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

Komal Basra,Robert French,Jonathan Levy,Raymond Holberger,M Fabian

doi:10.3390/ijerph14070730

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.

Highlights

There is a need to characterize patterns of risk factors within communities at a resolution that is meaningful to community-level public health efforts
We focused on New Bedford, Massachusetts (MA), USA, a low-income city with multiple environmental and public health challenges
We modeled the three primary behaviors/outcomes of interest by building multivariable logistic regression models from questions in the Behavioral Risk Factor Surveillance System (BRFSS) for exercise, fruit and vegetable consumption, and diabetes prevalence

Summary

Introduction

There is a need to characterize patterns of risk factors within communities at a resolution that is meaningful to community-level public health efforts. Public health and population health studies have been undertaken to address this need, including through the development of various spatial microsimulation methodologies in the US, UK, New Zealand, and Australia on topics such as poverty, obesity, smoking, and mental health [2,3,4,5,6]. In this context, we define spatial microsimulation as the use of simulation methods to generate individual-level data at higher geographic resolution than available in publicly available administrative datasets. Public Health 2017, 14, 730; doi:10.3390/ijerph14070730 www.mdpi.com/journal/ijerph

Methods

Results

Discussion

Conclusion