Social Determinants of Health (SDH), such as income, employment, education, and access to health care, have long served as a framework for studying health disparities and healthcare outcomes (Padilla, Kihal-Talantikit, Perez, & Deguen, 2016). Furthermore, the relationship between SDH and geography helps identify where health disparities are most impactful. Geographic Information Systems (GIS) can be used to identify where these vulnerable populations are located (Nykiforuk & Flaman, 2011). Based on methods established by Liaw et al. 2018, census tracts within the Jefferson Catchment Area (JCA) were examined using several SDH indicators. Census tracts with low standing SDH status were identified as geographic “cold spots” for community health. Selection measures for education, income, social deprivation, and life expectancy were used in the geospatial analysis. First, parameters were set at national averages to compare the JCA to census tracts across the USA. 64 census tracts met the criteria for this geospatial analysis, which showed the majority of the JCA community has worse SDH status than the average USA census tract. Next, parameters were changed to values representative of the 20th percentile among JCA census tracts only, which highlighted the six lowest SDH status census tracts in the JCA. Descriptive statistics were obtained for these six local cold spot census tracts. The results showed that minority populations were the most prevalent among the cold spots, and the racial impact of health disparities was evident. Further studies can be done looking at cold spot status as a potential indicator for different health care outcomes. Until individual patient data is routinely geocoded, this project represents a feasible alternative to creating a community health profile. These methods can help researchers identify target populations and decide where to dedicate public health resources.
Recommended CitationNowlan, Thomas and McIntire, PhD,MPH, Russell K., "Identifying Cold Spots within the Jefferson Catchment Area: A Geospatial Community Health Profile" (2019). Master of Public Health Thesis and Capstone Presentations. Presentation 288.