Division of General Medical Sciences, Washington University School of Medicine in St. Louis, St. Louis, Missouri (Drs Nagasako and Lian); Center for Clinical Excellence, BJC HealthCare, St. Louis, Missouri (Dr Nagasako); Missouri Hospital Association, Jefferson City, Missouri (Messrs Waterman and Reidhead); George Warren Brown School of Social Work, Washington University in St. Louis, St. Louis, Missouri (Dr Gehlert); and Alvin J. Siteman Cancer Center, St. Louis, Missouri (Drs Lian and Gehlert).
J Public Health Manag Pract. 2018 Jul/Aug;24(4):340-349. doi: 10.1097/PHH.0000000000000578.
Measures of population health at the subcounty level are needed to identify areas for focused interventions and to support local health improvement activities.
To extend the County Health Rankings population health measurement model to the ZIP code level using widely available hospital and census-derived data sources.
Retrospective administrative data study.
Missouri.
Missouri FY 2012-2014 hospital inpatient, outpatient, and emergency department discharge encounters (N = 36 176 377) and 2015 Nielsen data.
ZIP code-level health factors and health outcomes indices.
Statistically significant measures of association were observed between the ZIP code-level population health indices and published County Health Rankings indices. Variation within counties was observed in both urban and rural areas. Substantial variation of the derived measures was observed at the ZIP code level with 20 (17.4%) Missouri counties having ZIP codes in both the top and bottom quintiles of health factors and health outcomes. Thirty of the 46 (65.2%) counties in the top 2 county quintiles had ZIP codes in the bottom 2 quintiles.
This proof-of-concept analysis suggests that readily available hospital and census-derived data can be used to create measures of population health at the subcounty level. These widely available data sources could be used to identify areas of potential need within counties, engage community stakeholders, and target interventions.
需要在县级以下水平衡量人口健康状况,以确定需要重点干预的地区,并支持当地卫生改善活动。
利用广泛可用的医院和人口普查衍生数据源,将县健康排名人口健康衡量模型扩展到邮政编码级别。
回顾性行政数据研究。
密苏里州。
密苏里州 2012-2014 年 FY 医院住院、门诊和急诊部门出院记录(N=36176377)和 2015 年尼尔森数据。
邮政编码级别的健康因素和健康结果指数。
观察到邮政编码级别的人口健康指数与已发表的县健康排名指数之间存在统计学上显著的关联度量。在城乡地区都观察到了县内的差异。在邮政编码级别上,所得到的指标有很大的差异,有 20 个(17.4%)密苏里州的县,其邮政编码在健康因素和健康结果的前五个五分位数和后五个五分位数中都有分布。在排名前 2 的县的 5 个五分位数中,有 30 个(65.2%)县的邮政编码位于后 2 个五分位数中。
这项概念验证分析表明,现成的医院和人口普查衍生数据可用于创建县级以下的人口健康衡量指标。这些广泛可用的数据源可用于确定县内潜在需求区域,吸引社区利益相关者,并确定干预目标。