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The global COVID-19 pandemic had an unprecedented impact on transportation worldwide. Significant decreases in transportation across all modes were evident and sustained as governments worldwide implemented various countrywide closures and quarantine restrictions to slow the spread of the virus. This paper quantifies and assesses daily vehicle counts by Federal Highway Administration (FHWA) vehicle classifications during the COVID-19 pandemic in New York and Florida throughout 2020. The study found that duringMarch and April of 2020, traffic among all investigated FHWA categories was significantly reduced in both Florida and New York. However, commodity carriers in both states were able to recover faster and remained more consistent than passenger vehicles. This pattern was also observed in both urban and rural communities in Florida. The findings of this work demonstrate how commodity carrier movements, assessed through FHWA vehicle category counts, were less impacted by the governmental restrictions during the pandemic than passenger transportation. While overall traffic volume dropped by more than half in most places at the height of the pandemic, larger commodity-carrying vehicles remained nearly unchanged from the prior year by June of 2020. This was likely because of the critical need to maintain trucking movements to sustain populations. Understanding how truck traffic and freight movements more broadly were impacted during the COVID-19 pandemic is critical in preserving the continuity of service and preventing supply shortages in the event of future outbreaks. [ABSTRACT FROM AUTHOR]

Over the past decade (2010s), a growing number of American voters have abandoned their political affiliations, seemingly disillusioned with the actions of their respective party and/or its candidates. This phenomenon will undoubtedly shift the political spectrum, having unforeseen consequences at every level government and in every aspect of the electioneering process. At a minimum, this shift away from traditional party status will have consequential impacts on closed primary systems. Unaffiliated voters currently comprise roughly 28% of the electorate in Florida and approximately 23% in New York. From these numbers alone, it is clear to see that the unaffiliated voter will have substantial impact on elections, either from their absence in primaries or their inclusion in general elections. Therefore, this paper will investigate trends in the unaffiliated populous at the county level, in the states of Florida and New York between 2012 and 2022. Through statistical analysis as well as the use of a geographic information system (GIS) and its resulting visuals, this paper will assist in elucidating party desertion variations at the county level in these two politically important states. Despite background level trends, all but one of the variables assessed in this study, which included things such as population, majority affiliation and year of occurrence, were not significant. [ABSTRACT FROM AUTHOR]

Strong hurricane winds damage power grids and cause cascading power failures. Statistical and machine learning models have been proposed to predict the extent of power disruptions due to hurricanes. Existing outage models use inputs including power system information, environmental parameters, and demographic parameters. This paper reviews the existing power outage models, highlighting their strengths and limitations. Existing models were developed and validated with data from a few utility companies and regions, limiting the extent of their applicability across geographies and hurricane events. Instead, we train and validate these existing outage models using power outages from multiple regions and hurricanes, including hurricanes Harvey (2017), Michael (2018), and Isaias (2020), in 1910 US cities. The dataset includes outages from 39 utility companies in Texas, 5 in Florida, 5 in New Jersey, and 11 in New York. We discuss the limited ability of state-of-the-art machine learning models to (1) make bounded outage predictions, (2) extrapolate predictions to high winds, and (3) account for physics-informed outage uncertainties at low and high winds. For example, we observe that existing models can predict outages higher than the number of customers (in 19.8 % of cities with an average overprediction ratio of 5.2) and cannot capture well the outage variance for high winds, especially above 70 m s -1. Our findings suggest that further developments are needed for power outage models for proper representation of hurricane-induced outages. [ABSTRACT FROM AUTHOR]

There is a demand for noncontact, high-accuracy, high-spatial-resolution, wireless sensing technologies for various water level and coastal monitoring applications. This paper presents a low-cost, compact, easily configurable interferometry radar for noncontact water level monitoring, including its hardware design, signal processing algorithms, and wireless communication strategies. Interferometry radar measures distance by comparing the phase lag between reflected and transmitted signals. Water level measurements using this approach have been demonstrated in a solitary wave laboratory experiment, a field deployment observing wave run-up near Ponte Vedra, Florida, and a field deployment observing waves and tides in the Sparkill Creek located in Piermont, New York. The experimental results from the radars with millimeter-level accuracy have been compared with reference sensors and demonstrate the potential of continuous wave radar for water level observations. [ABSTRACT FROM AUTHOR]

Criminal justice system fines and fees are a source of revenue for courts, district attorneys, probation departments, and other actors both inside and outside of the justice system. Because these departments rely on fine and fee revenue to partially fund their budgets, they may be incentivized to oppose reforms that would reduce fine and fee collections. Using budget documents from counties and a sample of municipalities in Florida and New York, the authors find that budget reports provide more detailed data on fines and fees than are available from other data sources, including information on which types of fines and fees are collected and how much revenue criminal justice fines and fees provide, both on a per-capita basis and as a share of the total jurisdiction budget. This paper presents a replicable methodology for future research in how to use local government budget documents to evaluate fine and fee collections. A larger, forthcoming study will use this methodology to examine fine and fee collections in five states. [ABSTRACT FROM AUTHOR]

Strong hurricane winds damage power grids and cause cascading power failures. Statistical and machine learning models have been proposed to predict the extent of power disruptions due to hurricanes. Existing outage models use inputs including power system information, environmental, and demographic parameters. This paper reviews the existing power outage models, highlighting their strengths and limitations. Existing models were developed and validated with data on a few utility companies and regions, limiting the extent of their applicability across geographies and hurricane events. Instead, we train and validate these existing outage models using power outages for multiple regions and hurricanes, including Hurricanes Harvey (2017), Michael (2018), and Isaias (2020), in 1,833 cities along the U.S. coastline. The dataset includes outage data from 39 utility companies in Texas, 5 in Florida, 5 in New Jersey, and 11 in New York. We discuss the limited ability of state-of-the-art machine learning models to (1) make bounded outage predictions, (2) extrapolate predictions to high winds, and (3) account for physics-informed outage uncertainties at low and high winds. For example, we observe that existing models can predict outages as high as 25 times more than the number of customers and cannot capture well the outage variance for wind speeds over 70 m/s. Finally, we present a Beta regression outage modeling framework to address the shortcomings of existing power outage models. [ABSTRACT FROM AUTHOR]

Importance: The frequency and characteristics of post-acute sequelae of SARS-CoV-2 infection (PASC) may vary by SARS-CoV-2 variant. Objective: To characterize PASC-related conditions among individuals likely infected by the ancestral strain in 2020 and individuals likely infected by the Delta variant in 2021. Design: Retrospective cohort study of electronic medical record data for approximately 27 million patients from March 1, 2020-November 30, 2021. Setting: Healthcare facilities in New York and Florida. Participants: Patients who were at least 20 years old and had diagnosis codes that included at least one SARS-CoV-2 viral test during the study period. Exposure: Laboratory-confirmed COVID-19 infection, classified by the most common variant prevalent in those regions at the time. Main outcome(s) and measure(s): Relative risk (estimated by adjusted hazard ratio [aHR]) and absolute risk difference (estimated by adjusted excess burden) of new conditions, defined as new documentation of symptoms or diagnoses, in persons between 31–180 days after a positive COVID-19 test compared to persons without a COVID-19 test or diagnosis during the 31–180 days after the last negative test. Results: We analyzed data from 560,752 patients. The median age was 57 years; 60.3% were female, 20.0% non-Hispanic Black, and 19.6% Hispanic. During the study period, 57,616 patients had a positive SARS-CoV-2 test; 503,136 did not. For infections during the ancestral strain period, pulmonary fibrosis, edema (excess fluid), and inflammation had the largest aHR, comparing those with a positive test to those without a COVID-19 test or diagnosis (aHR 2.32 [95% CI 2.09 2.57]), and dyspnea (shortness of breath) carried the largest excess burden (47.6 more cases per 1,000 persons). For infections during the Delta period, pulmonary embolism had the largest aHR comparing those with a positive test to a negative test (aHR 2.18 [95% CI 1.57, 3.01]), and abdominal pain carried the largest excess burden (85.3 more cases per 1,000 persons). Conclusions and relevance: We documented a substantial relative risk of pulmonary embolism and a large absolute risk difference of abdomen-related symptoms after SARS-CoV-2 infection during the Delta variant period. As new SARS-CoV-2 variants emerge, researchers and clinicians should monitor patients for changing symptoms and conditions that develop after infection. [ABSTRACT FROM AUTHOR]

Background: Police violence is increasingly recognized as an urgent public health problem. Basic questions about police violence, however, remain unanswered, including which types of law enforcement agency are responsible for fatal police violence deaths. Methods: We estimated the proportion of police violence deaths in the U.S. (2013–2022) that were attributable to local, county, state, federal, or tribal police agencies, using mapping police violence data. We examined proportions overall, by decedent race/ethnicity, and by state. Results: Nationally, 60% of decedents were killed by municipal, 29% by county, 8% by state, and 3% by federal, police, with < 1% killed by tribal or other officers. These proportions varied by race/ethnicity, with 56% of Native American decedents killed by municipal police compared to 70–75% among other racially minoritized people. While municipal police were responsible for most deaths in most states, in the Southeast, county police predominated. In some Northeastern states (and Alaska), state police were responsible for > 40% of deaths. Conclusions: We identify wide geographic & racial/ethnic variation in the agencies responsible for fatal police violence. Findings suggest that the budgetary and infrastructural shifts required to prevent fatal police violence need to occur at multiple levels of government. [ABSTRACT FROM AUTHOR]

Climate changes are accelerating and leading to climate and weather extremes with the most destructive impacts and negative consequences on the planet. For these reasons, precise forecasting, and announcement of weather disasters of a convective nature, from local to synoptic scales, is very important. The Novel Thunderstorm Alert System (NOTHAS) has shown outstanding results in forecasting and early warning of different modes of convection, including local hazards in mid-latitudes. In this study, an attempt has been made to apply this tool in the prediction of different atmospheric systems that occur in different climatic regions. The upgraded prognostic and diagnostic algorithm with adjusted complex parameters and criteria representative of tropical storms and tropical cyclones showed good coincidence with the available observations. NOTHAS showed skill and success in assessing the dynamics and intensity of Hurricane Ian, which hit the west coast of Florida on 30 September 2022 and caused great material damage and human losses. This advanced tool also detected the most intense-extreme Level-5 on 1 September 2021, over New York, when catastrophic flooding occurred within the remnants of Hurricane Ida. Likewise, the upgraded model configuration very correctly predicted the trajectory, modifications, and strength of super typhoon Nanmadol over Japan (19 September 2022), 24–48 h in advance, and super typhoon Noru over the Philippines (25 September 2022). The system showed the temporal and spatial accuracy of the location of the heavy rainfall and flash flood. In general, the obtained results for all evaluated cases are encouraging and provide a good basis for further testing, verification, and severe weather warnings and guidance for weather services worldwide. [ABSTRACT FROM AUTHOR]

Background: Disparities in uninsured emergency department (ED) use are well documented. However, a comprehensive analysis evaluating how the Affordable Care Act (ACA) may have reduced racial and ethnic disparities is lacking. The goal was to assess the association of the ACA with racial and ethnic disparities in uninsured ED use. Methods: This study used data from the Healthcare Cost and Utilization Project (HCUP) State Emergency Department Databases (SEDD) for Georgia, Florida, Massachusetts, and New York from 2011 to 2017. Participants include non-elderly adults between 18 and 64 years old. Outcomes include uninsured rates of ED visits by racial and ethnic groups and stratified by medical urgency using the New York University ED algorithm. Visits were aggregated to year-quarter ED visits per 100,000 population and stratified for non-Hispanic White, non-Hispanic Black, and Hispanic non-elderly adults. Quasi-experimental difference-in-differences and triple differences regression analyses to identify the effect of the ACA and the separate effect of the Medicaid expansion were used comparing uninsured ED visits by race and ethnicity groups pre-post ACA. Results: The ACA was associated with a 14% reduction in the rate of uninsured ED visits per 100,000 population (from 10,258 pre-ACA to 8,877 ED visits per 100,000 population post-ACA) overall. The non-Hispanic Black compared to non-Hispanic White disparity decreased by 12.4% (-275.1 ED visits per 100,000) post-ACA. About 60% of the decline in the Black-White disparity was attributed to disproportionate declines in ED visit rates for conditions classified as not-emergent (-93.2 ED visits per 100,000), and primary care treatable/preventable (-64.1 ED visits per 100,000), while the disparity in ED visit rates for injuries and not preventable conditions also declined (-106.57 ED visits per 100,000). All reductions in disparities were driven by the Medicaid expansion. No significant decrease in Hispanic-White disparity was observed. Conclusions: The ACA was associated with fewer uninsured ED visits and reduced the Black-White ED disparity, driven mostly by a reduction in less emergent ED visits after the ACA in Medicaid expansion states. Disparities between Hispanic and non-Hispanic White adults did not decline after the ACA. Despite the positive momentum of declining disparities in uninsured ED visits, disparities, especially among Black people, remain. [ABSTRACT FROM AUTHOR]

Objective: To study the impact of Medicaid funding structures before and after the implementation of the Affordable Care Act (ACA) on health care access for Latinos in New York (Medicaid expansion), Florida (Medicaid non‐expansion), and Puerto Rico (Medicaid block grant). Data Sources: Pooled state‐level data for New York, Florida, and Puerto Rico from the 2011–2019 Behavioral Risk Factor Surveillance System and data from the 2011–2019 American Community Survey and Puerto Rico Community Survey. Study Design: Cross‐sectional study using probit with predicted margins to separately compare four health care access measures among Latinos in New York, Florida, and Puerto Rico (having health insurance coverage, having a personal doctor, delayed care due to cost, and having a routine checkup). We also used difference‐in‐differences to measure the probability percent change of having any health insurance and any public health insurance before (2011–2013) and after (2014–2019) the ACA implementation among citizen Latinos in low‐income households. Data Collection: The sample consisted of Latinos aged 18–64 residing in New York, Florida, and Puerto Rico from 2011 to 2019. Principal Findings: Latinos in Florida had the lowest probability of having health care access across all four measures and all time periods compared with those in New York and Puerto Rico. While Latinos in Puerto Rico had greater overall health care access compared with Latinos in both states, health care access in Puerto Rico did not change over time. Among citizen Latinos in low‐income households, New York had the greatest post‐ACA probability of having any health insurance and any public health insurance, with a growing disparity with Puerto Rico (9.7% any [1.6 SE], 5.2% public [1.8 SE]). Conclusions: Limited Medicaid eligibility (non‐expansion of Florida's Medicaid program) and capped Medicaid funds (Puerto Rico's Medicaid block grant) contributed to reduced health care access over time, particularly for citizen Latinos in low‐income households. [ABSTRACT FROM AUTHOR]

The U.S. electric-power infrastructure urgently needs renovation. Recent major power outages in California, New York, Texas, and Florida have highlighted U.S. electric-power unreliability. The media have discussed the U.S. aging power infrastructure and the Public Utilities Commission has demanded a comprehensive review of the causes of recent power outages. This paper explores geographic information systems (GIS) and a spatially enhanced predictive power-outage model to address: How may spatial analytics enhance our understanding of power outages? To answer this research question, we developed a spatial analysis framework that utilities can use to investigate power-failure events and their causes. Analysis revealed areas of statistically significant power outages due to multiple causes. This study's GIS model can help to advance smart-grid reliability by, for example, elucidating power-failure root causes, defining a data-responsive blackout solution, or implementing a continuous monitoring and management solution. We unveil a novel use of spatial analytics to enhance power-outage understanding. Future work may involve connecting to virtually any type of streaming-data feed and transforming GIS applications into frontline decision applications, showing power-outage incidents as they occur. GIS can be a major resource for electronic-inspection systems to lower the duration of customer outages, improve crew response time, as well as reduce labor and overtime costs. [ABSTRACT FROM AUTHOR]

The article discusses the costs associated with the efforts to comply with a publication requirement in the New York Limited Liability Company (LLC) Law as of 2015, and it provides a comparison of the LLC-related publication laws in places such as Wyoming and Florida. According to the article, an LLC in New York is mandated to report its existence in both a weekly publication and a daily publication. U.S. corporate taxation laws are examined, along with pass-through taxation status.

As the Soviet Union and its challenge to the West recede, we still have much to learn about the Slavic book trade and about the rise of the great Russian, East European, and Eurasian area studies collections in academic libraries in 20th-century North America. Who were the book dealers behind these collections, which still inform so much discovery and knowledge-making today? What can we learn about these personalities and their work? One such book dealer was George Sabo (1896–1983), who followed two brothers to Pittsburgh in 1913 but made his career in New York after 1920, first with a "steamship agency" for fellow immigrants. As a Carpatho-Rusyn from the Kingdom of Hungary, Sabo took his outlook and cultural capital from an ethno-religious group at the very center of the Slavic world and in remarkable symbiosis with nearly all its peoples, languages, identities, and states. Sabo's native village (Orechová) became part of Czechoslovakia after World War I and his wife's (Haidosh) part of the Soviet Union after World War II. Sabo's Carpatho-Rusyn-ness equipped him well as a Slavic-American book dealer and enterprising New Yorker, and we can illuminate much of his life, family, network, surroundings, and career in the city and beyond from many kinds of sources. [ABSTRACT FROM AUTHOR]

We investigate the association between nativity status (U.S.- vs foreignborn) and viral suppression among women of color (WOC) with HIV (HIV +) and whether this association was modified by education and housing. Methods: Data were from 549 HIV + WOC, who participated in the Health Resources and Services Administration-funded WOC Initiative 2009–2013. We used generalized estimating equation models to quantify the association between the respondents' nativity status and viral suppression. Results: After adjusting for covariates, foreign-born WOC were 2.2 times (95% confidence interval: 1.25, 3.85) more likely to achieve viral suppression than U.S.-born WOC. This association was not modified by education or housing status. Conclusions: Despite facing barriers to care, foreign-born WOC were more likely to achieve viral suppression than U.S.-born WOC. Programs aiming to end the HIV epidemic and reduce HIV disparities in the U.S. should consider these findings as they provide a more nuanced understanding of HIV + WOC. [ABSTRACT FROM AUTHOR]

Near-real time water segmentation with medium resolution satellite imagery plays a critical role in water management. Automated water segmentation of satellite imagery has traditionally been achieved using spectral indices. Spectral water segmentation is limited by environmental factors and requires human expertise to be applied effectively. In recent years, the use of convolutional neural networks (CNN's) for water segmentation has been successful when used on high-resolution satellite imagery, but to a lesser extent for medium resolution imagery. Existing studies have been limited to geographically localized datasets and reported metrics have been benchmarked against a limited range of spectral indices. This study seeks to determine if a single CNN based on Red, Green, Blue (RGB) image classification can effectively segment water on a global scale and outperform traditional spectral methods. Additionally, this study evaluates the extent to which smaller datasets (of very complex pattern, e.g harbour megacities) can be used to improve globally applicable CNNs within a specific region. Multispectral imagery from the European Space Agency, Sentinel-2 satellite (10 m spatial resolution) was sourced. Test sites were selected in Florida, New York, and Shanghai to represent a globally diverse range of waterbody typologies. Region-specific spectral water segmentation algorithms were developed on each test site, to represent benchmarks of spectral index performance. DeepLabV3-ResNet101 was trained on 33,311 semantically labelled true-colour samples. The resulting model was retrained on three smaller subsets of the data, specific to New York, Shanghai and Florida. CNN predictions reached a maximum mean intersection over union result of 0.986 and F1-Score of 0.983. At the Shanghai test site, the CNN's predictions outperformed the spectral benchmark, primarily due to the CNN's ability to process contextual features at multiple scales. In all test cases, retraining the networks to localized subsets of the dataset improved the localized region's segmentation predictions. The CNN's presented are suitable for cloud-based deployment and could contribute to the wider use of satellite imagery for water management. [ABSTRACT FROM AUTHOR]

Background: Patients with serious mental illness (SMI) are vulnerable to medical-surgical readmissions and emergency department visits.Methods: We studied 1,914,619 patients with SMI discharged after medical-surgical admissions in Florida and New York between 2012 and 2015 and their revisits to the hospital within 30 days of discharge.Results: Patients with SMI from the most disadvantaged communities had greater adjusted 30-day revisit rates than patients from less disadvantaged communities. Among those that experienced a revisit, patients from the most disadvantaged communities had 7.3 % greater 30-day observation stay revisits.Conclusions: These results suggest that additional investments are needed to ensure that patients with SMI from the most disadvantaged communities are receiving appropriate post-discharge care. [ABSTRACT FROM AUTHOR]

Socially vulnerable communities experience disproportionately negative outcomes following natural disasters and underscoring a need for well-validated measures to identify those at risk. However, questions have surfaced regarding the factor structure, internal consistency, and generalizability of social vulnerability measures. A reliance on data-driven techniques, which are susceptible to sample-specific characteristics, has likely exacerbated the difficulty generalizing social vulnerability measures across contexts. This study sought to validate previously published structures of SoVI using confirmatory factor analysis (CFA). We fit CFA models of 28 sociodemographic variables frequently used to calculate a commonly used measure, the social vulnerability index (SoVI), drawn from the American Community Survey across 4162 census tracts in Florida. Confirmatory models generally did not support theory-driven pillars of SoVI that were previously used to study vulnerability in the New York metropolitan area. Modified models and alternative SoVI factor structures also failed to fit the data. Many of the input variables displayed little to no variability, limiting their utility and explanatory power. Taken together, our results highlight the poor generalizability of SoVI across contexts, but raise several important considerations for reliability and validity, as well as issues related to source data and scale. We discuss the implications of these findings for improved theory-driven measurement of social vulnerability. [ABSTRACT FROM AUTHOR]

Adult day centers (ADCs) are nonresidential settings that support the health and social needs of vulnerable older adults. Due to ADCs' congregate nature and participants' compromised health status, many ADCs have been forced to close during the COVID-19 pandemic. It is unknown how closures have impacted service delivery at ADCs. Guided by the Resiliency Activation Framework, we (a) identified consequences resulting from closures of ADCs during the COVID-19 pandemic and (b) described factors that have enabled the ADC community to remain resilient in the wake of challenges brought on by the pandemic. We conducted 2 focus groups in California (n = 12), and individual interviews with ADC staff members (n = 8) in 7 other states. The results of a directed content analysis revealed perceived declines in physical, cognitive, and mental health of ADC users and increased caregiver strain. Access to human, social, economic, and political capital were essential for supporting ADCs in buffering the impacts of the pandemic on the older adults they serve but were not consistently available. Research is urgently needed that quantifies the impacts of the pandemic on ADC users and their caregivers to inform policy and advocacy efforts in the wake of the pandemic. [ABSTRACT FROM AUTHOR]

To compare the time to asthma-related readmission between children admitted to the intensive care unit (ICU) for asthma and those with a non-ICU hospitalization in the United States and to explore risk factors associated with readmission among children admitted in the ICU. In this retrospective cohort study, we included children aged 2–17 years in the State Inpatient Database (2005-2014) from four U.S. states who were hospitalized for asthma. We compared the time to asthma-related readmissions and emergency department (ED) visit between children admitted and not admitted to the ICU using the log-rank test. Among those admitted to the ICU, we explored factors associated with readmission using Cox regression. 66 835 children were hospitalized for asthma, with 14.0% admitted to the ICU, and 12 844 were readmitted for asthma while 22 915 had an asthma-related ED visit. The time to asthma-related readmission was shorter in the ICU group compared to the non-ICU group (p < 0.001), but the time to asthma-related ED visit did not differ between the two groups (p = 0.43). Being preschool-aged, female, Black, and having lower household income and a longer length of stay during the initial hospitalization conferred a higher risk of asthma-related readmission among children admitted to the ICU. Preschool age and Medicaid were Florida-specific risk factors while Hispanic ethnicity was New York-specific. Compared to children not admitted to the ICU, children admitted to the ICU for asthma were at increased risk of asthma-related readmission, with certain risk factors conferring an even higher risk. [ABSTRACT FROM AUTHOR]

An online survey in Florida and New York of community college librarians with responsibility for information literacy instruction provides a snapshot of instructional objectives and practices, including librarians' beliefs about students' information literacy needs, strengths, and weaknesses. Survey results point to the influence of the Association of College and Research Libraries Framework in the community college context, the challenges librarians face as they work to implement it, and their successes in doing so. These data reveal opportunities to support and improve instruction and to prepare future librarians to work successfully in community college contexts. [ABSTRACT FROM AUTHOR]

Objectives: Repeat visits (revisits) to emergency departments (EDs) for psychiatric care reflect poor continuity of care and impose a high financial cost. We test whether rapid expansion of community health centers (CHCs)—which provide regional, low‐cost primary care—correspond with fewer repeat psychiatric‐related ED visits (PREDVs). Methods: We obtained repeated cross‐sectional time‐series data for 7.8 million PREDVs from the State Emergency Department Database for four populous U.S. states (California, Florida, North Carolina, and New York) from 2006 to 2011. We specified as the outcome variable the count of repeat visits per ED visitor with a psychiatric diagnosis. We retrieved aggregate‐level mental health visits at CHCs from the Uniform Data System. Negative binomial regression methods controlled for individual‐level confounders, county health system and sociodemographic attributes, year fixed effects, and county fixed effects. Results: The risk of a repeat PREDV decreased with a county‐level increase in mental health patients seen at CHCs (incidence rate ratio = 0.986, 95% confidence interval = 0.98 to 0.99). Conversion of this rate ratio to the number of revisits averted indicated 34,000 fewer repeat PREDVs in these four states statistically associated with a 1% expansion in CHC mental health visits. Exploratory analyses found that revisits decline for relatively mild/moderate illnesses (e.g., mood, anxiety disorders) but not for severe illnesses (e.g., schizophrenia/psychoses). Conclusion: An increase in mental health services at CHCs corresponds with a modest decline in repeat PREDVs. This decline concentrates among those with less severe mental illnesses. [ABSTRACT FROM AUTHOR]

Since the discovery of the secondary preventive benefits of antiretroviral therapy, national and international governing bodies have called for countries to reach 90% diagnosis, ART engagement and viral suppression among people living with HIV/AIDS. The US HIV epidemic is dispersed primarily across large urban centers, each with different underlying epidemiological and structural features. We selected six US cities, including Atlanta, Baltimore, Los Angeles, Miami, New York, and Seattle, with the objective of demonstrating the breadth of epidemiological and structural differences affecting the HIV/AIDS response across the US. We synthesized current and publicly-available surveillance, legal statutes, entitlement and discretionary funding, and service location data for each city. The vast differences we observed in each domain reinforce disparities in access to HIV treatment and prevention, and necessitate targeted, localized strategies to optimize the limited resources available for each city’s HIV/AIDS response. [ABSTRACT FROM AUTHOR]

Black people have the highest rate of HIV/AIDS infection in the USA, and they are less likely to access quality physical and mental healthcare. To address these disparities as outlined in the National HIV/AIDS Strategy, there is a need for culturally congruent, innovative approaches to HIV/AIDS prevention. The first multi-denominational national study of Black faith leaders was conducted utilizing focus groups that were held in 11 US cities. The 265 participants were faith leaders who reported involvement in such prevention practices as sponsoring HIV/AIDS workshops, integrating HIV/AIDS messaging in the worship service, hosting HIV/AIDS screenings, distributing written materials about HIV/AIDS through the bulletin or flyers, pastoral counselling, advocating for policies that provide quality healthcare to the community and disseminating HIV/AIDS prevention messages through new media such as the Church website. These findings, including attention to barriers to engagement, provide insight into innovative practices that can be integrated into faith-based HIV/AIDS prevention programming. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In many post-election surveys, the proportion of respondents who claim to have voted is greater than government-reported turnout rates. These differences have often been attributed to respondent lying (e.g., Burden 2000). In a search for greater accuracy, scholars have replaced respondent self-reports of turnout with government records of their turnout (a.k.a. turnout validation). Some scholars have interpreted "validated" turnout estimates as more accurate than respondent selfreports because "validated" rates tend to be lower than aggregate selfreported rates and tend to be closer to government-reported rates. We explore the viability of turnout validation efforts. We find that several apparently viable methods of matching survey respondents to government records severely underestimate the proportion of Americans who were registered to vote. Matching errors that severely underestimate registration rates also drive down "validated" turnout estimates. As a result, when "validated" turnout estimates appear to be more accurate than self-reports because they produce lower turnout estimates, the apparent accuracy is likely an illusion. Also, among respondents whose selfreports can be validated against government records, the accuracy of self-reports is extremely high. This would not occur if lying was the primary explanation for differences between reported and official turnout rates. These findings challenge the notion that the practice of "turnout validation" offers a means of measuring turnout that is more accurate than survey respondents' self-reports. [ABSTRACT FROM AUTHOR]

Background Hospital discharge data are used for occupational injury surveillance, but observed hospitalisation trends are affected by trends in healthcare practices and workers' compensation coverage that may increasingly impair ascertainment of minor injuries relative to severe injuries. The objectives of this study were to (1) describe the development of a severe injury definition for surveillance purposes and (2) assess the impact of imposing a severity threshold on estimated occupational and non-occupational injury trends. Methods Three independent methods were used to estimate injury severity for the severe injury definition. 10 population-based hospital discharge databases were used to estimate trends (1998-2009), including the National Hospital Discharge Survey (NHDS) and State Inpatient Databases (SID) from the Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality. Negative binomial regression was used to model injury trends with and without severity restriction and to test trend divergence by severity. Results Trend estimates for occupational injuries were biased downwards in the absence of severity restriction, more so than for non-occupational injuries. Imposing a severity threshold resulted in a markedly different historical picture. Conclusions Severity restriction can be used as an injury surveillance methodology to increase the accuracy of trend estimates, which can then be used by occupational health researchers, practitioners and policymakers to identify prevention opportunities and to support state and national investments in occupational injury prevention efforts. The newly adopted state-based occupational health indicator, 'Work-Related Severe Traumatic Injury Hospitalizations', incorporates a severity threshold that will reduce temporal ascertainment threats to accurate trend estimates. [ABSTRACT FROM AUTHOR]

Why do workers participate in their own exploitation? This article moves beyond the situational production of consent that has dominated studies of the labor process and outlines the relational production of labor’s surplus value. Using a case of unpaid women who perform valuable work for VIP nightclubs, I present ethnographic data on the VIP party circuit from New York, the Hamptons, Miami, and Cannes, as well as 84 interviews with party organizers and guests. Party promoters, mostly male brokers, appropriate surplus value from women in four stages: recruitment, mobilization, performance, and control. Relational work between promoters and women, cemented by gifts and strategic intimacies, frames women’s labor as leisure and friendship, and boundary work legitimizes women’s work as distinct from sexual labor. When boundaries, media, and meanings of relationships do not appropriately align, as in relational mismatches, women experience the VIP party less as leisure and more as work, and they are less likely to participate. My findings embed the labor process in a relational infrastructure and hold insights for explaining why people work for free in culture and technology sectors of the post-Fordist economy. [ABSTRACT FROM AUTHOR]

The aim of this study was to assess the efficacy of stereotactic-arm assisted acetabular component placement during total hip arthroplasty (THA).~Aims~Objective~120 patients underwent primary THA at 4 different medical centres. A preoperative pelvic CT protocol was used to plan socket placement followed by robotic-arm assisted acetabular preparation and cup insertion. Intraoperative cup position was recorded and postoperative placement measured using Martell suite analysis software.~Methods~Methods~Using a 95% predictive intervals, robotic-arm cup placement was within +/-4 degrees of planned position in 95% of cases. Applying these data to the so-called safe zone, 96% of sockets were within the defined safe zone. Our data confirms that intraoperative robotic assistance improves the precision of preparation and position of the acetabular cup during total hip arthroplasty.~Results~Results [ABSTRACT FROM AUTHOR]

Aims The aims of this study are to describe: (1) the frequency of nurse-reported missed care in neonatal intensive care units; and (2) nurses' reports of factors contributing to missed care on their last shift worked. Background Missed nursing care, or necessary care that is not delivered, is increasingly cited as a contributor to adverse patient outcomes. Previous studies highlight the frequency of missed nursing care in adult settings; the occurrence of missed nursing care in neonatal intensive care units is unknown. Design A descriptive analysis of neonatal nurses' self-reports of missed care using data collected through a cross-sectional web-based survey. Methods A random sample of certified neonatal intensive care nurses in seven states was invited to participate in the survey in April 2012. Data were collected from nurses who provide direct patient care in a neonatal intensive care unit ( n = 230). Descriptive statistics constituted the primary analytic approach. Results Nurses reported missing a range of patient care activities on their last shift worked. Nurses most frequently missed rounds, oral care for ventilated infants, educating and involving parents in care and oral feedings. Hand hygiene, safety and physical assessment and medication administration were missed least often. The most common reasons for missed care included frequent interruptions, urgent patient situations and an unexpected rise in patient volume and/or acuity on the unit. Conclusion We find that basic nursing care in the neonatal intensive care unit is missed and that system factors may contribute to missed care in this setting. [ABSTRACT FROM AUTHOR]

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Medicaid is designed to ensure low-income populations can afford health care. However, not all health services are covered by the program. Most state Medicaid programs restrict abortion coverage, though a small number of state programs offer such coverage. Little is known about how low-income women are affected by differing Medicaid coverage policies regarding abortion. We conducted in-depth interviews with 98 low-income women who had abortions. We found that women's impressions about abortion costs and the availability of Medicaid coverage are generally accurate and that women rely predominantly on abortion facilities for confirmatory cost and coverage information. Additionally, when abortion is out of financial reach, women and the people in their lives experience numerous emotional and financial harms. Policies that aim to ensure abortion is affordable largely prevent these harms, though the availability of Medicaid coverage does not always guarantee access to affordable care. Findings can help advance evidence-based policies. [ABSTRACT FROM AUTHOR]

HIV testing behavior is important in understanding the high rates of undiagnosed infection among Hispanic/Latino men who have sex with men (MSM). Correlates of repeat/recent testing (within the past year and ≥5 tests during lifetime) and test avoidance (never or >5 years earlier) were examined among 608 sexually active Hispanic/Latino MSM (Miami-Dade County and New York City). Those who reported repeat/recent testing were more likely to have incomes over $30,000, speak English predominately, and have visited and disclosed same-sex behavior to a health care provider (HCP) in the past year. Those who were classified as test avoiders were less likely to have incomes over $10,000 and to have seen an HCP in the past year. The main reason for not testing (in both groups) was fear of HIV positivity; however, twice as many test avoiders considered this their main reason, and more test avoiders had confidentiality concerns. Results suggest that messages to encourage testing among Hispanic/Latino MSM may be most effective if past testing patterns and reasons for not testing are considered. HCPs can play an important role by consistently offering HIV tests to MSM and tailoring messages based on prior testing histories. [ABSTRACT FROM PUBLISHER]

A new decision rule based on net benefit per capita is proposed and exemplified with the aim of assisting policymakers in deciding whether to lockdown or reopen an economy—fully or partially—amidst a pandemic. Bayesian econometric models using Markov chain Monte Carlo algorithms are used to quantify this rule, which is illustrated via several sensitivity analyses. While we use COVID-19 data from the United States to demonstrate the ideas, our approach is invariant to the choice of pandemic and/or country. The actions suggested by our decision rule are consistent with the closing and reopening of the economies made by policymakers in Florida, Texas, and New York; these states were selected to exemplify the methodology since they capture the broad spectrum of COVID-19 outcomes in the U.S. [ABSTRACT FROM AUTHOR]

Simple Summary: Using tools from both mathematics (especially wavelet theory) and computer science (machine learning), we present a general new method for modelling the evolution of epidemics which is not restricted to human populations. A crucial novel feature of our approach is that it significantly takes into account that an epidemic may take place in certain types of waves which cannot only be of a global as well as local nature, but can also occur at multiple different times and locations. In the particular case of the current Covid-19 pandemic, based on recent figures from the Johns Hopkins database we apply our model to France, Germany, Italy, the Czech Republic, as well as the US federal states New York and Florida, and compare it and its predictions to established as well as other recently developed forecasting methods and techniques. We introduce the concept of epidemic-fitted wavelets which comprise, in particular, as special cases the number I (t) of infectious individuals at time t in classical SIR models and their derivatives. We present a novel method for modelling epidemic dynamics by a model selection method using wavelet theory and, for its applications, machine learning-based curve fitting techniques. Our universal models are functions that are finite linear combinations of epidemic-fitted wavelets. We apply our method by modelling and forecasting, based on the Johns Hopkins University dataset, the spread of the current Covid-19 (SARS-CoV-2) epidemic in France, Germany, Italy and the Czech Republic, as well as in the US federal states New York and Florida. [ABSTRACT FROM AUTHOR]

Background: To describe the supply, distribution, and characteristics of international medical graduates (IMGs) in family medicine who provide patient care in the U.S. Methods: A cross-sectional study design, using descriptive statistics on combined data from the Educational Commission for Foreign Medical Graduates and the American Medical Association, including medical school attended, country of medical school, and citizenship when entering medical school. Results: In total, 118,817 physicians in family medicine were identified, with IMGs representing 23.8% (n = 28,227) of the U.S. patient care workforce. Of all 9579 residents in family medicine, 36.0% (n = 3452) are IMGS. In total, 35.9% of IMGs attended medical school in the Caribbean (n = 10,136); 19.9% in South-Central Asia (n = 5607) and 9.1% in South-Eastern Asia (n = 2565). The most common countries of medical school training were Dominica, Mexico, and Sint Maarten. Of all IMGs in family medicine who attended medical school in the Caribbean, 74.5% were U.S. citizens. In total, 40.5% of all IMGs in family medicine held U.S. citizenship at entry to medical school. IMGs comprise almost 40% of the family medicine workforce in Florida, New Jersey and New York. Conclusions: IMGs play an important role in the U.S. family medicine workforce. Many IMGs are U.S. citizens who studied abroad and then returned to the U.S. for graduate training. Given the shortage of family physicians, and the large number of IMGs in graduate training programs, IMGs will continue to play a role in the U.S. physician workforce for some time to come. Many factors, including the supply of residency training positions, could eventually restrict the number of IMGs entering the U.S., including those contributing to family practice. [ABSTRACT FROM AUTHOR]