Assessing racial inequality in COVID-19 testing with Bayesian threshold tests
Applications
2020-11-03 v1
Abstract
There are racial disparities in the COVID-19 test positivity rate, suggesting that minorities may be under-tested. Here, drawing on the literature on statistically assessing racial disparities in policing, we 1) illuminate a statistical flaw, known as infra-marginality, in using the positivity rate as a metric for assessing racial disparities in under-testing; 2) develop a new type of Bayesian threshold test to measure disparities in COVID-19 testing and 3) apply the test to measure racial disparities in testing thresholds in a real-world COVID-19 dataset.
Cite
@article{arxiv.2011.01179,
title = {Assessing racial inequality in COVID-19 testing with Bayesian threshold tests},
author = {Emma Pierson},
journal= {arXiv preprint arXiv:2011.01179},
year = {2020}
}
Comments
Spotlight presentation, Machine Learning for Health (ML4H) at NeurIPS 2020 - Extended Abstract