■心血管疾病(CVD)会给患者带来巨大的经济负担,他们的家庭,以及中国的医疗体系。治疗CVD患者的医疗保健费用差异很大,但对与成本变化相关的因素知之甚少。本研究旨在对中国心血管疾病患者医疗费用的关键决定因素进行识别和排序,并评估其对医疗费用的影响。
■数据来自S市14家大型三级甲等综合医院的CVD患者调查,中国,在2018年至2020年之间。调查包括人口特征的信息,健康状况和合并症,医疗服务利用,和医疗费用。我们使用重新集中的影响函数回归来检验医疗保健成本集中度,分解和估计相关因素对成本分配的影响。我们还应用了分位数回归森林——一种机器学习方法——来识别预测第10位(低)的关键因素,第50名(中位数),和第90(高)分位数的医疗保健费用与CVD治疗相关。
■我们的样本包括28,213名CVD患者。第十,心血管疾病患者医疗费用的第50和第90分位数为6103元人民币,18,105CNY,和98,637CNY,分别。医疗保健费用高的患者更有可能年龄较大,男性,住院时间更长,更多的合并症,更复杂的医疗程序,紧急入院。更高的医疗保健费用也与特定的CVD类型有关,如心肌病,心力衰竭,和中风。
机器学习方法是确定中国心血管疾病患者医疗费用决定因素的有用工具。研究结果可能有助于改善政策制定,以减轻心血管疾病的财政负担,特别是在医疗保健费用高的患者中。
Cardiovascular disease (CVD) causes substantial financial burden to patients with the condition, their households, and the healthcare system in China. Health care costs for treating patients with CVD vary significantly, but little is known about the factors associated with the cost variation. This
study aims to identify and rank key determinants of health care costs in patients with CVD in China and to assess their effects on health care costs.
Data were from a survey of patients with CVD from 14 large tertiary grade-A general hospitals in S City, China, between 2018 and 2020. The survey included information on demographic characteristics, health conditions and comorbidities, medical service utilization, and health care costs. We used re-centered influence function regression to examine health care cost concentration, decomposing and estimating the effects of relevant factors on the distribution of costs. We also applied quantile regression forests-a machine learning approach-to identify the key factors for predicting the 10th (low), 50th (median), and 90th (high) quantiles of health care costs associated with CVD treatment.
Our sample included 28,213 patients with CVD. The 10th, 50th and 90th quantiles of health care cost for patients with CVD were 6,103 CNY, 18,105 CNY, and 98,637 CNY, respectively. Patients with high health care costs were more likely to be older, male, and have a longer length of hospital stay, more comorbidities, more complex medical procedures, and emergency admissions. Higher health care costs were also associated with specific CVD types such as cardiomyopathy, heart failure, and stroke.
Machine learning methods are useful tools to identify determinants of health care costs for patients with CVD in China. Findings may help improve policymaking to alleviate the financial burden of CVD, particularly among patients with high health care costs.