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Inverse probability weighting for selection bias in a Delaware community health center electronic medical record study of community deprivation and hepatitis C prevalence
Journal article   Open access   Peer reviewed

Inverse probability weighting for selection bias in a Delaware community health center electronic medical record study of community deprivation and hepatitis C prevalence

Neal D. Goldstein, Deborah Kahal, Karla Testa and Igor Burstyn
Annals of epidemiology, v 60, pp 1-7
Aug 2021
DOI: https://doi.org/10.1016/j.annepidem.2021.04.011
PMID: 33933628
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1016/j.annepidem.2021.04.011View
Accepted (AM)Maybe Open Access (Publisher Bronze),  Open

Abstract

Catchment Area Cohort Studies Electronic Health Records Health Hepatitis C, Chronic Residence Characteristics Selection Bias
To demonstrate how selection into a healthcare facility can induce bias in an electronic medical record-based study of community deprivation and chronic hepatitis C virus infection, in order to more accurately identify local risk factors and prevalence. We created a catchment model that attempted to define the probability of selection into a retrospective cohort. Then using the inverse of this probability, we compared naïve unweighted and weighted models to demonstrate the impact of selection bias. ZIP code-level ecological plots of the cohort demonstrated that there was a pattern of the community deprivation, hepatitis C outcome, and distance to the health center (an intuitive proxy for being within catchments). The naïve multilevel analysis found that living in an area with greater deprivation resulted in 1.25 times greater odds of HCV (95% CI: 1.06, 1.48), whereas the weighted analysis found less certainty of this effect due to a selection bias. We observed that selection into the catchment area of the studied healthcare facility may bias the association of community deprivation and hepatitis C. This may be mitigated through inverse probability weighting.

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11 citations in Web of Science
9 citations in Scopus

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Web of Science research areas
Public, Environmental & Occupational Health
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