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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]

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]

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]

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]