Who Became Financially Vulnerable After COVID-19? A Population-Level Machine Learning Analysis Using MEPS Data

2026-07-16

AI Summary

This study examines the populations experiencing heightened financial vulnerability in U.S. healthcare following the COVID-19 pandemic and identifies the underlying drivers of this trend. Leveraging Medical Expenditure Panel Survey (MEPS) data from 2019 and 2021, the authors employ survey-weighted subgroup analyses, interpretable logistic regression, and machine learning models—including random forests and gradient boosting—to assess shifts in the prevalence of high healthcare burden (defined as out-of-pocket expenditures exceeding 10% of household income) and the temporal stability of its predictors. The findings reveal that poverty status, inadequate insurance coverage, and prescription drug expenditures are key risk factors. Notably, despite increased financial strain among vulnerable groups during the pandemic, models trained on pre-pandemic data demonstrate strong generalizability to the post-pandemic period, underscoring the robustness of core predictors across time.

Abstract

The cost of healthcare remains a concern in the United States and may have been influenced by disruptions associated with the COVID-19 pandemic. This study examines healthcare financial vulnerability before and after the pandemic using Medical Expenditure Panel Survey (MEPS) data from 2019 and 2021. High financial burden was defined as out-of-pocket healthcare expenditures exceeding 10% of family income. Survey-weighted subgroup analyses were performed to obtain nationally representative estimates across demographic and socioeconomic groups. Descriptive analyses were complemented by interpretable logistic regression and machine learning models. Logistic regression was used to estimate adjusted odds ratios, while random forest and gradient boosting models were used to evaluate predictive performance. Temporal generalization assessed whether models trained on pre-pandemic data remained predictive when applied to post-pandemic observations. Financial vulnerability was strongly associated with poverty status, insurance coverage, and prescription drug spending. Subgroup analyses indicated persistent disparities across population groups, with some evidence of increased burden among vulnerable populations in 2021. Despite these differences, models trained on pre-pandemic data exhibited only modest reductions in predictive performance when evaluated on post-pandemic data, suggesting that the principal predictors of healthcare financial vulnerability remained relatively stable over time. These findings provide a population-level assessment of healthcare financial vulnerability during the COVID-19 period and demonstrate the value of combining interpretable statistical modeling with machine learning for population health research. The results may support future population health surveillance, risk stratification, and healthcare policy research aimed at reducing financial barriers to care.

Problem

• financial vulnerability
• healthcare affordability
• COVID-19 pandemic
• out-of-pocket expenditure
• health disparities

Innovation

• interpretable machine learning
• temporal generalization
• healthcare financial vulnerability
• population-level analysis
• survey-weighted modeling