Division of General Internal Medicine and Health Services Research, University of California, Los Angeles, USA.
Neurology. 2013 Feb 5;80(6):520-7. doi: 10.1212/WNL.0b013e31828154ae. Epub 2013 Jan 2.
Residence in a socioeconomically disadvantaged community is associated with mortality, but the mechanisms are not well understood. We examined whether socioeconomic features of the residential neighborhood contribute to poststroke mortality and whether neighborhood influences are mediated by traditional behavioral and biologic risk factors.
We used data from the Cardiovascular Health Study, a multicenter, population-based, longitudinal study of adults ≥65 years. Residential neighborhood disadvantage was measured using neighborhood socioeconomic status (NSES), a composite of 6 census tract variables representing income, education, employment, and wealth. Multilevel Cox proportional hazard models were constructed to determine the association of NSES to mortality after an incident stroke, adjusted for sociodemographic characteristics, stroke type, and behavioral and biologic risk factors.
Among the 3,834 participants with no prior stroke at baseline, 806 had a stroke over a mean 11.5 years of follow-up, with 168 (20%) deaths 30 days after stroke and 276 (34%) deaths at 1 year. In models adjusted for demographic characteristics, stroke type, and behavioral and biologic risk factors, mortality hazard 1 year after stroke was significantly higher among residents of neighborhoods with the lowest NSES than those in the highest NSES neighborhoods (hazard ratio 1.77, 95% confidence interval 1.17-2.68).
Living in a socioeconomically disadvantaged neighborhood is associated with higher mortality hazard at 1 year following an incident stroke. Further work is needed to understand the structural and social characteristics of neighborhoods that may contribute to mortality in the year after a stroke and the pathways through which these characteristics operate.
居住在社会经济处于不利地位的社区与死亡率有关，但具体机制尚不清楚。我们研究了居住社区的社会经济特征是否会导致中风后死亡，以及社区影响是否通过传统的行为和生物学危险因素来介导。
我们使用了心血管健康研究的数据，这是一项多中心、基于人群的、针对 65 岁及以上成年人的纵向研究。使用邻里社会经济地位（NSES）来衡量邻里劣势，这是一个由 6 个普查区变量组成的综合指标，代表收入、教育、就业和财富。构建了多层次 Cox 比例风险模型，以确定 NSES 与中风后死亡率的关联，调整了社会人口特征、中风类型以及行为和生物学危险因素。
在基线时没有既往中风的 3834 名参与者中，11.5 年的随访中有 806 人发生了中风，其中 168 人（20%）在中风后 30 天死亡，276 人（34%）在 1 年内死亡。在调整了人口特征、中风类型和行为及生物学危险因素的模型中，与居住在 NSES 最高社区的人相比，居住在 NSES 最低社区的人在中风后 1 年内的死亡风险显著更高（危险比 1.77，95%置信区间 1.17-2.68）。
居住在社会经济处于不利地位的社区与中风后 1 年内的死亡风险增加有关。需要进一步研究可能导致中风后 1 年内死亡的邻里结构和社会特征，以及这些特征的作用途径。
