Institute for Health Metrics and Evaluation, Hans Rosling Center, University of Washington, Seattle, Washington, USA.
Bluesquare SA, Brussels, Belgium.
Health Serv Res. 2022 Jun;57(3):557-567. doi: 10.1111/1475-6773.13676. Epub 2021 May 24.
To estimate health care systems' value in treating major illnesses for each US state and identify system characteristics associated with value.
Annual condition-specific death and incidence estimates for each US state from the Global Burden Disease 2019 Study and annual health care spending per person for each state from the National Health Expenditure Accounts.
Using non-linear meta-stochastic frontier analysis, mortality incidence ratios for 136 major treatable illnesses were regressed separately on per capita health care spending and key covariates such as age, obesity, smoking, and educational attainment. State- and year-specific inefficiency estimates were extracted for each health condition and combined to create a single estimate of health care delivery system value for each US state for each year, 1991-2014. The association between changes in health care value and changes in 23 key health care system characteristics and state policies was measured.
DATA COLLECTION/EXTRACTION METHODS: Not applicable.
US state with relatively high spending per person or relatively poor health-outcomes were shown to have low health care delivery system value. New Jersey, Maryland, Florida, Arizona, and New York attained the highest value scores in 2014 (81 [95% uncertainty interval 72-88], 80 [72-87], 80 [71-86], 77 [69-84], and 77 [66-85], respectively), after controlling for health care spending, age, obesity, smoking, physical activity, race, and educational attainment. Greater market concentration of hospitals and of insurers were associated with worse health care value (p-value ranging from <0.01 to 0.02). Higher hospital geographic density and use were also associated with worse health care value (p-value ranging from 0.03 to 0.05). Enrollment in Medicare Advantage HMOs was associated with better value, as was more generous Medicaid income eligibility (p-value 0.04 and 0.01).
Substantial variation in the value of health care exists across states. Key health system characteristics such as market concentration and provider density were associated with value.
估计美国各州医疗系统治疗重大疾病的价值,并确定与价值相关的系统特征。
全球疾病负担 2019 研究中每个美国州的特定疾病年度死亡率和发病率估计,以及国家卫生支出账户中每个州的人均卫生保健支出。
使用非线性元随机前沿分析,分别对 136 种可治疗疾病的死亡率和发病率比值进行回归分析,回归变量包括人均卫生保健支出以及年龄、肥胖、吸烟和教育程度等关键协变量。为每种卫生条件提取州和年份特定的效率估计值,并将其合并为每个美国州每年的医疗保健提供系统价值的单一估计值,时间范围为 1991 年至 2014 年。测量了医疗保健价值变化与 23 项关键医疗保健系统特征和州政策变化之间的关系。
数据收集/提取方法:不适用。
人均支出较高或健康结果较差的美国州,其医疗保健提供系统的价值较低。新泽西州、马里兰州、佛罗里达州、亚利桑那州和纽约州在 2014 年获得了最高的价值评分(分别为 81[95%置信区间 72-88]、80[72-87]、80[71-86]、77[69-84]和 77[66-85]),在控制了医疗保健支出、年龄、肥胖、吸烟、身体活动、种族和教育程度后。医院和保险公司的市场集中度较高与较差的医疗保健价值相关(p 值范围从<0.01 到 0.02)。较高的医院地理密度和使用率也与较差的医疗保健价值相关(p 值范围从 0.03 到 0.05)。参加医疗保险优势 HMO 与更好的价值相关,更慷慨的医疗补助收入资格也是如此(p 值分别为 0.04 和 0.01)。
各州之间的医疗保健价值存在很大差异。关键的卫生系统特征,如市场集中和提供者密度,与价值相关。