Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
JAMA Netw Open. 2019 Jan 4;2(1):e186816. doi: 10.1001/jamanetworkopen.2018.6816.
Health departments can be grouped together based on sociodemographic characteristics of the population served. Comparisons within these groups can then help with monitoring and improving the health of their populations.
To compare county-level percentile rankings on outcomes of smoking, motor vehicle crash deaths, and obesity within sociodemographic peer clusters vs nationwide rankings.
DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional, population-based study of demographic and health data from the 2014 Behavioral Risk Factor Surveillance System and the 2016 Robert Wood Johnson Foundation County Health Rankings data set was conducted at 3139 of 3143 US counties and county-equivalents. Four locations were excluded due to incomplete data. Data analysis was conducted between January and August 2017.
Random forest algorithms were used to identify sociodemographic characteristics most associated with the outcomes of interest. These characteristics were race and ethnicity, educational attainment, age, marital status, employment status, sex, and health insurance status. k-means clustering was used to cluster counties based on these sociodemographic characteristics and the percentage of the county classified as rural.
County-level smoking prevalence, motor vehicle crash death rate, and obesity prevalence. County percentile rankings on the outcomes of interest were compared in the national context and the within-cluster context.
A total of 318 856 967 individuals (mean [SD] individuals per county, 101 579.2 [326 315]; 161 911 910 women [50.8%]) were represented by the 3139 counties used in this analysis. Eight distinct sociodemographic clusters throughout the United States were found. Cluster-specific percentile rankings for both smoking prevalence and motor vehicle crash death rates improved more than 70 percentile points for several counties in the rural, American Indian cluster compared with the nationwide percentiles. Conversely, the young, urban, middle to high socioeconomic status cluster included counties with cluster-specific percentile rankings that declined by 60 percentile points or more compared with the nationwide rankings for all 3 outcomes of interest.
Comparing county health outcomes on a nationwide or statewide basis fails to adequately account for sociodemographic context. Clustering counties by sociodemographic factors related to the outcome of interest allows a better understanding of other factors that may be shaping the prevalence of health outcomes. These groupings may also aid learning exchange.
可以根据服务人群的社会人口特征将卫生部门分组。然后，对这些组内的比较可以帮助监测和改善其人群的健康状况。
比较按社会人口统计学同伴群内吸烟、机动车碰撞死亡和肥胖结果的县一级百分位排名与全国排名。
设计、设置和参与者：这项横断面、基于人群的研究使用了 2014 年行为风险因素监测系统和 2016 年罗伯特伍德约翰逊基金会县健康排名数据集中的人口统计和健康数据，对美国 3143 个县和县级等价物中的 3139 个进行了研究。由于数据不完整，排除了 4 个地点。数据分析于 2017 年 1 月至 8 月进行。
随机森林算法用于识别与研究结果最相关的社会人口统计学特征。这些特征包括种族和民族、教育程度、年龄、婚姻状况、就业状况、性别和健康保险状况。使用 K-均值聚类根据这些社会人口统计学特征以及被归类为农村的县的百分比对县进行聚类。
县一级的吸烟流行率、机动车碰撞死亡率和肥胖流行率。在全国范围内和集群内比较了对感兴趣的结果的县百分位排名。
共有 318856967 人（每个县平均个人数[标准差]，101579.2[326315]；161911910 名女性[50.8%]）由这 3139 个县的分析组成。在美国发现了八个不同的社会人口统计学集群。与全国百分位相比，农村、美洲印第安人集群中几个县的吸烟率和机动车碰撞死亡率的集群特定百分位排名均提高了 70 个以上。相反，年轻、城市、中高社会经济地位集群包括县的集群特定百分位排名下降了 60 个或更多，与所有 3 个感兴趣的结果的全国排名相比。
仅基于全国或全州的基础比较县的健康结果，无法充分说明社会人口统计学背景。按与研究结果相关的社会人口统计学因素对县进行聚类，可以更好地了解可能影响健康结果流行率的其他因素。这些分组还可以帮助交流学习。
