Linda Williams Pickle1, Yuchen Su. 1. Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD 20892-8317, USA. PICKLEL@mail.nih.gov
Abstract
BACKGROUND: A number of health risk factors have been associated with the incidence and mortality of common diseases. Although knowing risk factor patterns at a small-area level would be useful for ecologic analyses and prevention program planning, risk factor data are generally published only at the state or regional level in the United States. This study presents maps of within-state patterns of several such factors. METHODS: Responses to Behavioral Risk Factor Surveillance System (BRFSS) questions about smoking, obesity, health insurance, and mammography use were aggregated for 1992-1998 by county. These data were then geographically smoothed by adjusting each county's proportional response based on the responses of its neighboring counties. RESULTS: The maps show risk factor patterns consistent with published state-level maps, but also identify within-state variations masked by aggregation to the larger geographic units. CONCLUSIONS: The risk factor maps presented should permit a better understanding of localized patterns of health risk behaviors and access to health care as well as help to target intervention activities in the U.S. areas that most need them.
BACKGROUND: A number of health risk factors have been associated with the incidence and mortality of common diseases. Although knowing risk factor patterns at a small-area level would be useful for ecologic analyses and prevention program planning, risk factor data are generally published only at the state or regional level in the United States. This study presents maps of within-state patterns of several such factors. METHODS: Responses to Behavioral Risk Factor Surveillance System (BRFSS) questions about smoking, obesity, health insurance, and mammography use were aggregated for 1992-1998 by county. These data were then geographically smoothed by adjusting each county's proportional response based on the responses of its neighboring counties. RESULTS: The maps show risk factor patterns consistent with published state-level maps, but also identify within-state variations masked by aggregation to the larger geographic units. CONCLUSIONS: The risk factor maps presented should permit a better understanding of localized patterns of health risk behaviors and access to health care as well as help to target intervention activities in the U.S. areas that most need them.
Authors: Liora Sahar; Stephanie L Foster; Recinda L Sherman; Kevin A Henry; Daniel W Goldberg; David G Stinchcomb; Joseph E Bauer Journal: Cancer Date: 2019-05-30 Impact factor: 6.860
Authors: Katherine A Stamatakis; Scott T Leatherdale; Christine M Marx; Yan Yan; Graham A Colditz; Ross C Brownson Journal: J Public Health Manag Pract Date: 2012 Sep-Oct
Authors: Stephanie A Navarro Silvera; Elisa V Bandera; Beth A Jones; Alejandra M Kaplan; Kitaw Demisse Journal: Cancer Causes Control Date: 2019-11-12 Impact factor: 2.532