Community-engaged development of a GIS-based healthfulness index to shape health equity solutions

Abstract

Addressing health disparities requires both community engagement and an understanding of the social determinants of health. Although elements of the built environment can influence behavior change in public health interventions, such determinants have not been explicitly teased out via participatory mapping. An opportunity exists to integrate community voice in the development of such metrics. To fill this gap and inform the deployment of public health interventions in the Flint (USA) Center for Health Equity Solutions (FCHES), we created a means of assessing spatially-varying community needs and assets in a geographic information system (GIS), what we refer to as a healthfulness index. We engaged community and academic partners in their expert opinions on features of Flint's built environment that may promote or inhibit healthy behaviors via a multiple-criteria decision analysis framework. Experts selected from and ranked 29 variables in 6 categories (including amenities, environment, greenspace, housing, infrastructure, and social issues) using the analytic hierarchy process. The resulting matrices of expert opinions were aggregated and appended as weights for each variable's corresponding map layer. When combined through map algebra, composite scores yield spatially-varying healthfulness indices which signal any neighborhood's relative health promoting qualities (along a 0-100 scale). Results varied substantially across Flint, with the middle belt scoring highest and older neighborhoods in the northeast and north center of the city scoring lowest. Scores were aggregated to 38 Flint neighborhoods; for each of two project-specific indices, these ranged from lows of 38.7 (Hilborn Park) and 41.8 (Columbia Heights) to highs of 52.9 (College Cultural) and 58.0 (University Ave Corridor). We hypothesize that-even when controlling for individual-level factors-we will measure better and more sustained behavior change among participants living in neighborhoods with high healthfulness scores. Future work will examine this hypothesis and determine the importance of such indices in other similar communities.

The good, the bad, and the environment: developing an area-based measure of access to health-promoting and health-constraining environments in New Zealand

“…The healthy location index shares similarities with previous research conducted in New Zealand and elsewhere. Several other studies [ 65 , 67 ] utilised a similar approach to the topic evaluating both health-promoting and health-constraining environments at the same time. Sadler et al [ 67 ] created raster-based analysis of the presence of environmental features combined with decision-making processes within one U.S. city.…”

“…Several other studies [ 65 , 67 ] utilised a similar approach to the topic evaluating both health-promoting and health-constraining environments at the same time. Sadler et al [ 67 ] created raster-based analysis of the presence of environmental features combined with decision-making processes within one U.S. city. We build on and update the work of Pearson et al [ 65 ] who utilised accessibility measures and similar data to create resilience index.…”

“…Data were then classified as health-constraining ‘bads’ (fast-food outlets, takeaway outlets, dairy outlets and convenience stores, alcohol outlets, and gaming venues) and health-promoting ‘goods’ (green spaces, blue spaces, physical activity facilities, supermarkets, and fruit and vegetable outlets). There are a plethora of existing complex methods used for the construction of indices in socioeconomic geography; some of them utilising data transformation and weighting schemes [ 64 ], regression models [ 65 ], multivariate statistics [ 66 ] or multi-criteria decision analysis [ 67 ]. However, we opted for more straightforward method of creating typology based on the domain rankings that, to some extent, follows several papers which use this approach [ 64 , 68 ].…”

The Population Health OutcomEs aNd Information Exchange (PHOENIX) Program - A Transformative Approach to Reduce the Burden of Chronic Disease

“…We will develop novel solutions for specific risk classification problems by using hierarchical models, multiple machine learning algorithms (e.g., random tree forest, neural networks) and classical techniques based on evidence from previous studies including approaches employed Global Burden of Disease study group, as appropriate. We will also adapt novel methods that involve community members in quantitatively weighting the importance of specific health topics [15]. Community risk scores will be shared via the PHOENIX website; future efforts will emphasize incorporation of such risk scores into provider workflows either directly through their EHR or indirectly via secure web-based applications.…”

“…Despite such interest, few programs have demonstrated the capacity to build the necessary networks or resolve technical barriers that limit health information exchange (HIE) impact. While emerging approaches prompt excitement [15,16], additional research is necessary [17][18][19][20].…”