Time-varying consideration of health behaviours explains over 90% of the inequality in mortality associated with socioeconomic status

Background: Applying survival analysis techniques to epidemiological inference within research into ageing offers opportunities to estimate the association between exposure and outcome in longitudinal data. This study used Cox regression to investigate how socioeconomic inequality in mortality can be explained by exposure to various factors including smoking, diet, alcohol and physical activity. This study seeks to complement and extend previous work which found that the contribution of the socioeconomic gradient to inequalities in health was underestimated by baseline analysis. Methods: Data was obtained from Whitehall II, a British longitudinal cohort study, which investigated social determinants of health. Analysis is based on 11 waves of data collected over 32 years on 10,308 civil servants aged between 35 and 90. Socioeconomic position was defined by baseline employment grade (1-3). During the follow-up 2,427 participants died. Extensive experimental analysis was conducted using a vast number of health behaviours. Cox regression produced an age-and-sex-adjusted hazard ratio for the socioeconomic inequality in mortality. Health behaviours (smoking, physical activity, alcohol consumption, and diet) were then added as covariates to determine the extent to which they statistically explain this inequality, and how this differed from the last similar analysis from 2009. This was done at baseline and longitudinally. The health behaviours were then combined linearly, nonlinearly and new health behaviours were added. Results: Adding the above health behaviours as covariates statistically explained the socioeconomic gradient in mortality at baseline from 42% to 2009, to 51% to 2021. Longitudinal consideration increased the explanatory power, when all health behaviours were added as time-varying covariates, from 51% to 87%. Adding more variables in the form of a more comprehensive diet score statistically explained the gradient further, to 91%. The nonlinear model of smoking and exercise most accurately predicted mortality and had a 13% higher explanatory power when explaining the gradient compared to the linear model in longitudinal data. Conclusion: In the Whitehall II study, socioeconomic position and mortality showed an association. There is a gain in explanatory power of the set of health behaviours at baseline when follow-up is extended by 12 years, from 42% to 51%. When changes in behaviour over the 32 years of follow-up were also accounted for, this association was now significantly explained by over 90%, compared with 51% when considered at baseline. We suggest that reverse causation is partly responsible for the almost complete explanation of the social gradient in mortality by health behaviours. These results would therefore lead us to question why health behaviours are socially patterned in the way that has been observed, which would be significant for targeting health behaviours in lower socioeconomic statuses.