Who Became Financially Vulnerable After COVID-19? A Population-Level Machine Learning Analysis Using MEPS Data
Machine Learning
Summary
The authors studied how much money families in the U.S. had to spend on healthcare during and after the COVID-19 pandemic. They found that spending more than 10% of family income on healthcare was linked to poverty, insurance status, and prescription costs. Certain groups, especially vulnerable ones, faced greater financial struggles in 2021. Their models showed that the main reasons for financial problems stayed about the same before and after the pandemic. This work helps understand and track healthcare money issues across the population and could assist future health policies.
Authors
Alexey Kresin, Zien Cheng, Ammar Ahad, Ebiyomare Kelvin, Manish Sivaratri, Prabhjeet Singh, Omar Aljawfi, Olabisi Ojo, Nawar Shara
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.