing-care workers

1.69 0.45-6.35 1.66 0.44-6.27 1.73 0.45-6.61

Social/healthcare workers

1.46 0.33-6.34 1.42 0.32-6.24 1.45 0.33-6.44

Service/technical workers

3.18 0.84-12.04 3.07 0.80-11.74 3.11 0.81-12.02

Administrative workers

2.13 0.59-7.67 2.08 0.57-7.62 2.15 0.58-7.95

Seniority, yearse

1-3

1.94 0.53-7.17 1.96 0.53-7.23 1.91 0.52-7.04

11-20

1.39 0.37-5.30 1.39 0.37-5.31 1.40 0.37-5.34

21≤

2.30 0.61-8.60 2.29 0.61-8.59 2.29 0.61-8.64

Full-time, yesf

0.73 0.35-1.53 0.73 0.35-1.52 0.72 0.34-1.51

Managerg

0.91 0.29-2.88 0.89 0.28-2.84 0.91 0.28-2.90

Contractual employment, yesh

0.84 0.35-2.01 0.83 0.35-2.01 0.84 0.35-2.05

Personal gross income, DKKi

345,000-400,000

0.76 0.32-1.76 0.76 0.32-1.77 0.78 0.33-1.82

400,000-480,000

0.86 0.35-2.12 0.86 0.35-2.13 0.88 0.36-2.18

480,000<

1.46 0.55-3.84 1.45 0.55-3.82 1.49 0.57-3.93

Sickness absence in 2012, daysj

1-3

0.61 0.22-1.74 0.61 0.22-1.74 0.61 0.21-1.73

4-6

1.18 0.46-3.02 1.18 0.46-3.02 1.16 0.45-2.98

7-13

1.80 0.81-4.02 1.80 0.81-4.02 1.75 0.78-3.92

14≤

1.55 0.66-3.64 1.55 0.66-3.63 1.47 0.62-3.47

Perceived stressk

”Lesser degree”

1.18 0.51-2.72

”Some degree”

0.99 0.26-3.71

”High degree”

1.30 0.47-3.55

”Very high degree”

2.64 0.91-7.61

Non-respondents

1.44 0.55-3.74

Random part

12

Model 1 (null) Model 2 (null) Model 3 Model 4 Model 5 (main model) Model 5 + perceived stress

Fixed part HR 95% CI HR 95% CI HR 95% CI HR 95% CI

ICC for work units (L 2), (p-value) 0.41, (0.01) 0.42, (0.01) 0.41, (0.01) 0.28, (0.25) 0.30, (0.21) 0.32, (0.15)

ICC for institutions (L 3), (p-value) 0.06, (0.65) 0.03, (0.67) 0.03, (0.63)

Model 1: Random intercept for the work-unit level. Model 2: As model 1, but nesting the work-unit level within the institutional level. Model 3:

As model 1, but entering work-unit-level organizational changes in the fixed part.. Model 4: As model 3, but entering all employee- and work-

unit-level covariates to the model in the fixed part. Model 5: As model 4, but nesting the work-unit level within the institutional level.

Reference categories: aNo work-unit changes in 2013, b3-12 employees in work unit, cFemales, fMedical doctors and dentists, e1-4 years, fPart-

time employment, gNot manager, hPermanent contract, i345,000> DKK, jNo days, kNot at all.

DKK = Danish Kroner, L = Level, ICC = Intra-class Correlation Coefficient.

13

In Table 3, model 6 (main model) show that there was a higher risk of IHD following

relocation (HR 2.91, 95% CI: 1.07-7.90), change in management (HR 2.18, 95% CI:1.02-

4.68) or employee layoff (HR 2.90, 95% CI:1.36-6.16). Any change, mergers, split-ups, and

budget cuts were not associated with a higher risk of IHD. When adjusting for other

confounding work-unit organizational changes the HR point estimates of all change indicators

increased (models 5-6). Including perceived stress as a potential mediator in model 6

attenuated the HR point estimates slightly.

14

Table 3. Hazard ratios and 95% confidence intervals for incident ischemic heart disease through 2014 after each type of changes relative to no

changes through 2013. Intraclass correlation coefficients (ICC) for work-units (level 2) and institutions (level 3).

Model 5 Model 6 (main model) Model 6 + perceived stress

ICC, (p-value) ICC, (p-value) ICC, (p-value)

N Cases, n HR 95% CI Work units Institutions HR 95% CI Work units Institutions HR 95% CI Work units Institutions

No changes* 6712 18 1.00

1.00

1.00

Mergers 2532 4 0.57 0.18-1.76 0.29, (0.24) 0.04, (0.55) 0.75a 0.24-2.37 0.23, (0.40) 0.06, (0.48) 0.72a 0.23-2.30 0.26, (0.30) 0.06, (0.46)

Split-ups 950 *≤2 0.80 0.18-3.60 0.28, (0.25) 0.03, (0.64) 0.90b 0.20-4.07 0.24, (0.40) 0.04, (0.59) 0.87b 0.19-3.95 0.32, (0.14) 0.04, (0.54)

Relocation 1852 7 1.61 0.63-4.11 0.30, (0.21) 0.03, (0.67) 2.91c 1.07-7.90 0.28, (0.25) 0.05, (0.52) 2.81c 1.06-8.03 0.30, (0.19) 0.05, (0.49)

Change in management 3726 14 1.50 0.72-3.12 0.30, (0.21) 0.03, (0.67) 2.18c 1.02-4.68 0.27, (0.27) 0.05, (0.51) 2.10c 0.97-4.54 0.30, (0.21) 0.05, (0.49)

Employee layoff 3155 20 2.19 1.12-4.30 0.25, (0.34) 0.02, (0.79) 2.90d 1.36-6.16 0.17, (0.57) 0.04, (0.57) 2.78d 1.29-5.96 0.20, (0.46) 0.05, (0.54)

Budget cuts 2364 6 0.91 0.35-2.36 0.26, (0.32) 0.03, (0.63) 0.93e 0.35-2.50 0.26, (0.32) 0.03, (0.64) 0.91e 0.34-2.48 0.29, (0.24) 0.04, (0.59)

* Reporting of cells with ≤2 observations is restricted by Statistics Denmark.

Results from the employee-level variables are omitted, because these did not change noteworthy relative to those reported in Table 2. *

Reference category.

Model 5: In the fixed part, analyses adjusted for age, sex, occupation, seniority, full-time employment, manager status, contractual employment,

personal income, previous sickness absence at the employee-level (level 1), and number of employees within work units (level 2) at the work-

unit level. The work-unit (level 2) and the institutional level (level 3) were included as random intercepts. Exposure to each type of work-unit

organizational change (level 2) were modeled separately in the fixed part.

Model 6: As model 5, but analyses were adjusted for other types of work-unit changes as potential confounders (level 2), accordingly:

aSplit-ups and Budget cuts, bBudget cuts, cMergers and Split-ups, dMergers, Change in management, and Budget cuts, eChange in management.

15


To assess the impact of missing data on organizational changes, we conducted a sensitivity

analysis where all eligible employees with missing data on changes were assigned to the

reference category of “no changes”. Similar results were found for any change (HR 1.54, 95%

CI: 0.94-2.52) compared to those in Table 2 (HR 1.50, 95% CI: 0.81-2.77), indicating no

impact of missing data on changes.

We assessed if the marked risk directions of the highest categories of seniority and income

were due to residual age-confounding by stepwise adding age² and age³ in models 3-5 (Table

2). Including neither age² nor age² and age³ changed the point estimates for HR meaningfully,

suggesting no residual confounding by age.

Study participation required working in the same work unit through 2013, but some laid-off

employees could be included in the study population if their termination period extended into

2014. Employment termination periods ranged three to six months depending on seniority. To

assess if the employee-layoff effects were attributed to poor health status among those laid

off, we restricted model 6 for “employee layoff” to changes occurring only in the first

semester of 2013 (i.e., exposure and covariates at level 2), while keeping the follow-up period

through 2014 unaltered. An employee laid off in the first semester of 2013 would terminate

the employment in the last semester 2013 and thus not be included for follow-up on IHD.

Results from this sensitivity analysis supported the excess risk of IHD following employee

layoff (HR 2.59, 95% CI 1.09-6.18) relative to no changes.

16

DISCUSSION

Relocation, change in management or employee layoff in the work unit were associated with

a higher risk of hospital admission for IHD among the employees remaining during these

changes relative to no changes. Indication of any changes, mergers, split-ups or budgets cuts

were not associated with IHD. The HR point estimates of all change indicators decreased only

slightly when adjusting for perceived stress, indicating that this psychosocial factor is not an

important mediator of the association.

Previous findings and potential mechanisms

Our finding of a 2.9-fold higher risk of IHD in the year after employee layoff in the work unit

is consistent with the 5.1-fold higher cardiovascular mortality in the first 4 years following

major downsizing among employees who kept their job reported in a Finnish study.[13] In the

same study, minor downsizing (8-18% staff reduction) was not associated with a higher risk

of cardiovascular mortality (although estimates pointed in this direction),[13] indicating some

sensitivity towards the proportion of laid-off employees. As termination periods extended up

to six months in our study, some employees laid-off in 2013 may be included in the study

population with follow-up on IHD in 2014. However, a sensitivity analysis showed that

employee layoff occurring in the first semester of 2013 only was related to a similar high risk

of IHD through 2014 (HR: 2.6 vs. HR: 2.9), suggesting that the present employee-layoff

effects on IHD were attributed to the employees who kept their job in 2013.

We also found that relocation and change in management were associated with a marked

excess risk of IHD. To the extent of our knowledge this is the first study to demonstrate

associations between these types of organizational changes and cardiovascular diseases

although there is some prior evidence of associations with other adverse outcomes.[8,14,25]

The HR estimate of any changes pointed to a higher risk of IHD. Indeed, this result was

inadequately supported in the data.

Episodic stressors (e.g., anger, emotional upset) could lead to cardiovascular events among

individuals with advanced atherosclerotic plaque formation in coronary arteries.[26]

Organizational changes inducing job insecurity could be regarded such stressor.[27] However,

we found no convincing indications of perceived stress mediating the association between

changes and IHD. Indeed, this could also be due to using a perceived-stress measure of 1 item

only as indicated by the broad confidence intervals in Table 2. A previous study demonstrated

that the effects of major downsizing on medically certified sickness absence were mediated by

17

changes in physical demands, job control, and job insecurity.[28] Working in the public sector

of Denmark is generally considered as a secure employment. The relatively low

unemployment rate in this region decreased from 6.0% to 5.3% between 2013-2014,[29]

suggesting that long-term unemployment would not be a feared consequence following

organizational changes among many of the employees examined.

We did, however, find that latent factors at the work-unit level explained a large proportion of

the variation in IHD-events. Such factors may comprise the magnitude of the changes,

communication to the employees about the changes or the management style. Future studies

should examine mechanisms at the work-unit level potentially mediating the excess risk of

cardiovascular diseases that may follow organizational changes, such as employee layoff,

change in management or relocation.


Limitations are highlighted in the following. First, the potential impacts of reorganization on

IHD before and during the changes were not examined. We started follow-up at 1 January

2014 to ascertain that the IHD-event occurred after potential exposure to changes in 2013.

Second, we did not have data to account for organizational changes during the follow-up

period. This may have underestimated the results as the reference category of work units not

changed through 2013 would be more likely to be reorganized in 2014 than work units

changed recently. Third, the reorganization itself could layoff managers and therefore cause

missing data on changes as these were collected retrospectively via email. Indeed, the email

addresses was not changed if the managers remained employed within the Capital Region of

Denmark, and a sensitivity analysis suggested no impact of missing data on changes. The

statistical power of the analyses of specific types of changes is limited with a risk of type-II

statistical error as evidenced by broad 95% CIs. Since the hypothesis is addressing short-term

effects of organizational changes an extension of the follow-up period will not increase the

power.

This study has several strengths. First, data on changes, event of IHD, and perceived stress

originated from independent sources and thus common-method bias is not an issue.[30]

Second, organizational changes were measured at the work-unit level ensuring that the

employees did experience the potential reorganization. Third, we included only those

employees, who worked in the same work unit during the observation of changes, which,

again, ensured that the employees were actually affected by the changes. Fourth, we

accounted for clustering on two higher levels in the organizational structure, which allowed us

18

to assess variance explained by latent institutional and work-unit-level factors. Finally, we

consider it as a strength of the study that we assessed the relative risk of IHD following

various and frequently occurring types of changes. This also allowed us to establish a purer

reference group of no changes as compared to prior studies examining a single type of

change.

We were surprised to find such excess relative risks of IHD after various changes given the

widespread practice of workplace reorganization. Indeed, all findings should be interpreted

cautiously since associations could be observed by chance given the somewhat few IHD-

events examined (n=49).

This study demonstrated a higher risk of IHD among the employees who kept their job during

relocation, change in management or employee layoff in the work unit relative to no

organizational changes. There were no association with IHD after exposure to any change

examined, mergers, split-ups or budget cuts. Inferences to other workplace contexts should be

made cautiously because of the few IHD events and composition of the study population.

Funding

This work was supported by the Danish Working Environment Research Fund [13-2015-03].

Acknowledgements

JHJ thanks University Copenhagen, Julie Von Müllens Fond, Else & Mogens Wedell-

Wedellsborgs Fond, and the Graduate School of Public Health (at University of Copenhagen)

for their financial contribution to his stay as a visiting researcher in Department of Social and

Behavioral Sciences at Harvard T.H. Chan School of Public Health during this study.

Contributorship

JHJ had full access to all data provided in the present study, and JHJ takes responsibility for

the integrity and the accuracy of the data analyses. All authors were responsible for the

current study design. JHJ wrote the initial draft of the manuscript. All authors contributed to

the present study and approved the final draft of the manuscript.

Competing interests

None declared.

19

References

1 Gaynor M, Laudicella M, Propper C. Can governments do it better? Merger mania and

hospital outcomes in the English NHS. J Health Econ 2012;31:528–43.

doi:10.1016/j.jhealeco.2012.03.006

2 European Foundation for the Improvement of Living and Working Conditions

(Eurofound). ERM annual report 2014: restructuring in the public sector. Publications

Office of the European Union, Luxembourg: 2015. doi:10.2806/92093

3 Smaglik P. Job movements. Nature 2005;433:901. doi:10.1038/nj7028-901a

4 Finansministeriet. Sygehusene fritages for produktivitetskrav i 2018.

2017.https://www.fm.dk/nyheder/pressemeddelelser/2017/10/sygehusene-fritages-for-

produktivitetskrav-i-2018 (accessed 31 Jul 2018).

5 Bamberger SG, Vinding AL, Larsen A, et al. Impact of organisational change on

mental health: a systematic review. Occup Environ Med 2012;69:592–8.

doi:10.1136/oemed-2011-100381

6 de Jong T, Wiezer N, de Weerd M, et al. The impact of restructuring on employee

well-being: a systematic review of longitudinal studies. Work Stress 2016;30:91–114.

doi:10.1080/02678373.2015.1136710

7 Dahl MS. Organizational Change and Employee Stress. 2011;57:240–56.

doi:10.1287/mnsc.1100.1273

8 Jensen JH, Flachs EM, Skakon J, et al. Dual impact of organisational change on

subsequent exit from work unit and sickness absence: a longitudinal study among

public healthcare employees. Occup Environ Med 2018;75:479–85.

doi:10.1136/oemed-2017-104865

9 Kjekshus LE, Bernström VH, Dahl E, et al. The effect of hospital mergers on long-

term sickness absence among hospital employees: a fixed effects multivariate

regression analysis using panel data. BMC Heal Serv Res 2014;14:50.

doi:10.1186/1472-6963-14-50

10 Vahtera J, Kivimäki M, Pentti J. Effect of organisational downsizing on health of

employees. Lancet 1997;350:1124–8. doi:10.1016/S0140-6736(97)03216-9

11 Østhus S, Mastekaasa A. The impact of downsizing on remaining workers’ sickness

absence. Soc Sci Med 2010;71:1455–62. doi:10.1016/j.socscimed.2010.06.045

12 Theorell T, Oxenstierna G, Westerlund H, et al. Downsizing of staff is associated with

lowered medically certified sick leave in female employees. Occup Environ Med

2003;60:E9. doi:10.1136/oem.60.9.e9

20

13 Vahtera J, Kivimäki M, Pentti J, et al. Organisational downsizing, sickness absence,

and mortality: 10-town prospective cohort study. BMJ 2004;328:555.

doi:10.1136/bmj.37972.496262.0D

14 Ingelsrud MH. Reorganization increases long-term sickness absence at all levels of

hospital staff: panel data analysis of employees of Norwegian public hospitals. BMC

Health Serv Res 2014;14:411. doi:10.1186/1472-6963-14-411

15 Rod NH, Grønbæk M, Schnohr P, et al. Perceived stress as a risk factor for changes in

health behaviour and cardiac risk profile: A longitudinal study. J Intern Med

2009;266:467–75. doi:10.1111/j.1365-2796.2009.02124.x

16 Richardson S, Shaffer JA, Falzon L, et al. Meta-analysis of perceived stress and its

association with incident coronary heart disease. Am J Cardiol 2012;110:1711–6.

doi:10.1016/j.amjcard.2012.08.004

17 Kivimäki M, Honkonen T, Wahlbeck K, et al. Organisational downsizing and

increased use of psychotropic drugs among employees who remain in employment. J

Epidemiol Community Heal 2007;61:154–8. doi:10.1136/jech.2006.050955

18 Möller J, Theorell T, De Faire U, et al. Work related stressful life events and the risk of

myocardial infarction. Case-control and case-crossover analyses with the Stockholm

heart epidemiology programme (SHEEP). J Epidemiol Community Health 2005;59:23–

30. doi:10.1136/jech.2003.019349

19 Pollard TM. Changes in mental well-being, blood pressure and total cholesterol levels

during workplace reorganization: The impact of uncertainty. Work Stress 2001;15:14–

28. doi:10.1080/02678370110064609

20 Hvidtfeldt UA, Bjorner JB, Jensen JH, et al. Cohort Profile: The Well-being in

Hospital Employees (WHALE) study. Int J Epidemiol 2017;46:1758–1759h.

doi:http://doi.org/10.1093/ije/dyx073

21 Brandon George. SS and IA. Survival analysis and regression models. NIH Public

Access 2014;21:686–94. doi:10.1007/s12350-014-9908-2.Survival

22 Canette I. In the spotlight: Intraclass correlations after multilevel survival models. Stata

News. 2016;:31(2).https://www.stata.com/stata-news/news31-2/intraclass-correlations/

(accessed 31 Jul 2018).

23 Snijders T, Bosker R. Multilevel analysis. An Introduction to Basic and Advanced

Multilevel Modeling. 2nd ed. SAGE Publications 2012.

24 Baron RM, Kenny DA. The Moderator-Mediator Variable Distinction in Social The

Moderator-Mediator Variable Distinction in Social Psychological Research:

21

Conceptual, Strategic, and Statistical Considerations. J Pers Soc Psychol

1986;51:1173–82. doi:10.1037/0022-3514.51.6.1173

25 Greubel J, Kecklund G. The impact of organizational changes on work stress, sleep,

recovery and health. Ind Health 2011;49:353–64. doi:10.2486/indhealth.MS1211

26 Kivimäki M, Steptoe A. Effects of stress on the development and progression of

cardiovascular disease. Nat Rev Cardiol Published Online First: 2017.

doi:10.1038/nrcardio.2017.189

27 Westerlund H, Theorell T, Alfredsson L. Organizational instability and cardiovascular

risk factors in white-collar employees. Eur J Public Health 2004;14:37–42.

28 Kivimäki M, Vahtera J, Pentti J, et al. Factors underlying the effect of organisational

downsizing on health of employees : longitudinal cohort study. BMJ 2000;320:971–5.

29 Danmarks Statistik. Fuldtidsledige i pct af Arb efter køn, alder, område og tid.

http://www.statistikbanken.dk/AULP01 (accessed 31 Jul 2018).

30 Podsakoff PM, MacKenzie SB, Lee JY, et al. Common method biases in behavioral

research: a critical review of the literature and recommended remedies. J Appl Psychol

2003;88:879–903. doi:10.1037/0021-9010.88.5.879

Supplementary materials 1-4

Supplementary material 1. Diagram of the 3-level organizational data structure of the study

population.

Supplementary material 2. Directed acyclic graphs for other types of organizational changes

(level 2) confounding the relation between each of the six types of changes examined and

incident ischemic heart disease (IHD).

Supplementary material 3. Overlap in the six types of organizational change (level 2)

experienced by the employees (level 1) in study population (n=14,842).

Employees,

n

Mergers,

%

Split-ups,

%

Relocation,

%

Change in

management,

%

Employee

layoff, %

Budget

cuts, %

Any change 8130 31 12 23 46 39 29

Mergers 2532 20 41 53 28 25

Split-ups 950 54

46 55 31 21

Relocation 1852 56 23 46 26 17

Change in management 3726 36 14 23

28 22

Employee layoff 3155 22 9 15 33 33

Budget cuts 2364 27 8 13 35 45

Supplementary material 4. Overlap in the six types of organizational change (level 2)

experienced by the employees (level 1) with ischemic heart disease in 2014 (n=49).

Employees

with IHD-

event, n

Mergers,

%

Split-ups,

%

Relocation,

%

Change in

management,

%

Employee

layoff, %

Budget

cuts, %

Any change 31 13 6 23 45 65 19

Mergers 4 0 75 50 75 25

Split-ups *≤2 - - - - - -

Relocation 7 43 0 43 57 14

Change in management 14 14 14 21

64 7

Employee layoff 20 19 13 19 44 6

Budget cuts 6 17 17 17 17 17

* Reporting of cells with ≤2 observations is restricted by Statistics Denmark.

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