Multiple cluster analysis for the identification of high-risk census tracts for out-of-hospital cardiac arrest (OHCA) in Denver, Colorado

Abstract

Background Prior research has shown that high-risk census tracts for out-of-hospital cardiac arrest (OHCA) can be identified. High-risk neighborhoods are defined as having a high incidence of OHCA and a low prevalence of bystander cardiopulmonary resuscitation (CPR). However, there is no consensus regarding the process for identifying high-risk neighborhoods. Objective We propose a novel summary approach to identify high-risk neighborhoods through three separate spatial analysis methods: Empirical Bayes (EB), Local Moran’s I (LISA), and Getis Ord Gi* (Gi*) in Denver, Colorado. Methods We conducted a secondary analysis of prospectively collected Emergency Medical Services data of OHCA from January 1, 2009 to December 31, 2011 from the City and County of Denver, Colorado. OHCA incidents were restricted to those of cardiac etiology in adults ≥18 years. The OHCA incident locations were geocoded using Centrus. EB smoothed incidence rates were calculated for OHCA using Geoda and LISA and Gi* calculated using ArcGIS 10. Results A total of 1102 arrests in 142 census tracts occurred during the study period, with 887 arrests included in the final sample. Maps of clusters of high OHCA incidence were overlaid with maps identifying census tracts in the below the Denver County mean for bystander CPR prevalence. Five census tracts identified were designated as Tier 1 high-risk tracts, while an additional 7 census tracts where designated as Tier 2 high-risk tracts. Conclusion This is the first study to use these three spatial cluster analysis methods for the detection of high-risk census tracts. These census tracts are possible sites for targeted community-based interventions to improve both cardiovascular health education and CPR training.

Defining Sepsis Mortality Clusters in the United States*

“…Introduced by Nassel et al (2014), we combined three separate analytical spatial clustering methods to identify areas that were hot spots for sepsis mortality. 12 We categorized county-level sepsis clustering into three groups: strongly clustered, moderately clustered, and non-clustered. We considered a county to be strongly clustered if it was identified as high risk or sepsis hot spot using all three geospatial metrics (5th quartile of Empirical Bayes (EB) smoothed sepsis mortality rates, high-high cluster using Local Moran's I, and sepsis hot spot as defined by Getis-Ord Gi*).…”

“…12 We further categorized the EB smoothed sepsis mortality rates into quintiles, and we defined counties as high-risk if the EB sepsis mortality rates were in the top quintile. Second, we used Local Indicators of Spatial Autocorrelation 13 to measure similarity between counties and calculate values both within and across geographic boundaries, additionally identifying spatial outliers.…”

“…Second, we used Local Indicators of Spatial Autocorrelation 13 to measure similarity between counties and calculate values both within and across geographic boundaries, additionally identifying spatial outliers. 12,14,15 For each US county, we estimated Local Moran's I Statistic values, using associated z-scores and p-values to assess the magnitude of spatial autocorrelation and statistical significance, respectively. 12 Counties with statistically significant positive z-scores indicate areas surrounded by areas with similar sepsis-mortality rates – either similarly high or similarly low (positive spatial autocorrelation).…”

Barriers to Calling 911 and Learning and Performing Cardiopulmonary Resuscitation for Residents of Primarily Latino, High-Risk Neighborhoods in Denver, Colorado

“…Data were geocoded with ArcGIS (version 9.3; Environmental Systems Research Institute Inc., Redlands, CA) and Geoda software (http://geodacenter.asu.edu), and spatial analysis methods were used to identify high-risk census tracts. 16 …”

“…In accordance with previous research, 11,12,15 we identified 5 neighborhoods in Denver in which the incidence of out-of-hospital cardiac arrest was 2 to 5 times higher than the median for the county and rates of bystander CPR were below average. 16 These high-risk neighborhoods were composed of primarily Latinos and lower socioeconomic status residents.…”