Statistical Implications of Endogeneity Induced by Residential Segregation in Small-Area Modeling of Health Inequities

Rachel C. Nethery, Jarvis T. Chen, Nancy Krieger, Pamela D. Waterman, Emily Peterson, Lance A. Waller and Brent A. Coull

The American Statistician, 2022, vol. 76, issue 2, 142-151

Abstract: Health inequities are assessed by health departments to identify social groups disproportionately burdened by disease and by academic researchers to understand how social, economic, and environmental inequities manifest as health inequities. To characterize inequities, group-specific small-area health data are often modeled using log-linear generalized linear models (GLM) or generalized linear mixed models (GLMM) with a random intercept. These approaches estimate the same marginal rate ratio comparing disease rates across groups under standard assumptions. Here we explore how residential segregation combined with social group differences in disease risk can lead to contradictory findings from the GLM and GLMM. We show that this occurs because small-area disease rate data collected under these conditions induce endogeneity in the GLMM due to correlation between the model’s offset and random effect. This results in GLMM estimates that represent conditional rather than marginal associations. We refer to endogeneity arising from the offset, which to our knowledge has not been noted previously, as “offset endogeneity.” We illustrate this phenomenon in simulated data and real premature mortality data, and we propose alternative modeling approaches to address it. We also introduce to a statistical audience the social epidemiologic terminology for framing health inequities, which enables responsible interpretation of results.

Date: 2022
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DOI: 10.1080/00031305.2021.2003245

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