An important topic in the study of segregation are comparisons across space and time. This article extends current approaches in segregation measurement by presenting a five-term decomposition procedure that can be used to understand more clearly why segregation has changed or differs between two comparison points. Two of the five terms account for differences in segregation that are due to the differing marginal distributions (e.g., the gender and occupational distributions), while one term accounts for differences in segregation due the different structure of segregation (what might be termed “pure” segregation). The decomposition thus presents a solution to the problem of margin dependency, frequently discussed in the segregation literature. Finally, two terms account for the appearance or disappearance of units when analyzing change over time. The method can be further extended to attribute structural changes to individual units, which makes it possible, for instance, to quantify the effect of each occupation on changing gender segregation. The practical advantages of the decomposition are illustrated by two examples: a study of changing occupational gender segregation in the United States and a study of changing residential segregation in Brooklyn, New York.

Households behave systematically in family planning and intra-household resource allocation. A neglected area in historical health studies is the relationship between body mass index (BMI), health and family size. Using robust statistics and a large nineteenth-century BMI data set, this study uses an overlapping generations model to explain resource allocation within the household and illustrates that there is a positive relationship across the BMI distribution with family size. There is also a positive relationship between BMIs and average wealth, and an inverse relationship between BMI and inequality. After controlling for family size and wealth characteristics, there was a positive relationship between BMI and worker’s agricultural rural status.

David Wong, Professor, Geography and Geoinformation Science, received $50,000 from the University of Wisconsin-Madison for the project: "An intersectional approach to improve family and social support measures of county health - Incorporating racial-ethnic and age dimensions."

Cities with cleaner power plants and lower energy demand tend also to have tighter land-use restrictions; these restrictions increase housing prices and reduce the incentive for households to live in these lower greenhouse gas-emitting cities. We use a spatial equilibrium model to quantify the overall effects of land-use restrictions on the levels and spatial distribution of household carbon emissions. Our model features heterogeneous households, cities that vary in both their power plant technologies and their utility benefits of energy usage, as well as endogenous wages and rents. Relaxation of the current land-use restrictions in California to the level faced by the median urban household in the US leads to a 0.6% drop in national household carbon emissions and a decrease in the social cost of carbon of $310 million annually.

With the onset of the Covid-19 pandemic, Asians became the victims of a sudden increase in racial discrimination as public officials repeatedly referred to the virus as the "Chinese virus." We document that Asian entrepreneurship has been disproportionally hurt after January 2020, particularly among Asian immigrants, declining by 17 percent when compared to non-Hispanic whites. Examining the dynamics of transitions into and out of self-employment, we find a substantial increase in Asian immigrants' self-employment exits, increased necessity entries, and reductions in opportunity entries – patterns suggestive of customer and employer 'taste discrimination'. The pandemic has also proven particularly harmful on businesses owned by recently arrived immigrants and by East Asian immigrants. While Asian enclaves help palliate the pandemic's damaging impact, the latter has reached a broad spectrum of businesses. Gaining a better understanding of how the pandemic has impacted Asian businesses is crucial to inform about the emergence of discriminatory behaviors that widen inequities and endanger a fast recovery.

When it comes to studying the impacts of decision making, the research has been largely focused on examining the fairness of the decisions, the long-term effects of the decision pipelines, and utility-based perspectives considering both the decision-maker and the individuals. However, there has hardly been any focus on precarity which is the term that encapsulates the instability in people's lives. That is, a negative outcome can overspread to other decisions and measures of well-being. Studying precarity necessitates a shift in focus-from the point of view of the decision-maker to the perspective of the decision subject. This centering of the subject is an important direction that unlocks the importance of parting with aggregate measures to examine the long-term effects of decision making. To address this issue, in this paper, we propose a modeling framework that simulates the effects of compounded decision-making on precarity over time. Through our simulations, we are able to show the heterogeneity of precarity by the non-uniform ruinous aftereffects of negative decisions on different income classes of the underlying population and how policy interventions can help mitigate such effects.

Population censuses have never been and never will be perfect. The 2020 Census was not likely perfect, either. The challenges and obstacles to conducting the 2020 Census were numerous and varied—from its politicization to the pandemic—and accuracy and fairness were likely affected. Questions and concerns have been raised about the quality of the 2020 Census and whether the data will be as accurate as previous censuses (GAO 2020c; Thompson 2021). The goal of the Urban Institute’s study was to address such questions about quality and provide additional data about the 2020 Census’s accuracy and fairness. Urban created an innovative methodology—a simulation of the 2020 Census—to better understand the decennial census’s performance.

We hypothesize that when interpersonal comparisons, often referred to as “keeping up with the Joneses”, are operational, relative deprivation (income inequality) results in increased likelihood of morbidity among lower income households. Using a simple theoretical model, we show that the larger the income disparities between “the Joneses” and “the followers”, the higher is the followers' expenditure on conspicuous consumption and the lower is their expenditure on health. We empirically test our hypotheses using Canadian data from the Canadian Community Health Survey and the Survey of Household Spending and US data from the National Health Interview Survey. We find that, in peer groups defined by geographic proximity of residence or similar socio-economic background, larger income disparities are associated with higher spending by the followers on conspicuous consumption, lower health expenditure, worse self-reported health and younger age at death.

AT&T was the largest U.S. firm for most of the 20th century. Telephone operators once comprised over 50% of its workforce, but in the late 1910s it initiated a decades-long process of automating telephone operation with mechanical call switching—a technology invented in the 1880s. We study what drove AT&T to do so, and why it took nearly a century. Interdependencies between call switching and nearly every other activity in AT&T's business presented obstacles to change: telephone operators were the fulcrum of a complex production system which had developed around them, and automation only began after the firm and new technology were adapted to work together. Even then, automatic switching was only profitable in larger markets— hence diffusion expanded when the technology improved or service areas grew. The example suggests even narrowly-defined tasks can be difficult to automate if they interact with many others.

This paper examines if internal migrants at the turn of the twentieth century have influenced the long-term economic development of the counties where they settled over 100 years ago. Using Census microdata from 1880 and 1910, the distance travelled by American-born migrants between birthplace and county of residence is examined to assess its relevance for the economic development of US counties today. The settlement patterns of domestic migrants across the 48 continental states are then linked to current county-level development. Factors influencing both migration at the time and the level of development of the county today are controlled for. The results of the analysis underline the economic importance of internal migration. Counties that attracted American-born migrants more than 100 years ago are significantly richer today. Moreover, distance is crucial for the impact of internal migration on long-term economic development; the larger the distance travelled by domestic migrants, the greater the long-term economic impact on the receiving territories.

Objective This short communication investigates the usefulness of time-use measures to validate subjective health measures such as self-reported health (SRH). It does this by examining time-use patterns and SRH among middle-age adults in the United States distinguished by race/ethnicity and with additional attention to differences in responses based on language of interview for Hispanics. Methods Data for this study come from the 2013–2016 American Time Use Survey. We calculated average time-use for personal care; housework; paid work; leisure; volunteering/travel; caregiving; and education for every racial/ethnic group differentiating by SRH for 27,063 adults aged 25–64 years. A series of ANOVAs were computed to assess differences in time-use by SRH. Results Non-Hispanic whites and non-Hispanic Blacks who reported poor/fair SRH spent more time in personal care and leisure, and less time in paid work, volunteering/travel, caregiving and education, in comparison to those who reported Excellent/Very Good/Good SRH. Among Hispanics, differences by SRH were found for personal care, paid work, leisure and volunteering/travel. Hispanics who answered in English displayed partially similar patterns in SRH found for non-Hispanic whites and Blacks. Hispanics who answered in Spanish demonstrated differences in SRH in the areas of paid work, leisure and education, diverging from the other groups. Conclusions Time-use differences by health status are consistent between non-Hispanic whites, non-Hispanic blacks, but not so for Hispanics. To some extent, Hispanics who answered in English have more comparable patterns to non-Hispanic whites and non-Hispanic Blacks than Spanish respondents. Caution should be exercised when self-reported health measures are used to compare diverse samples collected with surveys that are administered in different languages.

There remains a large gender imbalance in the science, technology, engineering and mathematics (STEM) workforce deriving from a leaky pipeline where women start losing interest and confidence in science and engineering as early as primary school. To address this disparity, the Science Research &amp; Engineering Program (SREP) at Hathaway Brown School was established in 1998 to engage and expose their all-female high school students to STEM fields through an internship-like multi-year research experience at partnering institutions. We compare data from existing Hathaway Brown School SREP alumnae records from 1998–2018 ( <italic>n = 495</italic> ) to Non-SREP students and national datasets (National Center for Educational Statistics, National Science Foundation, and US Census data) to assess how SREP participation may influence persistence in the STEM pipeline and whether SREP alumnae attribute differences in these outcomes to the confidence and skill sets they learned from the SREP experience. The results reveal that women who participate in the SREP are more likely to pursue a major in a STEM field and continue on to a STEM occupation compared to non-SREP students, national female averages, and national subsets. Participants attribute their outcomes to an increase in confidence, establishment of technical and professional skills, and other traits strengthened through the SREP experience. These data suggest that implementing similar experiential programs for women in science and engineering at the high school stage could be a promising way to combat the remaining gender gap in STEM fields.

Policymakers at all levels of government are currently working to distribute the new COVID-19 vaccine to the U.S. population. Prioritizing vaccine resources across population groups poses difficult decisions for policymakers (Biggerstaff, 2020; Dooling et al., 2021). What are the implications of alternative prioritizations for national economic security versus public health and safety? Should resources be prioritized for individuals with the greatest risk of exposure to SARS-CoV-2, those most likely to develop severe COVID-19 if they become infected, those most likely to transmit the virus to others, or some combination? Should distribution be based on information regarding employment and health, or more simply based on age, and how should that information be verified? The Centers for Disease Control and Prevention (CDC) recently published guidelines for vaccination priority that seek to balance these competing objectives (CDC, 2021; White House, 2021). The guidelines prioritize groups based on long-term care residence, age, health status, and occupation, reflecting guidance from the Advisory Committee on Immunization Practices (ACIP) (McClung et al., 2020; Dooling et al., 2021). In addition to these national recommendations, every state is tailoring its vaccination plan to local factors, resulting in considerable variation across the country. Against this backdrop, this Research Findings report explores how different prioritization strategies can affect vaccine eligibility across poverty levels and across race/ethnicity. Understanding the implications of prioritization strategies on these dimensions takes on particular importance given striking inequities across the population in health risk factors and in COVID-19 mortality (Centers for Disease Control and Prevention, 2020a). We use data from the 2014–2017 Medical Expenditure Panel Survey (MEPS), sponsored by the Agency for Healthcare Research and Quality (AHRQ, 2019), to construct hierarchical population estimates for potential vaccine priority groups. MEPS is a household survey of the civilian noninstitutionalized population that collects a wide range of data including demographic characteristics, health conditions, use of medical services, charges and source of payments, access to care, satisfaction with care, health insurance coverage, income, and employment. It does not include nursing home residents or adults who are incarcerated, two groups with particularly high rates of COVID-19 morbidity and mortality. Nevertheless, MEPS is a valuable resource for conducting vaccine prioritization analyses of the rest of the population, because many key variables, including detailed employment and detailed health risk data, as well as income and race and ethnicity, are collected in the same survey—thereby capturing patterns of overlap across vaccine groups. We present three scenarios for vaccine prioritization. All three scenarios begin by prioritizing healthcare workers and then individuals age 65 and older (reflecting announcements already made in many states as of February 2021). The first scenario next prioritizes other groups of essential workers based on their having above-average risk of infection and potential to spread infection to others. The second scenario instead prioritizes adults at increased risk of severe COVID-19, conditional on infection. The third scenario examines the most administratively simple strategy of focusing solely on age (after healthcare workers). The priority groups we examine differ from those used in ACIP and CDC recommendations in several ways. Our definition of essential workers is based on U.S. Department of Homeland Security guidelines, and we combine this with information on ability to work at home. This likely differs from ACIP’s essential frontline and other essential worker categories. Also, our definition of adults with increased risk or potentially increased risk of severe COVID-19 includes adults with treated high blood pressure and current smokers, whereas neither risk factor would qualify an adult for inclusion in ACIP or CDC groups with underlying medical conditions. Finally, note also that all three prioritization scenarios follow many states in giving high priority to adults age 65 to 74. For each of the three prioritization scenarios we present estimates of eligibility group sizes and cumulative distributions stratified by health risk, occupation, and, of particular interest in this study, poverty level and race and ethnicity. Our focus is primarily on early prioritization, when infection rates are high and before the share of the population with immunity reaches levels that substantially reduce new infections. Our estimates focus on the characteristics of adults who would be eligible for the vaccine. The rates at which eligible populations become vaccinated will depend on a range of factors outside the scope of this analysis.

Range aggregate queries (RAQs) are an integral part of many real-world applications, where, often, fast and approximate answers for the queries are desired. Recent work has studied answering RAQs using machine learning models, where a model of the data is learned to answer the queries. However, such modelling choices fail to utilize any query specific information. To capture such information, we observe that RAQs can be represented by query functions, which are functions that take a query instance (i.e., a specific RAQ) as an input and output its corresponding answer. Using this representation, we formulate the problem of learning to approximate the query function, and propose NeuroDB, a query specialized neural network framework, that answers RAQs efficiently. We experimentally show that NeuroDB answers RAQs orders of magnitude faster than the state-of-the-art on real-world, benchmark and synthetic datasets. Furthermore, NeuroDB is query-type agnostic (i.e., it does not make any assumption about the underlying query type) and our observation that queries can be represented by functions is not specific to RAQs. Thus, we investigate whether NeuroDB can be used for other query types, by applying it to distance to nearest neighbour queries. We experimentally show that NeuroDB outperforms the state-of-the-art for this query type, often by orders of magnitude. Moreover, the same neural network architecture as for RAQs is used, bringing to light the possibility of using a generic framework to answer any query type efficiently.

Background/objectives Experimental studies of time-restricted eating suggest that limiting the daily eating window, shifting intake to the biological morning, and avoiding eating close to the biological night may promote metabolic health and prevent weight gain. Subjects/methods We used the Eating & Health Module of the 2006–2008 and 2014–2016 American Time Use Survey to examine cross-sectional associations of timing of eating in relation to sleep/wake times as a proxy for circadian timing with body mass index (BMI). The analytical sample included 38 302 respondents (18–64 years; BMI 18.5–50.0 kg/m2). A single 24-hour time use diary was used to calculate circadian timing of eating variables: eating window (time between first and last eating activity); morning fast (time between end of sleep and start of eating window); and evening fast (time between end of eating window and start of sleep). Multinomial logistic regression and predictive margins were used to estimate adjusted population prevalences (AP) by BMI categories and changes in prevalences associated with a one-hour change in circadian timing of eating, controlling for sociodemographic and temporal characteristics. Results A one-hour increase in eating window was associated with lower adjusted prevalence of obesity (AP = 27.1%, SE = 0.1%). Conversely, a one-hour increase in morning fast (AP = 28.7%, SE = 0.1%) and evening fast (AP = 28.5%, SE = 0.1%) were each associated with higher prevalence of obesity; interactions revealed differing patterns of association by combination of eating window with morning/evening fast (p < 0.0001). Conclusions Contrary to hypotheses, longer eating windows were associated with a lower adjusted prevalence of obesity and longer evening fasts were associated with a higher prevalence of obesity. However, as expected, longer morning fast was associated with a higher adjusted prevalence of obesity. Studies are needed to disentangle the contributions of diet quality/quantity and social desirability bias in the relationship between circadian timing of eating and BMI.

Objective: The objective of this study was to determine whether the Affordable Care Act’s (ACA) major coverage expansions mitigated the impact of unemployment on health insurance coverage status. Data Source: A 2011−2019 versions of the American Community Survey developed by the University of Minnesota Integrated Public Use Microdata Series program. Research Design: We use difference-in-difference-in-differences (ie, triple difference) regressions to compare changes in the short-run impacts of local unemployment rates before and after the ACA. Principal Findings: Before the ACA, rises in local unemployment were associated with uninsurance due to losses in private coverage (ie, both nongroup and employer sponsored). Following the ACA’s full implementation, the link between employment and coverage was attenuated by access to publicly subsidized qualified health plans on the ACA’s nongroup market, and enhanced access to Medicaid in states that expanded. Our findings suggest protections from unemployment-linked uninsured spells are largest in states that expanded Medicaid. Conclusions: Expanded access to coverage under the ACA could mitigate the adverse effects on insurance status and access to care historically linked to job loss. However, should the ACA be repealed, many households stand to lose their ability to turn to Medicaid or subsidized nongroup coverage as safety-net resources to offset the burdens of job loss. J.B. received support provided by the Robert Wood Johnson Foundation Policies for Action Program (grant #77341) as well as the DREAM Scholar Program in University of Kentucky’s Center for Clinical and Translational Sciences. The remaining authors declare no conflict of interest. Correspondence to: Joseph Benitez, PhD, Department of Health Management and Policy, College of Public Health, University of Kentucky, 111 Washington Avenue, Lexington, KY 40536. E-mail: joseph.benitez@uky.edu. Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.

While several areas in the United States have asked nurses and physicians who are not in the labor force to return to help with the COVID-19 pandemic, little is known about the characteristics of these clinicians that may present barriers to returning. We studied age, disability, and household composition of clinicians not in the workforce using the American Community Survey from 2014 to 2018, a nationally-representative survey of US households administered by the US Census. Overall, we found that, for nurses and physicians not in the labor force, over three-quarters were 55 and over and about 15 percent had a disability. For female nurses and physicians not in the labor force, over half of those ages 20–54 had a child under 15 at home and over half of those ages 65+ had another adult 65 and over at home. These characteristics may present challenges and risks to returning.

Nearly 1 in 5 children in the United States live in rural areas. Rural children experience health and health care disparities compared to their urban peers and represent a unique and vulnerable pediatric patient population. Important disparities exist in all-cause mortality, suicide, firearm-related unintentional injury, and obesity. Rural children experience decreased availability and accessibility of primary care and specialty care (especially mental health care) due to a decreased number of health care providers as well as geographical and transportation-related barriers. Other geographic and socioeconomic determinants, especially concerning poverty and substandard housing conditions, are likely important contributors to the observed health disparities. Increased funding for research focused on rural populations is needed to provide innovative solutions for the unique health needs of rural children. Policy changes positioned to correct the trajectory of poor health among children should consider the needs of rural children as an under-researched and under-resourced vulnerable population.

OBJECTIVES: The purpose of this study was to employ simulations to model the probability of mortality from COVID-19 (i.e., coronavirus) for older adults in the United States given at best and at worst cases. METHODS: This study first examined current epidemiological reports to better understand the risk of mortality from COVID-19. Past epidemiological studies from severe acute respiratory syndrome were also examined given similar virology. Next, at best and at worst mortality cases were considered with the goal of estimating the probability of mortality. To accomplish this for the general population, microdata from the National Health Interview Survey pooled sample (2016, 2017, and 2018 public-use NHIS with a sample of 34,881 adults at least 60 years of age) were utilized. Primary measures included age and health status (diabetes, body mass index, and hypertension). A logit regression with 100,000 simulations was employed to derive the estimates and probabilities. RESULTS: Age exhibited a positive association for the probability of death with an odds ratio (OR) of 1.22 (p < .05, 95% confidence interval [CI]: 1.05-1.42). A positive association was also found for body mass index (BMI) (OR 1.03, p < .01, 95% CI: 1.02-1.04) and hypertension (OR 1.36, p < .01, 95% CI: 1.09-1.66) for the at best case. Diabetes was significant but only for the at best case. DISCUSSION: This study found mortality increased with age and was notable for the 74-79 age group for the at best case and the 70-79 age group of the at worst case. Obesity was also important and suggested a higher risk for mortality. Hypertension also exhibited greater risk but the increase was minimal. Given the volume of information and misinformation, these findings can be applied by health professionals, gerontologists, social workers, and local policymakers to better inform older adults about mortality risks and, in the process, reestablish public trust.