Neighborhood income and individual education: Effect on survival after myocardial infarction
Neighborhood income and individual education: Effect on survival after myocardial infarction
复制标题
DOI：
10.4065/83.6.663
复制
发表时间：
2008-06-01
影响因子：
8.9
通讯作者：
Roger, Veronique L.
中科院分区：
文献类型：
--
作者：
Gerber, Yariv;Weston, Susan A.;Roger, Veronique L.
目的：评价社区收入和个人教育水平与社区患者心肌梗死（MI）后死亡率的关系。患者和方法：从2002年11月1日至2006年5月31日，705名明尼苏达州奥姆斯特德县（Olmsted County）符合标准化标准的MI患者（平均+/- SD年龄69 +/- 15岁，44%为女性）被前瞻性纳入并随访。该社区的家庭收入中位数是根据人口普查区的数据估算的；教育程度是自我报告的。从医疗记录中获得人口统计学和临床变量。结果：生活在较不富裕的社区和教育水平较低都与年龄和更多的合并症有关。在随访期间（中位13个月），155例患者死亡。社区收入（风险比[HR]， 2.10； 95%可信区间[CI]，：1.42-3.12；最低[中位数，34,205美元]对最高[中位数，60,6521美元]）和个人教育（风险比，2.21;95% CI, 1.47-3.32; < 12年vs > 12年）与死亡风险独立相关。人口统计学和各种心肌梗死后预后指标的调整削弱了这些估计，但低邻里收入的过度风险仍然存在（HR, 1.62; 95% CI, 1.08-2.45）。作为一个连续变量建模，年收入每增加110,000美元与死亡风险降低10%相关（调整HR， 0.90; 95% CI, 0.82-0.99）。结论：在这个地理上明确的米尔患者队列中，低个人教育水平和贫穷的社区收入与较差的临床表现相关。即使在控制了各种潜在的混杂因素之后，贫穷的邻里收入仍然是死亡率的一个强有力的预测因子。这些数据证实了心肌梗死后健康方面的社会经济差异。
OBJECTIVE: To evaluate the association of neighborhood-level income and individual-level education with post-myocardial infarction (MI) mortality in community patients.PATIENTS AND METHODS: From November 1, 2002, through May 31, 2006, 705 (mean +/- SD age, 69 +/- 15 years; 44% women) residents of Olmsted County, MN, who experienced an MI meeting standardized criteria were prospectively enrolled and followed up. The neighborhood's median household income was estimated by census tract data; education was self-reported. Demographic and clinical variables were obtained from the medical records.RESULTS: Living in a less affluent neighborhood and having a low educational level were both associated with older age and more comorbidity. During follow-up (median, 13 months), 155 patients died. Neighborhood income (hazard ratio [HR], 2.10; 95% confidence interval [CI], :1.42-3.12; for lowest [median, $34,205] vs highest [median, $60,6521 tertile) and individual education (HR, 2.21; 95% CI, 1.47-3.32; for < 12 vs > 12 years) were independently associated with mortality risk. Adjustment for demographics and various post-MI prognostic indicators attenuated these estimates, yet excess risk persisted for low neighborhood income (HR, 1.62; 95% CI, 1.08-2.45). Modeled as a continuous variable, each $11.0,000 increase in annual income was associated with a 10% reduction in mortality risk (adjusted HR, 0.90; 95% CI, 0.82-0.99).CONCLUSION: In this geographically defined cohort of patients with Mill, low individual education and poor neighborhood income were associated with a worse clinical presentation. Poor neighborhood income was a powerful predictor of mortality even after controlling for a variety of potential confounding factors. These data confirm the socioeconomic disparities in health after MI.