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

Hugh M. Semple, Michael T. Cudnik, Michael Sayre, David Keseg, Craig Warden, Comilla Sasson

Research output: Contribution to journalArticle

13 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)277-284
Number of pages8
JournalJournal of Community Health
Volume38
Issue number2
DOIs
StatePublished - Apr 2013

Fingerprint

Out-of-Hospital Cardiac Arrest
Geographical Information System
community
Heart Arrest
methodology
Community Hospital
Cardiopulmonary Resuscitation
incident
incidence
Survival Rate

Keywords

  • Bystander CPR
  • Local Moran's I
  • Out-of-hospital cardiac arrest
  • Single and multiple criteria clusters

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health
  • Health(social science)
  • Medicine(all)

Cite this

Identification of high-risk communities for unattended out-of-hospital cardiac arrests using GIS. / Semple, Hugh M.; Cudnik, Michael T.; Sayre, Michael; Keseg, David; Warden, Craig; Sasson, Comilla.

In: Journal of Community Health, Vol. 38, No. 2, 04.2013, p. 277-284.

Research output: Contribution to journalArticle

Semple, Hugh M. ; Cudnik, Michael T. ; Sayre, Michael ; Keseg, David ; Warden, Craig ; Sasson, Comilla. / Identification of high-risk communities for unattended out-of-hospital cardiac arrests using GIS. In: Journal of Community Health. 2013 ; Vol. 38, No. 2. pp. 277-284.
@article{eb3f5850f0e845dfa0f410e8609b2059,
title = "Identification of high-risk communities for unattended out-of-hospital cardiac arrests using GIS",
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.",
keywords = "Bystander CPR, Local Moran's I, Out-of-hospital cardiac arrest, Single and multiple criteria clusters",
author = "Semple, {Hugh M.} and Cudnik, {Michael T.} and Michael Sayre and David Keseg and Craig Warden and Comilla Sasson",
year = "2013",
month = "4",
doi = "10.1007/s10900-012-9611-7",
language = "English (US)",
volume = "38",
pages = "277--284",
journal = "Journal of Community Health",
issn = "0094-5145",
publisher = "Springer Netherlands",
number = "2",

}

TY - JOUR

T1 - Identification of high-risk communities for unattended out-of-hospital cardiac arrests using GIS

AU - Semple, Hugh M.

AU - Cudnik, Michael T.

AU - Sayre, Michael

AU - Keseg, David

AU - Warden, Craig

AU - Sasson, Comilla

PY - 2013/4

Y1 - 2013/4

N2 - 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.

AB - 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.

KW - Bystander CPR

KW - Local Moran's I

KW - Out-of-hospital cardiac arrest

KW - Single and multiple criteria clusters

UR - http://www.scopus.com/inward/record.url?scp=84879604619&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84879604619&partnerID=8YFLogxK

U2 - 10.1007/s10900-012-9611-7

DO - 10.1007/s10900-012-9611-7

M3 - Article

VL - 38

SP - 277

EP - 284

JO - Journal of Community Health

JF - Journal of Community Health

SN - 0094-5145

IS - 2

ER -