Avoiding overadjustment bias in social epidemiology through appropriate covariate selection: a primer.

Obtaining accurate estimates of the causal effects of socioeconomic position (SEP) on health is important for public health interventions. To do this, researchers must identify and adjust for all potential confounding variables, while avoiding inappropriate adjustment for mediator variables on a causal pathway between the exposure and outcome. Unfortunately, 'overadjustment bias' remains a common and under-recognized problem in social epidemiology. This paper offers an introduction on selecting appropriate variables for adjustment when examining effects of SEP on health, with a focus on overadjustment bias. We discuss the challenges of estimating different causal effects including overadjustment bias, provide guidance on overcoming them, and consider specific issues including the timing of variables across the life-course, mutual adjustment for socioeconomic indicators, and conducting systematic reviews. We recommend three key steps to select the most appropriate variables for adjustment. First, researchers should be clear about their research question and causal effect of interest. Second, using expert knowledge and theory, researchers should draw causal diagrams representing their assumptions about the interrelationships between their variables of interest. Third, based on their causal diagram(s) and causal effect(s) of interest, researchers should select the most appropriate set of variables, which maximizes adjustment for confounding while minimizing adjustment for mediators.