Oregon Health & Science University, Portland, Oregon 97239, USA.
Prehosp Emerg Care. 2012 Jul-Sep;16(3):338-46. doi: 10.3109/10903127.2012.664244. Epub 2012 Mar 27.
Scarce resources in disease prevention and emergency medical services (EMS) need to be focused on high-risk areas of out-of-hospital cardiac arrest (OHCA).
Cluster analysis using geographic information systems (GISs) was used to find these high-risk areas and test potential predictive variables.
This was a retrospective cohort analysis of EMS-treated adults with OHCAs occurring in Columbus, Ohio, from April 1, 2004, through March 31, 2009. The OHCAs were aggregated to census tracts and incidence rates were calculated based on their adult populations. Poisson cluster analysis determined significant clusters of high-risk census tracts. Both census tract-level and case-level characteristics were tested for association with high-risk areas by multivariate logistic regression.
A total of 2,037 eligible OHCAs occurred within the city limits during the study period. The mean incidence rate was 0.85 OHCAs/1,000 population/year. There were five significant geographic clusters with 76 high-risk census tracts out of the total of 245 census tracts. In the case-level analysis, being in a high-risk cluster was associated with a slightly younger age (-3 years, adjusted odds ratio [OR] 0.99, 95% confidence interval [CI] 0.99-1.00), not being white, non-Hispanic (OR 0.54, 95% CI 0.45-0.64), cardiac arrest occurring at home (OR 1.53, 95% CI 1.23-1.71), and not receiving bystander cardiopulmonary resuscitation (CPR) (OR 0.77, 95% CI 0.62-0.96), but with higher survival to hospital discharge (OR 1.78, 95% CI 1.30-2.46). In the census tract-level analysis, high-risk census tracts were also associated with a slightly lower average age (-0.1 years, OR 1.14, 95% CI 1.06-1.22) and a lower proportion of white, non-Hispanic patients (-0.298, OR 0.04, 95% CI 0.01-0.19), but also a lower proportion of high-school graduates (-0.184, OR 0.00, 95% CI 0.00-0.00).
This analysis identified high-risk census tracts and associated census tract-level and case-level characteristics that can be used to target public education efforts to prevent OHCA and to mitigate its occurrence with CPR and automated external defibrillator training. In addition, EMS resources can be redeployed to minimize response times to these census tracts.
疾病预防和紧急医疗服务（EMS）的稀缺资源需要集中在院外心脏骤停（OHCA）的高风险地区。
使用地理信息系统（GIS）进行聚类分析，以找到这些高风险地区并测试潜在的预测变量。
这是对俄亥俄州哥伦布市 2004 年 4 月 1 日至 2009 年 3 月 31 日期间接受 EMS 治疗的成年人 OHCA 的回顾性队列分析。OHCA 被聚集到普查区，并根据其成年人口计算发病率。泊松聚类分析确定了高风险普查区的显著集群。通过多变量逻辑回归检验普查区和病例水平特征与高风险地区的关联。
在研究期间，共有 2037 例符合条件的 OHCA 发生在城市范围内。平均发病率为每 1000 人/年 0.85 例 OHCA。有五个显著的地理集群，共有 76 个高风险普查区，占 245 个普查区的总数。在病例水平分析中，处于高风险集群与年龄稍轻（-3 岁，调整后优势比 [OR] 0.99，95%置信区间 [CI] 0.99-1.00）、非白人、非西班牙裔（OR 0.54，95% CI 0.45-0.64）、心脏骤停发生在家庭（OR 1.53，95% CI 1.23-1.71）和未接受旁观者心肺复苏术（CPR）（OR 0.77，95% CI 0.62-0.96）有关，但存活至出院的比例较高（OR 1.78，95% CI 1.30-2.46）。在普查区水平分析中，高风险普查区也与平均年龄略低（-0.1 岁，OR 1.14，95% CI 1.06-1.22）和白人、非西班牙裔患者比例较低（-0.298，OR 0.04，95% CI 0.01-0.19）有关，但高中毕业生比例也较低（-0.184，OR 0.00，95% CI 0.00-0.00）。
本分析确定了高风险普查区以及与之相关的普查区和病例水平特征，可用于针对公众教育工作，以预防 OHCA，并通过 CPR 和自动体外除颤器培训减轻其发生。此外，还可以重新部署 EMS 资源，以尽量减少对这些普查区的响应时间。
