A Machine Learning Approach to Predicting Higher COVID-19 Care Burden in the Primary Care Safety Net: Hispanic Patient Population Size a Key Factor

Evan V Goldstein; Fernando A Wilson

doi:10.1177/23333928221115894

A Machine Learning Approach to Predicting Higher COVID-19 Care Burden in the Primary Care Safety Net: Hispanic Patient Population Size a Key Factor

Health Serv Res Manag Epidemiol. 2022 Aug 2:9:23333928221115894. doi: 10.1177/23333928221115894. eCollection 2022 Jan-Dec.

Authors

Evan V Goldstein¹, Fernando A Wilson^{1

2

3}

Affiliations

¹ Department of Population Health Sciences, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA.
² Matheson Center for Health Care Studies, University of Utah, Salt Lake City, UT, USA.
³ Department of Economics, University of Utah, Salt Lake City, UT, USA.

Abstract

Introduction: The federal government legislated supplemental funding to support community health centers (CHCs) in response to the COVID-19 pandemic. Supplemental funding included standard base payments and adjustments for the number of total and uninsured patients served before the pandemic. However, not all CHCs share similar patient population characteristics and health risks.

Objective: To use machine learning to identify the most important factors for predicting whether CHCs had a high burden of patients diagnosed with COVID-19 during the first year of the pandemic.

Methods: Our analytic sample included data from 1342 CHCs across the 50 states and D.C. in 2020. We trained a random forest (RF) classifier model, incorporating 5-fold cross-validation to validate the RF model while optimizing the model's hyperparameters. Final performance metrics were calculated following the application of the model that had the best fit to the held-out test set.

Results: CHCs with a high burden of COVID-19 had an average of 65.3 patients diagnosed with COVID-19 per 1000 patients in 2020. Our RF model had 80.9% accuracy, 80.1% precision, 25.0% sensitivity, and 98.1% specificity. The percentage of Hispanic patients served in 2020 was the most important feature for predicting whether CHCs had high COVID-19 burden.

Conclusions: Findings from our RF model suggest patient population race and ethnicity characteristics were most important for predicting whether CHCs had a high burden of patients diagnosed with COVID-19 in 2020, though sensitivity was low. Enhanced support for CHCs serving large Hispanic patient populations may have an impact on addressing future COVID-19 waves.

Keywords: COVID-19; community health; community health centers; health promotion; prevention; primary care.