21-3-20151Herhaling titel van presentatie Estimating mortality by socio-economic status using...

11-04-23 1Herhaling titel van presentatie

Estimating mortality by socio-economic status using unlinked data

Estimating social inequalities in HLE: Challenges and opportunities10 February, 2012

Sylvie Gadeyne, Patrick DeboosereInterface Demography, Vrije Universiteit Brussel

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy

2Herhaling titel van presentatie

Objective

• OBJECTIVE

o Investigate the differences between mortality and social inequalities in mortality estimated with linked data and mortality and social inequalities in mortality estimated with unlinked data

o Focus on mortality

• CONTEXT: DATA AVAILABILITY?

o Before the 1990s, relatively few studies on social inequalities in mortality in Belgium

o At the individual level, there were few data to investigate these social inequalities

o Some studies: infant and child mortality, research at the aggregate level

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy


New data sources

o Research on mortality inequalities has been boosted in Belgium by

the availability of new data sources since the nineties

o Surveys

o For example: the Belgian Health Interview Survey

o Mortality follow-up of the census

o Mid 1990s: construction of the ‘National Mortality Databank’ (Interface Demography in cooperation with Statistics Belgium)

o The National Mortality Databank: several phases o Linkage census and mortality data 1991-1996o Linkage census and mortality data 2001-2004

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy


National Mortality Databank

o The National Mortality Databank: first phase

o Linkage for all-cause mortality: March 1991 - March 1996

Socio-economic data < Census 1991 Mortality data 1991-1996 < National Register

follow-up of five years (60 months)

o Linkage for cause specific mortality: March 1991 - December 1995

Socio-economic data < Census 1991 Mortality data 1991-1995 < Death Certificates

follow-up of almost five years (58 months)

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy


National Mortality Databank

o The National Mortality Databank: second phase

o Linkage for all-cause mortality: October 2001 - December 2004Socio-economic data < Census 2001

Mortality data 2001-2004 < National Register

follow-up of more than 3 years (39 months)

o Linkage for cause specific mortality: January 2004 - December 2005Socio-economic data < Census 2001

Mortality data 2004-2005 < Death Register

follow-up of two years (24 months)

Brussels CR: follow-up 2001-2004 (39 months)

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy


A ‘new’ reserach line

o The new data availability has allowed ID for developing a research

line concerning inequalities in mortality, morbidity and health

o Some examples

o Educational differences in life expectancy (1991-1995)

o Applying prevalence of good health: healthy life expectancy by education, region… in cooperation with the WIV

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy



o Research in all cause mortality

o Socio-economic inequalities in mortality risks among men and womeno Using several indicators of socioeconomic status: education, long term

income (housing quality), activity status, professional class and type of income

o Focusing on detailed indicators: sector of employment (ISCO)

o Focus on less obvious age groups: young-aged and old-aged

o Doctoral research of P. Deboosere (2007)

o European projects: comparative research of mortality inequalities

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy

8


o Inequalities in all cause mortality

o Investigating the evolution of educational inequalities between 1991-1996 (TAHIB) and 2001-2004, in cooperation with WIV

P. Deboosere, S. Gadeyne, H. Van Oyen (2009)

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy


A ‘new’ reserach line o Inequalities in cause specific mortality

o Distinguishing the most important cause groups of deatho Doctoral research of S. Gadeyne 2005

o Focusing on specific causes of death o Doctoral research of H. Vandenheede (2011): diabetes (mortality

using the linked data, morbidity using the Health Surveys)o Focus on breast cancer (as one of the exception on the general

pattern of a negative gradient)

o International comparative studies: Belgium & Europe (stroke, lung cancer, suicide, diabetes, …)

o The most important advantage of linked data?

o Linkage based on the register number or unique identification key, deterministic linkage

o Linked information applies to the same person

o Numerator-denominator bias is excluded

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy


Question Mark???

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy

11

‘Linked’ & ‘unlinked’ life expectancy

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy


‘Linked’ & ‘unlinked’ life expectancy

• COMPARING THE LINKED AND THE UNLINKED METHOD

o Life expectancy by educational status

o Point of departure of life tables o Age-specific mortality rates by educational status o Numerator: number of deaths by educational status and ageo Denominator: number of person years lived by educational status and

age

o Brussels Capital Region @ IDo Linked data: mortality follow-up of the census 2001 (2001-2004)o Unlinked data: census 2001 and death certificates (2001-2004)

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy


Comparing methods

• COMPARING THE LINKED AND UNLINKED METHOD

o Linked datao Numerator (number of deaths by educational class) and denominator

(number of person years lived by educational class) both come from the same dataset = mortality follow-up of the census 2001

o Linkage o census 2001 data linked to register data on mortality for 2001-

2004o unique identification keyo information applies to the same person o numerator and denominator in the same dataset

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy


Comparing methods

• COMPARING THE LINKED AND UNLINKED METHOD

o Unlinked methodo Denominator (person years lived by socioeconomic status) < census

2001o Numerator (number of deaths by educational status) < death certificates

numerator and denominator are provided by different data sources

numerator-denominator bias

o Calculation o Mortality rates are calculated using the linked and unlinked datao Rates are transformed into probabilities to establish life tables o Indicators resulting from both datasets are compared

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy


Results

• LIFE EXPECTANCY IN THE LINKED AND THE UNLINKED METHOD

Table 1: Life expectancy at age 20 by educational level and sex, the linked and the unlinked method compared

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy


Results

• USE OF UNLINKED DATA

o Considerable bias

o Mortality rates by educational level are significantly underestimated in the unlinked method (table 1)

o Both among men and women o And for all educational levels o Exception: the group for which information on educational level is

missing: overestimation of mortalityo Resulting in a considerable bias:

o life expectancy by educational level is higher using the unlinked data, except for the missing group

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy


Results

• DIFFERENCES BETWEEN AGE-SPECIFIC MORTALITY RATES IN THE LINKED AND THE UNLINKED METHOD

Figure 1: Absolute difference between the age-specific mortality rates estimated with unlinked data and those estimated with linked data

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy


Results

• DIFFERENCES BETWEEN AGE-SPECIFIC MORTALITY RATES IN THE LINKED AND THE UNLINKED METHOD

Figure 2: Relative difference between the age-specific mortality rates estimated with unlinked data and those estimated with linked data

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy


Results

• DIFFERENCES BETWEEN SOCIAL INEQUALITIES IN THE LINKED AND THE UNLINKED METHOD

Figure 3a: Rate ratio (primary education/tertiary education) by age and sex, the linked (L) and unlinked method (U-L) compared, men

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy


Results

• DIFFERENCES BETWEEN SOCIAL INEQUALITIES IN THE LINKED AND THE UNLINKED METHOD

Figure 3b: Rate ratio (primary education/tertiary education) by age and sex, the linked (L) and unlinked method (U-L) compared, women

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy


Results


o Bias o Not only are the levels of mortality biasedo Social inequalities in mortality are biased too (figure 3a and figure 3b) o For example: mortality ratio primary/tertiary

o Men:o At young age: overestimation of inequalities (factor 2,5)o From 45-49 and 70 years on: underestimationo From 75-79 years on: comparable inequalities

o Women:o Young age: overestimation of inequalitieso Between 40-44 ad 70-74: comparable inequalitieso Old age: overestimation of inequalities

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy


Results


o Explaining the bias

o Quality of the educational information in the death certificates

o Very high percentage of missing information for education

o More than 70% of the deaths in the certificates have a missing value for education

o This group with a missing value for education has a very high mortality in the unlinked method (table 1)

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy


Results


o Figure 4: Percentage of missing values for educational levels by sex and

age: certificates and census data compared

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy


Recommendations

• IMPLICATIONS OF THE RESULTS

o If mortality and social inequalities need to be investigated through unlinked data, educational level should be registered correctly and exhaustively in the certificates

o Alternatively, respondents for whom educational data are missing

o could be distributed in such a way that the educational distribution of the total population in the census 2001 is respected

o educational information could be derived from other socioeconomic characteristics, such as professional class

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy


Recommendations

• IMPLICATIONS OF THE RESULTS

o even if educational level is registered correctly in the certificates, errors can occur when using non-linked datasets to compute mortality rates by educational level o differing registration of education in both data sets, technical problems,

registration errors in mortality

o Best option: linked data o the firmest basis to calculate mortality rates by educational levelo the 2001 census was the last ‘classical’ census in Belgium

o it will be replaced by an administrative censuso in this administrative census the registration of education is not

assured for the total population in Belgiumo this definitely has repercussions for the follow-up of social

inequalities in mortality

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy


Recommendations

• OTHER POSSIBILITIES?

o Crossroads Bank Social Security?

o Explorative analyses of these data

o Sample of the total population

o Mortality by class of income: quite good results

o Integration of information on educational level?

o To be continued!

Vrij

e U

niv

ers

iteit

Bru

ssel

Inte

rface

Dem

ogra

phy

Thank you for your attention

[email protected]

21-3-20151Herhaling titel van presentatie Estimating mortality by socio-economic status using...

Documents

Transcript of 21-3-20151Herhaling titel van presentatie Estimating mortality by socio-economic status using...