Constructing U.S. Life Tables by Educational Status, 1990-2011

 描述(由申请人提供)：众所周知，在美国，受教育程度较低的人的健康和存活率更差。人们发现，受教育程度不同的人在生存方面的差异越来越大，这引发了争论，即社会经济地位较低的人是否越来越落后，或者这种差异是否可以通过群体组成的变化来解释。例如，随着没有完成高中或高等教育的人变得越来越少，这一群体在预测未来健康的特征方面变得更有选择性。然而，按教育水平衡量预期寿命的数据并不理想。生命统计数据准确地衡量了死亡人数，但众所周知，关于死亡证明的教育报告陈述有误，而且各州的差异导致数据缺失。教育在通过国家死亡指数(NDI)与随后的死亡率联系起来的调查数据中更为准确，例如人口普查局的国家纵向死亡率研究(NLMS)。然而，在NLMS等调查中的后续行动并不完美，这使得很难单独估计死亡率，而不是调查自然减员和未能与死亡记录联系起来。在估计选定年份受教育程度预期寿命差异的为数不多的研究中，只有一项研究试图对这些数据问题进行调整，这被认为不够准确，不能根据其他年份的受教育情况编制寿命表。我们的第一个目标是将MCD和NLMS数据结合起来，并进行分析，以解决这些数据中的这些问题，并按年龄、性别、种族和教育估计死亡率。由于我们非常了解总死亡率，我们将确保我们的估计死亡率与全国总死亡率相加。此外，NLMS中的许多样本量在特定的年龄、性别、种族和教育群体中都很小，因此我们需要使各年龄段的死亡率保持平稳，并可能随着时间的推移保持平稳。所有这一切使用贝叶斯估计程序是很自然的，我们将开发和实施该程序。我们的方法提供了一种独特、灵活和强大的跨两个数据源借用信息的方法，以弥补每个数据源的不足。在相似年龄和教育程度的子组中的死亡率之间的平滑减少了调查数据中的噪声，而分层模型将不确定性传播到最终估计，而先验分布提供了一种一致的方法来纳入重要的统计派生的约束 死亡率参数，特别是各教育类别的死亡率总和。我们的第二个目标是根据教育程度的变化进行调整，并重新计算生命表，使不同教育群体中的人口比例随着时间的推移保持一致。我们的第三个目标是与更广泛的研究人员、政策制定者和任何其他对结果感兴趣的人分享这些生命表。我们将在网上发布生命表，并通过多种渠道宣布，包括新闻稿、在公共和私人论坛上的公告，以及发表同行评议的研究文章。这将是一项有价值的服务，具有为研究和政策提供信息的潜力，使我们能够更好地理解和解决寿命方面的社会经济差距。

 DESCRIPTION (provided by applicant): Health and survival are known to be worse for those with less education in the United States. The discovery of substantial and growing differences in survival by education level has sparked controversy regarding whether those of lower socioeconomic status are increasingly being left behind, or whether differences can be explained by changes in the composition of groups over time. For example, as it becomes rarer for individuals not to complete high school or post-secondary education, this group becomes more selected in terms of characteristics that predict future health. However, the data for examining life expectancy by education level are not ideal. Vital Statistics data accurately measure deaths, but education reports on death certificates are known to be stated with error, and differences across states result in missing data. Education is more accurate in survey data that is linked to subsequent mortality via the National Death Index (NDI), such as the Census Bureau's National Longitudinal Mortality Study (NLMS). However, follow-up in surveys such as the NLMS is less than perfect, making it difficult to estimate mortality separate from survey attrition and failure to link to death records. Of the few studies estimating educational differences in life expectancy in selected years, only one attempted an adjustment for these data issues, and that was not considered precise enough for production of life tables by education in other years. Our first aim is to combine the MCD and NLMS data and perform analyses to address these problems with the data and estimate mortality by age, sex, race and education. Since we know total mortality well, we will ensure that our estimated mortality rates add up to national totals. Also, many of the sample sizes in the NLMS are small within specific age, gender, race, and education groups, so we need to smooth mortality rates across ages, and potentially over time. All of this is natural using a Bayesian estimation procedure, which we will develop and implement. Our methods provide a unique, flexible, and powerful means of borrowing information across two data sources to make up for deficiencies in each. Smoothing across mortality in similar age and education subgroups reduces noisiness in the survey data, while hierarchical models propagate uncertainty through to the final estimates, and prior distributions provide a consistent means to incorporate vital statistics-derived constraints on the mortality rate parameters, particularly rates summed across the education categories. Our second aim is to adjust for shifting educational attainment and re-calculate life tables that equalize the proportion of people in the different education groups over time. Our third aim is to share these life tables with the wider population of researchers, policy-makers, and anyone else with an interest in the results. We will post the life tables on the web, and announce it through multiple channels, including press-releases, announcements on public and private forums, and the publication of peer- reviewed research articles. This will be a valuable service, with potentia to inform both research and policy, enabling us to better understand and address socioeconomic disparities in longevity.