Matthew C Stiefel1, Tasha Straszewski1, Jennifer C Taylor2, Christina Huang3, Jessica An4, Folasade J Wilson-Anumudu5, Allen Cheadle6. 1. Center for Population Health, Kaiser Permanente Care Management Institute, Oakland, CA. 2. Center for Wireless and Population Health Systems, University of California, San Diego, CA. 3. Medicare Strategy, Kaiser Permanente Northern California, Oakland, CA. 4. Continuum of Care, Kaiser Permanente Northern California, Oakland, CA. 5. Omada Health, Incorporated, San Francisco, CA. 6. Center for Community Health and Evaluation, Kaiser Permanente Washington, Seattle, WA.
Abstract
INTRODUCTION: As a means of conceptualizing population health, the County Health Rankings & Roadmaps program developed a methodology to rank counties within each state on Health Outcomes and Health Factors. We built on this framework by introducing an additional application that utilized national percentile scores and population size weighting to compare counties on a national, rather than a state, level. METHODS: We created national percentile scores for 3078 US counties and used population size weighting in our calculations so that values for counties with larger populations would be weighted more heavily than values for counties with smaller populations. RESULTS: We demonstrated how this application can be used to 1) compare counties nationally, 2) examine clustering and variability among counties, and 3) compare the health of states and regions. To underscore its utility, we included an example application by Kaiser Permanente. As a form of method validation, the results of this application are in line with other ranking systems (eg, US News and World Report and United Health Foundation; ρ = 0.39 to 0.91, p < 0.001). DISCUSSION: This application can be used by communities and organizations that may be interested in comparing the health of counties, service areas, and regions in which they operate. We included additional considerations and highlighted some limitations for those interested in utilizing this application. CONCLUSION: By comparing counties nationally and utilizing population size weighting, community partners can focus on areas that may be of greatest need in moving toward a national Culture of Health.
INTRODUCTION: As a means of conceptualizing population health, the County Health Rankings & Roadmaps program developed a methodology to rank counties within each state on Health Outcomes and Health Factors. We built on this framework by introducing an additional application that utilized national percentile scores and population size weighting to compare counties on a national, rather than a state, level. METHODS: We created national percentile scores for 3078 US counties and used population size weighting in our calculations so that values for counties with larger populations would be weighted more heavily than values for counties with smaller populations. RESULTS: We demonstrated how this application can be used to 1) compare counties nationally, 2) examine clustering and variability among counties, and 3) compare the health of states and regions. To underscore its utility, we included an example application by Kaiser Permanente. As a form of method validation, the results of this application are in line with other ranking systems (eg, US News and World Report and United Health Foundation; ρ = 0.39 to 0.91, p < 0.001). DISCUSSION: This application can be used by communities and organizations that may be interested in comparing the health of counties, service areas, and regions in which they operate. We included additional considerations and highlighted some limitations for those interested in utilizing this application. CONCLUSION: By comparing counties nationally and utilizing population size weighting, community partners can focus on areas that may be of greatest need in moving toward a national Culture of Health.
Authors: Lynne C Messer; Barbara A Laraia; Jay S Kaufman; Janet Eyster; Claudia Holzman; Jennifer Culhane; Irma Elo; Jessica G Burke; Patricia O'Campo Journal: J Urban Health Date: 2006-11 Impact factor: 3.671