Importance: Deaths from self-injury are increasing. Understanding the sources of risk is important for prevention and treatment. Objective: To estimate the risks of suicide and drug poisoning deaths among adult men whose adolescent occupational expectations were not met in adulthood. Design, Setting, and Participants: This cohort study included a sample of men interviewed as part of the High School and Beyond study, a nationally representative study of US high school sophomores and seniors in 1980, who were interviewed every 2 years through 1986; those who were sophomores in 1980 were reinterviewed in 1992. Men who survived to 1992 and reported occupational expectations were included in the present study. Death records prior to 2018 were linked to mortality databases and released in 2019. Data analysis was conducted from May to October 2020. Exposure: Occupational expectations. Main Outcomes and Measures: Survival or death by suicide, drug poisoning, chronic liver disease, heart disease, cancer, or some other cause, categorized from International Classification of Diseases, Ninth Revision and Tenth Revision codes. Competing risk Fine-Gray survival models regressed cause of death on adolescent occupational expectations and covariates. Results: The 11680 men in the High School and Beyond cohort study had a median (interquartile range) age of 29 (28-30) years in 1992, when the analysis of their future mortality began. Most men survived until 2015 (11060 [weighted percentage, 95.0%]). Reported causes of death were suicide (60 [weighted percentage, 0.5%]), drug poisoning (40 [weighted percentage, 0.4%]), chronic liver disease (20 [weighted percentage, 0.2%]), heart disease (130 [weighted percentage, 1.0%]), cancer (100 [weighted percentage, 1.0%]), and other (280 [weighted percentage, 2.0%]). Subhazard ratios for death by suicide and drug poisoning were 2.91 (95% CI, 1.07-7.88; P =.04) and 2.62 (95% CI, 1.15-5.94; P =.02) times higher, respectively, among those who in 1980 expected to hold a subbaccalaureate occupation that later declined in labor market share compared with those with professional occupational expectations. The actual job held by men did not attenuate the hazards of deaths from suicide and drug poisoning. Conclusions and Relevance: In this cohort study, men whose occupational expectations were not met because of labor market declines were at a higher risk of death from suicide or drug poisoning than men with different occupational expectations. Interventions to mitigate labor market changes should account for individuals' expectational ideals.

Evidence of a strong negative correlation between adolescent academic performance and mortality points to the importance of not only cognitive, but also non-cognitive, skills in predicting survival. We integrated two bodies of research to evaluate expectations regarding the role of educational attainment and trajectories of employment and marriage experience in mediating relationships between high school class rank and longevity. In particular, we used data from the Wisconsin Longitudinal Study (n = 9,232) to fit parametric mortality models from age 55 to age 77. Multiple mediator models allowed for quantification of the degree to which the association between high school class rank and mortality is mediated by life trajectories and educational attainment. Our results show that high school class rank is a statistically significant and substantively meaningful predictor of survival beyond age 55 and that this relationship is partially, but not fully, mediated by trajectories of employment and marriage experience across the life course. Higher educational attainment also mediates a substantial part of the relationship, but to varying degrees for men and women.

<p>What dimensions of education matter for people’s chances of surviving young adulthood? Do cognitive skills, noncognitive skills, course-taking patterns, and school social contexts matter for young adult mortality, even net of educational attainment? The authors analyze data from High School and Beyond, a nationally representative cohort of about 25,000 high school students first interviewed in 1980. Many dimensions of education are associated with young adult mortality, and high school students’ math course taking retains its association with mortality net of educational attainment. This work draws on theories and measures from sociological and educational research and enriches public health, economic, and demographic research on educational gradients in mortality that has relied almost exclusively on ideas of human capital accumulation and measures of degree attainment. The findings also call on social and education researchers to engage together in research on the lifelong consequences of educational processes, school structures, and inequalities in opportunities to learn.</p>

Does education change people’s lives in a way that delays mortality? Or is education primarily a proxy for unobserved endowments that promote longevity? Most scholars conclude that the former is true, but recent evidence based on Danish twin data calls this conclusion into question. Unfortunately, these potentially field-changing findings—that obtaining additional schooling has no independent effect on survival net of other hard-to-observe characteristics—have not yet been subject to replication outside Scandinavia. In this article, we produce the first U.S.-based estimates of the effects of education on mortality using a representative panel of male twin pairs drawn from linked complete-count census and death records. For comparison purposes, and to shed additional light on the roles that neighborhood, family, and genetic factors play in confounding associations between education and mortality, we also produce parallel estimates of the education-mortality relationship using data on (1) unrelated males who lived in different neighborhoods during childhood, (2) unrelated males who shared the same neighborhood growing up, and (3) non-twin siblings who shared the same family environment but whose genetic endowments vary to a greater degree. We find robust associations between education and mortality across all four samples, although estimates are modestly attenuated among twins and non-twin siblings. These findings—coupled with several robustness checks and sensitivity analyses—support a causal interpretation of the association between education and mortality for cohorts of boys born in the United States in the first part of the twentieth century.

This paper presents a new probabilistic method of record linkage, developed using the U.S. full count censuses of 1900 and 1910 but applicable to a range of different sources of historical records. The method was designed to exploit a more comprehensive set of individual and contextual characteristics present in historical census data, aiming to obtain a machine learning algorithm that better distinguishes between multiple potential matches. Our results demonstrate that the method achieves a match rate that is twice as high other currently popular methods in the literature while at the same time also achieving greater accuracy. In addition, the method only performs negligibly worse than other algorithms in resembling the target population.

Many sociologists suspect that publication expectations have risen over time-that how much graduate students have published to get assistant professor jobs and how much assistant professors have published to be promoted have gone up. Using information about faculty in 21 top sociology departments from the American Sociological Association's Guide to Graduate Departments of Sociology, online curricula vitae, and other public records, I provide empirical evidence to support this suspicion. On the day they start their first jobs, new assistant professors in recent years have already published roughly twice as much as their counterparts did in the early 1990s. Trends for promotion to associate professor are not as dramatic but are still remarkable. I evaluate several potential explanations for these trends and conclude that they are driven mainly by changes over time in the fiscal and organizational realities of universities and departments.

© 2019 Elsevier Inc. Background: Land use regression (LUR) models have been widely used to estimate air pollution exposures at high spatial resolution. However, few LUR models were developed for rapidly developing urban cores, which have substantially higher densities of population and built-up areas than the surrounding areas within a city's administrative boundary. Further, few studies incorporated vertical variations of air pollution in exposure assessment, which might be important to estimate exposures for people living in high-rise buildings. Objective: A LUR model was developed for the urban core of Lanzhou, China, along with a model of vertical concentration gradients in high-rise buildings. Methods: In each of four seasons in 2016–2017, NO2 was measured using Ogawa badges for 2 weeks at 75 ground-level sites. PM2.5 was measured using DataRAM for shorter time intervals at a subset (N = 38) of the 75 sites. Vertical profile measurements were conducted on 9 stories at 2 high-rise buildings (N = 18), with one building facing traffic and another facing away from traffic. The average seasonal concentrations of NO2 and PM2.5 at ground level were regressed against spatial predictors, including elevation, population, road network, land cover, and land use. The vertical variations were investigated and linked to ground-level predictions with exponential models. Results: We developed robust LUR models at the ground level for estimated annual averages of NO2 (R2: 0.71, adjusted R2: 0.67, and Leave-One-Out Cross Validation (LOOCV) R2: 0.64) and PM2.5 (R2: 0.77, adjusted R2: of 0.73, and LOOCV R2: 0.67) in the urban core of Lanzhou, China. The LUR models for the estimated seasonal averages of NO2 showed similar patterns. Vertical variation of NO2 and PM2.5 differed by windows orientation with respect to traffic, by season or by time of a day. Vertical variation functions incorporated the ground-level LUR predictions, in a form that could allow for exposure assessment in future epidemiological investigations. Conclusions: Ground-level NO2 and PM2.5 showed substantial spatial variations, explained by traffic and land use patterns. Further, vertical variation of air pollution levels is significant under certain conditions, suggesting that exposure misclassification could occur with traditional LUR that ignores vertical variation. More studies are needed to fully characterize three-dimensional concentration patterns to accurately estimate air pollution exposures for residents in high-rise buildings, but our LUR models reinforce that concentration heterogeneity is not captured by the limited government monitors in the Lanzhou urban area.

Past research finds that the effect of socioeconomic origin on the probability of making educational transitions decreases over the educational career from primary to graduate school. Some have argued that this pattern of waning is the result of selective attrition, since those of modest social origins who make a given transition may have exceptional cognitive or noncognitive skills while more advantaged individuals may rely less heavily on these skills to continue their education. We study a sample of American 10th graders from 1980 to assess how much the pattern of waning effects is due to selective attrition along noncognitive skills for this cohort. We find that controlling for noncognitive skills does not make the effect of socioeconomic origin more stable across transitions. Still, socioeconomic advantage does not decline uniformly across transitions, and it appears most pronounced at the transition into college, whether accounting for noncognitive skills or not. Our results suggest that origins continue to drive educational attainment even among those who make it to postsecondary transitions.

Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, noncoding variants from which pinpointing causal genes remains challenging. Here we combined data from 718,734 individuals to discover rare and low-frequency (minor allele frequency (MAF) < 5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which 8 variants were in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants were in genes (MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants were in GIPR. The effect sizes of rare variants are ~10 times larger than those of common variants, with the largest effect observed in carriers of an MC4R mutation introducing a stop codon (p.Tyr35Ter, MAF = 0.01%), who weighed ~7 kg more than non-carriers. Pathway analyses based on the variants associated with BMI confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity.

Educational gradients in health status, morbidity, and mortality are well established, but which aspects of schooling produce those gradients is only partially understood. We draw on newly available data from the midlife follow-up of the High School and Beyond sophomore cohort to analyze the relationship between students' level of coursework in high school and their long-term health outcomes. We additionally evaluate the mediating roles of skill development, postsecondary attendance and degree attainment, and occupational characteristics. We find that students who took a medium- to high-level course of study in high school have better self-reported health and physical functioning in midlife, even net of family background, adolescent health, baseline skills, and school characteristics. The association partially operates through pathways into postsecondary education. Our findings have implications for both educational policy and research on the educational gradient in health.

Does participation in one wave of a survey have an effect on respondents' answers to questions in subsequent waves? In this article, we investigate the presence and magnitude of "panel conditioning" effects in one of the most frequently used data sets in the social sciences: the General Social Survey (GSS). Using longitudinal records from the 2006, 2008, and 2010 surveys, we find evidence that at least some GSS items suffer from this form of bias. To rule out the possibility of contamination due to selective attrition and/or unobserved heterogeneity, we strategically exploit a series of between-person comparisons across time-in-survey groups. This methodology, which can be implemented whenever researchers have access to at least three waves of rotating panel data, is described in some detail so as to facilitate future applications in data sets with similar design elements.

Concern with childhood nutrition prompted numerous surveys of children's growth in the United States after 1870. The Children's Bureau's 1918 "Weighing and Measuring Test" measured two million children to produce the first official American growth norms. Individual data for 14,000 children survives from the Saint Paul, Minnesota survey whose stature closely approximated national norms. As well as anthropometry the survey recorded exact ages, street address and full name. These variables allow linkage to the 1920 census to obtain demographic and socioeconomic information. We matched 72% of children to census families creating a sample of nearly 10,000 children. Children in the entire survey (linked set) averaged 0.74 (0.72) standard deviations below modern WHO height-for-age standards, and 0.48 (0.46) standard deviations below modern weight-for-age norms. Sibship size strongly influenced height-for-age, and had weaker influence on weight-for-age. Each additional child six or underreduced height-for-age scores by 0.07 standard deviations (95% CI: -0.03, 0.11). Teenage siblings had little effect on height-forage. Social class effects were substantial. Children of laborers averaged half a standard deviation shorter than children of professionals. Family structure and socio-economic status had compounding impacts on children's stature.

Purpose Researchers who study mortality among survey participants have multiple options for obtaining information about which participants died (and when and how they died). Some use public record and commercial databases; others use the National Death Index; some use the Social Security Death Master File; and still others triangulate sources and use Internet searches and genealogic methods. We ask how inferences about mortality rates and disparities depend on the choice of source of mortality information. Methods Using data on a large, nationally representative cohort of people who were first interviewed as high school sophomores in 1980 and for whom we have extensive identifying information, we describe mortality rates and disparities through about age 50 using four separate sources of mortality data. We rely on cross-tabular and multivariate logistic regression models. Results These sources of mortality information often disagree about which of our panelists died by about age 50 and also about overall mortality rates. However, differences in mortality rates (i.e., by sex, race/ethnicity, education) are similar across of sources of mortality data. Conclusion Researchers' source of mortality information affects estimates of overall mortality rates but not estimates of differential mortality by sex, race and/or ethnicity, or education.

Over the past half century, American children have experienced increasingly unequal childhoods. The goal of this article is to begin to understand the implications of recent trends in social and economic inequalities among children for the future of inequalities in health among adults. The relative importance of many of the causal pathways linking childhood social and economic circumstances to adult health remains underexplored, and we know even less about how these causal pathways have changed over time. I combine a series of original analyses with reviews of relevant literature in a number of fields to inform a discussion of what growing childhood inequalities might mean for future inequalities in adult health. In the end, I argue that there is good reason to suppose that growing inequalities in children’s social and economic circumstances will lead to greater heterogeneity in adults’ morbidity and mortality.

The National Center for Education Statistics’ (NCES) longitudinal student surveys have long been exceptionally useful for many purpose. Despite their many virtues, however, these surveys cannot be used to monitor trends at short time intervals, they do not allow for flexible changes to survey content, they cannot generally be used to infer policy effects, they are not useful for international comparisons, and they are of limited value to local stakeholders. NCES should consider doing to its longitudinal students surveys what the Census Bureau did to the decennial census long form and what NORC has long done for the General Social Survey: Move to annual rotating panels and allow outside investigators to field (and fund) supplemental topical modules. NCES should also continue to work with the research community to explore new survey content areas and modes of observation, improve the quality of spatial measures, and pursue record linkage to administrative data.

Data on age-sequenced trajectories of individuals' attributes are used for a growing number of research purposes. However, there is no consensus about which method to use to identify the number of discrete trajectories in a population or to assign individuals to a specific trajectory group. We modeled real and simulated trajectory data using "naïve" methods, optimal matching, grade of membership models, and three types of finite mixture models. We found that these methods produced inferences about the number of trajectories that frequently differ (1) from one another and (2) from the truth as represented by simulation parameters. We also found that they differed in the assignment of individuals to trajectory groups. In light of these findings, we argue that researchers should interpret results based on these methods cautiously, neither reifying point estimates about the number of trajectories nor treating individuals' trajectory group assignments as certain.

In this article I define the main criteria that ought to be considered in evaluating the costs and benefits of various data resources that might be used for a new study of social and economic mobility in the United States. These criteria include population definition and coverage, sample size, topical coverage, temporal issues, spatial issues, sustainability, financial expense, and privacy and data access. I use these criteria to evaluate the strengths and weakness of several possible data resources for a new study of mobility, including existing smaller-scale surveys, the Current Population Survey, the American Community Survey, linked administrative data, and a new stand-alone survey. No option is perfect, and all involve trade-offs. I conclude by recommending five possible designs that are particularly strong on the criteria listed above.

In December 2014, the Center for Migration Studies of New York (CMS) released a paper that provides new estimates of the US unauthorized resident population (Warren 2014). The paper describes the development of a new dataset which has detailed information about unauthorized residents, derived from data collected in the US Census Bureaus American Community Survey (ACS). The dataset will be useful to scholars, researchers, service-providers, and government officials in crafting, implementing, and evaluating programs that serve noncitizens, including the unauthorized. In addition, the new estimates provide an opportunity to examine the dramatic changes in unauthorized immigration in the past two decades and the assumptions that have shaped US policies and public opinion.