Utilizing GIS to visualize hypertension spread: A comparative study using community collected data

introduction: The use of Geographic Information Systems (GIS) and spatial statistics in the community health discipline is underutilized in detecting significant fine scale patterns among community-sized environments. The capabilities of these techniques allow public health workers to identify areas of high importance and to target limited funds to places where it will have the greatest impact. Hypertension is a common antecedent to numerous chronic diseases including coronary heart disease, stroke and end-stage renal failure. This study aimed to compare the efficacy of different spatial analysis approaches in examining geographic patterns of hypertension prevalence in urbanized Alachua County, Florida.

methods: HealthStreet is a community-engaged research program that utilizes community health workers (CHW) model to assess the various health needs of the community and link them to appropriate medical and social services and health research at UF. Three types of spatial analyses (SatScan, Spatial Empirical Bayesian Rate Smoothing (SEBRS) and Indicators of spatial association) were used to determine areas of significantly high prevalence and “hotspots” of hypertension among HealthStreet participants residing in the urban areas of Alachua County (N = 1307).  Four separate models were developed using logistic regression; and classification tables determined the most accurate model.

results: Statistically significant clusters/hotspots, located in the northeastern region of urbanized Alachua County, were found in all three spatial analyses. The Satscan/SEBRS model performed the best against the testing data with 96.8% sensitivity and 94.6% specificity.

conclusions: The combined use of Satscan and SEBRS provides the best predictive model of hypertension in Alachua County, FL. The advantage of comparing spatial analyses provides public health professionals with substantiated methods that spatially interpolate chronic diseases, among underrepresented populations. The analyses allow for increased spatial accuracy for locating areas that would have the biggest health-related impact with the least amount of funds. The data also allows public health professionals to concentrate interventions on areas where it is more needed.