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Int. J. Environ. Res. Public Health 2017, 14(7), 730; doi:10.3390/ijerph14070730

Community-Engaged Modeling of Geographic and Demographic Patterns of Multiple Public Health Risk Factors

1
Department of Environmental Health, Boston University School of Public Health, Boston, MA 02118, USA
2
Office of Environmental Stewardship, City of New Bedford, New Bedford, MA 02740, USA
3
NorthStar Learning Centers, New Bedford, MA 02740, USA
*
Author to whom correspondence should be addressed.
Received: 18 May 2017 / Revised: 28 June 2017 / Accepted: 3 July 2017 / Published: 6 July 2017
(This article belongs to the Special Issue Spatial Modelling for Public Health Research)
View Full-Text   |   Download PDF [496 KB, uploaded 6 July 2017]   |  

Abstract

Many health risk factors are intervention targets within communities, but information regarding high-risk subpopulations is rarely available at a geographic resolution that is relevant for community-scale interventions. Researchers and community partners in New Bedford, Massachusetts (USA) collaboratively identified high-priority behaviors and health outcomes of interest available in the Behavioral Risk Factor Surveillance System (BRFSS). We developed multivariable regression models from the BRFSS explaining variability in exercise, fruit and vegetable consumption, body mass index, and diabetes prevalence as a function of demographic and behavioral characteristics, and linked these models with population microdata developed using spatial microsimulation to characterize high-risk populations and locations. Individuals with lower income and educational attainment had lower rates of multiple health-promoting behaviors (e.g., fruit and vegetable consumption and exercise) and higher rates of self-reported diabetes. Our models in combination with the simulated population microdata identified census tracts with an elevated percentage of high-risk subpopulations, information community partners can use to prioritize funding and intervention programs. Multi-stressor modeling using data from public databases and microsimulation methods for characterizing high-resolution spatial patterns of population attributes, coupled with strong community partner engagement, can provide significant insight for intervention. Our methodology is transferrable to other communities. View Full-Text
Keywords: GIS; spatial microsimulation; community partnerships; diabetes; exercise GIS; spatial microsimulation; community partnerships; diabetes; exercise
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Basra, K.; Fabian, M.P.; Holberger, R.R.; French, R.; Levy, J.I. Community-Engaged Modeling of Geographic and Demographic Patterns of Multiple Public Health Risk Factors. Int. J. Environ. Res. Public Health 2017, 14, 730.

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