At least 38 states have created service areas or “districts” for each of their community colleges. However, little is known about the geographic boundaries of community college districts and the policymaking process that defines them. We studied state policy documents nationally and the actual district boundaries of Texas community colleges to investigate the larger policymaking processes of determining boundaries. We found significant variation across the United States, including in who determines the boundaries and whether the districts have associated tuition reductions. In our case study, we also found evidence that the majority of Texas’s community college districts appear to reflect their larger local environments, although a small number may exhibit evidence of racial imbalance.

Two distinct patterns have emerged among groups of metropolitan areas and neighborhoods in the United States since 1980. While some have experienced rapid growth in population and new housing units, others have experienced rapid growth in housing prices instead. Between 1980 and 2018, the number of occupied housing units in US metropolitan areas grew from 80 million to 122 million. The location of this growth in housing stock is disproportionately oriented toward smaller, less-dense cities and neighborhoods rather than highpopulation metropolitan areas. Low-density suburbs have grown much faster than all other types of neighborhoods, accounting for 78 percent of the growth in housing units in the set of neighborhoods observed in 1980. However, home price growth has been fastest in high-density suburbs and the most prosperous and high-density areas of central cities. Moreover, the rate of new housing construction has been low or falling in all types of locations since 2000, but particularly so in the low-density suburbs and rural areas where most of the recent quantity growth has occurred. Altogether, the US housing stock has been getting older, more crowded, and less affordable in recent years.

More than one in five U.S. adults missed a medical appointment last year because they didn't have a way to get to it, according to a report by the Urban Institute. It's a problem that disproportionately affects women and households of color. Why it matters: Transportation is a key social driver of health equity. While telehealth has reduced some transportation barriers, it's not accessible to all and can't replace in-person care for all medical needs, Axios' Adriel Bettelheim reports. What they found: Though 91% of adults reported they had access to a vehicle, the figure was substantially lower for Black adults (81%), those with low family incomes (78%) or a disability (83%), and for individuals with public health insurance (79%) or no coverage (83%). Zoom in: In Atlanta, 17% of households don't have access to a vehicle, per the National Equity Atlas. Broken down by race, Black households are almost three times as likely to be without a vehicle as white households. There's also a glaring gender disparity. About 20% of Atlanta women are without a vehicle, compared to 13% of men. The big picture: A study in the American Journal of Preventive Medicine found as much as 40% of a person's health can be attributed to socioeconomic factors like education, employment and transportation availability. What's happening: Some health care providers and nonprofits have programs to fill the gap locally, including CVS Health's partnership with Uber Health in Atlanta's 30318 zip code that provides free rides to medical appointments. Uber has also partnered with Health Force of Georgia to give rides to seniors. The state Department of Community Health gives rides to Medicaid-covered, non-urgent appointments. The American Cancer Society's Road to Recovery program will also drive cancer patients to appointments. (You can volunteer to drive patients, too!) Go deeper: As Emma reported, statistics show rural Georgians have less access to health care and must travel farther for it, which can mean life or death.

In recent years, the USA observed a substantial increase in the adoption of robotic technology. The use of industrial robots in the US economy increased rapidly from about 1 robot per 1,000 workers in 2005 to 1.7 robots per 1,000 workers in 2017, a 70% increase. At the same time, there is a concern that the rapid adoption of robots will transform our society in a way that we have never seen before. In this article, we investigate whether individuals are responding to the increasing use of robots in their locality by altering their schooling decision. The results of the analysis suggest that a 10% increase in robot exposure is associated with an approximately 2.5% rise in college enrolment rate. In the long run, we find evidence that more intense exposure to robots during school ages is associated with an increase in the probability of an individual obtaining a college degree.

Neighborhood conditions are dynamic; association of changing neighborhood socioeconomic factors with cancer preventive behaviors remains unclear. We examined associations of neighborhood socioeconomic deprivation, gentrification, and change in income inequality with adherence to the American Cancer Society (ACS) Guidelines on Nutrition and Physical Activity for Cancer Prevention in The Hispanic Community Health Study/Study of Latinos (HCHS/SOL). The HCHS/SOL enrolled 16,415 adults, ages 18-74 years, at baseline (2008-2011), from communities in the Bronx, NY, Chicago, IL, Miami, FL, and San Diego, CA. Geocoded baseline addresses were linked to the 2000 decennial Census and 5-year American Community Survey (2005-2009 and 2012-2016) tracts to operationalize neighborhood deprivation index (NDI), gentrification and income inequality. Complex survey multinominal logistic regression models estimated the relative risk ratio (RRR) with overall guideline adherence level (low, moderate, high) and by components—diet, physical activity, body mass index (BMI), and alcohol intake. Overall, 14%, 60%, and 26% of the population had low, moderate, and high ACS guideline adherence, respectively. NDI was negatively associated with risk of high (vs. low) guideline adherence (RRR=0.87, 95%CI=0.78-0.98), although attenuated after controlling for individual SES (RRR=0.89, 95%CI = 0.80-1.00), and associated with lower adherence to BMI recommendations (low vs. moderate RRR=0.90, 95%CI=0.84-0.97; high RRR=0.86, 95%CI=0.77-0.97). Gentrification associated with higher likelihood of meeting the dietary recommendations (low vs. moderate RRR=1.04, 95%CI=1.01-1.07), but not with overall adherence or individual components. Change in income inequality was not associated outcomes. Neighborhood deprivation may be negatively associated with ACS guideline adherence among Hispanic/Latino adults.

The Patient pillar represents the demand side of health. It includes the entire US population (i.e., all potential patients) and analyzes overall patient health, outcomes, and the actions patients take to promote and maintain their health. While COVID-19 negatively impacted several Patient metrics, most show worsening or stagnant long-term trends with limited to no recovery post-pandemic. American life expectancy exemplifies this finding. While stagnant since 2010, life expectancy fell by 2.4 years between 2019 and 2021. Therefore, while this metric showed a modest improvement in 2022 for the first time since the pandemic's start, its overall recovery is still incomplete. During the pandemic, long-term improvements in infant mortality stalled while maternal mortality rose steadily. Further, significant racial disparities persist across the metrics for life expectancy, infant mortality, and maternal mortality.8 The prevalence of mental illness is a growing challenge for many patients. Self-reported rates of mental illness have been on the rise since 2018. Similarly, suicide rates have increased steadily since 2005. Despite an initial decline during the pandemic, they reached an all-time peak at 14.3 suicide deaths per 100,000 people in 2022.9 More encouragingly, mental healthcare utilization is rapidly growing, resulting in the gap between patients reporting mental illness and those receiving treatment narrowing significantly.

IPUMS Demographic and Health Surveys (IPUMS DHS), through its intuitive website (http://dhs.ipums.org/), eliminate barriers to overtime and cross-national analyses with the DHS. IPUMS DHS recently released simplified reproductive calendar data. These calendar data are harmonized across samples, distinguish "not in universe" cases from "no" responses, and do not require de-stringing. Variable names are hot links to important documentation, such as survey-question text and comparability concerns. Analysts can also select consistently coded variables relating to the woman, her household, and her social and environmental context without merging files.

Marriages to same-sex couples were declared legal in all states by the U.S. Supreme Court in 2015. Starting in 2019 the US Census Bureau included a revised household roster in the American Community Survey (ACS) that directly counts same-sex and different-sex married and cohabiting couples. We rely on data from the ACS 1-year PUMS files from 2019 and 2021 to provide the most recent ACS estimates of changing numbers of individuals entering a same-sex marriage. We compare same-sex newlyweds to different-sex newlyweds based on the couples' marital history, age, and education. Analyses are limited to householders and spouses of householders. Note that although the terms "sex" and "gender" are not interchangeable, because the ACS survey questionnaire uses the term "sex," we have opted to do so as well. Check out the NCFMR profiles by topic "LGBTQ+ Relationships" for more resources on LGBTQ+ individuals and couples, Kamp Dush and Manning (2021) for marriage rates to same-sex couples and Julian et al. (2022) for changes in marriages to same-sex and different-sex couples during the pandemic.

Introduction: Women of color and immigrant women are more likely to report mistreatment and poor quality of care during their reproductive health care. Surprisingly few data exist on how language access may impact immigrant women's experiences of maternity care, particularly by race and ethnicity. Methods: We conducted qualitative in-depth, one-on-one semi-structured interviews from August 2018 to August 2019 with 10 Mexican and eight Chinese/Taiwanese women (n = 18) living in Los Angeles or Orange County who gave birth within the past 2 years. Interviews were transcribed and translated, and data were initially coded based on the interview guide questions. We identified patterns and themes using thematic analysis methods. Results: Participants described how a lack of translators and language- and cultural-concordant health care providers and staff impeded their access to maternity care services; in particular, they described barriers to communication with receptionists, providers, and ultrasound technicians. Despite Mexican immigrants’ ability to access Spanish-language health care, both Mexican and Chinese immigrant women described how lack of understanding medical concepts and terminology resulted in poor quality of care, lack of informed consent for reproductive procedures, and subsequent psychological and emotional distress. Undocumented women were less likely to use strategies that leveraged social resources to improve language access and quality care. Conclusions: Reproductive autonomy cannot be achieved without access to culturally and linguistically appropriate health care. Health care systems should ensure that comprehensive information is given to women, in a language and manner they can understand, with particular attention toward providing in-language services across multiple ethnicities. Multilingual staff and health care providers are critical in providing care that is responsive to immigrant women.

Background Early-life stressful experiences are associated with increased risk of adverse psychological outcomes in later life. However, much less is known about associations between early-life positive experiences, such as participation in cognitively stimulating activities, and late-life mental health. We investigated whether greater engagement in cognitively stimulating activities in early life is associated with lower risk of depression and anxiety in late life.Methods. We surveyed former participants of the St. Louis Baby Tooth study, between 22 June 2021 and 25 March 2022 to collect information on participants' current depression/anxiety symptoms and their early-life activities (N = 2187 responded). A composite activity score was created to represent the early-life activity level by averaging the frequency of self-reported participation in common cognitively stimulating activities in participants' early life (age 6, 12, 18), each rated on a 1 (least frequent) to 5 (most frequent) point scale. Depression/anxiety symptoms were measured by Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder Screener (GAD-7). We used logistic regressions to estimate odds ratios (OR) and 95% confidence intervals (CI) of outcome risk associated with frequency of early-life activity.Results. Each one-point increase in the early-life composite cognitive activity score was associated with an OR of 0.54 (95% CI 0.38–0.77) for late-life depression and an OR of 0.94 (95% CI 0.61–1.43) for late-life anxiety, adjusting for age, sex, race, parental education, childhood family structure, and socioeconomic status.Conclusions. More frequent participation in cognitively stimulating activities during early life was associated with reduced risk of late-life depression.

How can racial discrimination harm innovation? We study this question using data on US inventors linked to population censuses in 1895-1925. Our novel identification strategy leverages plausibly exogenous variation in the timing of lynchings and the name of the victims. We find an immediate and persistent decrease in patents granted to inventors who share their names with the victims of lynchings, but only when victims are Black. We hypothesize that lynchings accentuate the racial content of the victim's name to patent examiners, who do not observe inventor race from patent applications. We interpret these findings as evidence of discrimination by patent examiners and provide evidence against alternative mechanisms. for in-sightful comments and discussions, as well as seminar audiences at Bocconi University, CERGE-EI, LSE, Northwestern University , RWI Essen, and the UNCE Workshop for helpful suggestions. We are grateful to Enrico Berkes for sharing data with us. Michael Giordano provided outstanding research assistance by verifying the content of newspaper articles. We gratefully acknowledge financial support from the Center for Economic History at Northwestern University, Fondazione Romeo and Enrica Invernizzi, the Czech Academy of Sciences, and the UNCE project (UNCE/HUM/035). All errors are our own.

The centralization of housing units in 56 large urban areas from 1970 to 2020 is measured using an index based on the ratio of the mean distance housing units are located from the center to the mean distance if the housing units were uniformly distributed across the urban area. Mean centralization declines from 1970 to 2010 and then increases in 2020. Highest levels of centralization tend to occur in old large urban areas, especially in the Northeast. Centralization increases with urban area size and declines as areas grow more rapidly. Measures based on units in the urban core and suburban periphery, and the central density from the negative exponential model are reasonable measures of centralization. The negative exponential density gradient is not. Several of these measures show patterns over time similar to the centralization index, first declining and then increasing in recent decades. This raises the possibility that long-standing trends in urban decentralization may be ending.

Private synthetic data sharing is preferred as it keeps the distribution and nuances of original data compared to summary statistics. The state-of-the-art methods adopt a select-measure-generate paradigm, but measuring large domain marginals still results in much error and allocating privacy budget iteratively is still difficult. To address these issues, our method employs a partition-based approach that effectively reduces errors and improves the quality of synthetic data, even with a limited privacy budget. Results from our experiments demonstrate the superiority of our method over existing approaches. The synthetic data produced using our approach exhibits improved quality and utility, making it a preferable choice for private synthetic data sharing.

The LA100 Equity Strategies project integrates community guidance with robust research, modeling, and analysis to identify strategy options that can increase equitable outcomes in Los Angeles’ clean energy transition. This chapter focuses on housing weatherization and cooling technologies as means to increase access to safe and comfortable home temperatures. Lack of cooling access and use can have severe health impacts on building occupants during heat waves. Specifically, NREL developed and used a residential building stock model to simulate the energy use of 50,000 dwellings representing the diversity of housing types, appliances, climate zones, and household incomes across Los Angeles. We compared a baseline scenario with seven upgrade scenarios. Five scenarios cooled the entire household and featured cooling systems at varying efficiency levels with various improvements to the envelope, roof, and shading, and two scenarios cooled a single room in a household with no prior cooling using either a room air-conditioning or a heat pump system. For each scenario, we evaluated impacts on utility bills, payback periods, and changes in energy burdens, as well as ability to achieve safe and comfortable temperatures. We also examined the effects of building types ( multifamily vs. single-family) on indoor air temperatures. Based on the results of modeling, analysis, and community guidance, we identified six short-term and two long-term strategies for improving access to building envelope upgrades and cooling strategies that could save lives and maintain safe home temperatures for Los Angeles’ low-income households during heat waves. Research was guided by input from the community engagement process, and associated equity strategies are presented in alignment with that guidance.

This project examines the impact of the COVID-19 pandemic on the housing sector for Asian Americans in California. The pandemic was not solely a public health crisis, but also massively disrupted the economy—displacing nearly one in five workers and forcing millions to fall behind on their housing payments (rents and mortgages). The housing impacts were not evenly distributed along race and class lines, and previous research found that Asian Americans were disproportionately affected and less likely to receive assistance. This project provides additional insights on the magnitude and patterns of housing difficulties encountered by this group. The analysis draws on micro-level data (individual household records) from two surveys conducted by the U.S. Census Bureau, the American Community Survey to examine pre-pandemic vulnerability, and the Household Pulse Survey to measure outcomes during the pandemic. The analysis produced the following major findings: while Asian had similar pre-pandemic housing vulnerability as non-Hispanic Whites (NHWs); Asians with lower educational attainment had significantly higher housing burden; there was noticeable variation among Asian ethnic subgroups; during the pandemic, the estimated percent of Asians behind on housing payment was greater than for NHWs and Others; and Asians with lower educational attainment were particularly hard hit by the pandemic. We recommend the following: it is critical to work with the U.S. Census Bureau and other governmental agencies to further disaggregate the Asian population to identify those with the greatest needs; it is equally important to collaborate with state agencies to evaluate housing relief and assistance programs to identify the barriers that hindered Asians participation. Research should also continue to monitor the housing problems Asians will face in the future.

Decades of austerity measures and privatization have reduced funding for public sector employees across all agencies in New York State. Increasingly, state agencies have outsourced work traditionally done by public sector employees to private contractors, and localities have been forced to cut or limit services on account of budget cutbacks or shortfalls, resulting in a loss of public sector jobs. The mental healthcare sector exemplifies this trend, as policymakers’ goals of reducing public spending and healthcare costs have intersected with changing models of care provision, ultimately shrinking the public sector mental healthcare workforce.

The U.S. technological and economic boom of the 1990s generally resulted in decreased poverty yet left some areas of the country behind. Since then, policymakers have sought to eliminate concentrated poverty nationwide. The latest attempt to eradicate spatial poverty in the U.S. – tax incentives for private investment – was adopted in 2018. While research into this new program is sparse, other place-based policy research aimed at eradicating poverty is mixed. This study seeks to expand on existing research into this program by using a unique data source and model specification. Using six five-year estimates for 42,160 census tracts between 2012 and 2021, this study employs a fixed effects approach, including a difference-in-differences supplement, to estimate the association between a census tract being designated as an Opportunity Zone and various economic indicators. It finds statistically modest but substantively mixed associations between Opportunity Zone designation and economic indicators that ultimately support arguments for and against place-based policies.

IMPORTANCE Prior research suggests significant social value associated with increased longevity due to preventing and treating cancer. Other social costs associated with cancer, such as unemployment, public medical spending, and public assistance, may also be sizable. OBJECTIVE To examine whether a cancer history is associated with receipt of disability insurance, income, employment, and medical spending. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study used data from the Medical Expenditure Panel Study (MEPS) (2010-2016) for a nationally representative sample of US adults aged 50 to 79 years. Data were analyzed from December 2021 to March 2023. EXPOSURE Cancer history. MAIN OUTCOMES AND MEASURES The main outcomes were employment, public assistance receipt, disability, and medical expenditures. Variables for race, ethnicity, and age were used as controls. A series of multivariate regression models were used to assess the immediate and 2-year association of a cancer history with disability, income, employment, and medical spending. RESULTS Of 39 439 unique MEPS respondents included in the study, 52% were female, and the mean (SD) age was 61.44 (8.32) years; 12% of respondents had a history of cancer. Individuals with a cancer history who were aged 50 to 64 years were 9.80 (95% CI, 7.35-12.25) percentage points more likely to have a work-limiting disability and were 9.08 (95% CI, 6.22-11.94) percentage points less likely to be employed compared with individuals in the same age group without a history of cancer. Nationally, cancer accounted for 505 768 fewer employed individuals in the population aged 50 to 64 years. A cancer history was also associated with an increase of $2722 (95% CI, $2131-$3313) in medical spending, $6460 (95% CI, $5254-$7667) in public medical spending, and $515 (95% CI, $337-$692) in other public assistance spending. CONCLUSIONS AND RELEVANCE In this cross-sectional study, a history of cancer was associated with increased likelihood of disability, higher medical spending, and decreased likelihood of employment. These findings suggest there may be gains beyond increased longevity if cancer can be detected and treated earlier.

Aircraft noise is a key concern for communities surrounding airports, with increasing evidence for health effects and inequitable distributions of exposure. However, there have been limited national-scale assessments of aircraft noise exposure over time and across noise metrics, limiting evaluation of population exposure patterns. We evaluated national-scale temporal trends in aviation noise exposure by airport characteristics and across racial/ethnic populations in the U.S. Noise contours were modeled for 90 U.S. airports in 5-year intervals between 1995 and 2015 using the Federal Aviation Administration’s Aviation Environmental Design Tool. We utilized linear fixed effects models to estimate changes in noise exposure areas for day-night average sound levels (DNL) of 45, 65, and a nighttime equivalent sound level (Lnight) of 45 A-weighted decibels (dB[A]). We used group-based trajectory modeling to identify distinct groups of airports sharing underlying characteristics. We overlaid noise contours and Census tract data from the U.S. Census Bureau and American Community Surveys for 2000 to 2015 to estimate exposure changes overall and by race/ethnicity. National-scale analyses showed non-monotonic trends in mean exposed areas that peaked in 2000, followed by a 37% decrease from 2005 to 2010 and a subsequent increase in 2015. We identified four distinct trajectory groups of airports sharing latent characteristics related to size and activity patterns. Those populations identifying as minority (e.g., Hispanic/Latino, Black/African American, Asian) experienced higher proportions of exposure relative to their subgroup populations compared to non-Hispanic or White populations across all years, indicating ethnic and racial disparities in airport noise exposure that persist over time. Overall, these data identified differential exposure trends across airports and subpopulations, helping to identify vulnerable communities for aviation noise in the U.S. We conducted a descriptive analysis of temporal trends in aviation noise exposure in the U.S. at a national level. Using data from 90 U.S. airports over a span of two decades, we characterized the noise exposure trends overall and by airport characteristics, while estimating the numbers of exposed by population demographics to help identify the impact on vulnerable communities who may bear the burden of aircraft noise exposure.

vii Executive Summary The LA100 Equity Strategies project integrates community guidance with robust research, modeling, and analysis to identify strategy options that can increase equitable outcomes in Los Angeles’ clean energy transition. This chapter focuses on housing weatherization and access to cooling as means to achieve more equitable resilience to heat waves during unplanned power outages. Specifically, NREL used weather, housing, and socioeconomic data to characterize LA’s residential building stock. We developed a residential building stock model to simulate the energy use of 50,000 dwellings representing the diversity of housing types, appliances, climate zones, and household incomes across Los Angeles. We then simulated and evaluated the impacts of 10 building envelope and cooling upgrades on indoor temperature—a main cause of heat-induced health risks—over a 4- day power outage during a heat wave. We examined occupant exposure to extreme heat and how heat exposure changes with each upgrade across income, tenure (renter/owner status), building type, and disadvantaged community (DAC) status. We also examined upgrade costs and utility bills. Based on the results of our analysis and community guidance, we identified building envelope upgrades and cooling strategies that could save lives and maintain safe home temperatures for LA’s low-income households in the event of a planned or unplanned power outage during a summer heat wave. Research was guided by input from the community engagement process, and associated equity strategies are presented in alignment with that guidance.