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

按教育状况构建美国生命表，1990-2011

• 批准号: 9326122
• 负责人: DAVID M CUTLER
• 金额: $ 8.21万
• 依托单位: NATIONAL BUREAU OF ECONOMIC RESEARCH
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-15 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9326122
• 关键词: Address; Affect; Age; Bayesian Modeling; Categories; Cause of Death; Censuses; Cessation of life; Characteristics; Code; Data; Data Adjustments; Data Sources; Death Certificates; Death Records; Differential Mortality; Drops; Education; Educational Background; Educational Status; Eligibility Determination; Ensure; Failure; Follow-Up Studies; Future; Gender; Goals; Health; Individual; Internet; Left; Life Expectancy; Life Tables; Link; Longevity; Measures; Medicare; Methods; Modeling; Paper; Policies; Policy Maker; Population; Press Releases; Privatization; Procedures; Production; Public Policy; Publications; Race; Reporting; Research; Research Peer Review; Research Personnel; Sample Size; Secondary Schools; Services; Social Security; Socioeconomic Status; Subgroup; Sum; Surveys; Time; Uncertainty; United States; Vital Statistics; Woman; base; flexibility; follow-up; high school; indexing; interest; low socioeconomic status; mortality; population survey; public health relevance; sex; socioeconomic disparity; trend

 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.

 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生存状况更差。人们发现，受教育程度不同的人在生存方面的差异很大，而且越来越大，这引发了一场争论，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用群体组成随时间的变化来解释。例如，由于没有完成高中或中学后教育的人越来越少，这个群体在预测未来健康的特征方面变得更加挑剔。但是，按教育水平审查预期寿命的数据并不理想。生命统计数据准确地衡量死亡，但死亡证明上的教育报告是错误的，各州之间的差异导致数据缺失。教育在通过国家死亡指数（NDI）与随后的死亡率相关的调查数据中更准确，例如人口普查局的国家纵向死亡率研究（NLMS）。然而，NLMS等调查的后续行动并不完美，因此很难估计死亡率，而不考虑调查减员和未能与死亡记录联系。在估计某些年份教育对预期寿命的影响的少数研究中，只有一项研究试图对这些数据问题进行调整，但据认为这不够精确，无法按教育程度编制其他年份的寿命表。我们的第一个目标是结合联合收割机和NLMS数据，并进行分析，以解决这些问题的数据和估计死亡率的年龄，性别，种族和教育。由于我们对总死亡率非常了解，我们将确保我们估计的死亡率加起来与全国总数相符。此外，NLMS中的许多样本量在特定年龄，性别，种族和教育群体中都很小，因此我们需要平滑不同年龄段的死亡率，并可能随着时间的推移。所有这一切都是自然使用贝叶斯估计过程，我们将开发和实现。我们的方法提供了一种独特、灵活和强大的方法，可以跨两个数据源借用信息，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，对死亡率进行平滑可以减少调查数据中的噪声，而分层模型将不确定性传播到最终估计值，先验分布提供了一种一致的方法，可以将重要的统计学衍生的约束纳入调查数据。 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调整教育程度的变化，重新计算生命表，使不同教育群体的人口比例随着时间的推移而均衡。我们的第三个目标是与更广泛的研究人员、政策制定者和其他对结果感兴趣的人分享这些生命表。我们会把生命表放在网上，并透过多个渠道公布，包括新闻稿、在公开及私人论坛公布，以及发表经同行评审的研究文章。这将是一项有价值的服务，有可能为研究和政策提供信息，使我们能够更好地理解和解决长寿方面的社会经济差异。