分析疫情前后美国医疗支出脆弱性变化，发现贫困与医保是关键因素。
Who Became Financially Vulnerable After COVID-19? A Population-Level Machine Learning Analysis Using MEPS Data
- 用MEPS数据对比2019与2021年医疗支出负担，定义超收入10%为脆弱。
- 2021年弱势群体负担加重，但预测模型在疫情后仍保持稳定表现。
- 结合统计与机器学习，适合政策制定者和公共卫生研究者参考。
美国医疗成本仍是关注焦点，可能受新冠疫情影响。本研究利用2019年与2021年医疗支出面板调查（MEPS）数据，分析疫情前后医疗财务脆弱性。将自付医疗支出超过家庭收入10%定义为高财务负担。采用加权子群分析获得全国代表性估计，结合可解释的逻辑回归与机器学习模型。逻辑回归用于估算调整后优势比，随机森林与梯度提升模型评估预测性能。通过时间泛化检验，评估基于疫情前数据训练的模型在疫情后数据上的表现。结果显示，财务脆弱性与贫困状态、保险覆盖及处方药支出密切相关。子群分析表明不同人群间持续存在差异，部分弱势群体在2021年负担更重。尽管如此，基于疫情前数据训练的模型在疫情后仅出现小幅预测性能下降，表明主要预测因子在时间上相对稳定。研究提供了疫情期间医疗财务脆弱性的宏观评估，并展示了将可解释统计建模与机器学习结合在人群健康研究中的价值，结果可支持未来健康监测、风险分层及减少医疗经济障碍的政策研究。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。