{"title":"心脏骤停的地理模式:一个探索性模型","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":"{\"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}","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}
Geographical patterns of cardiac arrests: An exploratory model
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.