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Decomposition Analysis to Identify Intervention Targets for Reducing Disparities

Jackson, John W.a,b,c; VanderWeele, Tyler J.c,d

doi: 10.1097/EDE.0000000000000901
Social Epidemiology

There has been considerable interest in using decomposition methods in epidemiology (mediation analysis) and economics (Oaxaca–Blinder decomposition) to understand how health disparities arise and how they might change upon intervention. It has not been clear when estimates from the Oaxaca–Blinder decomposition can be interpreted causally because its implementation does not explicitly address potential confounding of target variables. While mediation analysis does explicitly adjust for confounders of target variables, it typically does so in a way that effectively entails equalizing confounders across racial groups, which may not reflect the intended intervention. Revisiting prior analyses in the National Longitudinal Survey of Youth on disparities in wages, unemployment, incarceration, and overall health with test scores, taken as a proxy for educational attainment, as a target intervention, we propose and demonstrate a novel decomposition that controls for confounders of test scores (e.g., measures of childhood socioeconomic status [SES]) while leaving their association with race intact. We compare this decomposition with others that use standardization (to equalize childhood SES [the confounders] alone), mediation analysis (to equalize test scores within levels of childhood SES), and one that equalizes both childhood SES and test scores. We also show how these decompositions, including our novel proposals, are equivalent to implementations of the Oaxaca–Blinder decomposition but provide a more formal causal interpretation for these decompositions.

From the aDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD

bDepartment of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD

cDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA

dDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.

Submitted March 17, 2017; accepted July 24, 2018.

Data and Code Availability:

John Jackson was partly funded by the Alonzo Smythe Yerby Fellowship, and Tyler J. VanderWeele was funded by the National Institutes of health grant ES017876.

The authors report no conflicts of interest.

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Correspondence: John W. Jackson, Departments of Epidemiology and Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD. E-mail:

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