Identifying high-risk geographic areas for cardiac arrest using three methods for cluster analysis

Comilla Sasson, Michael T. Cudnik, Ariann Nassel, Hugh Semple, David J. Magid, Michael Sayre, David Keseg, Jason S. Haukoos, Craig R. Warden

Research output: Contribution to journalArticlepeer-review

46 Scopus citations


Objectives: The objective was to identify high-risk census tracts, defined as those areas that have both a high incidence of out-of-hospital cardiac arrest (OHCA) and a low prevalence of bystander cardiopulmonary resuscitation (CPR), by using three spatial statistical methods. Methods: This was a secondary analysis of two prospectively collected registries in the city of Columbus, Ohio. Consecutive adult (¥18 years) OHCA patients, restricted to those of cardiac etiology and treated by emergency medical services (EMS) from April 1, 2004, to April 30, 2009, were studied. Three different spatial analysis methods (Global Empirical Bayes, Local Moran's I, and SaTScan's spatial scan statistic) were used to identify high-risk census tracts. Results: A total of 4,553 arrests in 200 census tracts occurred during the study period, with 1,632 arrests included in the final sample after exclusions for no resuscitation attempt, noncardiac etiology, etc. The overall incidence for OHCA was 0.70 per 1,000 people for the 6-year study period (SD = ±0.52). Bystander CPR occurred in 20.2% (n = 329), with 10.0% (n = 167) surviving to hospital discharge. Five high-risk census tracts were identified by all three analytic methods. Conclusions: The five high-risk census tracts identified may be possible sites for high-yield targeted community-based interventions to improve CPR training and cardiovascular disease education efforts and ultimately improve survival from OHCA.

Original languageEnglish (US)
Pages (from-to)139-146
Number of pages8
JournalAcademic Emergency Medicine
Issue number2
StatePublished - Feb 2012
Externally publishedYes

ASJC Scopus subject areas

  • Emergency Medicine


Dive into the research topics of 'Identifying high-risk geographic areas for cardiac arrest using three methods for cluster analysis'. Together they form a unique fingerprint.

Cite this