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This study utilizes provincial panel data from China spanning from 2013 to 2019 and considers indices such as residents' income, employment, housing, education, and medical care to comprehensively measure the welfare index of each province using the coefficient of variation. It also examines the impact and mechanism of digital financial inclusion on welfare and evaluates its influence on welfare imbalance through panel data models and RIF regression. The estimation results of digital financial inclusion on welfare indicate that digital financial inclusion is an important driver elevating the income, employment, housing, education, medical treatment and other welfares of Chinese residents, and the conclusion remains robust after using instrumental variable estimation and replacing the measurement method of welfare. Regional differences reveal that digital financial inclusion has a greater contribution to the welfare in the Midwest. The effect mechanism test shows that digital financial inclusion improves the welfare by creating employment opportunities and plays a greater role in the Midwest, and will also improve the welfare by increasing the welfare expenditure. However, this mechanism will exacerbate the welfare imbalance between the Midwest and East. The estimation results of digital financial inclusion on the welfare imbalance show that digital financial inclusion reduces welfare imbalance between the central and western regions and the eastern regions, as well as within the central and western regions. [ABSTRACT FROM AUTHOR]

Mounting evidence shows overall insect abundances are in decline globally. Habitat loss, climate change, and pesticides have all been implicated, but their relative effects have never been evaluated in a comprehensive large-scale study. We harmonized 17 years of land use, climate, multiple classes of pesticides, and butterfly survey data across 81 counties in five states in the US Midwest. We find community-wide declines in total butterfly abundance and species richness to be most strongly associated with insecticides in general, and for butterfly species richness the use of neonicotinoid-treated seeds in particular. This included the abundance of the migratory monarch (Danaus plexippus), whose decline is the focus of intensive debate and public concern. Insect declines cannot be understood without comprehensive data on all putative drivers, and the 2015 cessation of neonicotinoid data releases in the US will impede future research. [ABSTRACT FROM AUTHOR]

Reliable population estimates are important for making informed management decisions about wildlife species. Standardized survey protocols have been developed for monitoring population trends of the wood turtle (Glyptemys insculpta), a semi-aquatic freshwater turtle species of conservation concern throughout its distribution in east-central North America. The protocols use repeated active search surveys of defined areas, allowing for estimation of survey-specific detection probability (p) and site-specific abundance. These protocols assume population closure within the survey area during the survey period, which is unlikely to be met as wood turtles are a highly mobile species. Additionally, current protocols use a single-pass design that does not allow for separation of availability (pa) and detectability (pd). If there are systematic influences on pa or pd that are not accounted for in the survey design or data analysis, then resulting abundance estimates could be biased. The objectives of this study were to determine if pa is a random process and if pa and pd are influenced by demographic characteristics. We modified the wood turtle survey protocol used in the upper Midwest to include a double-pass design, allowing us to estimate pa and pd using a robust design capture-recapture model. The modified protocol was implemented at 14 wood turtle monitoring sites in Minnesota and Wisconsin between 2017 and 2022. Our results indicated that pa was non-random and that pd increased with turtle carapace length. Our study suggests that model assumptions for current wood turtle population models may be violated, likely resulting in an overestimation of abundance. We discuss possible protocol and modeling modifications that could result in more accurate wood turtle abundance estimates. [ABSTRACT FROM AUTHOR]

Forensic facial reconstruction aims to assemble and provide the appearance of a face over a skull, in order to lead to recognition of that individual, making possible the application of primary identification methods. The scientific literature presents facial soft tissue thickness (FSTT) tables for reference from a range of different geographic regions. However, the consensus on its importance or on how to use specific population data related to FSTT is not unanimous. Brazil is formed by geographic regions with diverse populations, which are reflected in facial features. This paper aimed to measure and compare FSTT of distinct Brazilian samples to ascertain the need for specific data sets for different regions. A specific protocol for cone beam computed tomography was used to standardize measurement, and it was applied in a sample of 101 subjects. The FSTT measurements of a Brazilian population from the Midwest Region was compared to a previous sample from Southeast, which was collected using the same protocol. High compatibility was observed when comparing the averages of FSTT among samples of these two different geographic regions. Regarding age groups, notable differences on the medium and inferior face were observed in females. Minor variances found are unlikely to affect the practice of forensic facial reconstruction. Facial features, such as eyes, lips, nose, and skin may also be relevant in the differentiation of people from these two areas in Brazil. Therefore, concerning the Southeast and Midwest Brazilian regions, the need to apply different data sets is unnecessary. [ABSTRACT FROM AUTHOR]

Purpose: The U.S. federal Earned Income Tax Credit (EITC) is often considered the most effective antipoverty program for families in the U.S., leading to a variety of improved outcomes such as educational attainment, work incentives, economic activity, income, and health benefits for mothers, infants and children. State EITC supplements to the federal credit can significantly enhance the magnitude of this intervention. In this paper we advance EITC and health research by: 1) describing the diffusion of state EITC policies over 40 years, 2) presenting patterns in important EITC policy dimensions across space and time, and 3) disseminating a robust data set to advance future research by policy analysts and scientists. Methods: We used current public health law research methods to systematically collect, conduct textual legal analysis, and numerically code all EITC legislative changes from 1980 through 2020 in the 50 states and Washington, D.C. Results: First, the pattern of diffusion across states and time shows initial introductions during the 1990s in the Midwest, then spreading to the Northeast, with more recent expansions in the West and South. Second, differences by state and time of important policy dimensions are evident, including size of credit and refundability. Third, state EITC benefits vary considerably by household structure. Conclusion: Continued research on health outcomes is warranted to capture the full range of potential beneficial effects of EITCs on family and child wellbeing. Lawyers and policy analysts can collaborate with epidemiologists and economists on other high-quality empirical studies to assess the many dimensions of policy and law that potentially affect the social determinants of health. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic and related restrictions led to major transit demand decline for many public transit systems in the United States. This paper is a systematic analysis of the dynamics and dimensions of this unprecedented decline. Using transit demand data derived from a widely used transit navigation app, we fit logistic functions to model the decline in daily demand and derive key parameters: base value, the apparent minimal level of demand and cliff and base points, representing the initial date when transit demand decline began and the final date when the decline rate attenuated. Regression analyses reveal that communities with higher proportions of essential workers, vulnerable populations (African American, Hispanic, Female, and people over 45 years old), and more coronavirus Google searches tend to maintain higher levels of minimal demand during COVID-19. Approximately half of the agencies experienced their decline before the local spread of COVID-19 likely began; most of these are in the US Midwest. Almost no transit systems finished their decline periods before local community spread. We also compare hourly demand profiles for each system before and during COVID-19 using ordinary Procrustes distance analysis. The results show substantial departures from typical weekday hourly demand profiles. Our results provide insights into public transit as an essential service during a pandemic. [ABSTRACT FROM AUTHOR]

Introduction: Traditionally, the study of aphasia focused on brain trauma, clinical biomarkers, and cognitive processes, rarely considering the social determinants of health. This study evaluates the relationship between aphasia impairment and demographic, socioeconomic, and contextual determinants among people with aphasia (PWA). Methods: PWA indexed within AphasiaBank—a database populated by multiple clinical aphasiology centers with standardized protocols characterizing language, neuropsychological functioning, and demographic information—were matched with respondents in the Medical Expenditure Panel Survey based on response year, age, sex, race, ethnicity, time post stroke, and mental health status. Generalized log-linear regression models with bootstrapped standard errors evaluated the association between scores on the Western Aphasia Battery-Revised Aphasia Quotient (WAB-R AQ) and demographic, economic, and contextual characteristics accounting for clustering of respondents and the stratification of data collection. Region, age, and income specific models tested the sensitivity of results. Results: PWA over age 60 had 2.4% (SE = 0.020) lower WAB-R AQ scores compared with younger PWA. Compared to White PWA, Black and Hispanic PWA had 4.7% (SE = 0.03) and 0.81% (SE = 0.06) lower WAB-R AQ scores, respectively, as did those and living in the Southern US (-2.2%, SE = 0.03) even after controlling for age, family size, and aphasia type. Those living in larger families (β = 0.005, SE = 0.008), with income over $30,000 (β = 0.017, SE = 0.022), and a college degree (β = 0.030, SE = 0.035) had higher WAB-R AQ relative to their counterparts. Region-specific models showed that racial differences were only significant in the South and Midwest, while ethnic differences are only significant in the West. Sex differences only appeared in age-specific models. Racial and ethnic differences were not significant in the high-income group regression. Conclusion: These findings support evidence that circumstances in which individuals live, work, and age are significantly associated with their health outcomes including aphasia impairment. [ABSTRACT FROM AUTHOR]

Objective: There is currently inconclusive evidence regarding the relationship between recidivism and mental illness. This retrospective study aimed to use rigorous machine learning methods to understand the unique predictive utility of mental illness for recidivism in a general population (i.e.; not only those with mental illness) prison sample in the United States. Method: Participants were adult men (n = 322) and women (n = 72) who were recruited from three prisons in the Midwest region of the United States. Three model comparisons using Bayesian correlated t-tests were conducted to understand the incremental predictive utility of mental illness, substance use, and crime and demographic variables for recidivism prediction. Three classification statistical algorithms were considered while evaluating model configurations for the t-tests: elastic net logistic regression (GLMnet), k-nearest neighbors (KNN), and random forests (RF). Results: Rates of substance use disorders were particularly high in our sample (86.29%). Mental illness variables and substance use variables did not add predictive utility for recidivism prediction over and above crime and demographic variables. Exploratory analyses comparing the crime and demographic, substance use, and mental illness feature sets to null models found that only the crime and demographics model had an increased likelihood of improving recidivism prediction accuracy. Conclusions: Despite not finding a direct relationship between mental illness and recidivism, treatment of mental illness in incarcerated populations is still essential due to the high rates of mental illnesses, the legal imperative, the possibility of decreasing institutional disciplinary burden, the opportunity to increase the effectiveness of rehabilitation programs in prison, and the potential to improve meaningful outcomes beyond recidivism following release. [ABSTRACT FROM AUTHOR]

The Karner blue butterfly (Lycaeides melissa samuelis, or Kbb), a federally endangered species under the U.S. Endangered Species Act in decline due to habitat loss, can be further threatened by climate change. Evaluating how climate shapes the population trend of the Kbb can help in the development of adaptive management plans. Current demographic models for the Kbb incorporate in either a density-dependent or density-independent manner. We instead created mixed density-dependent and -independent (hereafter "endo-exogenous") models for Kbbs based on long-term count data of five isolated populations in the upper Midwest, United States during two flight periods (May to June and July to August) to understand how the growth rates were related to previous population densities and abiotic environmental conditions, including various macro- and micro-climatic variables. Our endo-exogenous extinction risk models showed that both density-dependent and -independent components were vital drivers of the historical population trends. However, climate change impacts were not always detrimental to Kbbs. Despite the decrease of population growth rate with higher overwinter temperatures and spring precipitations in the first generation, the growth rate increased with higher summer temperatures and precipitations in the second generation. We concluded that finer spatiotemporally scaled models could be more rewarding in guiding the decision-making process of Kbb restoration under climate change. [ABSTRACT FROM AUTHOR]

Agricultural development has been causing changes to the environment and the abundance and distribution of avian species. Agriculture is dynamic with changes in products occurring at large scales over relatively short time periods. The catfish aquaculture industry is one such agriculture industry that has undergone dramatic changes over the last 25 years. The double-crested cormorant (Nannopterum auritum) is a piscivorous bird that has an extensive history with the aquaculture industry of Mississippi due to its depredation of cultured catfish. A large-scale monitoring program began in 1989 to estimate the abundance and location of cormorants at every known roost in the primary catfish producing region of the state, regionally known as the Delta. We used this data set to address hypotheses pertaining to cormorant ecology within the Delta over time, particularly in relation to aquaculture. We found that, although the Midwest breeding population of cormorants has been increasing, the abundance of cormorants wintering in the Delta has been decreasing, closely following the decline of aquaculture, suggesting aquaculture area is the primary reason for cormorant inhabitation of the region. We also modeled cormorant presence and abundance at all roost sites to determine what factors most influenced cormorant distribution. Aquaculture area around roosts was a significant predictor of both cormorant presence and abundance. However, the influence of aquaculture area was seasonally dependent, with greater positive influences occurring prior to migration. Lastly, we found peak cormorant abundance in the Delta is occurring 2.14 days earlier each year, which may be indicative of changes to migration phenology. Information gained using this large dataset aids in cormorant damage mitigation and to further our understanding of cormorant ecology. Data indicate changes in agriculture, and potentially climate change, can influence phenology, distribution, and abundance of avian species at large geographic scales. [ABSTRACT FROM AUTHOR]

Importance: The SARS-CoV-2 pandemic has overwhelmed hospital capacity, prioritizing the need to understand factors associated with type of discharge disposition. Objective: Characterization of disposition associated factors following SARS-CoV-2. Design: Retrospective study of SARS-CoV-2 positive patients from March 7th, 2020, to May 4th, 2022, requiring hospitalization. Setting: Midwest academic health-system. Participants: Patients above the age 18 years admitted with PCR + SARS-CoV-2. Intervention: None. Main outcomes: Discharge to home versus PAC (inpatient rehabilitation facility (IRF), skilled-nursing facility (SNF), long-term acute care (LTACH)), or died/hospice while hospitalized (DH). Results: We identified 62,279 SARS-CoV-2 PCR+ patients; 6,248 required hospitalizations, of whom 4611(73.8%) were discharged home, 985 (15.8%) to PAC and 652 (10.4%) died in hospital (DH). Patients discharged to PAC had a higher median age (75.7 years, IQR: 65.6–85.1) compared to those discharged home (57.0 years, IQR: 38.2–69.9), and had longer mean length of stay (LOS) 14.7 days, SD: 14.0) compared to discharge home (5.8 days, SD: 5.9). Older age (RRR:1.04, 95% CI:1.041–1.055), and higher Elixhauser comorbidity index [EI] (RRR:1.19, 95% CI:1.168–1.218) were associated with higher rate of discharge to PAC versus home. Older age (RRR:1.069, 95% CI:1.060–1.077) and higher EI (RRR:1.09, 95% CI:1.071–1.126) were associated with more frequent DH versus home. Blacks, Asians, and Hispanics were less likely to be discharged to PAC (RRR, 0.64 CI 0.47–0.88), (RRR 0.48 CI 0.34–0.67) and (RRR 0.586 CI 0.352–0.975). Having alpha variant was associated with less frequent PAC discharge versus home (RRR 0.589 CI 0.444–780). The relative risks for DH were lower with a higher platelet count 0.998 (CI 0.99–0.99) and albumin levels 0.342 (CI 0.26–0.45), and higher with increased CRP (RRR 1.006 CI 1.004–1.007) and D-Dimer (RRR 1.070 CI 1.039–1.101). Increased albumin had lower risk to PAC discharge (RRR 0.630 CI 0.497–0.798. An increase in D-Dimer (RRR1.033 CI 1.002–1.064) and CRP (RRR1.002 CI1.001–1.004) was associated with higher risk of PAC discharge. A breakthrough (BT) infection was associated with lower likelihood of DH and PAC. Conclusion: Older age, higher EI, CRP and D-Dimer are associated with PAC and DH discharges following hospitalization with COVID-19 infection. BT infection reduces the likelihood of being discharged to PAC and DH. [ABSTRACT FROM AUTHOR]

This study serves to provide evidence on how the increase in people working from home due to government induced social distancing measures is contributing to the frequency of individuals suffering from depression or anxiety. Using a compilation of datasets from the NHIS, Household Pulse Survey, and the Oxford Covid-19 Response Tracker, we find a general trend of increased rates of depression and anxiety in those who moved to a remote working format. However, while all regions have an increased frequency in anxiety for those who switched to telework, those in the Northeast and West (that also have implemented strict lockdown measures related to social distancing) have slightly higher rates of anxiety compared to those in the South and Midwest. [ABSTRACT FROM AUTHOR]

Importance: Changes in insurance coverage after the Affordable Care Act (ACA) among non-elderly adults with self-reported chronic conditions across income categories have not been described. Objective: To examine changes in insurance coverage after the ACA among non-elderly adults with chronic conditions across income categories, by geographic region. Design: We compared self-reported access to health insurance pre-ACA (2010-2013) and post-ACA (2014-2017) for individuals 18-64 years of age with ≥ 2 chronic conditions, including hypertension, heart disease/stroke, emphysema, diabetes, asthma, cancer, and arthritis, across regions using a logistic regression approach, adjusted for covariates. We also assessed U.S. Census regional differences in insurance coverage post-ACA using modified Poisson regression models with robust variance and calculated the risk ratio (RR) of being uninsured by region, with the Northeast as the reference category. Within each region, we then examined changes in insurance coverage by income level among non-elderly individuals with any chronic condition. Setting: 2010–2017 household component of the nationally representative Medical Expenditure Panel Survey (MEPS). Participants: All members of surveyed households during five interviews over a two-year period. Intervention: Start of insurance coverage expansion under the ACA. Main outcomes: Health insurance status. Results: On average nationwide, non-elderly adults with self-reported chronic conditions experienced increased insurance coverage associated with the ACA (diabetes: +6.41%, high-blood pressure: +6.09%, heart disease: +6.50%, asthma: +6.37%, arthritis: +6.77%, and ≥ 2 chronic conditions: +6.39%). Individuals in the West region reported the largest increases (diabetes +9.71%, high blood pressure +8.10%, and heart disease/stroke +8.83 %, asthma +9.10%, arthritis +8.39%, and ≥ 2 chronic conditions +8.58). In contrast, individuals in the South region reported smaller increases in insurance coverage post-ACA among those with diabetes, heart disease/stroke, and asthma compared to the Midwest and West. The Northeast region, which had the highest levels of insurance coverage pre-ACA, exhibited the smallest increase in reported coverage post-ACA. Reported insurance coverage improved across all regions for adults with any chronic condition across income levels, most notably for very low- and low-income individuals. A further cross-sectional comparison after the ACA demonstrated important residual differences in insurance coverage, despite the gains in all regions. When compared to the Northeast, adults with any self-reported chronic conditions living in the South were more likely to report no insurance coverage (diabetes: RR 1.99, p-value <0.001, high blood pressure: RR 2.02, p-value <0.001, heart diseases/stroke: RR 2.55, p-value <0.001, asthma RR 2.21, p-value <0.001, arthritis: RR 2.25, p-value <0.001), and ≥ 2 chronic condition (RR 2.29, p-value <0.001). Conclusion and relevance: The ACA was associated with meaningful increases in insurance coverage for adults with any self-reported chronic condition in all US regions, most notably in the West region and among those with lower incomes, suggesting a nation-wide trend to improved access to health insurance following implementation. However, intra-regional comparisons after ACA implementation showed important differences. Individuals with ≥2 chronic conditions in the South were 2.29 times less likely to have insurance coverage in comparison to their peers in the Northeast. Though the post-ACA improvements in reported access to health insurance coverage affected all US regions, the reported experience of those with multiple chronic conditions in the South point to continued barriers for those most likely to benefit from access to health insurance coverage. Medicaid expansion in the South would likely result in increased insurance coverage for individuals with chronic conditions and improve health care outcomes. [ABSTRACT FROM AUTHOR]

Background: Policies that restrict access to and use of the Supplemental Nutrition Assistance Program (SNAP) and Special Supplemental Nutrition Assistance Program for Women, Infants, and Children (WIC) by legal status may disproportionately disadvantage particular racial and ethnic groups. While immigrant legal status, race, and ethnicity are recognized as independent social determinants of health, studies examining the extent to which legal status structures racial and ethnic health disparities are limited. Research is needed to identify factors that mitigate disparate health outcomes, such as SNAP and WIC. Methods: Cross-sectional data from the 2009/2010 National Agricultural Workers Survey (N = 3,961) were analyzed. Chi-square tests and logistic regressions examined associations among self-reported health, race, ethnicity, legal status, and SNAP/WIC participation. Results: Farmworkers reporting excellent or good health were more likely to be non-Hispanic White, U.S. citizen, aged 18–25, single, male, educated beyond primary school, living above the poverty level, without chronic health conditions, and located in the Midwest. Hispanic farmworkers had lower odds of reporting excellent or good health (OR 0.27, 95% CI 0.12–0.62). Among SNAP/WIC participants, Hispanic farmworkers had higher odds of reporting excellent or good health (OR 6.74, 95% CI 1.54–29.57) compared to non-Hispanic White farmworkers. There was no significant association between self-reported health and legal status. Discussion: This study complements the extant literature showing racial and ethnic health disparities among the U.S. farmworker population. Results provide valuable insight on the health-protective potential of programs like SNAP and WIC, particularly among Hispanic farmworkers, who may be both less likely to be eligible and more hesitant to participate. These findings underscore the need to expand U.S. farmworkers' eligibility and participation in SNAP and WIC. [ABSTRACT FROM AUTHOR]

To explore the attitude towards COVID-19 vaccination among healthcare workers in the US, we surveyed three groups of individuals (essential non-healthcare workers, general healthcare workers, and correctional healthcare workers). We found surprisingly high portions of healthcare workers with COVID-19 vaccine hesitancy/resistance, with 23% of correctional healthcare workers and 17% general healthcare workers (as compared to 12%) refusing to be vaccinated against COVID-19. Multivariate regression models suggest that current season flu vaccination (aOR = 3.34), relying on employer for COVID-19 information (aOR = 3.69), and living in the Midwest (aOR = 5.04) to be strongly associated with COVID-19 vaccine acceptance among essential workers and general healthcare workers. Current season flu vaccination (aOR = 7.52) is also strongly associated with COVID-19 vaccine acceptance among correctional healthcare workers. Potential mechanisms of vaccine hesitancy/resistance among healthcare workers involves low health literacy and employer mistrust. Our findings are highly relevant as we try to reach COVID-19 vaccination goals in the US. [ABSTRACT FROM AUTHOR]

Lyme disease is the most widely reported vector-borne disease in the United States. 95% of confirmed human cases are reported in the Northeast and upper Midwest (25,778 total confirmed cases from Northeast and upper Midwest / 27,203 total US confirmed cases). Human cases typically occur in the spring and summer months when an infected nymph Ixodid tick takes a blood meal. Current federal surveillance strategies report data on an annual basis, leading to nearly a year lag in national data reporting. These lags in reporting make it difficult for public health agencies to assess and plan for the current burden of Lyme disease. Implementation of a nowcasting model, using historical data to predict current trends, provides a means for public health agencies to evaluate current Lyme disease burden and make timely priority-based budgeting decisions. The objective of the study was to develop and compare the performance of nowcasting models using free data from Google Trends and Centers of Disease Control and Prevention surveillance reports. We developed two sets of elastic net models for five regions of the United States: 1. Using only monthly proportional hit data from the 21 disease symptoms and tick related terms, and 2. Using monthly proportional hit data from terms identified via Google correlate and the disease symptom and vector terms. Elastic net models using the full-term list were highly accurate (Root Mean Square Error: 0.74, Mean Absolute Error: 0.52, R2: 0.97) for four of the five regions of the United States and improved accuracy 1.33-fold while reducing error 0.5-fold compared to predictions from models using disease symptom and vector terms alone. Many of the terms included and found to be important for model performance were environmentally related. These models can be implemented to help local and state public health agencies accurately monitor Lyme disease burden during times of reporting lag from federal public health reporting agencies. [ABSTRACT FROM AUTHOR]

Consumers perceive organic foods as more nutritious, natural, and environmentally friendly than non-organic or conventional foods. Since organic foods developed, studies on consumer behavior and organic foods have contributed significantly to its development. The presesent study aims to identify the factors affecting consumer buying behaviour toward organic foods in the United States. Survey data are collected from 770 consumers in the Midwest, United States. ANOVA, multiple linear regression, factor analysis, independent t-tests, and hierarchical multiple regression analysis are used to analyze the collected primary data. This research confirms health consciousness, consumer knowledge, perceived or subjective norms, and perception of price influence consumers' attitudes toward buying organic foods. Availability is another factor that affected the purchase intentions of consumers. Age, education, and income are demographic factors that also impact consumers' buying behavior. The findings help marketers of organic foods design strategies to succeed in the US's fast-growing organic foods market. [ABSTRACT FROM AUTHOR]

Early in the pandemic, slowing the spread of novel coronavirus disease 2019 (COVID-19) relied on non-pharmaceutical interventions. All U.S. states adopted social-distancing restrictions in March and April of 2020, though policies varied both in timing and scope. Compared to states with Democratic governors, those with Republican governors often adopted measures for shorter durations and with greater resistance from their residents. In Kentucky, an extremely close gubernatorial election immediately prior to the discovery of SARS-CoV-2 replaced a Republican incumbent with a Democrat, despite Republicans easily winning all other statewide races. This chance election result offers a unique opportunity to examine the impact of early social distancing policies in a relatively conservative, rural, white-working-class state. Our study begins by estimating an event-study model to link adoption of several common social distancing measures–public school closures, bans on large gatherings, closures of entertainment-related businesses such as restaurants, and shelter-in-place orders (SIPOs)–to the growth rate of cases across counties in the Midwest and South in the early stages of the pandemic. These policies combined to slow the daily growth rate of COVID-19 cases by 9 percentage points after 16 days, with SIPOs and entertainment establishment closures accounting for the entire effect. In order to obtain results with more direct applicability to Kentucky, we then estimate a model that interacts the policy variables with a "white working class" index characterized by political conservatism, rurality, and high percentages of white, evangelical Christian residents without college degrees. We find that the effectiveness of early social distancing measures decreased with higher values of this index. The results imply that the restrictions combined to slow the spread of COVID-19 by 12 percentage points per day in Kentucky's two largest urban counties but had no statistically detectable effect across the rest of the state. [ABSTRACT FROM AUTHOR]

Benign paroxysmal positional vertigo (BPPV) is the most common cause of vertigo in humans, yet the molecular etiology is currently unknown. Evidence suggests that genetic factors may play an important role in some cases of idiopathic BPPV, particularly in familial cases, but the responsible genetic variants have not been identified. In this study, we performed whole exome sequencing [including untranslated regions (UTRs)] of 12 families and Sanger sequencing of additional 30 families with recurrent BPPV in Caucasians from the United States (US) Midwest region, to identify the genetic variants responsible for heightened susceptibility to BPPV. Fifty non-BPPV families were included as controls. In silico and experimental analyses of candidate variants show that an insertion variant rs113784532 (frameshift causing truncation) in the neural cadherin gene PCDHGA10 (protocadherin-gamma A10) is an exceedingly strong candidate (p = 1.80x10-4 vs. sample controls; p = 5.85x10-19 vs. ExAC data; p = 4.9x10-3 vs. NHLBI exome data). The mutant protein forms large aggregates in BPPV samples even at young ages, and affected subjects carrying this variant have an earlier onset of the condition than those without [average 44.0±14.0 (n = 16) versus 54.4±16.1 (n = 36) years old, p = 0.054]. In both human and mouse inner ear tissues, PCDHGA10 is expressed in ganglia, hair cells and vestibular transitional epithelia. Fluorescent RNA in situ hybridization using mouse inner ear tissues shows that expression increases with age. In summary, our data show that a variant in the PCDHGA10 gene may be involved in causing or aggravating some familial cases of recurrent idiopathic BPPV. [ABSTRACT FROM AUTHOR]

The aim of this study is to determine whether basic housing, financial, and food insecurities in part explain the relationship between childhood neglect and violence as documented in the "cycle of violence" literature. Using a prospective cohort design, neglected children (under the age of 12) with court substantiated histories (1967–1971) in one metropolitan Midwest area and demographically matched non-neglected children were followed into adulthood. Housing, financial, and food insecurities were assessed in 2003–2005 interviews at mean age 41. Official arrest data were used to measure violence ever and from 2003 through 2013. Mediation was tested using probit structural equation modeling. Controlling for age, sex, and race, childhood neglect predicted violent arrests and housing, financial, and food insecurities in middle adulthood. Housing and financial securities predicted violent arrests ever and after 2003, whereas food insecurity only predicted any violent arrest ever. Housing and financial insecurities partially mediated the relationship between childhood neglect and violent criminal behavior. Greater attention and efforts need to focus on providing basic housing, financial, and food support for neglected children to reduce their risk for violent criminal behavior. [ABSTRACT FROM AUTHOR]

Background & methods: Recent social movements have highlighted fatal police violence as an enduring public health problem in the United States. To solve it, the public requires basic information, such as understanding where rates of fatal police violence are particularly high, and for which groups. Existing mapping efforts, though critically important, often use inappropriate statistical methods and can produce misleading, unstable rates when denominators are small. To fill this gap, we use inverse-variance-weighted multilevel models to estimate overall and race-stratified rates of fatal police violence for all Metropolitan Statistical Areas (MSAs) in the U.S. (2013–2017), as well as racial inequities in these rates. We analyzed the most recent, reliable data from Fatal Encounters, a citizen science initiative that aggregates and verifies media reports. Results: Rates of police-related fatalities varied dramatically, with the deadliest MSAs exhibiting rates nine times those of the least deadly. Overall rates in Southwestern MSAs were highest, with lower rates in the northern Midwest and Northeast. Yet this pattern was reversed for Black-White inequities, with Northeast and Midwest MSAs exhibiting the highest inequities nationwide. Our main results excluded deaths that could be considered accidents (e.g., vehicular collisions), but sensitivity analyses demonstrated that doing so may underestimate the rate of fatal police violence in some MSAs by 60%. Black-White and Latinx-White inequities were slightly underestimated nationally by excluding reportedly 'accidental' deaths, but MSA-specific inequities were sometimes severely under- or over-estimated. Conclusions: Preventing fatal police violence in different areas of the country will likely require unique solutions. Estimates of the severity of these problems (overall rates, racial inequities, specific causes of death) in any given MSA are quite sensitive to which types of deaths are analyzed, and whether race and cause of death are attributed correctly. Monitoring and mapping these rates using appropriate methods is critical for government accountability and successful prevention. [ABSTRACT FROM AUTHOR]

The incorporation of cover crops into the maize (Zea mays L.)-soybean [Glycine max (L.) Merr.] rotation in the U.S. upper Midwest may improve sustainability. Long, cold winters in the region make identifying successful cover crop species and management practices a challenge. Two experiments were conducted in Minnesota, USA from fall 2016 through spring 2019 to examine the effect of cover crops interseeded at four- to six-leaf collar (early-interseeded) and dent to physiological maturity (late-interseeded) on biomass and grain yield of maize. Annual ryegrass (Lolium multiflorum L.) and cereal rye (Secale cereale L.) were evaluated as monocultures and in mixtures with crimson clover (Trifolium incarnatum L.) and forage radish (Raphanus sativus L.). Differences in canopy cover and biomass of late-interseeded cover crops were observed at the southernmost location in 2018. Additional accumulated growing-degree days in fall 2018 did not translate into increased cover crop canopy coverage of late-interseeded cover crops. Differences in cover crop canopy cover and biomass of early-interseeded cover crops were observed by fall frost at all locations in 2017 and at the northernmost location in 2018. Cover crop canopy cover and biomass at termination before planting maize, soil moisture at maize planting as well as maize aboveground biomass and yield were not affected by spring cereal rye regrowth of cover crops late-interseeded the previous year. Similarly, early-interseeded cover crops did not affect maize aboveground biomass or yield. We attribute these results to limited cover crop growth. This highlights the potential of a variety of cover crop strategies interseeded into maize in the U.S. upper Midwest; however, efforts to fine-tuning cover crop management and weather conditions are needed to benefit from such practice. [ABSTRACT FROM AUTHOR]

Understanding demographic parameters such as survival is important for scientifically sound wildlife management. Survival can vary by region, sex, age-class, habitat, and other factors. White-tailed deer fawn survival is highly variable across the species' range. While recent studies have investigated fawn survival in several Midwestern states, there have been no published estimates from Iowa for 30 years. We radio-collared 48 fawns in central Iowa from 2015–2017 to estimate survival, home range size, and habitat composition and identity causes of mortality. Estimated fawn survival (± SE) was similar to other Midwest studies at 30 (0.78 ± 0.07)) and 60 days (0.69 ± 0.08), but considerably lower at 7 months (0.31 ± 0.02). Survival was positively associated with woodland habitat through 30 and 60 days, but not related to habitat at 7 months. Female fawns avoided agricultural habitat in their home ranges. Fawn 95% kernel density home ranges were smaller than in other studies in the Midwest (21.22 ± 2.74 ha at 30 days, 25.47 ± 2.87 ha at 60 days, and 30.59 ± 2.37 ha at 7 months). The large amount of woodland and grassland (>90%) in our study area meant that fawns did not have to travel far to find suitable cover, which may explain their small home ranges. We recorded 21 mortalities, the leading cause of which was disease (n = 9; 56% epizootic hemorrhagic disease [EHD]) followed by suspected predation (4) and harvest (3). The mortality associated with an outbreak of EHD in 2016, all of which occurred after 60 days post-capture, is the most likely explanation for our low survival estimate at 7 months. While predation, usually early in life, is the leading cause of mortality in most studies, sporadic diseases like EHD can be a major source of mortality in older fawns in some years. [ABSTRACT FROM AUTHOR]

Climate change is likely to increase the frequency of drought and more extreme precipitation events. The objectives of this study were i) to assess the impact of extended drought followed by heavy precipitation events on yield and soil organic carbon (SOC) under historical and future climate, and ii) to evaluate the effectiveness of climate adaptation strategies (no-tillage and new cultivars) in mitigating impacts of increased frequencies of extreme events and warming. We used the validated SALUS crop model to simulate long-term maize and wheat yield and SOC changes of maize-soybean-wheat rotation cropping systems in the northern Midwest USA under conventional tillage and no-till for three climate change scenarios (one historical and two projected climates under the Representative Concentration Path (RCP) 4.5 and RCP6) and two precipitation changes (extreme precipitation occurring early or late season). Extended drought events caused additional yield reduction when they occurred later in the season (10–22% for maize and 5–13% for wheat) rather than in early season (5–17% for maize and 2–18% for wheat). We found maize grain yield declined under the projected climates, whereas wheat grain yield increased. No-tillage is able to reduce yield loss compared to conventional tillage and increased SOC levels (1.4–2.0 t/ha under the three climates), but could not reverse the adverse impact of climate change, unless early and new improved maize cultivars are introduced to increase yield and SOC under climate change. This study demonstrated the need to consider extreme weather events, particularly drought and extreme precipitation events, in climate impact assessment on crop yield and adaptation through no-tillage and new genetics reduces yield losses. [ABSTRACT FROM AUTHOR]

Background: Geographic range limits and the factors structuring them are of great interest to biologists, in part because of concerns about how global change may shift range boundaries. However, scientists lack strong mechanistic understanding of the factors that set geographic range limits in empirical systems, especially in animals. Methodology/Principal Findings: Across dozens of populations spread over six degrees of latitude in the American Midwest, female mating success of the evergreen bagworm Thyridopteryx ephemeraeformis (Lepidoptera: Psychidae) declines from ∼100% to ∼0% near the edge of the species range. When coupled with additional latitudinal declines in fecundity and in egg and pupal survivorship, a spatial gradient of bagworm reproductive success emerges. This gradient is associated with a progressive decline in local abundance and an increased risk of local population extinction, up to a latitudinal threshold where extremely low female fitness meshes spatially with the species' geographic range boundary. Conclusions/Significance: The reduction in fitness of female bagworms near the geographic range limit, which concords with the abundant centre hypothesis from biogeography, provides a concrete, empirical example of how an Allee effect (increased pre-reproductive mortality of females in sparsely populated areas) may interact with other demographic factors to induce a geographic range limit. [ABSTRACT FROM AUTHOR]

This study examines peer and seating effects on overall class performance and exams from a spatial perspective for principles of economics courses at a major Land Grand institution in the Midwest. Both spatial and student specific factors were identified that impact performance. The spatial relationships in the classroom were explored to determine if students' peers and seating choice affect their performance. Endogenous spatial peer effects were only found to impact performance on the first exam. Other findings found gender, being an economics major, sitting in the back of the class, and the year the class was taken all impacted overall exam and class performance. [ABSTRACT FROM AUTHOR]