DeVille Nicole V, Iyer Hari S, Holland Isabel, Bhupathiraju Shilpa N, Chai Boyang, James Peter, Kawachi Ichiro, Laden Francine, Hart Jaime E
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
Environ Epidemiol. 2022 Dec 14;7(1):e235. doi: 10.1097/EE9.0000000000000235. eCollection 2023 Feb.
Few studies have prospectively examined long-term associations between neighborhood socioeconomic status (nSES) and mortality risk, independent of demographic and lifestyle risk factors.
We assessed associations between nSES and all-cause, nonaccidental mortality among women in the Nurses' Health Study (NHS) 1986-2014 (N = 101,701) and Nurses' Health Study II (NHSII) 1989-2015 (N = 101,230). Mortality was ascertained from the National Death Index (NHS: 19,228 deaths; NHSII: 1556 deaths). Time-varying nSES was determined for the Census tract of each residential address. We used principal component analysis (PCA) to identify nSES variable groups. Multivariable Cox proportional hazards models were conditioned on age and calendar period and included time-varying demographic, lifestyle, and individual SES factors.
For NHS, hazard ratios (HRs) comparing the fifth to first nSES quintiles ranged from 0.89 (95% confidence interval [CI] = 0.84, 0.94) for percent of households receiving interest/dividends, to 1.11 (95% CI = 1.06, 1.17) for percent of households receiving public assistance income. In NHSII, HRs ranged from 0.72 (95% CI: 0.58, 0.88) for the percent of households receiving interest/dividends, to 1.27 (95% CI: 1.07, 1.49) for the proportion of households headed by a single female. PCA revealed three constructs: education/income, poverty/wealth, and racial composition. The racial composition construct was associated with mortality (HR: 1.03; 95% CI = 1.01, 1.04).
In two cohorts with extensive follow-up, individual nSES variables and PCA component scores were associated with mortality. nSES is an important population-level predictor of mortality, even among a cohort of women with little individual-level variability in SES.
很少有研究前瞻性地考察邻里社会经济地位(nSES)与死亡风险之间的长期关联,且独立于人口统计学和生活方式风险因素。
我们评估了1986 - 2014年护士健康研究(NHS,N = 101,701)和1989 - 2015年护士健康研究II(NHSII,N = 101,230)中nSES与女性全因、非意外死亡率之间的关联。死亡率通过国家死亡指数确定(NHS:19,228例死亡;NHSII:1556例死亡)。为每个居住地址的人口普查区确定随时间变化的nSES。我们使用主成分分析(PCA)来识别nSES变量组。多变量Cox比例风险模型以年龄和日历时间为条件,并纳入随时间变化的人口统计学、生活方式和个体社会经济因素。
在NHS中,比较第五个与第一个nSES五分位数的风险比(HRs)范围从接受利息/股息家庭百分比的0.89(95%置信区间[CI] = 0.84, 0.94)到接受公共援助收入家庭百分比的1.11(95% CI = 1.06, 1.17)。在NHSII中,HRs范围从接受利息/股息家庭百分比的0.72(95% CI:0.58, 0.88)到单身女性为户主家庭比例的1.27(95% CI:1.07, 1.49)。PCA揭示了三个结构:教育/收入、贫困/财富和种族构成。种族构成结构与死亡率相关(HR:1.03;95% CI = 1.01, 1.04)。
在两个进行了广泛随访的队列中,个体nSES变量和PCA成分得分与死亡率相关。nSES是死亡率的一个重要的人群水平预测因素,即使在一组个体社会经济水平差异很小的女性队列中也是如此。