Mapping Community Needs: Visualizing the Social Vulnerability Index and Stroke Hospitalization Dynamics for Informed Interventions
Abstract
Background:
The interplay between social determinants of health and adverse health outcomes requires innovative tools for estimating and disseminating information. Social Vulnerability Index (SVI) is a multidimensional measure of a community's susceptibility to external stressors and its capacity to cope, offering valuable insights into health disparities. Stroke, on the other hand, is the 5th leading cause of death in Georgia. Understanding the SVI-stroke relationship guides targeted interventions and resource allocation. This abstract explores the development and utilization of a novel ArcGIS dashboard integrating SVI and stroke hospitalization data.
Methods:
The annual age-adjusted stroke hospitalization rate was measured at the census tract level using the 2016-2020 hospital discharge data. While the social determinants of health were captured from the 2018 SVI data released by CDC/ATSDR. Combining the two datasets, a dashboard was built using ArcGIS Dashboards application for an interactive graphic display of the data.
Results:
The dashboard presents interactive bivariate choropleth maps pairing SVI indices with stroke hospitalization rates at the census tract level. User-friendly map filters, enable visualization of the overall SVI and four sub-themes: Socioeconomic Status, Household Composition & Disability, Minority Status & Language, and Housing & Transportation. The dashboard allows searches by address or county, displaying American Community Survey-derived measures for selected census tracts used in the SVI calculations. Users can identify patterns, hotspots, and correlations between SVI components and stroke incidence.
Conclusion:
The dashboard stands as a pivotal planning tool; its integration of SVI and stroke hospitalization data at the census tract level offers a nuanced perspective for public health districts, hospitals, and other stakeholders. This approach not only identifies vulnerable populations but also facilitates targeted interventions and resource optimization. The platform allows for integration of additional social determinants of health and stroke risk factors, creating a dynamic, informative tool for ongoing public health planning efforts.
Keywords
Dashboard, GIS, Stroke, Data Visualization, Hospitalization Rate, Social Vulnerability Index, American Community Survey
Mapping Community Needs: Visualizing the Social Vulnerability Index and Stroke Hospitalization Dynamics for Informed Interventions
Background:
The interplay between social determinants of health and adverse health outcomes requires innovative tools for estimating and disseminating information. Social Vulnerability Index (SVI) is a multidimensional measure of a community's susceptibility to external stressors and its capacity to cope, offering valuable insights into health disparities. Stroke, on the other hand, is the 5th leading cause of death in Georgia. Understanding the SVI-stroke relationship guides targeted interventions and resource allocation. This abstract explores the development and utilization of a novel ArcGIS dashboard integrating SVI and stroke hospitalization data.
Methods:
The annual age-adjusted stroke hospitalization rate was measured at the census tract level using the 2016-2020 hospital discharge data. While the social determinants of health were captured from the 2018 SVI data released by CDC/ATSDR. Combining the two datasets, a dashboard was built using ArcGIS Dashboards application for an interactive graphic display of the data.
Results:
The dashboard presents interactive bivariate choropleth maps pairing SVI indices with stroke hospitalization rates at the census tract level. User-friendly map filters, enable visualization of the overall SVI and four sub-themes: Socioeconomic Status, Household Composition & Disability, Minority Status & Language, and Housing & Transportation. The dashboard allows searches by address or county, displaying American Community Survey-derived measures for selected census tracts used in the SVI calculations. Users can identify patterns, hotspots, and correlations between SVI components and stroke incidence.
Conclusion:
The dashboard stands as a pivotal planning tool; its integration of SVI and stroke hospitalization data at the census tract level offers a nuanced perspective for public health districts, hospitals, and other stakeholders. This approach not only identifies vulnerable populations but also facilitates targeted interventions and resource optimization. The platform allows for integration of additional social determinants of health and stroke risk factors, creating a dynamic, informative tool for ongoing public health planning efforts.
