Hood Carlyn M, Gennuso Keith P, Swain Geoffrey R, Catlin Bridget B
Population Health Institute, University of Wisconsin-Madison, Madison, Wisconsin.
Population Health Institute, University of Wisconsin-Madison, Madison, Wisconsin.
Am J Prev Med. 2016 Feb;50(2):129-35. doi: 10.1016/j.amepre.2015.08.024. Epub 2015 Oct 31.
The County Health Rankings (CHR) provides data for nearly every county in the U.S. on four modifiable groups of health factors, including healthy behaviors, clinical care, physical environment, and socioeconomic conditions, and on health outcomes such as length and quality of life. The purpose of this study was to empirically estimate the strength of association between these health factors and health outcomes and to describe the performance of the CHR model factor weightings by state.
Data for the current study were from the 2015 CHR. Thirty-five measures for 45 states were compiled into four health factors composite scores and one health outcomes composite score. The relative contributions of health factors to health outcomes were estimated using hierarchical linear regression modeling in March 2015. County population size; rural/urban status; and gender, race, and age distributions were included as control variables.
Overall, the relative contributions of socioeconomic factors, health behaviors, clinical care, and the physical environment to the health outcomes composite score were 47%, 34%, 16%, and 3%, respectively. Although the CHR model performed better in some states than others, these results provide broad empirical support for the CHR model and weightings.
This paper further provides a framework by which to prioritize health-related investments, and a call to action for healthcare providers and the schools that educate them. Realizing the greatest improvements in population health will require addressing the social and economic determinants of health.
县健康排名(CHR)为美国几乎每个县提供了关于四类可改变的健康因素的数据,包括健康行为、临床护理、物理环境和社会经济状况,以及诸如寿命和生活质量等健康结果的数据。本研究的目的是实证估计这些健康因素与健康结果之间的关联强度,并描述CHR模型因子加权在各州的表现。
本研究的数据来自2015年的CHR。将45个州的35项指标编制成四个健康因素综合得分和一个健康结果综合得分。2015年3月,使用分层线性回归模型估计了健康因素对健康结果的相对贡献。县人口规模、农村/城市状况以及性别、种族和年龄分布作为控制变量。
总体而言,社会经济因素、健康行为、临床护理和物理环境对健康结果综合得分的相对贡献分别为47%、34%、16%和3%。尽管CHR模型在某些州的表现优于其他州,但这些结果为CHR模型及其加权提供了广泛的实证支持。
本文进一步提供了一个确定与健康相关投资优先级的框架,并呼吁医疗服务提供者和培养他们的学校采取行动。要实现人口健康的最大改善,需要解决健康的社会和经济决定因素。