Robust Machine Learning Variable Importance Analyses of Medical Conditions for Health Care Spending
本文提出非参数双重稳健机器学习方法，分析26种常见医疗条件对商业保险参保者年均支出的增量影响，发现多发性硬化症等疾病成本被传统参数方法低估。
OBJECTIVE: To propose nonparametric double robust machine learning in variable importance analyses of medical conditions for health spending. DATA SOURCES: 2011-2012 Truven MarketScan database. STUDY DESIGN: I evaluate how much more, on average, commercially insured enrollees with each of 26 of the most prevalent medical conditions cost per year after controlling for demographics and other medical conditions. This is accomplished within the nonparametric targeted learning framework, which incorporates ensemble machine learning. Previous literature studying the impact of medical conditions on health care spending has almost exclusively focused on parametric risk adjustment; thus, I compare my approach to parametric regression. PRINCIPAL FINDINGS: My results demonstrate that multiple sclerosis, congestive heart failure, severe cancers, major depression and bipolar disorders, and chronic hepatitis are the most costly medical conditions on average per individual. These findings differed from those obtained using parametric regression. CONCLUSIONS: The literature may be underestimating the spending contributions of several medical conditions, which is a potentially critical oversight. If current methods are not capturing the true incremental effect of medical conditions, undesirable incentives related to care may remain. Further work is needed to directly study these issues in the context of federal formulas.