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2004 VOL. 28 NO. 3 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 259
Job characteristics and the subjective oral health
of Australian workers
Anne E. Sanders and A. John Spencer
Australian Research Centre for Population Oral Health, Dental School,
University of Adelaide, South Australia
Abstract
The way in which work is structured and
organised is associated with the health and
well-being of workers.
Objectives:To examine the associations
between hours worked, job security, skill
maintenance and work and home
interference and subjective oral health; and
to compare findings for different
occupational groups.
Methods:Data were collected in 1999 froma random stratified sample of households in
all Australian States and Territories using a
telephone interview and a questionnaire
survey. Subjective oral health was
evaluated with the short form Oral Health
Impact Profile (OHIP-14), which assesses
the adverse impact of oral conditions on
quality of life.
Results:Data were obtained for 2,347
dentate adults in the workforce. In the
12 months preceding the survey, 51.9%
had experienced oral pain and 31.0%reported psychological discomfort from
dental problems. Males, young adults,
Australian-born workers, and those in
upper-white collar occupations reported
lower mean OHIP-14 scores (ANOVA
p40 hours a week was associated
with higher OHIP-14 scores for other
workers. Conclusions:Aspects of the work
environment are associated with the
subjective oral health of workers. Because
these contexts are subject to only limited
control by individual workers, their influence
is a public health issue.
(Aust N Z J Public Health2004; 28: 259-66)
Submitted:December 2003
Revision requested:March 2004
Accepted:April 2004
Correspondence to:Professor A. John Spencer, Australian Research Centre for Population Oral Health,Dental School, University of Adelaide, South Australia 5005. Fax: (08) 8303 4858;e-mail: [email protected]
Article Eating, Drinking and Oral Health
The restructuring of the labour mar-
ket has altered several features of the
labour force in Australia. Changes
in working hours are one example. The Aus-
tralian Bureau of Statistics monthly labour
force surveys1show that not only has theaverage number of hours worked by full-time
workers increased over two decades, but also
the proportion working long hours has in-
creased. According to the Australian Coun-
cil of Trade Unions, Australia ranks second
behind Korea for average working hours and
has the highest proportion of its labour force
working more than 50 hours per week among
OECD countries.2Yet not all workers are
working longer hours, because the propor-
tion of the labour force working part timehas also increased.3Changes are also appar-
ent in perceived job security. Time series data
show that the proportion of Australian work-
ers who believed their job to be secure de-
clined in the early to mid 1990s.4,5In 1999,
a national poll of Australian workers found
that 74% believed their job to be safe, which
represented a decrease of seven percentage
points since the previous year.6Organisa-
tional downsizing and job creation schemes
have spurred a need for retraining programs
and professional development to maintain a
skilled workforce.
Coinciding with these changes, labour
force participation rates for females in-
creased from 46% in 1985 to 55% in 2001.7
Workers, especially those combining parent-
hood and paid work, require flexibility to
balance work and home demands. Currently,
Australia and the United States remain the
only two OECD countries not to offer a paid
parenting or maternity leave scheme, with
New Zealand introducing a scheme in 2002.
Because these changes were introduced
rapidly, they are likely to have an impact on
the health and well-being of workers. The
negative effect on employee health of organi-sational downsizing has been reported in
several longitudinal studies.8-10A recent US
study found that physical and mental symp-
toms associated with downsizing were not
confined to those directly targeted by struc-
tural changes but were, to a milder extent,
also reported by workers less immediately
affected.11To date, Australian research in this
area is limited and no studies have exam-
ined job characteristics and the subjective
oral health of workers. Unlike objectivelyassessed measures of dental disease, subjec-
tive measures of oral health convey infor-
mation about the impact of oral disease on
quality of life from the individuals perspec-
tive.
The first objective of the study was to ex-
amine the associations of hours worked, per-
ceived job security, perceived risk of skill
obsolescence, and the strain of work and
home interference on the subjective oral
health of workers in Australia. The second
objective was to compare findings for dif-
ferent occupational groups.
Methods
Data were from the 1999 National Dental
Telephone Interview Survey (NDTIS) and a
self-complete questionnaire sent to first per-
son adult interviewees immediately follow-
ing the interview. NDTIS is a periodic
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260 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 2004 VOL. 28 NO. 3
Table 1: Socio-demographic characteristics of the
weighted sample.
n %
Occupational group
Upper white-collar 791 38.5
Lower white-collar 1,009 49.1
Blue-collar 256 12.5
Sex
Male 1,301 55.5
Female 1,045 44.5
Age group
18-24 years 392 16.7
25-34 years 571 24.3
35-44 years 700 29.8
45-54 years 477 20.3
55+ years 206 8.8
Country of birth
Australia 1,900 81.8
Other 421 18.2
Education
Tertiary 961 41.2
No tertiary 1,373 58.8Household income
$50,000 1,000 44.8
Sanders and Spencer Article
cross-sectional population survey that monitors the self-reported
oral health of Australian residents aged five years and over and
their use of dental services. In 1999, a random stratified sample
was drawn for all States and Territories from the electronic tele-
phone listings and one randomly selected household member was
interviewed. To maximise participation and response the meth-
ods recommended by Dillman were used.12These included an
information letter sent to all households in advance of telephone
contact and up to four personalised approaches for the question-
naire.
Subjective oral health was evaluated with the 14-item Oral
Health Impact Profile (OHIP-14).13This short form is useful when
space constraints and the risk of respondent burden do not permit
use of the full 49-item scale. This 49-item OHIP14was based on
the international classif ication of impairments, disabilities and
handicaps developed in 1980 by the World Health Organization
and adapted for oral health by Locker.15The OHIP explores seven
dimensions of impact arranged in ascending hierarchical order
from functional limitation, pain and discomfort, psychological
discomfort, through to physical, psychological and social disability
and finally handicap. In the short form OHIP-14 two questions
tap each of the seven dimensions. Participants are asked to report
the frequency with which they experienced impacts over the
12 months preceding the survey. Responses are coded on a five-
point scale of 0=never, 1=hardly ever, 2=occasionally, 3=fairly
often and 4=very often. We used two summary statistics from
this scale: the percentage of persons reporting an impact occa-
sionally or more often, and the mean scale score with higher scores
reflecting more adverse impact.The questionnaire asked about occupation, working hours, per-
ceptions of job security and skill obsolescence, and work-home
interference. Occupational title and main task descriptors were
coded according to the Australian Standard Classification of
Occupations16and then collapsed into three groups: upper white-
collar (manager, administrator, professional), lower white-collar
(paraprofessional, tradesperson, clerk, salesperson, personal serv-
ice work) and blue-collar (plant or machine operator, driver,
labourer or related). Response options for hours worked were up
to 30, 30-40 and >40, representing part time, standard working
week and overtime hours worked. Perceived job security was as-sessed with the question Do you expect that your job will be
secure for the next five years? Response options were yes, prob-
ably, unlikely, and no. The same response options were used
for the question Do you expect that your present job skills will
be obsolete within 10 years? Scoring on this item was reversed
so that an affirmative response reflected a high expectation of
skill maintenance. Finally, work-home interference was evaluated
using an eight-item scale tested by Gutek and colleagues.17Four
items assessed the degree to which work interfered with home
life and the remaining items assessed the level of home-to-work
interference. Responses were recorded on a five-point scale coded
from 0 to 4 with higher scores indicating greater interference. In
an exploratory factor analysis of the items, a two-factor solution
emerged that conceptually supported the scales bi-directional
structure and which was empirically appropriate. Both factors had
eigenvalues greater than one that together accounted for 60.0%
of the total variance. The first factor was labelled work interferes
with home (=0.80) and the second home interferes with work
(=0.72). Continuous scores on the overall scale and two subscales
were categorised into five groups labelled low, low-moderate,
moderate, moderate-high, and high interference, with higher scores
reflecting a higher level of conflict. It was not possible to con-
struct equal-sized quintiles because of the clustering of scores.
Consequently, the five groups approximated quintile ranges as
closely as data permitted.
Data were weighted to account for differing sampling prob-
abilities due to the sampling design to be representative of the
Australian population in its age and sex composition for each
sampling stratum.
Bivariate associations between the explanatory variables and
OHIP-14 scores were examined using one-way ANOVA with the
level of statistical significance set to 5%. All explanatory vari-
ables were retained and were entered into a multiple regression
model to estimate the association between job characteristics and
the social impact of oral conditions. In multivariate analysis, the
ordinal variables of hours worked, job security and skill mainte-
nance were transformed to dummy variables. Blocks of explana-
tory variables were entered in two steps so that the relative
contribution of the job characteristics entered at step two could
be distinguished from the effect of socio-demographic variables
entered in step one. A separate model was constructed for each of
the three occupational groups.
Both unstandardised and standardised beta coefficients are
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2004 VOL. 28 NO. 3 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 261
Eating, Drinking and Oral Health Job characteristics and subjective oral health
reported. The beta value (B) is a measure of how strongly each
independent variable is associated with mean OHIP-14 scores. It
indicates the change in mean OHIP-14 score that is due to a change
of one unit of each independent variable. To compare the relative
contribution of each independent variable across different mod-
els that is, between different occupational groups these
unstandardised beta coeff icients (B) are reported. However, within
a single model (occupational group), comparisons of beta values
are difficult to interpret as the units in which these variables are
measured differ. To facilitate interpretation of the relative contri-
bution of each independent variable, the standardised beta coef-
ficients (beta) are also reported. These values standardise the
different units to standard deviations that vary from 1 to +1.
Results
Participation in the NDTIS was 56.6% (n=7,829). Of the 6,152
adults who were sent the self-complete questionnaire, 3,973
responded (64.6%). Analysis was limited to dentate adults who
were aged 18 to 65 years and in paid work (n=2,347). Males domi-
nated blue-collar (82.0%) occupations and to a lesser extent up-
per white-collar occupations (57.9%), but contributed less than
half of the lower white-collar occupations (48.6%). Other sample
characteristics are presented in Table 1. Workers in upper white-
collar (UWC) occupations comprised 38.5%, lower white-collar
occupations (LWC) comprised nearly half (49.1%) and blue-col-
lar (BC) workers comprised 12.5%. A sizeable minority (41.2%)
had tertiary education, and 44.8% had household income of more
than $50,000.
One-quarter (25.3%) worked 40 hours is higher than the na-
tional estimate of 37%.19Overtime was twice as common among
UWC workers (61.1%) than among LWC workers (30.5%).
Table 2: Distribution in hours worked, job security, skill maintenance and work-home interference for occupational
groups.
Upper Lower Blue collarc Total
white collara white collarb
n % n % n % n %
Hours worked
Up to 30 hours 119 15.1 344 34.4 54 21.1 517 25.3
31-40 hours 187 23.8 352 35.2 110 43.0 649 31.8
More than 40 hours 481 61.1 305 30.5 92 35.9 878 43.0
Total 787 100.0 1,001 100.0 256 100.0 2,044 100.0
Job securityYes 401 50.8 410 40.8 92 36.4 903 44.1
Probably 281 35.6 424 42.2 102 40.3 807 39.4
Unlikely 67 8.5 114 11.3 30 11.9 211 10.3
No 41 5.2 57 5.7 29 11.5 127 6.2
Total 790 100.0 1,005 100.0 253 100.0 2,048 100.0
Skill maintenance
Yes 369 46.6 391 39.1 97 38.0 857 41.9
Probably 309 39.1 403 40.3 103 40.4 815 39.8
Unlikely 75 9.5 155 15.5 44 17.3 274 13.4
No 38 4.8 52 5.2 11 4.3 101 4.9
Total 791 100.0 1,001 100.0 255 100.0 2,047 100.0
Work interferes with home
Low interference 154 19.7 353 36.2 73 28.8 592 28.1
Low-moderate 172 22.0 235 24.1 58 22.9 490 23.2
Moderate 154 19.7 170 17.4 38 15.0 379 18.0
Moderate-high 138 17.7 127 13.1 30 11.8 308 14.6
High interference 163 20.9 89 9.1 54 21.4 340 16.1
Total 782 100.0 976 100.0 252 100.0 2,109 100.0
Home interferes with work
Low interference 223 28.5 286 29.3 53 20.8 578 27.4
Low-moderate 110 14.1 119 12.2 42 16.5 283 13.4
Moderate 246 31.5 318 32.6 71 27.9 669 31.7
Moderate-high 64 8.2 72 7.4 49 19.2 190 9.0
High interference 139 17.7 181 18.5 39 15.5 389 18.5
Total 782 100.0 976 100.0 252 100.0 2,109 100.0Notes:(a) Manager/administrator; professional.(b) Paraprofessional; tradesperson; clerk; sales or personal service worker.(c) Plant or machine operator or driver; labourer or related worker.
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Table 3: Mean (se) OHIP-14 scores for socio-
demographic characteristics.
Mean (se) OHIP-14
Occupational groupc
Upper white collar 0.46 (0.02)
Lower white collar 0.58 (0.02)
Blue collar 0.56 (0.04)
Sexa
Male 0.50 (0.01)
Female 0.56 (0.02)
Age groupb
18-24 years 0.45 (0.02)
25-34 years 0.52 (0.02)
35-44 years 0.57 (0.02)
45-54 years 0.53 (0.02)
55+ years 0.57 (0.04)
Country of birthc
Australia 0.50 (0.01)
Other 0.61 (0.03)
Educationa
Tertiary 0.50 (0.02)
No tertiary 0.55 (0.01)
Household incomeb
$50,000 0.58 (0.02)
Notes:(a) p
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Eating, Drinking and Oral Health Job characteristics and subjective oral health
for BC workers, differences failed to reach statistical significance.
For LWC workers, the increasing risk to security was associated
with a stepwise increase in mean OHIP-14 scores. Differences in
skill maintenance expectations were significantly associated with
OHIP-14 scores for each occupational group. For all workers,
those who were certain that their job skills would be maintained
reported lowest mean OHIP-14 scores. For white-collar workers
a monotonic gradient was observed characterised by decreasing
mean OHIP-14 scores with increasing certainty of skill mainte-
nance.
For both upper and lower white-collar workers, highest levels
of work interference and home interference were associated with
highest mean OHIP scores. Although those BC workers who ex-
perienced least interference also reported least social impact, there
was not a clear relationship between level of interference and the
impact of dental problems.
The correlations between the independent variables were ex-
amined for collinearity as high correlation would reduce the pre-
cision of estimates in the multivariate regression analysis.
Although significant, associations were weak, with Spearmans
rank correlation coefficients ranging from 0.05 for the associa-
tion between hours worked and job security, to 0.31 for the asso-
ciation between work-to-home interference and job security.
In multivariate regression analysis, the potential confounding
effect of socio-demographic factors was taken into account by
entering sex, age in years, country of birth (Australia or other),
education (tertiary or not tertiary) and household income
(>$A50,000 or $A50,000) in the models before the explana-
tory variables. The results are presented in Table 6. For UWC
workers, female sex and age were positively associated with mean
OHIP-14 scores but country of birth and socio-economic indica-
tors were not. Compared with those working standard hours, part-
time workers had greater impact scores. Workers with uncertain
job security (but not those whose job was definitely not secure)
reported greater social impact than workers in secure jobs.
Workers in jobs where skill maintenance was unlikely reported
significantly greater social impact than workers whose skill main-
tenance was assured. Both work interference with home and home
Table 5: Mean (se) social impact scores according to work-related characteristics for occupational groups.
Upper Lower Blue collar
white collar white collar
Mean (se) Mean (se) Mean (se)
Hours worked b a d
Up to 30 hours 0.56 (0.04) 0.60 (0.03) 0.59 (0.10)
31-40 hours 0.41 (0.03) 0.58 (0.03) 0.40 (0.05)
More than 40 hours 0.45 (0.02) 0.56 (0.03) 0.74 (0.06)
Total 0.46 (0.02) 0.58 (0.02) 0.56 (0.04)
Job security d d a
Yes 0.35 (0.02) 0.50 (0.02) 0.47 (0.05)
Probably 0.59 (0.04) 0.61 (0.03) 0.65 (0.06)
Unlikely 0.58 (0.06) 0.63 (0.04) 0.43 (0.11)
No 0.45 (0.10) 0.77 (0.09) 0.53 (0.10)
Total 0.46 (0.02) 0.58 (0.02) 0.54 (0.04)
Skill maintenance c d b
Yes 0.41 (0.02) 0.51 (0.02) 0.44 (0.05)
Probably 0.48 (0.03) 0.58 (0.03) 0.71 (0.07)
Unlikely 0.56 (0.07) 0.69 (0.05) 0.50 (0.07)
No 0.65 (0.10) 0.82 (0.11) 0.58 (0.15)
Total 0.46 (0.02) 0.58 (0.02) 0.56 (0.04)Work interferes with home d d c
Low interference 0.32 (0.03) 0.58 (0.03) 0.41 (0.07)
Low-moderate 0.45 (0.04) 0.49 (0.03) 0.52 (0.07)
Moderate 0.35 (0.03) 0.51 (0.03) 0.80 (0.10)
Moderate-high 0.55 (0.04) 0.67 (0.05) 0.48 (0.08)
High interference 0.62 (0.04) 0.92 (0.08) 0.72 (0.09)
Total 0.46 (0.02) 0.59 (0.02) 0.57 (0.04)
Home interferes with work d d d
Low 0.30 (0.02) 0.44 (0.03) 0.32 (0.06)
Low-moderate 0.44 (0.04) 0.64 (0.04) 0.98 (0.12)
Moderate 0.53 (0.03) 0.55 (0.03) 0.40 (0.06)
Moderate-high 0.38 (0.05) 0.51 (0.05) 0.61 (0.06)
High 0.63 (0.05) 0.87 (0.05) 0.72 (0.11)
Total 0.46 (0.02) 0.59 (0.02) 0.57 (0.04)
Notes:(a) p>0.05; (b) p
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Table 6: Multiple regression unstandardised coefficients (se) and standardised coefficients for the social impact of oral
problems for occupational groups.
Upper white collar Lower white collar Blue collar
Ba SE Betab Sig Ba SE Betab Sig Ba SE Betab Sig
1 (Constant) -0.31 (0.09) f 0.29 (0.08) f -0.11 (0.17) c
Sex
Male (ref)
Female 0.09 (0.04) 0.09 d -0.09 (0.04) -0.08 d 0.07 (0.10) 0.05 c
Age in years 0.01 (0.00) 0.14 f 0.00 (0.00) -0.01 c 0.00 (0.00) 0.02 c
Country of birth
Australia (ref)
Overseas 0.08 (0.04) 0.06c
0.05 (0.05) 0.04c
0.26 (0.10) 0.18e
Education
Tertiary (ref)
No tertiary 0.06 (0.04) 0.06 c 0.00 (0.04) 0.00 c -0.04 (0.11) -0.02 c
Household income
>$50,000 (ref)
0.05; (d) p
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Eating, Drinking and Oral Health Job characteristics and subjective oral health
explained in the model for BC workers (21.5%) than for UWC
(14.3%) or LWC workers (10.9%). An examination of the change
statistics revealed that in each model, the job characteristics en-
tered in the second step accounted for a substantially greater pro-
portion of the explained variance than did the socio-demographic
factors entered at step one. For UWC and BC workers, the job
characteristics explained about three times more variance than
did the socio-demographic factors.
Discussion
The main finding of this study was a strong association between
job characteristics and the subjective oral health of workers. Com-
parative studies of workers in the oral health literature are very
limited. Marcenes and Sheiham found that work-related mental
demand was related to periodontal disease in male workers aged
35 to 44 years,20and in other research the flexibility of working
hours was associated with dental self-care behaviour in workers
aged 24-44 years.21The general health literature has reported
widely the relationship between work-related psychosocial fac-
tors such as decision latitude, job demands and social support
and workers health. Yet fewer studies have examined the associa-
tions between health and ways that work is structured in terms of
hours worked, job security, continuing education and flexibility
to manage competing work and home commitments. Moreover,
many studies have been limited to white-collar workers, omitting
those in manual occupations.
The OHIP-14 questionnaire and the original 49-item OHIP have
been widely used to evaluate subjective oral health in more than25 studies, including randomised clinical trials and nationally
representative population surveys.22The importance of this studys
findings from a population perspective is the extent to which oral
health problems are experienced. In all, 61.5% of workers reported
impacts occasionally or more often. More than half (51.9%) re-
called that dental problems had caused oral pain and almost one-
third (31.0%) reported feeling self-conscious or tense as a
consequence of dental problems.
Our findings are limited by the cross-sectional design of the
study. It is not possible, for instance, to infer that changing char-
acteristics of the labour force have affected the health of workers.However, f indings from the Whitehall II prospective cohort study
of British civil servants support this argument. Like Australia,
Britain underwent economic reform to improve productivity and
international competitiveness. Whitehall II showed that the threat
of privatisation of the civil service had a greater adverse effect on
the subjective health of employees than the actual change in em-
ployment status that followed.23This finding also supports our
observation among UWC workers that a perceived threat to job
security was associated with greater impact than the knowledge
that the job was not secure.
Because both the OHIP-14 and job characteristics were self-
reported, a second limitation is self-reporting bias. In reviewing
the literature on organisational stress, Zapf and colleagues24de-
scribed this as bias whereby underlying factors such as negative
affect can lead to a tendency to report in one direction potentially
altering the association between perceived stress and subjective
health status. We argue that this is a threat when measuring sub-
jective oral health with the global self-rated health item. Responses
to this global item reflect multiple dimensions of oral health that
are not specified by the researcher, and which are consequently
prone to personality traits of the respondent. Because OHIP items
address specific impacts such as the sense of taste, pain, inter-
ruption to meals, and social irritability, their clearly defined
boundaries minimise the potential impact of subjective interpre-
tation resulting in bias.
In Australia, there is a strong occupational dimension to work-
ing overtime. Overall, managers are most likely to work the long-
est hours, while professionals have the greatest proportion of
workers who routinely work overtime.25We found that although
a greater proportion of UWC workers worked overtime, this fac-
tor was not associated with elevated mean OHIP-14 scores among
these workers. Yet for BC workers, and to a lesser extent for LWC
workers, working overtime was a key risk factor. It is likely that
the long hours worked by managers and professionals are chosen
rather than obligated by financial need or employer demand.
Clearly, substantial variation exists in levels and types of stressors
experienced by different occupational groups in the Australian
labour force. Implicit in this finding is the implication that differ-
ent interventions are required for different groups to optimise the
health of workers.
Two job characteristics were associated with the social impact
of oral conditions for all three occupational groups. One was the
perception that the maintenance of job skills was unlikely and theother was the interference of work demands on home obligations.
For white-collar workers the interference of home obligations on
work demands was also associated with greater impact of oral
conditions on daily living.
Conclusion
Job characteristics in the Australian labour force are associated
with subjective oral health. This is one of an increasing number
of health outcomes that have been linked to conditions in the
workplace. Our study underscores the importance of recognising
that people are kept healthy or become ill in the environments in
which they live and work. Because job characteristics that shape
the work environment are subject to only limited control by the
individual, their influence is a public health issue.
Acknowledgement
The research on which this paper is based was supported by the
Australian Dental Research Foundation.
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