发表机构
Hood College; Georgetown University; University of Benin; NIST University; AI CoLab: MedStar–Georgetown Collaborative Center for Artificial Intelligence in Healthcare Research and Education; MedStar Health Research Institute; Albany State University（胡德学院; 乔治敦大学; 贝宁大学; NIST大学; 人工智能合作实验室：梅斯达星 - 乔治敦医疗保健研究与教育人工智能协作中心; 梅斯达星健康研究所; 奥尔巴尼州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究利用MEPS数据，通过调查加权亚组分析、逻辑回归和机器学习模型，考察新冠疫情前后医疗财务脆弱性，发现其与贫困、保险等因素相关，模型在疫情前后预测性能稳定，为相关研究提供了评估及方法支持。
AI 中文摘要
美国医疗保健成本仍是一个关注点，可能受新冠疫情相关干扰影响。本研究利用2019年和2021年医疗支出面板调查（MEPS）数据，考察疫情前后医疗财务脆弱性。将高财务负担定义为自付医疗支出超过家庭收入的10%。进行调查加权亚组分析以获取各人口和社会经济群体具有全国代表性的估计值。描述性分析辅以可解释逻辑回归和机器学习模型。逻辑回归用于估计调整后的优势比，随机森林和梯度提升模型用于评估预测性能。时间泛化评估了基于疫情前数据训练的模型应用于疫情后观察时是否仍具预测性。财务脆弱性与贫困状况、保险覆盖范围和处方药支出密切相关。亚组分析表明不同人群间存在持续差异，2021年弱势群体负担有增加迹象。尽管存在这些差异，但基于疫情前数据训练的模型在疫情后数据上评估时预测性能仅略有下降，表明医疗财务脆弱性的主要预测因素随时间相对稳定。这些发现提供了新冠疫情期间医疗财务脆弱性的全人群评估，并证明了将可解释统计建模与机器学习相结合用于人群健康研究的价值。结果可能支持未来旨在减少医疗财务障碍的人群健康监测、风险分层和医疗政策研究。
英文摘要
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.
Comments14 pages, 3 figures
