Need for More Individual-level Meta-Analyses in Social Epidemiology: Example of Job Strain and Coronary Heart Disease

In genetics, major progress was made after pooling of data sets to mega-studies became the norm in the field. In the present commentary, the authors ask whether such an approach would also be worthy of broader application in the field of social epidemiology. Research on job strain and coronary heart disease provides an illustrative example. Over 3 decades, debate has continued as to the relative importance of high psychological demands versus low control—that is, whether one component of job strain is more toxic than the other—and differences by age and sex. Recently, these controversies were largely resolved in an individual-participant meta-analysis of 200,000 participants from 13 cohorts: The combination of both high demands and low control was a greater risk factor than either of the components alone, there were no differences in the associations of job strain with CHD between men and women, between the young and old, or at different levels of socioeconomic position, and the impact was more modest when unpublished data were included but was still robust to all adjustments. The fact that longstanding debates in the job strain literature were resolved by applying an individual-participant data meta-analysis approach suggests that lessons learned in genetics might also apply to social epidemiology.