Ariann F Nassel1, Elisabeth D Root2, Jason S Haukoos3, Kevin McVaney4, Christopher Colwell4, James Robinson5, Brian Eigel6, David J Magid7, Comilla Sasson8. 1. University of Alabama, Birmingham, AL, United States. 2. University of Colorado, Boulder, CO, United States. 3. Department of Emergency Medicine, University of Colorado, Aurora, CO, United States; Denver Health and Hospital Authority, Denver, CO, United States; Colorado School of Public Health, Aurora, CO, United States. 4. Department of Emergency Medicine, University of Colorado, Aurora, CO, United States; Denver Health and Hospital Authority, Denver, CO, United States. 5. Denver Health and Hospital Authority, Denver, CO, United States. 6. American Heart Association, Dallas, TX, United States. 7. Kaiser Permanente, Denver, CO, United States. 8. Department of Emergency Medicine, University of Colorado, Aurora, CO, United States; Colorado School of Public Health, Aurora, CO, United States; American Heart Association, Dallas, TX, United States. Electronic address: comilla.sasson@ucdenver.edu.
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.
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.
Keywords:
Cardiopulmonary resuscitation; Geographic information systems; Out-of-hospital cardiac arrest; Public health interventions; Spatial statistics
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