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arXiv cs.LG
arXiv cs.LG
7/20/2026
Who Became Financially Vulnerable After COVID-19? A Population-Level Machine Learning Analysis Using MEPS Data

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

Short summary

This study uses MEPS data from 2019 and 2021 to analyze healthcare financial vulnerability before and after COVID-19, combining interpretable logistic regression with random forest and gradient boosting models. Financial vulnerability was strongly linked to poverty status, insurance coverage, and prescription drug spending, with persistent disparities across population groups. Models trained on pre-pandemic data showed only modest performance drops on post-pandemic data, suggesting key predictors of healthcare financial burden remained stable.

  • •Healthcare financial vulnerability analyzed using MEPS 2019/2021 with interpretable ML models
  • •Poverty, insurance coverage, and prescription spending are strongest predictors of financial burden
  • •Pre-pandemic models generalize well to post-pandemic data, indicating stable predictor structure

Generated with AI, which can make mistakes.

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Read full article at arXiv cs.LG

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