Thomas Avis J, Eberly Lynn E, Davey Smith George, Neaton James D
Coordinating Centers for Biometric Research, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55414, USA.
Am J Epidemiol. 2006 Sep 15;164(6):586-90. doi: 10.1093/aje/kwj234. Epub 2006 Aug 7.
There is a well-established, strong association between socioeconomic position and mortality. Public health mortality analyses thus routinely consider the confounding effect of socioeconomic position when possible. Particularly in the absence of personally reported data, researchers often use area-based measures to estimate the effects of socioeconomic position. Data are limited regarding the relative merits of measures based on US Census tract versus ZIP code (postal code). ZIP-code measures have more within-unit variation but are also more easily obtained. The current study reports on 293,138 middle-aged men screened in 14 states in 1973-1975 for the Multiple Risk Factor Intervention Trial and having 25-year mortality follow-up. In risk-adjusted proportional hazards models containing either ZIP-code-based or tract-based median household income, all-cause mortality hazard ratios were 1.16 (95% confidence interval: 1.14, 1.17) per Dollars 10,000 less ZIP-code-based income and 1.15 (95% confidence interval: 1.13, 1.16) per Dollars 10,000 less tract-based income; adding either income variable to a risk-adjusted model improved model fit substantially. Both were significant independent predictors in a combined model; tract-based income was a slightly stronger mortality predictor (hazard ratios = 1.05 and 1.11 for ZIP-code-based and tract-based income, respectively). These patterns held across various causes of death, for both Blacks and non-Blacks, and with or without adjustment for ZIP-code-based income diversity or tract-based proportion below poverty.
社会经济地位与死亡率之间存在着既定的紧密关联。因此，公共卫生死亡率分析在可能的情况下通常会考虑社会经济地位的混杂效应。特别是在缺乏个人报告数据时，研究人员常常使用基于区域的指标来估计社会经济地位的影响。关于基于美国人口普查区与邮政编码的指标的相对优缺点，数据有限。邮政编码指标在单位内部的差异更大，但也更容易获得。当前的研究报告了1973年至1975年在14个州为多重危险因素干预试验筛查的293,138名中年男性，并对其进行了25年的死亡率随访。在包含基于邮政编码或基于普查区的家庭收入中位数的风险调整比例风险模型中，基于邮政编码的收入每减少1万美元，全因死亡率风险比为1.16（95%置信区间：1.14, 1.17），基于普查区的收入每减少1万美元，全因死亡率风险比为1.15（95%置信区间：1.13, 1.16）；将任一收入变量添加到风险调整模型中，都能显著改善模型拟合。在一个综合模型中，两者都是显著的独立预测因素；基于普查区的收入是一个稍强的死亡率预测因素（基于邮政编码和基于普查区的收入的风险比分别为1.05和1.11）。这些模式在各种死因中均成立，对黑人和非黑人而言都是如此，且无论是否对基于邮政编码的收入多样性或基于普查区的贫困以下比例进行调整。
