TY - JOUR
T1 - Measuring Subcounty Differences in Population Health Using Hospital and Census-Derived Data Sets
T2 - The Missouri ZIP Health Rankings Project
AU - Nagasako, Elna
AU - Waterman, Brian
AU - Reidhead, Mathew
AU - Lian, Min
AU - Gehlert, Sarah
N1 - Funding Information:
This research was supported by the County Health Rankings & Roadmaps as a collaboration of University of Wisconsin Population Health Institute and the Robert Wood Johnson Foundation, grant 607K390. Additional research support was provided by BJC HealthCare, Missouri Hospital Association, and NIH/NCATS Washington University-ICTS grants UL1 TR000448 and KL2 TR000450. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH/NCATS. The authors thank Emily Schenk for her coordination of project advisory group activities and assistance with manuscript preparation, Josh Grotzinger and Mario Schootman for assistance with manuscript preparation and review, respectively, and the members of the project advisory group for their input to the project. This project was accepted for oral presentation in a panel at the American Public Health Association Annual Meeting on October 31, 2016.
Publisher Copyright:
Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - health factors
KW - health outcomes
KW - health rankings
KW - population health
KW - public health surveillance
KW - small-area health estimates
KW - subcounty-level health estimates
UR - http://www.scopus.com/inward/record.url?scp=85048013139&partnerID=8YFLogxK
U2 - 10.1097/PHH.0000000000000578
DO - 10.1097/PHH.0000000000000578
M3 - Article
C2 - 28492449
AN - SCOPUS:85048013139
VL - 24
SP - 340
EP - 349
JO - Journal of Public Health Management and Practice
JF - Journal of Public Health Management and Practice
SN - 1078-4659
IS - 4
ER -