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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: 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]

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]