EURO-GBD-SE WP 4 Development of methods to assess potential for reduction of health inequalities

1. EURO-GBD-SE WP 4 Development of methods to assess potential for reduction of health inequalities Work package leader: EMC Rasmus Hoffmann

2. Objectives • To adapt methods as developed elsewhere (including methods developed in the Global Burden of Disease study) to the needs of this project. • To develop rules for the specification of "counterfactual" scenarios for the distribution of socioeconomic determinants and risk factors. • To implement these methods and scenarios in a plan for analysis of data on socioeconomic, morbidity, mortality, diseases and risk factors in Europe.

3. Time schedule July 2009 – June 2010

4. Tasks/Deliverables • Conceptualize our understanding of causality in a network of causal factors including SES (e.g. education), proximate risk factors (e.g. smoking), health and mortality. • Develop metrics and methods (1) to quantify the contribution of these factors to health inequalities, taking into account their interrelation, and (2) to estimate to what extent modifying SES differences in health determinants will lead to reducing health inequalities. • D1: Research protocol for “counterfactual” analysis (M-12)

5. Causality in the relationship between SES and health (1) In our project, we aim to estimating the changes that would be achieved in health if exposure to risk factors were altered. This necessarily involves an assumption of causality from these risk factors to health (Steenland & Armstrong, 2006). Observed health differences between (social) groups do not necessarily mean that the parameter used to differentiate between the groups is causal. The parameter may be a risk indicator and not a risk factor (Olsen, 2003). Clear indicators for causality are difficult to find in the literature (e.g. Hill, 1965; Hoover, 2003). As an example, one criteria that all authors agree on is that causality includes a chronological order, i.e. the cause must be prior to the effect.

6. Causality in the relationship between SES and health (2) e.g. through less knowledge, motivation and resources to live healthy ? risk factors SES health How important is reverse causality ? SES health through 1. health knowledge 2. coping strategies 3. self-efficacy (Davey Smith 1998; Hummer et al. 1998; Preston 1992) through 1. health costs 2. ability to work (J.P. Smith 2005) health Education income

7. Some variables • For SES: mainly education, possibly income and occupational status. GBD also uses social factors (“systemic risk factors”) • Proximate risk factors: alcohol, smoking, high blood pressure, high BMI, physical inactivity (all present in GBD) • Outcome: overall mortality, cause specific mortality, self-rated health

8. Starting point for methodology: Population Attributable Fraction The PAF belongs to the Comparative Risk Assessment (CRA) methods and represents the fraction of cases or deaths from a specific disease that would not have occurred in the absence of exposure to a specific risk factor. PIF is a similar concept, sometimes even used synonymously. It is more flexible for the creation of counterfactuals (flexible proportions and RR’s). PAF equals PIF where the prevalence becomes zero. n = number of exposure categories Pi = proportion of population currently in the ith exposure category P′i = proportion of population in the ith exposure category in the counterfactual (alternative) scenario RRi= relative risk of disease-specific mortality for the ith exposure category

9. How to deal with multiple causes and multiple outcomes? (1) As a result of multicausality, the PAFs for interventions related to multiple risk factors overlap and cannot be combined by simple addition (Gakidou et al. 2007, JAMA; Ezzati et al. 2003, Lancet) PAFi= the proportion of the disease preventable by reducing exposure to the ith risk factor Product of all (1-PAFi)’s = is the fraction of disease not preventable through interventions on any of the n risk factors. The preventable deaths can then be summed across diseases to obtain the total (all-cause) mortality that would be prevented through interventions on all risks.

10. How to deal with multiple causes and multiple outcomes? (2) • This approach is based on two strong assumptions (Gakidou et al 2007, JAMA): • exposures to risks are uncorrelated • The hazardous effects of one risk are not mediated through other risks • Workaround for 1 (easy): Conducting all analysis stratified by control variables • (see also Steenland/Armstrong 2006, Epidemiology) • Workaround for 2 (problematic): Using existing knowledge, on how much e.g. the effect of education is mediated by smoking, in order to adjust for it in a sensitivity analysis.

11. Causal model physical activity BMI IHD education lung cancer smoking CVD alcohol

12. Steps for quantifying hazardous effects (GBD Operations Manual 2009) • Choosing a list of health problems and causes of death causally affected by each of the chosen risk factors. Questions: - What is a satisfactory level of evidence for causality? - Does a risk factor affect health, mortality, or both? • Finding information on prevalence and rate ratios for these risk factors, either from GBD, meta-analysis, or elsewhere. For example our prevalences come from the Eurothine data and our rate ratios mainly come from Danaei et al. 2009 in PLOSmedicine. Question: Do we accept estimates that do not control for potentially confounding variables? Or estimates that do control for mediating variables. • Developing or choosing an alternative distribution (scenario)(WP 6)

13. Calculations in the working paper Table 3: Calculate the impact of a change in the educational distribution. We show 4 scenarios and the age-specific results. Table 4: Calculate the impact of changing the health behavior (=risk factors). We show this differentiated by risk factor and by cause of death. Table 5: Define the impact of a behavioral change on SES mortality differences (by risk factor).

14. Problems and tasks for the next 3 months (until June 2010): • Correct and finalize the working paper • Further elaboration of the causal framework • Further development of methods for our special needs: There are more applications of PAF for settings with direct and indirect effects (causal pathways) that we will explore. • Close cooperation with WP 5 (database): - to define our data needs and the choice of variables - to find solutions for unperfect data situations. 5. In cooperation with WP 6: Defining the scenarios • How to compute standard errors and confidence intervals for PAF? This is not straightforward but there are ideas in the literature (Steenland/Armstrong, 2006). • How to deal with continuous variables? • How to connect to other work packages in order to calculate life-table based outcomes? • Do we need to change the calculation tool/software? • Write the research protocol for “counterfactual” analysis (D 1) due in June 2010

15. Acknowledgements Co-funded by the European Community Thank you !