Search

The measurement of crime in the United States is one that has constantly evolved since national efforts began in 1930. However, the measurements of victim and offender characteristics, specifically race and ethnicity, have not developed at the same pace, nor as rapidly for crime data as it has for other fields. This paper examines two primary criminal justice data sources, the Uniform Crime Reports (UCR) and the National Incident-Based Reporting System (NIBRS) and compares them for context to the United States Census. Analysis shows that the Census has continually expanded racial and ethnic categories while the crime data instruments have mostly refined them only by name. The paper concludes with suggestions for revision of the crime data collection instruments, specifically NIBRS, and a discussion of research and policy implications. [ABSTRACT FROM AUTHOR]

In the United States, truck rates for perishable food, the per‐mile rate charged for trucking services to move perishable food from farms to stores, rose substantially in the post‐COVID‐19 pandemic era. We argue that rising truck rates is a signal of a broader shortage of truckers, but the connection between labor shortages, rising truck rates, and a lack of trucking services has yet to be established empirically. In this paper, we develop an empirical examination based on an equilibrium job search, matching, and bargaining framework in which we estimate the role of labor shortages in accelerating driver‐wage growth, and truck rates for agricultural products. We estimate the model by combining US Bureau of Census Current Population Survey data on truck driver wages with USDA‐Agricultural Marketing Service Service data on truck rates to establish the linkage between trucker supply and the demand for trucking services. We find that the COVID‐19 pandemic was responsible for a rise in for‐hire trucker wages of some 38%$38\%$, a rise in average truck rates of nearly 50%$50\%$ and that the gap between trucker‐job openings and successful matches explains a significant, but small, rise in truck rates. [ABSTRACT FROM AUTHOR]

BACKGROUND Timely, accurate, and precise demographic estimates at various levels of geography are crucial for planning, policymaking, and analysis. In the United States, data from the decennial census and annual American Community Survey (ACS) serve as the main sources for subnational demographic estimates. While estimates derived from these sources are widely regarded as accurate, their timeliness is limited and variability sizable for small geographic units like towns and neighborhoods. OBJECTIVE This paper investigates the potential for using nonrepresentative consumer trace data assembled by commercial vendors to produce valid and timely estimates. We focus on data purchased from Data Axle, which contains the names and addresses of over 150 million Americans annually. METHODS We identify the predictors of over- and undercounts of households as measured with consumer trace data and compare a range of calibration approaches to assess the extent to which systematic errors in the data can be adjusted for over time. We also demonstrate the utility of the data for predicting contemporaneous (nowcasting) tract-level household counts in the 2020 Decennial Census. RESULTS We find that adjusted counts at the county, ZIP Code Tabulation Areas (ZCTA), and tract levels deviate from ACS survey-based estimates by an amount roughly equivalent to the ACS margins of error. Machine-learning methods perform best for calibration of countyand tract-level data. The estimates are stable over time and across regions of the country. We also find that when doing nowcasts, incorporating Data Axle estimates improved prediction bias relative to using the most recent ACS five-year estimates alone. CONTRIBUTION Despite its affordability and timeliness compared to survey-based measures, consumer trace data remains underexplored by demographers. This paper examines one consumer trace data source and demonstrates that challenges with representativeness can be overcome to produce household estimates that align with survey-based estimates and improve demographic forecasts. At the same time, the analysis also underscores the need for researchers to examine the limits of the data carefully before using them for specific applications. [ABSTRACT FROM AUTHOR]

This paper describes the creation of the Longitudinal, Intergenerational Family Electronic Micro-Database (LIFE-M), a new data resource linking vital records and decennial censuses for millions of individuals and families living in the late 19th and 20th centuries in the United States. This combination of records provides a life-course and intergenerational perspective on the evolution of health and economic outcomes. Vital records also enable the linkage of women, because they contain a crosswalk between women's birth (i.e., "maiden") and married names. We describe (1) the data sources, coverage, and linking sequence; (2) the process and supervised machine-learning methods used to link records longitudinally and across generations; and (3) the resulting linked samples, including linking rates, representativeness, and weights. [ABSTRACT FROM AUTHOR]

This paper presents a probabilistic method of record linkage, developed using the U.S. full count censuses of 1900 and 1910 but applicable to many sources of digitized historical records. The method links records using a two-step approach, first establishing high confidence matches among men by exploiting a comprehensive set of individual and contextual characteristics. The method then proceeds to link both men and women by leveraging links between households established in the first step. While only the first stage links can be directly comparable to other popular methods in research on the U.S., our method yields both considerably higher linkage rates and greater accuracy while only performing negligibly worse than other algorithms in resembling the target population. [ABSTRACT FROM AUTHOR]

There are many public health situations within the United States that require fine geographical scale data to effectively inform response and intervention strategies. However, a condition for accessing and analyzing such data, especially when multiple institutions are involved, is being able to preserve a degree of spatial privacy and confidentiality. Hospitals and state health departments, who are generally the custodians of these fine-scale health data, are sometimes understandably hesitant to collaborate with each other due to these concerns. This paper looks at the utility and pitfalls of using Zip4 codes, a data layer often included as it is believed to be "safe", as a source for sharing fine-scale spatial health data that enables privacy preservation while maintaining a suitable precision for spatial analysis. While the Zip4 is widely supplied, researchers seldom utilize it. Nor is its spatial characteristics known by data guardians. To address this gap, we use the context of a near-real time spatial response to an emerging health threat to show how the Zip4 aggregation preserves an underlying spatial structure making it potentially suitable dataset for analysis. Our results suggest that based on the density of urbanization, Zip4 centroids are within 150 meters of the real location almost 99% of the time. Spatial analysis experiments performed on these Zip4 data suggest a far more insightful geographic output than if using more commonly used aggregation units such as street lines and census block groups. However, this improvement in analytical output comes at a spatial privy cost as Zip4 centroids have a higher potential of compromising spatial anonymity with 73% of addresses having a spatial k anonymity value less than 5 when compared to other aggregations. We conclude that while offers an exciting opportunity to share data between organizations, researchers and analysts need to be made aware of the potential for serious confidentiality violations. [ABSTRACT FROM AUTHOR]

Europeans at the end of the eighteenth century had settled across the globe, from North and South America to Australia to the southern tip of Africa. While theories of institutional persistence explain the 'reversal of fortunes' between settled and unsettled regions, few studies consider the large differences in early living standards between settler societies. This paper uses newly transcribed household‐level tax censuses from the Dutch and British Cape Colony and the United States shortly after independence to show comparative levels of income and wealth over four decades both between the two regions and within them. Cape farmers were, on average, more affluent than their American counterparts. While crop output and livestock were more unequally distributed at the Cape, ownership of enslaved people in America was more unequal. There was little indication of an imminent reversal of fortunes. [ABSTRACT FROM AUTHOR]

Before the racial integration of schools in the southern United States, predominantly African American schools were staffed almost exclusively by African American teachers as well, and teaching constituted an extraordinarily large share of professional employment among southern Blacks. The large-scale desegregation of southern schools that occurred after passage of the 1964 Civil Rights Act represented a potential threat to this employment base, and this paper estimates how student integration affected Black teacher employment. Using newly assembled archival data from 759 southern school districts observed between 1960 and 1972, I estimate that a school district transitioning from fully segregated to fully integrated education, which approximates the experience of the modal southern district in this period, led to a 41.7% reduction in Black teacher employment. Additional results, including event-study specifications and models with extensive controls for concurrent policy changes, strongly suggest that these employment reductions were a causal effect of integration and not due to school district self-selection into desegregation. To study the broader impacts of reduced teaching employment, I estimate race-specific changes in occupations and earnings in the decennial Censuses and find that displaced southern Black teachers either entered lower skill occupations within the South or migrated out of the region to continue teaching and that integration-induced displacement led to substantial earnings reductions. [ABSTRACT FROM AUTHOR]

BACKGROUND: The US life expectancy has been stagnating in recent decades, and along with this, the time trends of life span variation have shown stagnation and even increases with respect to historical levels. OBJECTIVE: We aim to disentangle contributions from subnational levels (US regions) to national changes in life expectancy and life span variation in 2010-2019 and 2019-2020. METHODS: A decomposition of the change in the national life expectancy and life disparity into the contribution of changing mortality and population structure among subnational regions is presented. The US Census regions are the Midwest, Northeast, South, and West. RESULTS: From 2010 to 2019, the South substantially contributed to the life span variation increase due to increasing mortality contributions. The old-age survival improvements across all regions further contributed to increasing life span variation at the national level. Different population growth patterns across regions, especially at older ages, are a further source of change in national life span variation and life expectancy. From 2019 to 2020, during the COVID-19 pandemic, an increase in life span variation and a decrease in life expectancy across all regions were observed. CONTRIBUTION: We present continuous-time decompositions for changes in life expectancy and life span variation. When decomposing subnational contributions to national changes, we also demonstrate the role of the composition effect through subnational-national growth differences. This paper quantifies and highlights the specific contributions of regions and age groups to the national mortality increase in the United States between 2010 and 2019, as well as between 2019 and 2020. [ABSTRACT FROM AUTHOR]

The US fertility transition in the nineteenth century is unusual. Not only did it start from a very high fertility level and very early in the nation's development, but it also took place long before the nation's mortality transition, industrialization, and urbanization. This paper assembles new county-level, householdlevel, and individual-level data, including new complete-count IPUMS microdata databases of the 1830-1880 censuses, to evaluate different theories for the nineteenth-century American fertility transition. We construct cross-sectional models of net fertility for currently-married white couples in census years 1830-1880 and test the results with a subset of couples linked between the 1850-1860, 1860-1870, and 1870-1880 censuses. We find evidence of marital fertility control consistent with hypotheses as early as 1830. The results indicate support for several different but complementary theories of the early US fertility decline, including the land availability, conventional structuralist, ideational, child demand/quality-quantity tradeoff, and life cycle savings theories. [ABSTRACT FROM AUTHOR]

Purpose and Method: In this study, the author employed a positivist perspective and framework, utilizing a scoping methodology to systematically explore the extant research literature in speech-language pathology pertaining to literacy interventions for children from culturally and linguistically diverse (CLD) backgrounds. Results: The scoping review encompassed 22 studies selected from a pool of 908 sources, uncovering 34 literacy strategies presented in an evidence map. These strategies, intended for both school-age and preschool children, were implemented across diverse populations, including Indigenous communities, dual language learners, and children with disabilities. The strategies identified offer valuable insights for intervention planning and piloting. Conclusions: The review underscores the significance of culturally responsive literacy interventions in fostering inclusive and supportive learning environments for CLD learners. Additionally, two illustrative case studies are presented to exemplify the application of the identified strategies and discussed in the context of implementing Universal Design for Learning principles for all students. [ABSTRACT FROM AUTHOR]

We conduct this research with a two-fold aim: providing a quantitative analysis of the opioid epidemic in the United States (U.S.), and exploring the impact of the COVID-19 pandemic on opioid-related mortality. The duration and persistence of the opioid epidemic lends itself to the need for an overarching analysis with extensive scope. Additionally, studying the ramifications of these concurrent severe public health crises is vital for informing policies to avoid preventable mortality. Using data from CDC WONDER, we consider opioid-related deaths grouped by Census Region spanning January 1999 to October 2022 inclusive, and later add on a demographic component with gender-stratification. Through the lens of key events in the opioid epidemic, we build an interrupted time series model to reveal statistically significant drivers of opioid-related mortality. We then employ a counterfactual to approximate trends in the absence of COVID-19, and estimate excess opioid-related deaths (defined as observed opioid-related deaths minus projected opioid-related deaths) associated with the pandemic. According to our model, the proliferation of fentanyl contributed to sustained increases in opioid-related death rates across three of the four U.S. census regions, corroborating existing knowledge in the field. Critically, each region has an immediate increase to its opioid-related monthly death rate of at least 0.31 deaths per 100,000 persons at the start of the pandemic, highlighting the nationwide knock-on effects of COVID-19. There are consistent positive deviations from the expected monthly opioid-related death rate and a sizable burden from cumulative excess opioid-related deaths, surpassing 60,000 additional deaths nationally from March 2020 to October 2022, ∼70% of which were male. These results suggest that robust, multi-faceted measures are even more important in light of the COVID-19 pandemic to prevent overdoses and educate users on the risks associated with potent synthetic opioids such as fentanyl. [ABSTRACT FROM AUTHOR]

In this research note, we describe the results of the first validation study of the U.S. Census Bureau's new Community Resilience Estimates (CRE), which uses Census microdata to develop a tract-level vulnerability index for the United States. By employing administrative microdata to link Social Security Administration mortality records to CRE, we show that CRE quartiles provide more stable predictions of COVID-19 excess deaths than single demographic categorizations such as race or age, as well as other vulnerability measures including the U.S. Centers for Disease Control and Prevention's Social Vulnerability Index (SVI) and the Federal Emergency Management Agency's National Risk Index (NRI). We also use machine learning techniques to show that CRE provides more predictive power of COVID-19 excess deaths than standard socioeconomic predictors of vulnerability such as poverty and unemployment, as well as SVI and NRI. We find that a 10-percentage-point increase in a key CRE risk measure is associated with one additional death per neighborhood during the initial outbreak of COVID-19 in the United States. We conclude that, compared with alternative measures, CRE provides a more accurate predictor of community vulnerability to a disaster such as a pandemic. [ABSTRACT FROM AUTHOR]

The recent release of 2020 U.S. Census data reflects the continued growth of the Hispanic/Latino population over the last four decades. The Hispanic/Latino population has increased by a factor of 3.25 since 1980, with nearly one in five inhabitants of the United States identifying as Hispanic or Latino. With these demographic changes, language maintenance figures have shown significant change as well. In every state, a language shift is evident, as all of the western states have lower measures of Spanish language maintenance among the Hispanic population than they did a generation ago. So, while Spanish language use is growing with regard to overall numbers in most of the Western United States, language shift is still a reality among the Latino population. Social variables such as education, income, and employment also show a different relationship with the Spanish language than they did 40 years ago. While there were strong negative correlations between Spanish language use and these variables in the previous generation, many (but not all) of these correlations have weakened, many to the point of no statistical significance in the data from 2020. [ABSTRACT FROM AUTHOR]

Fertility is a life course process that is strongly shaped by geographic and sociodemographic subgroup contexts. In the United States, scholars face a choice: they can situate fertility in a life course perspective using panel data, which is typically representative only at the national level; or they can attend to subnational contexts using rate schedules, which do not include information on life course statuses. The method and data source we introduce here, Census-Held Linked Administrative Records for Fertility Estimation (CLAR-FE), permits both. It derives fertility histories and rate schedules from U.S. Census Bureau–held data for the nation and by state, racial and ethnic subgroups, and the important life course status of parity. We generate three types of rates for 2000–2020 at the national and state levels by race and ethnicity: age-specific rates and both unconditional and conditional parity- and age-specific rates. Where possible, we compare these rates with those produced by the National Center for Health Statistics. Our new rate schedules illuminate state and racial and ethnic differences in transitions to parenthood, providing evidence of the important subgroup heterogeneity that characterizes the United States. CLAR-FE covers nearly the entire U.S. population and is available to researchers on approved projects through the Census Bureau's Federal Statistical Research Data Centers. [ABSTRACT FROM AUTHOR]

This study evaluates the extent to which metropolitan racial segregation occurs between neighborhoods—from tract to tract—and within neighborhoods—from block to block—and is framed theoretically by Putnam's (2007) "hunkering down" hypothesis. Analyses are based on complete-count block, tract, and metropolitan data from the last four U.S. decennial censuses. We document recent patterns of block-to-block segregation between Whites and racial and ethnic minorities (Blacks, Asians, and Hispanics) and between different minority pairs. For example, roughly 40% of all metro Black–White segregation is due to segregation from block to block within neighborhoods. Among Asians, the between-neighborhood component of metropolitan segregation has increased over time but was largely compensated by declines in the within-neighborhood (or block) component. Metropolitan fixed-effects models show that trends and racial and ethnic differences in segregation—overall and within and between neighborhoods—are broadly observed across metro areas but are most evident in the largest, oldest, and most highly segregated metro areas. The results are robust to alternative estimates that adjust for differential privacy, metropolitan reclassification, and neighborhood boundary changes. Analyses of neighborhood change in Atlanta, Georgia, further reinforce the generality of our multiscale approach. [ABSTRACT FROM AUTHOR]

Intensive agriculture and deep plowing caused topsoil erosion and dust storms during the 1930s, affecting agricultural income and land values for years. Given the growing literature on the relevance of in utero and early-life exposures, it is surprising that studies focusing on links between the Dust Bowl and later-life health have produced inconclusive and mixed results. We reevaluate this literature and study the long-term effects of in utero and early-life exposure to topsoil erosion caused by the 1930s Dust Bowl on old-age longevity. Specifically, using Social Security Administration death records linked with the full-count 1940 census, we conduct event studies with difference-in-differences designs to compare the longevity of individuals in high- versus low-topsoil-erosion counties before versus after 1930. We find intent-to-treat reductions in longevity of approximately 0.85 months for those born in high-erosion counties after 1930. We show that these effects are not an artifact of preexisting trends in longevity. Additional analyses suggest that the effects are more pronounced among children raised in farm households, females, and those whose mothers had lower education. We also provide suggestive evidence that reductions in adulthood income are a likely mechanism for the effects we document. [ABSTRACT FROM AUTHOR]

Mobile location data has emerged as a valuable data source for studying human mobility patterns in various contexts, including virus spreading, urban planning, and hazard evacuation. However, these data are often anonymized overviews derived from a panel of traced mobile devices, and the representativeness of these panels is not well documented. Without a clear understanding of the data representativeness, the interpretations of research based on mobile location data may be questionable. This article presents a comprehensive examination of the potential biases associated with mobile location data using SafeGraph Patterns data in the United States as a case study. The research rigorously scrutinizes and documents the bias from multiple dimensions, including spatial, temporal, urbanization, demographic, and socioeconomic, over a five-year period from 2018 to 2022 across diverse geographic levels, including state, county, census tract, and census block group. Our analysis of the SafeGraph Patterns dataset revealed an average sampling rate of 7.5% with notable temporal dynamics, geographic disparities, and urban-rural differences. The number of sampled devices was strongly correlated with the census population at the county level over the five years for both urban (r > 0.97) and rural counties (r > 0.91), but less so at the census tract and block group levels. We observed minor sampling biases among groups such as gender, age, and moderate-income, with biases typically ranging from -0.05 to +0.05. However, minority groups such as Hispanic populations, low-income households, and individuals with low levels of education generally exhibited higher levels of underrepresentation bias that varied over space, time, urbanization, and across geographic levels. These findings provide important insights for future studies that utilize SafeGraph data or other mobile location datasets, highlighting the need to thoroughly evaluate the spatiotemporal dynamics of the bias across spatial scales when employing such data sources. [ABSTRACT FROM AUTHOR]

The functions of hospital chaplains and the corresponding staffing of spiritual care departments remain persistent and parallel questions within the profession. No consensus exists on services provided by spiritual care departments nor the staffing patterns to meet those expectations. This study describes the key activities and staffing at the 20 U.S. News and World Report Best Hospitals 2020–2021 as well as the connections between services, staffing, and select hospital characteristics such as average daily census. Information about each hospital's chaplaincy department was gathered via a Zoom/telephone assisted survey with its spiritual care manager. Findings reveal that while spiritual care departments are structurally integrated into their organizations and chaplains respond consistently to requests for care, involvement in established organizational protocols varies. Study findings support the notion that staffing levels are a function of chaplain integration into an organization and the activities organizations expect chaplains to fulfill. [ABSTRACT FROM AUTHOR]

Within coastal communities, sea level rise (SLR) will result in widespread intermittent flooding and long-term inundation. Inundation effects will be evident, but isolation that arises from the loss of accessibility to critical services due to inundation of transportation networks may be less obvious. We examine who is most at risk of isolation due to SLR, which can inform community adaptation plans and help ensure that existing social vulnerabilities are not exacerbated. Combining socio-demographic data with an isolation metric, we identify social and economic disparities in risk of isolation under different SLR scenarios (1-10 ft) for the coastal U.S. We show that Black and Hispanic populations face a disproportionate risk of isolation at intermediate levels of SLR (4 ft and greater). Further, census tracts with higher rates of renters and older adults consistently face higher risk of isolation. These insights point to significant inequity in the burdens associated with SLR. Risk of isolation is expected to disproportionately affect racial minority populations in the U.S. as sea level rise increases. Communities with more renters, older adults, and lower-income populations will also be impacted. [ABSTRACT FROM AUTHOR]

In the United States, much has been learned about the determinants of longevity from survey data and aggregated tabulations. However, the lack of large-scale, individual-level administrative mortality records has proven to be a barrier to further progress. We introduce the CenSoc datasets, which link the complete-count 1940 U.S. Census to Social Security mortality records. These datasets—CenSoc-DMF (N = 4.7 million) and CenSoc-Numident (N = 7.0 million)—primarily cover deaths among individuals aged 65 and older. The size and richness of CenSoc allows investigators to make new discoveries into geographic, racial, and class-based disparities in old-age mortality in the United States. This article gives an overview of the technical steps taken to construct these datasets, validates them using external aggregate mortality data, and discusses best practices for working with these datasets. The CenSoc datasets are publicly available, enabling new avenues of research into the determinants of mortality disparities in the United States. [ABSTRACT FROM AUTHOR]

Objective: It is important to identify gaps in access and reduce health outcome disparities, understanding access to intensive care unit (ICU) beds, especially by race and ethnicity, is crucial. Our objective was to evaluate the race and ethnicity-specific 60-minute drive time accessibility of ICU beds in the United States (US). Design: We conducted a cross-sectional study using road network analysis to determine the number of ICU beds within a 60-minute drive time, and calculated adult intensive care bed ratios per 100,000 adults. We evaluated the US population at the Census block group level and stratified our analysis by race and ethnicity and by urbanicity. We classified block groups into four access levels: no access (0 adult intensive care beds/100,000 adults), below average access (>0–19.5), average access (19.6–32.0), and above average access (>32.0). We calculated the proportion of adults in each racial and ethnic group within the four access levels. Setting: All 50 US states and the District of Columbia. Participants: Adults ≥15 years old. Main outcome measures: Adult intensive care beds/100,000 adults and percentage of adults national and state) within four access levels by race and ethnicity. Results: High variability existed in access to ICU beds by state, and substantial disparities by race and ethnicity. 1.8% (n = 5,038,797) of Americans had no access to an ICU bed, and 26.8% (n = 73,095,752) had below average access, within a 60-minute drive time. Racial and ethnic analysis showed high rates of disparities (no access/below average access): American Indians/Alaskan Native 12.6%/28.5%, Asian 0.7%/23.1%, Black or African American 0.6%/16.5%, Hispanic or Latino 1.4%/23.0%, Native Hawaiian and other Pacific Islander 5.2%/35.0%, and White 2.1%/29.0%. A higher percentage of rural block groups had no (5.2%) or below average access (41.2%), compared to urban block groups (0.2% no access, 26.8% below average access). Conclusion: ICU bed availability varied substantially by geography, race and ethnicity, and by urbanicity, creating significant disparities in critical care access. The variability in ICU bed access may indicate inequalities in healthcare access overall by limiting resources for the management of critically ill patients. [ABSTRACT FROM AUTHOR]

We examined the association between social vulnerability and HIV diagnoses, linkage to HIV medical care, and viral suppression among adults in the Southern U.S. Data from CDC's National HIV Surveillance System (NHSS) were used to determine census tract-level HIV diagnosis rates and percentages of persons linked to care within one month and with viral suppression within six months of diagnosis among Black/African American, Hispanic/Latino, and White adults aged ≥ 18 years residing in the Southern U.S. in 2018. Census tract-level social vulnerability data were obtained from the 2018 CDC Social Vulnerability Index (SVI). Rate and proportion ratios were used to determine the difference between the lowest quartile of SVI scores (Q1) and the highest quartile (Q4) by age group, transmission category, and region of residence and stratified by sex assigned at birth. Areas with the highest social vulnerability (Q4) had the highest rates of HIV diagnoses (Black: 56.5, Hispanic/Latino: 27.2, and White: 10.3). Those in Q4 also had the lowest percentages of adults linked to care (Black: 76.1%, Hispanic/Latino: 81.2%, and White: 77.8%), and the lowest percentages of adults with viral suppression (Black: 59.8%, Hispanic/Latino: 68.4%, and White: 65.7%). This ecological study found an association between social vulnerability, HIV diagnoses, and poorer care outcomes among Black/African American, Hispanic/Latino, and White adults. Tailoring interventions and improving access for persons residing in areas with the highest social vulnerability is necessary to reduce HIV transmission and improve health outcomes in the Southern U.S. [ABSTRACT FROM AUTHOR]

Objectives: This study describes the supply of Latino dentists in the United States from 1980 to 2019, as tabulated by the Census. The number of Latino dentists per 100,000 Latino population was compared to the number of non‐Hispanic White (NHW) dentists per 100,000 NHW population. These four‐decade comparisons were made for the entire country as well as the five states having the largest Latino populations. Methods: Data from the decennial census and the American Community Survey were used to identify the nationwide population, the number of dentists, and their respective Spanish‐language abilities, stratified by race/ethnic group (Latinos and non‐Hispanic Whites). Results: In 1980, there were only 18 Latino dentists for every 100,000 Latino population in the entire nation, compared to 70 NHW dentists per 100,000 NHW population. While there was an increase to 21 Latino dentists per 100,000 in 1990, the supply remained virtually the same over this almost 40‐year period, ending back at 18 per 100,000 in 2019. In comparison, there were about four times as many non‐Hispanic White dentists as Latino dentists. This national discrepancy was also reflected in the five states that were evaluated. Similarly, Latino dentists were far more likely to speak Spanish than NHW dentists at both the national and state levels. Conclusions: The Latino dentist supply, already inadequate in 1980, has remained virtually unchanged over the past almost 40 years. The authors believe that this deficiency will have profound consequences, and recommend that initiatives be undertaken to increase the number of Latino dentists. [ABSTRACT FROM AUTHOR]

Objectives Retrospective measures of childhood socioeconomic status (SES) in cohort studies of aging that first observe people late in life—such as the Health and Retirement Study (HRS)—are widely used. However, their measurement validity and reliability are unknown. We assess the reliability and validity of the HRS's retrospective measures of parental education and childhood family finances. Methods We use records for 6,343 HRS sample members who were children in 1940 that have been linked to records from the complete-count 1940 U.S. Census. We assess interrater reliability by comparing (a) retrospective reports of childhood SES collected from sample members in the 1992–2018 HRS to (b) prospective measures of parallel concepts collected from HRS sample members' parents in the 1940 Census. We assess predictive validity by comparing the results of analyses that model later-life outcomes as a function of childhood SES as measured both prospectively and retrospectively. Results Interrater reliabilities of retrospective measures of parental education are high; however, the same is not true of the retrospective measure of childhood family finances. Both retrospective and prospective measures of childhood SES are predictive of later-life outcomes, and with similar strengths and directions of associations for most outcomes. Discussion Researchers who rely on retrospective indicators of childhood SES from the HRS should be aware of their measurement properties. They are measured with error, and that error modestly attenuates estimates of their associations with later-life outcomes. However, prospective and retrospective measures of childhood SES have similar predictive validity. These findings should reassure researchers who rely on retrospective measures of childhood SES in the HRS and similarly designed surveys. [ABSTRACT FROM AUTHOR]

The data reported here characterize spatial and temporal variation in the ratio of short-to-long-duration visits in public places (i.e., points of interest) in the United States for each week between January 2019 and December 2020. The underlying data on anonymized and aggregated foot traffic to public places is curated by SafeGraph, a geospatial data provider. In this work, we report the estimated number and duration of "short" (i.e., <4 hours) and "long" (i.e., >4 hours) visits to public places at the US census block group level. Long visits are shown to be a good proxy for workers based on formal economic data. We propose that short visits are more likely to represent nonobligate activities: people visiting a public place for leisure, shopping, entertainment, or civic or cultural engagement. Our work constructs a ratio of short to long visits, which can be used to inform population estimates for nonworker use of public space. These data may be useful for understanding how people's use of public space has changed during the COVID-19 pandemic and, more generally, for understanding activity patterns in public. Measurement(s) Visits to a Public Venue Technology Type(s) mobile phone Sample Characteristic - Organism Homo Sample Characteristic - Environment public building Sample Characteristic - Location United States of America [ABSTRACT FROM AUTHOR]

This study investigated the number of outdoor orientation programs--high-impact transition programs for college students--in the United States. Researchers used a census methodology to identify the number of outdoor orientation programs existing at accredited, residential, bachelor's degree-granting colleges and universities within the United States (N = 1,217). Researchers found 212 outdoor orientation programs (OOPs), representing 17% of the identified colleges, the largest number of OOPs in the history of these programs. A follow-up survey found OOPs were notably aligned in risk management procedures (medical training of leaders, tracking incidents, etc.) and programmatic similarities (use of name games, problem-solving and initiative activities). Programs diverged in the use of curricular activities (i.e., solo experiences) and the specifics of risk management (i.e., physician oversight). The COVID-19 pandemic led to 83% of programs being cancelled in 2021. A question remains as to how well these high-impact programs will recover after the pandemic. [ABSTRACT FROM AUTHOR]

Many topics social scientists study are sensitive in nature. Although we know some people may be reluctant to respond to sensitive questions in surveys, we know less about how such questions could influence responses to other questions appearing later in a survey. In this study, we use the Trump administration's proposal to include a citizenship question on the 2020 Census to demonstrate how such spillover effects can undermine important survey-based estimates. Using a large survey experiment (n = 9,035 respondents), we find that asking about citizenship status significantly increases the percentage of questions skipped and makes respondents less likely to report that members of their household are Hispanic. Not only does this demonstrate that sensitive questions can have important downstream effects on survey responses, but our results also speak to an important public policy debate that will likely arise in the future. [ABSTRACT FROM AUTHOR]

The wildland–urban interface (WUI) is the focus of many important land management issues, such as wildfire, habitat fragmentation, invasive species, and human–wildlife conflicts. Wildfire is an especially critical issue, because housing growth in the WUI increases wildfire ignitions and the number of homes at risk. Identifying the WUI is important for assessing and mitigating impacts of development on wildlands and for protecting homes from natural hazards, but data on housing development for large areas are often coarse. We created new WUI maps for the conterminous United States based on 125 million individual building locations, offering higher spatial precision compared to existing maps based on U.S. census housing data. Building point locations were based on a building footprint data set from Microsoft. We classified WUI across the conterminous United States at 30‐m resolution using a circular neighborhood mapping algorithm with a variable radius to determine thresholds of housing density and vegetation cover. We used our maps to (1) determine the total area of the WUI and number of buildings included, (2) assess the sensitivity of WUI area included and spatial pattern of WUI maps to choice of neighborhood size, (3) assess regional differences between building‐based WUI maps and census‐based WUI maps, and (4) determine how building location accuracy affected WUI map accuracy. Our building‐based WUI maps identified 5.6%–18.8% of the conterminous United States as being in the WUI, with larger neighborhoods increasing WUI area but excluding isolated building clusters. Building‐based maps identified more WUI area relative to census‐based maps for all but the smallest neighborhoods, particularly in the north‐central states, and large differences were attributable to high numbers of non‐housing structures in rural areas. Overall WUI classification accuracy was 98.0%. For wildfire risk mapping and for general purposes, WUI maps based on the 500‐m neighborhood represent the original Federal Register definition of the WUI; these maps include clusters of buildings in and adjacent to wildlands and exclude remote, isolated buildings. Our approach for mapping the WUI offers flexibility and high spatial detail and can be widely applied to take advantage of the growing availability of high‐resolution building footprint data sets and classification methods. [ABSTRACT FROM AUTHOR]

The cost of ASD in the U.S. is estimated using a forecast model that for the first time accounts for the true historical increase in ASD. Model inputs include ASD prevalence, census population projections, six cost categories, ten age brackets, inflation projections, and three future prevalence scenarios. Future ASD costs increase dramatically: total base-case costs of $223 (175–271) billion/year are estimated in 2020; $589 billion/year in 2030, $1.36 trillion/year in 2040, and $5.54 (4.29–6.78) trillion/year by 2060, with substantial potential savings through ASD prevention. Rising prevalence, the shift from child to adult-dominated costs, the transfer of costs from parents onto government, and the soaring total costs raise pressing policy questions and demand an urgent focus on prevention strategies. [ABSTRACT FROM AUTHOR]

Although local policies aimed at reducing childhood health inequities can benefit from local data, sample size constraints in population representative health surveys often prevent rigorous evaluations of child health disparities and health care patterns at local levels. Electronic Health Records (EHRs) offer a possible solution as they contain large amounts of information on pediatric patients within a health system. In this paper, we consider the suitability of using EHRs from a large health system to study local children's health by evaluating the extent to which the EHRs capture the county's child population. First, we compare the demographic characteristics of Duke University Health System pediatric patients who live in Durham County, NC (USA) to the child population estimates in the 2015–2019 American Community Survey. We then examine geographic variation in census tract rates of children captured in the EHR data and estimate negative binomial models to assess how tract characteristics are associated with these rates. We also perform these analyses for the subset of pediatric patients who have a well-child encounter. We find that the demographic characteristics of pediatric patients captured by the EHRs are similar to those of the county's child population. Although the county rate of children captured in the EHRs is high, there is variation across census tracts. On average, census tracts with higher concentrations of non-Hispanic Black residents have lower capture rates and tracts with higher concentrations of poverty have higher capture rates, with the poorest tracts showing the largest racial gap in rates of children captured by EHRs. Our findings suggest that EHRs from a large health system can be used to assess children's population health, but that EHR-based evaluations of children's health disparities and health care patterns should account for differences in who is captured by the EHRs based on census tract characteristics. • EHRs from a large health system are suitable for studying local children's health. • Local children in EHRs are demographically similar to county population estimates. • Rates of children captured in health system differ by census tract characteristics. • EHR-based analyses should adjust for local differences in capture rates. [ABSTRACT FROM AUTHOR]

The main purpose of studying the dynamics of the geometric parameters of the country's living premises is to determine the patterns of their transformation as a result of changes in the lifestyle of the population. An analysis of official statistical information presented in the reports on population censuses and inventory of residential premises conducted in recent years in Russia, Japan and the United States made it possible to identify the dependence of changes in the number and area of living premises on the direction of development of the property structure in the country. Despite the relative stability of the reproduction of the traditional spatial image of the country's living premises, the change in its characteristics also depends on the level and character of the distribution of housing provision among the population. In the 19th century, a wide variety of living conditions with a low level of housing provision for the population led to an increase in state support for the construction of housing, which made it possible to narrow the geometric parameters of housing under construction and increase the average level of provision in countries. With the increase in the economic opportunities of households, it becomes preferable to purchase housing in private ownership, which makes it possible to expand the variety of living conditions and increase the average provision of housing for the population. The complete privatization of dwellings, observed at the end of the 20th century, leads to the emergence of private rent, which narrows the geometric characteristics of the dwelling under construction and increases the uneven housing supply again. During periods of economic crises, there are noticeable shifts in the transformation of the spatial image of a dwelling, associated with the search for the highest quality geometric forms of a dwelling, which change the direction of its reproduction during periods of restoration. [ABSTRACT FROM AUTHOR]

Subcounty housing unit counts are important for studying geo-historical patterns of (sub)urbanization, land-use change, and residential loss and gain. The most commonly used subcounty geographical unit for social research in the United States is the census tract. However, the changing geometries and historically incomplete coverage of tracts present significant obstacles for longitudinal analysis that existing datasets do not sufficiently address. Overcoming these barriers, we provide housing unit estimates in consistent 2010 tract boundaries for every census year from 1940 to 2010 plus 2019 for the entire continental US. Moreover, we develop an "urbanization year" indicator that denotes if and when tracts became "urbanized" during this timeframe. We produce these data by blending existing interpolation techniques with a novel procedure we call "maximum reabsorption." Conducting out-of-sample validation, we find that our hybrid approach generally produces more reliable estimates than existing alternatives. The final dataset, Historical Housing Unit and Urbanization Database 2010 (HHUUD10), has myriad potential uses for research involving housing, population, and land-use change, as well as (sub)urbanization. Measurement(s) human dwelling Technology Type(s) Geographic Information System Factor Type(s) housing units • urbanization Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment anthropogenic environment Sample Characteristic - Location contiguous United States of America [ABSTRACT FROM AUTHOR]

In this study of nonprofit engagement with the census, we argue for an expansion of the research toolbox to include critical race methods as an essential lens on public and nonprofit administration. By foregrounding race and racism, illuminating power structures and discourses, and centering the knowledge and practices of nonprofit staff as street-level workers, critical race methods reveal the processes of construction that shape and influence a seemingly objective count and highlight the roles that staff play as mediators in the conflicts between fear and trust that are inherent to racial governance in the United States. [ABSTRACT FROM AUTHOR]

Copyright of Professional Geographer is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Given the recognition that the U.S. government lacks a consensus definition of the word rural , the purpose of this scoping review was to uncover how the federal government defines the term and to establish a nuanced understanding of what criterion is used to designate an area as rural. Methods: Arksey and O'Malley's framework was used to synthesize, analyze, and summarize the existing literature. A multi-system search was conducted, and articles were screened for eligibility by two independent reviewers using pretested forms. Results: Initially, 929 articles were screened that used the search terms rural and some variation of the word definition. After eliminating all ineligble studies, 49 documents were included in the final analysis. These documents revealed 33 federal definitions of rural. The majority of definitions centered on either population, population density, or urban integration provisions. Additionally, the analysis showed that the literature could be separated into two categories: how rural was defined in a particular industry or for a specific population and the multiple adverse effects of having multiple definitions of rural. Conclusion: The discrepancies found in current classification systems reveal the need for a standardized definition of rural. Ultimately, policies centered on securing health care services for rural populations are impacted by whatever definition of rural is used. Failing to establish a gold standard definition of rural could have harmful consequences to the health and wellbeing of the many people living in rural communities across the U.S. [ABSTRACT FROM AUTHOR]

Multi-temporal and spatially explicit population data are vital in many fields, such as demography, urban planning, disaster prevention,economics, and environmental modeling. Population data used in these studies are typically aggregated at census enumeration units, which are too coarse for many applications. Accurate population downscaling methods are needed to obtain population data at finer spatial resolutions. We use a novel settlement-related database, Built-Up Property Records (BUPR) from the Historical Settlement Data Compilation for the United States (HISDAC-US) to downscale population from census tracts to block groups. The BUPR dataset provides the number of built-up property records for each 250-m grid at 5-year temporal resolution from 1810 to 2015 for most contiguous United States (CONUS). The ability of BUPR to downscale population from census tracts to block groups for four states, representing a range of population densities, is evaluated here by comparing against other commonly-used ancillary datasets. The BUPR-based method outperforms all other methods in all but one state with highly-incomplete BUPR. A more detailed accuracy assessment is performed by dividing each state into low, medium, and high population density categories. The BUPR method produces more accurate downscaled population estimates for low and medium categories, though its performance deteriorates in the high density category due to its relatively coarse spatial resolution. BUPR-based dasymetric mapping is subsequently applied to the CONUS and found to generalize well beyond the four comparison states with high downscaling accuracy. The long-term record of the HISDAC-US dataset enables the potential construction of fine-grained population data back to 1810. [ABSTRACT FROM AUTHOR]

One of the most common methods for estimating the U.S. unauthorized foreign-born population is the residual method. Over the last decade, residual estimates have typically fallen within a narrow range of 10.5 to 12 million. Yet it remains unclear how sensitive residual estimates are to their underlying assumptions. We examine the extent to which estimates may plausibly vary owing to uncertainties in their underlying assumptions about coverage error, emigration, and mortality. Findings show that most of the range in residual estimates derives from uncertainty about emigration rates among legal permanent residents, naturalized citizens, and humanitarian entrants (LNH); estimates are less sensitive to assumptions about mortality among the LNH foreign-born and coverage error for the unauthorized and LNH populations in U.S. Census Bureau surveys. Nevertheless, uncertainty in all three assumptions contributes to a range of estimates, whereby there is a 50% chance that the unauthorized foreign-born population falls between 9.1 and 12.2 million and a 95% chance that it falls between 7.0 and 15.7 million. [ABSTRACT FROM AUTHOR]

In a first-ever nation-wide census tract evaluation, we assess the accuracy of the Hamilton–Perry population projection method for 65,221 census tracts. We started with 73,607 census tracts but eliminated those for which zeros appeared in age/sex groups. The test uses 1990 and 2000 census tract data by age and gender to construct cohort change ratios, which are then applied to 2000 census tract data to generate 2010 Hamilton–Perry projections that are evaluated in an ex post facto test against the reported 2010 census tract data by age and gender. The projections include: (1) uncontrolled age and gender projections; and (2) age and gender projections controlled to a projection of the population total by census tract. Mean Absolute Percent Error (MAPE) is used to evaluate precision and Mean Algebraic Percent Error (MALPE) is used to evaluate bias. We find that controlling the Hamilton–Perry projections by age for each tract to the linearly projected total population of each tract reduces both MAPE and MALPE within age groups by gender and for total females and total males. As this result suggests, simple linear extrapolation provides more accurate projections of the total population than does the Hamilton–Perry Method. However, even with controlling we find the Hamilton–Perry projections by age to be biased upward. Finally, we use MAPE-R (MAPE- Revised) to evaluate the effect of extreme outliers and find that high MAPEs in the uncontrolled projections are largely driven by extreme errors (outliers) found in less than 1 percent of the 65,221 census tracts used in the study. [ABSTRACT FROM AUTHOR]

Copyright of Urban Studies (Sage Publications, Ltd.) is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Since its founding, the United States has counted democratic elections as a fundamental tenet of democracy. Redistricting ensures that elections are free, fair, and representative of the people. This process requires that every ten years, after the national census, congressional, state, and local districts are redrawn, if necessary, to reflect changes in population to ensure that district populations are equal. What should be a simple calculation, has become step one in a political party's bid to maintain or gain power. This has led to countless legal battles and minority populations left without adequate representation. In turn, this lack of representation perpetuates the systemic racism upon which this country was founded. Gerrymandering, particularly racial gerrymandering, is a foundational step in ensuring the exclusion of underrepresented communities from voting. The 2020 election concluded with many seat flips by a slim margin of votes, along with the record voter turn-out and resulting litigation, which suggests that the battle of redistricting following the 2020 census is bound to be an intense one. This Comment defines gerrymandering, its types, and strategies as well as lays out the legal framework for racial gerrymandering litigation. It focuses past on censuses and what can be expected out of the 2020 census results. The impact of racial gerrymandering on minority and underrepresented communities including voter suppression, systematic racism, and a lack of representation in government cannot be understated. The Comment concludes with possible solutions to prevent racial gerrymandering in the future. Ultimately, the author demonstrates that the fight for fair and free access to voting is stronger than ever. [ABSTRACT FROM AUTHOR]

Purpose: We examined the combined influences of race/ethnicity and neighborhood socioeconomic status (SES) on long-term survival among patients with multiple myeloma (MM).Methods: Data from the 2000-2015 NCI Surveillance, Epidemiology, and End Results Program (SEER-18) were used. Census tract-level SES index was assessed in tertiles (low, medium, high SES). Competing-risk modeling was used to estimate sub-hazard ratios (SHR) and 95% confidence intervals (CIs) for SES tertile adjusted for sex and age at diagnosis and stratified by race/ethnicity.Results: Overall, living in a low SES neighborhood was associated with worse MM survival. However, we observed some variation in the association by racial/ethnic group. Living in a low versus a high SES neighborhood was associated with a 35% (95% CI = 1.16-1.57) increase in MM-specific mortality risk among Asian/Pacific Islander cases, a 17% (95% CI = 1.12-1.22) increase among White cases, a 14% (95% CI = 1.04-1.23) increase among Black cases, and a 7% (95% CI = 0.96-1.19) increase among Hispanic cases.Conclusion: These results suggest that the influence of both SES and race/ethnicity should be considered when considering interventions to remedy disparities in MM survival. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Planning Education & Research is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Researchers highlight the importance of urban form and sprawling development patterns as determinants of economic mobility in the United States. However, the relationship is complex because sprawl affects other variables too, which themselves may affect upward mobility. This study uses a structural equation model to estimate the direct and indirect effects of urban sprawl, and related components of the built environment, on mobility at the census tract level. The results suggest that compactness has a positive direct effect on mobility, but an overall negative indirect effect on mobility through the tested mediating factors. Hence, there are both positive and negative side-effects associated with compactness in regards to economic mobility. In light of these findings, this paper recommends policies, programs, and procedures for the planning community to aid in alleviating the negative consequences of sprawl on economic mobility, while maintaining the positive ones. • Urban sprawl directly and indirectly effects upward economic mobility at the census tract level in the United States. • The direct effect of compactness on mobility is positive, though the total indirect effect is negative. • More compact census tracts have higher rates of upward mobility than more sprawling ones. • Indirect effects through social capital, poverty, job access, housing stock, and education quality are negative. • Indirect effects through concentrations of non-white minorities and food deserts are positive. [ABSTRACT FROM AUTHOR]