Estimating mortality by socio-economic status using unlinked data

1. Estimating mortality by socio-economic status using unlinked data Estimating social inequalities in HLE: Challenges and opportunities 10 February, 2012 Sylvie Gadeyne, Patrick Deboosere Interface Demography, Vrije Universiteit Brussel Herhaling titel van presentatie

2. Objective • Objective • 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 • Focus on mortality • Context: data availability? • Before the 1990s, relatively few studies on social inequalities in mortality in Belgium • At the individual level, there were few data to investigate these social inequalities • Some studies: infant and child mortality, research at the aggregate level Herhaling titel van presentatie

3. New data sources • Research on mortality inequalities has been boosted in Belgium by the availability of new data sources since the nineties • Surveys • For example: the Belgian Health Interview Survey • Mortality follow-up of the census • Mid 1990s: construction of the ‘National Mortality Databank’ (Interface Demography in cooperation with Statistics Belgium) • The National Mortality Databank: several phases • Linkage census and mortality data 1991-1996 • Linkage census and mortality data 2001-2004 Herhaling titel van presentatie

4. National Mortality Databank • The National Mortality Databank: first phase • 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) • 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) Herhaling titel van presentatie

5. National Mortality Databank • The National Mortality Databank: second phase • Linkage for all-cause mortality: October 2001 - December 2004 Socio-economic data < Census 2001 Mortality data 2001-2004 < National Register follow-up of more than 3 years (39 months) • Linkage for cause specific mortality: January 2004 - December 2005 Socio-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) Herhaling titel van presentatie

6. A ‘new’ reserach line • The new data availability has allowed ID for developing a research line concerning inequalities in mortality, morbidity and health • Some examples • Educational differences in life expectancy (1991-1995) • Applying prevalence of good health: healthy life expectancy by education, region… in cooperation with the WIV Herhaling titel van presentatie

7. A ‘new’ reserach line • Research in all cause mortality • Socio-economic inequalities in mortality risks among men and women • Using several indicators of socioeconomic status: education, long term income (housing quality), activity status, professional class and type of income • Focusing on detailed indicators: sector of employment (ISCO) • Focus on less obvious age groups: young-aged and old-aged • Doctoral research of P. Deboosere (2007) • European projects: comparative research of mortality inequalities Herhaling titel van presentatie

8. A ‘new’ reserach line • Inequalities in all cause mortality • 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)

9. A ‘new’ reserach line • Inequalities in cause specific mortality • Distinguishing the most important cause groups of death • Doctoral research of S. Gadeyne 2005 • Focusing on specific causes of death • Doctoral research of H. Vandenheede (2011): diabetes (mortality using the linked data, morbidity using the Health Surveys) • Focus on breast cancer (as one of the exception on the general pattern of a negative gradient) • International comparative studies: Belgium & Europe (stroke, lung cancer, suicide, diabetes, …) • The most important advantage of linked data? • Linkage based on the register number or unique identification key, deterministic linkage • Linked information applies to the same person • Numerator-denominator bias is excluded Herhaling titel van presentatie

10. Question Mark??? • How will we study mortality and morbidity inequalities in the future? • Health studies: Health Surveys and other surveys • Mortality? • Classic census does not exist anymore • Linkage between other data sources and register data? The Crossroads Bank for Social Security • Unlinked method? • Numerator and denominator of the mortality rates by SES come from different sources • Consequences? Objective: To study the differences resulting from the use of the linked versustheunlinked data in calculating socio-economic inequalities in mortality and life expectancy Herhaling titel van presentatie

11. ‘Linked’ & ‘unlinked’ life expectancy • Healthy life expectancy by socio-economic status • Healthy life expectancy • Several possibilities • Most often: Sullivan Method • Prevalence of good health • Life tables: based on death rates • Linked and unlinked data to calculate death rates and establish life tables • Socio-economic status • Indicator: education • Data on education available for all individuals aged 20-25 and older • Relatively stable indicator of socioeconomic status • Most important: data available in the census and in the certificates

12. ‘Linked’ & ‘unlinked’ life expectancy • Comparing the linked and the unlinked method • Life expectancy by educational status • Point of departure of life tables • Age-specific mortality rates by educational status • Numerator: number of deaths by educational status and age • Denominator: number of person years lived by educational status and age • Brussels Capital Region @ ID • Linked data: mortality follow-up of the census 2001 (2001-2004) • Unlinked data: census 2001 and death certificates (2001-2004) Herhaling titel van presentatie

13. Comparing methods • Comparing the linked and unlinked method • Linked data • 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 • Linkage • census 2001 data linked to register data on mortality for 2001-2004 • unique identification key • information applies to the same person • numerator and denominator in the same dataset Herhaling titel van presentatie

14. Comparing methods • Comparing the linked and unlinked method • Unlinked method • Denominator (person years lived by socioeconomic status) < census 2001 • Numerator (number of deaths by educational status) < death certificates  numerator and denominator are provided by different data sources  numerator-denominator bias • Calculation • Mortality rates are calculated using the linked and unlinked data • Rates are transformed into probabilities to establish life tables • Indicators resulting from both datasets are compared Herhaling titel van presentatie

15. 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 Herhaling titel van presentatie

16. Results • Use of unlinked data • Considerable bias • Mortality rates by educational level are significantly underestimated in the unlinked method (table 1) • Both among men and women • And for all educational levels • Exception: the group for which information on educational level is missing: overestimation of mortality • Resulting in a considerable bias: • life expectancy by educational level is higher using the unlinked data, except for the missing group Herhaling titel van presentatie

17. 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 Herhaling titel van presentatie

18. 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 Herhaling titel van presentatie

19. 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 Herhaling titel van presentatie

20. 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 Herhaling titel van presentatie

21. Results • Use of unlinked data • Bias • Not only are the levels of mortality biased • Social inequalities in mortality are biased too (figure 3a and figure 3b) • For example: mortality ratio primary/tertiary • Men: • At young age: overestimation of inequalities (factor 2,5) • From 45-49 and 70 years on: underestimation • From 75-79 years on: comparable inequalities • Women: • Young age: overestimation of inequalities • Between 40-44 ad 70-74: comparable inequalities • Old age: overestimation of inequalities Herhaling titel van presentatie

22. Results • Use of unlinked data • Explaining the bias • Quality of the educational information in the death certificates • Very high percentage of missing information for education • More than 70% of the deaths in the certificates have a missing value for education • This group with a missing value for education has a very high mortality in the unlinked method (table 1) Herhaling titel van presentatie

23. Results • Use of unlinked data • Figure 4: Percentage of missing values for educational levels by sex and age: certificates and census data compared Herhaling titel van presentatie

24. Recommendations • Implications of the results • If mortality and social inequalities need to be investigated through unlinked data, educational level should be registered correctly and exhaustively in the certificates • Alternatively, respondents for whom educational data are missing • could be distributed in such a way that the educational distribution of the total population in the census 2001 is respected • educational information could be derived from other socioeconomic characteristics, such as professional class Herhaling titel van presentatie

25. Recommendations • Implications of the results • 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 • differing registration of education in both data sets, technical problems, registration errors in mortality • Best option: linked data • the firmest basis to calculate mortality rates by educational level • the 2001 census was the last ‘classical’ census in Belgium • it will be replaced by an administrative census • in this administrative census the registration of education is not assured for the total population in Belgium • this definitely has repercussions for the follow-up of social inequalities in mortality Herhaling titel van presentatie

26. Recommendations • Other possibilities? • Crossroads Bank Social Security? • Explorative analyses of these data • Sample of the total population • Mortality by class of income: quite good results • Integration of information on educational level? • To be continued! Herhaling titel van presentatie

27. Thank you for your attentionsylvie.gadeyne@vub.ac.be