Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, 13201 Bruce B. Downs Blvd, MDC 56, Tampa FL 33612, USA.
Prev Chronic Dis. 2010 May;7(3):A59. Epub 2010 Apr 15.
Prompt transportation to a hospital and aggressive medical treatment can often prevent acute cardiac events from becoming fatal. Consequently, lack of transport before death may represent lost opportunities for life-saving interventions. We investigated the effect of individual characteristics (age, sex, race/ethnicity, education, and marital status) and small-area factors (population density and social cohesion) on the probability of premature cardiac decedents dying without transport to a hospital.
We analyzed death data for adults aged 25 to 69 years who resided in the Tampa, Florida, metropolitan statistical area and died from an acute cardiac event from 1998 through 2002 (N = 2,570). Geocoding of decedent addresses allowed the use of multilevel (hierarchical) logistic regression models for analysis.
The strongest predictor of dying without transport was being unmarried (odds ratio, 2.13; 95% confidence interval, 1.79-2.52, P < .001). There was no effect of education; however, white race was modestly predictive of dying without transport. Younger decedent age was a strong predictor. Multilevel statistical modeling revealed that less than 1% of the variance in our data was found at the small-area level.
Results contradicted our hypothesis that small-area characteristics would increase the probability of cardiac patients receiving transport before death. Instead we found that being unmarried, a proxy of living alone and perhaps low social support, was the most important predictor of people who died from a cardiac event dying without transport to a hospital.
及时送往医院并进行积极的治疗通常可以防止急性心脏事件演变为致命事件。因此，在死亡前未能及时转运可能意味着错失了挽救生命的干预机会。我们研究了个体特征（年龄、性别、种族/民族、教育程度和婚姻状况）和小区域因素（人口密度和社会凝聚力）对心脏性猝死患者在未被送往医院前死亡的可能性的影响。
我们分析了 1998 年至 2002 年期间居住在佛罗里达州坦帕都会统计区、年龄在 25 至 69 岁之间、因急性心脏事件死亡的成年人（n=2570）的死亡数据。通过对死者地址进行地理编码，我们可以使用多层次（分层）逻辑回归模型进行分析。
未被转运而死亡的最强预测因素是未婚（优势比，2.13；95%置信区间，1.79-2.52，P<.001）。教育程度没有影响；然而，白人种族在一定程度上预示着未被转运而死亡。死者年龄越小，死亡风险越大。多层次统计模型显示，我们数据中的方差不到 1%是在小区域层面上发现的。
结果与我们的假设相矛盾，即小区域特征会增加心脏患者在死亡前接受转运的可能性。相反，我们发现，未婚（独居和可能缺乏社会支持的代名词）是死于心脏事件的人未被送往医院而死亡的最重要预测因素。
