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.
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ABSTRACT: Background: The goal of this study is to better understand the trend in epidemiological features and the outcomes of emergency medical service (EMS)-assessed out-of-hospital cardiac arrest (OHCA) according to the community urbanization level: metropolitan, urban, and rural. Methods: This study was performed within a nationwide EMS system with a single-tiered basic-to-intermediate service level and approximately 900 destination hospitals for eligible OHCA cases in South Korea (with 48 million people). A nationwide OHCA database, which included information regarding demographics, Utstein criteria, EMS, and hospital factors and outcomes, was constructed using the EMS run sheets of eligible cases who were transported by 119 EMS ambulances and followed by a medical record review from 2006 to 2010. Cases with an unknown outcome were excluded. The community urbanization level was categorized according to population size, with metropolitan areas (more than 500,000 residents), urban areas (100,000-500,000 residents), and rural areas (<100,000 residents). The primary end point was the survival to discharge rate. Age- and sex-adjusted survival rates (ASRs) and standardized survival ratios (SSRs) with 95% confidence intervals (CIs) were calculated compared to a standard population. The adjusted odds ratios (AORs) and 95% CIs for survival were calculated and adjusted for potential risk factors using stratified multivariable logistic regression analysis. Results: There were 97,291 EMS-assessed OHCAs with 73,826 (75.9%) EMS-treated cases analyzed, after excluding the patients with unknown outcome (N=4172). The standardized incidence rate increased from 37.5 in 2006 to 46.8 in 2010 per 100,000 person-years for EMS-assessed OHCAs, and the survival rate was 3.0% for EMS-assessed OHCAs (3.3% for cardiac etiology and 2.3% for non-cardiac etiology) and 3.6% for EMS-treated OHCAs. Significantly different trends were found by urbanization level for bystander CPR, EMS performance, and the level of the destination hospital. The ASRs for survival were significantly improved by year in the metropolitan areas (3.6% in 2006 to 5.3% in 2010) but remained low in the urban areas (1.4% in 2006 to 2.3% in 2010) and very low in the rural areas (0.5 in 2006 and 0.8 in 2010). The SSRs (95% CIs) in the metropolitan areas were 1.19 (1.06-1.34) in 2006 and 1.77 (1.64-1.92) in 2010, whereas the SSRs were observed to be less than 1.00 during the five-year period in both urban and rural areas. The AORs (95% CIs) for survival significantly increased to 1.42 (1.22-1.66) in the metropolitan areas and to 1.58 (1.18-2.11) in the urban areas while not increasing in the rural areas, compared to the level of each group of areas in 2006. Conclusions: In this nationwide cohort study from 2006 to 2010, the standardized incidence rate and survival to discharge rate of EMS-assessed OHCAs increased annually in metropolitan and urban communities but did not increase in rural communities. Further investigations should be undertaken to improve the performance and outcomes in rural communities.Resuscitation 01/2013; 84(5). DOI:10.1016/j.resuscitation.2012.12.020 · 4.17 Impact Factor
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ABSTRACT: Improving survival rates at the neighborhood level is increasingly seen as a priority for reducing overall rates of out-of-hospital cardiac arrest (OHCA) in the United States. Since wide disparities exist in OHCA rates at the neighborhood level, it is important for public health officials and residents to be able to quickly locate neighborhoods where people are at elevated risk for cardiac arrest and to target these areas for educational outreach and other mitigation strategies. This paper describes an OHCA web mapping application that was developed to provide users with interactive maps and data for them to quickly visualize and analyze the geographic pattern of cardiac arrest rates, bystander CPR rates, and survival rates at the neighborhood level in different U.S. cities. The data comes from the CARES Registry and is provided over a period spanning several years so users can visualize trends in neighborhood out-of-hospital cardiac arrest patterns. Users can also visualize areas that are statistical hot and cold spots for cardiac arrest and compare OHCA and bystander CPR rates in the hot and cold spots. Although not designed as a public participation GIS (PPGIS), this application seeks to provide a forum around which data and maps about local patterns of OHCA can be shared, analyzed and discussed with a view of empowering local communities to take action to address the high rates of OHCA in their vicinity.06/2013; 5(2):212. DOI:10.5210/ojphi.v5i2.4587
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ABSTRACT: Objective: A 10-fold regional variation in survival after out-of-hospital cardiac arrest (OHCA) has been reported in the United States, which partly relates to variability in bystander cardiopulmonary resuscitation (CPR) rates. In order for resources to be focused on areas of greatest need, we conducted a geospatial analysis of variation of CPR rates. Methods: Using 2010-2011 data from Durham, Mecklenburg, and Wake counties in North Carolina participating in the Cardiac Arrest Registry to Enhance Survival (CARES) program, we included all patients with OHCA for whom resuscitation was attempted. Geocoded data and logistic regression modeling were used to assess incidence of OHCA and patterns of bystander CPR according to census tracts and factors associated herewith. Results: In total, 1466 patients were included (median age, 65 years [interquartile range 25]; 63.4% men). Bystander CPR by a layperson was initiated in 37.9% of these patients. High-incidence OHCA areas were characterized partly by higher population densities and higher percentages of black race as well as lower levels of education and income. Low rates of bystander CPR were associated with population composition (percent black: OR, 3.73; 95% CI, 2.00-6.97 per 1% increment in black patients; percent elderly: 3.25; 1.41-7.48 per 1% increment in elderly patients; percent living in poverty: 1.77, 1.16-2.71 per 1% increase in patients living in poverty). Conclusions: In 3 counties in North Carolina, areas with low rates of bystander CPR can be identified using geospatial data, and education efforts can be targeted to improve recognition of cardiac arrest and to augment bystander CPR rates.Resuscitation 08/2014; 85(11). DOI:10.1016/j.resuscitation.2014.08.013 · 4.17 Impact Factor