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Abstract:
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Unconscious/implicit biases may play a role in healthcare disparities. We estimate the association between implicit racial bias and county-level healthcare disparities, hypothesizing that counties with more negative attitudes towards Blacks, relative to Whites, have greater healthcare disparities. County-level disparities are estimated using nationally-representative Medicare data. National data on implicit biases were collected online through an Implicit Association Test (IAT), a computer-based measure that relies on differences in response latency to reveal implicit bias. The county-level IAT data suffer from issues of scarcity due to low (county-level) sample sizes, and potential bias stemming from non-representative respondent samples. We applied a combination of nonresponse weighting and model-based shrinkage utilizing socio-demographics (e.g., percent Black, socio-economics) from the American Community Survey, and borrowing information across counties using multi-level modeling. Using this approach, we derived model-based and population-weighted county-level IAT measures (e.g., attitudes towards Blacks versus Whites) and estimated its association with healthcare disparities
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