Uncovering Life Course Constellations of Exposures through Big Data on Place, Time, and Family Factors

通过关于地点、时间和家庭的大数据揭示暴露的生命历程结论 因素 项目摘要 这个项目将按地点、时间和家庭追踪20世纪初美国出生队列的死亡率 各种因素。将大数据与大量背景曝光相结合，我们大幅深化了我们的 理解儿童如何接触疾病、经济变化和自然环境的复杂性 灾难塑造了出生于1910-1930年龄段人群的老年死亡率概况。我们将假设驱动的信息融合在一起 测试、数据驱动的发现，以及来自方差分解的综合测量。我们的建议结合了 海量的CenSoc数据，其中包含1975-2005年间的1500万条死亡记录，以测试特定的 假设，并围绕主要的、交互的和累积的影响生成新的假设 在发育的敏感时期暴露，可能会影响这些队列的死亡经历。 我们由社会学家、人口学家、经济学家、流行病学家和其他人组成的跨学科小组 我们在CenSoc数据以及测试成人发育起源假说方面的专业知识 健康和疾病理论追求一套相互关联的具体目标，以推动 了解早期生命暴露和后来的生命死亡率之间的复杂联系。 目标1以一组按时间和地点分层的方差分解开始，跨越20世纪初 为了编制一份估计家庭背景(兄弟姐妹关系)重要性的“地图集” 由于共同的环境因素(童年邻居的相关性)决定了老年人的死亡经历 20世纪末。然后我们问，这些估计是否受到重大疾病事件的影响 这些模式在中年通过社会经济地位标志得到解释的程度。AIM 2枢轴 通过利用“自然实验”研究设计从森林到树木来估计因果关系的主要和 特定早期生活暴露的交互影响以及这些影响如何因性别、地理和家庭而异 背景资料。然后，我们使用机器学习工具来综合估计，这些估计可能会因年龄而异 曝光、曝光顺序和早期生命中的曝光范围。这些模型探讨了 累积暴露、暴露的动态互补性和早期侮辱的可逆性 使用其他地方没有的强大的分析。目标3总结了我们的分析，推动了 通过使用以前的数据进行代际分析，目的是链接回父母关于接触和 问父母的暴露是否会影响下一代的老年死亡率，以及 暴露会在几代人之间产生互动。

Uncovering Life Course Constellations of Exposures through Big Data on Place, Time, and Family Factors Project Abstract This project will trace the mortality of birth cohorts of the early 20th century in the US by place, time, and family factors. Combining “big data” with a large array of contextual exposures, we substantially deepen our understanding of the complexities of how childhood exposures to disease, economic change, and natural disasters shape old age mortality profiles of cohorts born ~1910-1930. We fuse together hypothesis driven tests, data driven discoveries, and omnibus measures from variance decompositions. Our proposal combines the massive CenSoc data, which contains >15 million death records between 1975-2005 to test specific hypotheses as well as generate new hypotheses around the main, interactive, and cumulative effects of exposures during sensitive periods of development that may shape mortality experiences of these cohorts. Our interdisciplinary group of sociologists, demographers, economists, epidemiologists and others combines our expertise with the CenSoc data as well as with testing hypotheses from the Developmental Origins of Adult Health and Disease theories to pursue an interconnected set of specific aims to push forward the frontier of understanding the complex links between early life exposures and later life mortality. Aim 1 begins with a set of variance decompositions stratified by time and place across the early 20th century in order to construct an “Atlas” of estimates of the importance of family background (sibling correlations) as well as shared environmental factors (childhood neighbor correlations) determining old age mortality experiences at the close of the 20th century. We then ask whether these estimates are shaped by major disease events and the extent to which the patterns are explained through socioeconomic status markers in mid life. Aim 2 pivots from the forest to the trees by leveraging “natural experiment” research designs to estimate causal main and interactive effects of specific early life exposures and how these effects vary by sex, geography, and family background. We then make use of machine learning tools to synthesize estimates that may vary by age of exposure, sequence of exposures, and domain of exposures during early life. These models explore impacts of cumulative exposures, dynamic complementarity of exposures and potential for reversibility of early insults using well powered analysis not available elsewhere. Aim 3 concludes our analysis by pushing the frontier of intergenerational analysis by using data in previous aims to link back to parental information on exposures and ask whether parental exposures affect the next generation’s old age mortality as well as whether the effects of exposures interact across generations.