Display Settings:

Format

Send to:

Choose Destination
We are sorry, but NCBI web applications do not support your browser and may not function properly. More information
J Community Health. 2013 Apr;38(2):277-84. doi: 10.1007/s10900-012-9611-7.

Identification of high-risk communities for unattended out-of-hospital cardiac arrests using GIS.

Author information

  • 1Department of Geography and Geology, Eastern Michigan University, Ypsilanti, MI, USA. hsemple@emich.edu

Abstract

Improving survival rates for out of hospital cardiac arrest (OHCA) at the neighborhood level is increasingly seen as priority in US cities. Since wide disparities exist in OHCA rates at the neighborhood level, it is necessary to locate neighborhoods where people are at elevated risk for cardiac arrest and target these for educational outreach and other mitigation strategies. This paper describes a GIS-based methodology that was used to identify communities with high risk for cardiac arrests in Franklin County, Ohio during the period 2004-2009. Prior work in this area used a single criterion, i.e., the density of OHCA events, to define the high-risk areas, and a single analytical technique, i.e., kernel density analysis, to identify the high-risk communities. In this paper, two criteria are used to identify the high-risk communities, the rate of OHCA incidents and the level of bystander CPR participation. We also used Local Moran's I combined with traditional map overlay techniques to add robustness to the methodology for identifying high-risk communities for OHCA. Based on the criteria established for this study, we successfully identified several communities that were at higher risk for OHCA than neighboring communities. These communities had incidence rates of OHCA that were significantly higher than neighboring communities and bystander rates that were significantly lower than neighboring communities. Other risk factors for OHCA were also high in the selected communities. The methodology employed in this study provides for a measurement conceptualization of OHCA clusters that is much broader than what has been previously offered. It is also statistically reliable and can be easily executed using a GIS.

PMID:
22983677
[PubMed - indexed for MEDLINE]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Icon for Springer
    Loading ...
    Write to the Help Desk