Distributed Counterfactual Modeling Approach for Investigating Hospital-Associated Racial Disparities in COVID-19 Mortality

Several studies have found that black patients are more likely than white patients to test positive for or be hospitalized with COVID-19, but many of these same studies have found no difference in in-hospital mortality. These studies may have underestimated racial differences due to reliance on data from a single hospital system, as adequate control of patient-level characteristics requires aggregation of highly granular data from several institutions. Further, one factor thought to contribute to disparities in health outcomes by race is site of care. Several differences between black and white patient populations, such as access to care and referral patterns among clinicians, can lead to patients of different races largely attending different hospitals. We sought to develop a method that could study the potential association between attending hospital and racial disparity in mortality for COVID-19 patients without requiring patient-level data sharing among collaborating institutions. We propose a novel application of a distributed algorithm for generalized linear mixed modeling (GLMM) to perform counterfactual modeling and investigate the role of hospital in differences in COVID-19 mortality by race. Our counterfactual modeling approach uses simulation to randomly assign black patients to hospitals in the same distribution as those attended by white patients, quantifying the difference between observed mortality rates and simulated mortality risk following random hospital assignment. To illustrate our method, we perform a proof-of-concept analysis using data from four hospitals within the OneFlorida Clinical Research Consortium. Our approach can be used by investigators from several institutions to study the impact of admitting hospital on COVID-19 mortality, a critical step in addressing systemic racism in modern healthcare.