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

Purpose: 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.

Methods: 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.

Results: 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.

Conclusions: 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.