Measuring Subcounty Differences in Population Health Using Hospital and Census-Derived Data Sets: The Missouri ZIP Health Rankings Project. Issue 4 (July 2018)

• Record Type: Journal Article
• Authors: Nagasako, Elna; Waterman, Brian; Reidhead, Mathew; Lian, Min; Gehlert, Sarah
• Is Part Of: Journal of public health management and practice. Volume 24:Issue 4(2018)
• Publication Date: 2018-07
• Subjects: health factors -- health outcomes -- health rankings -- population health -- public health surveillance -- small-area health estimates -- subcounty-level health estimates; Public health administration -- United States -- Periodicals; 253.6
• DOI: 10.1097/PHH.0000000000000578
• Languages: English
• ISSNs: 1078-4659

Abstract

Abstract : Context: Measures of population health at the subcounty level are needed to identify areas for focused interventions and to support local health improvement activities. Objective: To extend the County Health Rankings population health measurement model to the ZIP code level using widely available hospital and census-derived data sources. Design: Retrospective administrative data study. Setting: Missouri. Population: Missouri FY 2012-2014 hospital inpatient, outpatient, and emergency department discharge encounters (N = 36 176 377) and 2015 Nielsen data. Main Outcome Measures: ZIP code–level health factors and health outcomes indices. Results: 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. Conclusions: 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. Abstract : Supplemental Digital Content is Available in the Text.