{"title":"Geographical patterns of cardiac arrests: An exploratory model","authors":"Jonathan D. Mayer","doi":"10.1016/0160-8002(81)90051-4","DOIUrl":null,"url":null,"abstract":"<div><p>The geographical distribution of out-of-hospital cardiac arrest has not been studied but is of importance both epidemiologically and programmatically, for the planning of pre-hospital emergency care. In this study, 525 cardiac arrests in Seattle are sampled and the census tract of their occupance noted. A predictive model is developed to explain the geographical distribution of the cardiac arrest cases. The regression model indicates a high degree of statistical explanation (<em>R</em><sup>2</sup> = 0.94), based upon 5 independent variables. Using population alone as an independent variable, the model is only marginally less powerful (<em>R</em><sup>2</sup> = 0.91). The study concludes that such a prediction model is of use in the geographical allocation of emergency units based upon response time minimization.</p></div>","PeriodicalId":79263,"journal":{"name":"Social science & medicine. Part D, Medical geography","volume":"15 3","pages":"Pages 329-334"},"PeriodicalIF":0.0000,"publicationDate":"1981-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0160-8002(81)90051-4","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Social science & medicine. Part D, Medical geography","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/0160800281900514","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The geographical distribution of out-of-hospital cardiac arrest has not been studied but is of importance both epidemiologically and programmatically, for the planning of pre-hospital emergency care. In this study, 525 cardiac arrests in Seattle are sampled and the census tract of their occupance noted. A predictive model is developed to explain the geographical distribution of the cardiac arrest cases. The regression model indicates a high degree of statistical explanation (R2 = 0.94), based upon 5 independent variables. Using population alone as an independent variable, the model is only marginally less powerful (R2 = 0.91). The study concludes that such a prediction model is of use in the geographical allocation of emergency units based upon response time minimization.