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Skin cancer has a significant impact on the lives of many individuals annually and is recognized as the most prevalent type of cancer. In the United States, an estimated annual incidence of approximately 3.5 million people receiving a diagnosis of skin cancer underscores its widespread prevalence. Furthermore, the prognosis for individuals afflicted with advancing stages of skin cancer experiences a substantial decline in survival rates. This paper is dedicated to aiding healthcare experts in distinguishing between benign and malignant skin cancer cases by employing a range of machine learning and deep learning techniques and different feature extractors and feature selectors to enhance the evaluation metrics. In this paper, different transfer learning models are employed as feature extractors, and to enhance the evaluation metrics, a feature selection layer is designed, which includes diverse techniques such as Univariate, Mutual Information, ANOVA, PCA, XGB, Lasso, Random Forest, and Variance. Among transfer models, DenseNet-201 was selected as the primary feature extractor to identify features from data. Subsequently, the Lasso method was applied for feature selection, utilizing diverse machine learning approaches such as MLP, XGB, RF, and NB. To optimize accuracy and precision, ensemble methods were employed to identify and enhance the best-performing models. The study provides accuracy and sensitivity rates of 87.72% and 92.15%, respectively. [ABSTRACT FROM AUTHOR]

China's export benefits from the significant fiscal stimulus in the United States. This paper analyzes the global spillover effect of the American economy on China's macro-economy using the Markov Chain Monte Carlo (MCMC)-Gibbs sampling approach, with the goal of improving the ability of China's financial system to protect against foreign threats. This paper examines the theories of the consequences of uncertainty on macroeconomics first. Then, using medium-sized economic and financial data, the uncertainty index of the American and Chinese economies is built. In order to complete the test and analysis of the dynamic relationship between American economic uncertainty and China's macro-economy, a Time Varying Parameter-Stochastic Volatility-Vector Autoregression (TVP- VAR) model with random volatility is constructed. The model is estimated using the Gibbs sampling method based on MCMC. For the empirical analysis, samples of China's and the United States' economic data from January 2001 to January 2022 were taken from the WIND database and the FRED database, respectively. The data reveal that there are typically fewer than 5 erroneous components in the most estimated parameters of the MCMC model, which suggests that the model's sampling results are good. China's pricing level reacted to the consequences of the unpredictability of the American economy by steadily declining, reaching its lowest point during the financial crisis in 2009, and then gradually diminishing. After 2012, the greatest probability density range of 68% is extremely wide and contains 0, indicating that the impact of economic uncertainty in the United States on China's pricing level is no longer significant. China should therefore focus on creating a community of destiny by working with nations that have economic cooperation to lower systemic financial risks and guarantee the stability of the capital market. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic prompted governments worldwide to implement a range of containment measures, including mass gathering restrictions, social distancing, and school closures. Despite these efforts, vaccines continue to be the safest and most effective means of combating such viruses. Yet, vaccine hesitancy persists, posing a significant public health concern, particularly with the emergence of new COVID-19 variants. To effectively address this issue, timely data is crucial for understanding the various factors contributing to vaccine hesitancy. While previous research has largely relied on traditional surveys for this information, recent sources of data, such as social media, have gained attention. However, the potential of social media data as a reliable proxy for information on population hesitancy, especially when compared with survey data, remains underexplored. This paper aims to bridge this gap. Our approach uses social, demographic, and economic data to predict vaccine hesitancy levels in the ten most populous US metropolitan areas. We employ machine learning algorithms to compare a set of baseline models that contain only these variables with models that incorporate survey data and social media data separately. Our results show that XGBoost algorithm consistently outperforms Random Forest and Linear Regression, with marginal differences between Random Forest and XGBoost. This was especially the case with models that incorporate survey or social media data, thus highlighting the promise of the latter data as a complementary information source. Results also reveal variations in influential variables across the five hesitancy classes, such as age, ethnicity, occupation, and political inclination. Further, the application of models to different MSAs yields mixed results, emphasizing the uniqueness of communities and the need for complementary data approaches. In summary, this study underscores social media data's potential for understanding vaccine hesitancy, emphasizes the importance of tailoring interventions to specific communities, and suggests the value of combining different data sources. [ABSTRACT FROM AUTHOR]

Socio-economic and geographical inequalities in breast cancer mortality have been widely described in European countries and the United States. To investigate the combined effects of geographic access and socio-economic characteristics on breast cancer outcomes, a systematic review was conducted exploring the relationships between: (i) geographic access to healthcare facilities (oncology services, mammography screening), defined as travel time and/or travel distance; (ii) breast cancer-related outcomes (mammography screening, stage of cancer at diagnosis, type of treatment and rate of mortality); (iii) socioeconomic status (SES) at individuals and residential context levels. In total, n = 25 studies (29 relationships tested) were included in our systematic review. The four main results are: The statistical significance of the relationship between geographic access and breast cancer-related outcomes is heterogeneous: 15 were identified as significant and 14 as non-significant. Women with better geographic access to healthcare facilities had a statistically significant fewer mastectomy (n = 4/6) than women with poorer geographic access. The relationship with the stage of the cancer is more balanced (n = 8/17) and the relationship with cancer screening rate is not observed (n = 1/4). The type of measures of geographic access (distance, time or geographical capacity) does not seem to have any influence on the results. For example, studies which compared two different measures (travel distance and travel time) of geographic access obtained similar results. The relationship between SES characteristics and breast cancer-related outcomes is significant for several variables: at individual level, age and health insurance status; at contextual level, poverty rate and deprivation index. Of the 25 papers included in the review, the large majority (n = 24) tested the independent effect of geographic access. Only one study explored the combined effect of geographic access to breast cancer facilities and SES characteristics by developing stratified models. [ABSTRACT FROM AUTHOR]

Traditional sociodemographic disparities in adolescent vaccination initiation for the HPV, Tdap, and MenACWY vaccines have declined in the United States of America. This decline raises the question of whether inequities in access have been successfully addressed. This paper synthesizes research on the resource barriers that inhibit vaccination alongside research on vaccine hesitancy where parents actively refuse vaccination. To do so, I classify the primary reason why teens are unvaccinated in the National Immunization Survey-Teen 2012–2022 into three categories: resource failure, agentic refusal, and other reasons. I use three non-exclusive subsamples of teens who are unvaccinated against the HPV (N = 87,163), MenACWY (N = 54,726), and Tdap (N = 10,947) vaccines to examine the relative importance of resource failure reasons and agentic refusal reasons for non-vaccination across time and teens' sociodemographic characteristics. Results indicate that resource failure reasons continue to explain a substantial portion of the reasons why teens are unvaccinated and disproportionately affect racially/ethnically and economically marginalized teens. Thus, even as sociodemographic inequalities in rates of vaccination have declined, inequities in access remain consequential. [ABSTRACT FROM AUTHOR]

Opioid use disorder (OUD) represents a public health crisis in the United States. Medication for opioid use disorder (MOUD) with buprenorphine in primary care is a proven OUD treatment strategy. MOUD induction is when patients begin withdrawal and receive the first doses of buprenorphine. Differences between induction methods might influence short-term stabilization, long-term maintenance, and quality of life. This paper describes the protocol for a study designed to: (1) compare short-term stabilization and long-term maintenance treatment engagement in MOUD in patients receiving office, home, or telehealth induction and (2) identify clinically-relevant practice and patient characteristics associated with successful long-term treatment. The study design is a randomized, parallel group, pragmatic comparative effectiveness trial of three care models of MOUD induction in 100 primary care practices in the United States. Eligible patients are at least 16 years old, have been identified by their clinician as having opioid dependence and would benefit from MOUD. Patients will be randomized to one of three induction comparators: office, home, or telehealth induction. Primary outcomes are buprenorphine medication-taking and illicit opioid use at 30, 90, and 270 days post-induction. Secondary outcomes include quality of life and potential mediators of treatment maintenance (intentions, planning, automaticity). Potential moderators include social determinants of health, substance use history and appeal, and executive function. An intent to treat analysis will assess effects of the interventions on long-term treatment, using general/generalized linear mixed models, adjusted for covariates, for the outcomes analysis. Analysis includes practice- and patient-level random effects for hierarchical/longitudinal data. No large-scale, randomized comparative effectiveness research has compared home induction to office or telehealth MOUD induction on long-term outcomes for patients with OUD seen in primary care settings. The results of this study will offer primary care providers evidence and guidance in selecting the most beneficial induction method(s) for specific patients. [ABSTRACT FROM AUTHOR]

Despite the proven efficacy of pre-exposure prophylaxis (PrEP) in preventing HIV acquisition, PrEP is underutilized by sexual and gender minority young adults in the 13 states in the United States "South." In this paper, we describe the process we used to develop a digital campaign to promote awareness and engagement of sexual and gender minority young adults in the Louisiana Department of Health's TelePrEP Program and provide campaign performance metrics. In Phase 1, we conducted formative research that informed campaign development and strategy. In total, 109 sexual and gender minority young adults completed a survey of PrEP constructs (e.g., facilitators, barriers). We also conducted three, sequential focus groups to iteratively generate, revise and refine the digital material. In collaboration with our strategic marketing partner (SMP), we developed 3 different ads and 1 video ad to promote on web and mobile-in app display, as well as Facebook, Instagram, and YouTube. Phase 2 focused on campaign implementation and evaluation (e.g., number of impressions, user activities on LA TelePrEP landing page). In the first few weeks of the campaign, data from tracking pixels indicated minimal activities on the landing page. We paused to revamp the campaign. Our SMP determined that a more young adult-focused landing page could bolster engagement. We created a new landing page and reran the campaign for 33 days. We saw substantially more user activities on the new landing page (n = 382) compared to the LA TelePrEP landing page (n = 185). Overall, we had 730,665 impressions and 475 link clicks. By collaborating with our SMP, we effectively and efficiently translated our community-engaged formative research into relevant and engaging digital content. This pilot study is one of the first to demonstrate the importance of using tracking pixels to monitor real-time user data to optimize performance of a digital PrEP campaign. [ABSTRACT FROM AUTHOR]

Parkinson's disease which is the second most prevalent neurodegenerative disorder in the United States is a serious and complex disease that may progress to mild cognitive impairment and dementia. The early detection of the mild cognitive impairment and the identification of its biomarkers is crucial to support neurologists in monitoring the progression of the disease and allow an early initiation of effective therapeutic treatments that will improve the quality of life for the patients. In this paper, we propose the first deep-learning based approaches to detect mild cognitive impairment in the sleep Electroencephalography for patients with Parkinson's disease and further identify the discriminative features of the disease. The proposed frameworks start by segmenting the sleep Electroencephalography time series into three sleep stages (i.e., two non-rapid eye movement sleep-stages and one rapid eye movement sleep stage), further transforming the segmented signals in the time-frequency domain using the continuous wavelet transform and the variational mode decomposition and finally applying novel convolutional neural networks on the time-frequency representations. The gradient-weighted class activation mapping was also used to visualize the features based on which the proposed deep-learning approaches reached an accurate prediction of mild cognitive impairment in Parkinson's disease. The proposed variational mode decomposition-based model offered a superior accuracy, sensitivity, specificity, area under curve, and quadratic weighted Kappa score, all above 99% as compared with the continuous wavelet transform-based model (that achieved a performance that is almost above 92%) in differentiating mild cognitive impairment from normal cognition in sleep Electroencephalography for patients with Parkinson's disease. In addition, the features attributed to the mild cognitive impairment in Parkinson's disease were demonstrated by changes in the middle and high frequency variational mode decomposition components across the three sleep-stages. The use of the proposed model on the time-frequency representation of the sleep Electroencephalography signals will provide a promising and precise computer-aided diagnostic tool for detecting mild cognitive impairment and hence, monitoring the progression of Parkinson's disease. [ABSTRACT FROM AUTHOR]

The worldwide outbreak of the coronavirus was first identified in 2019 in Wuhan, China. Since then, the disease has spread worldwide. As it is currently spreading in the United States, policy makers, public health officials and citizens are racing to understand the impact of this virus on the United States healthcare system. They fear a rapid influx of patients overwhelming the healthcare system and leading to unnecessary fatalities. Most countries and states in America have introduced mitigation strategies, such as using social distancing to decrease the rate of newly infected people. This is what is usually meant by flattening the curve. In this paper, we use queueing theoretic methods to analyze the time evolution of the number of people hospitalized due to the coronavirus. Given that the rate of new infections varies over time as the pandemic evolves, we model the number of coronavirus patients as a dynamical system based on the theory of infinite server queues with time inhomogeneous Poisson arrival rates. With this model we are able to quantify how flattening the curve affects the peak demand for hospital resources. This allows us to characterize how aggressive societal policy needs to be to avoid overwhelming the capacity of healthcare system. We also demonstrate how curve flattening impacts the elapsed lag between the times of the peak rate of hospitalizations and the peak demand for the hospital resources. Finally, we present empirical evidence from Italy and the United States that supports the insights from our model analysis. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has been particularly deadly for residents of nursing homes and other long-term care facilities. This paper analyzes COVID-19 deaths at nursing homes during the first wave of the pandemic in the United States during the spring and early summer 2020. By combining data on facility-level COVID-19 deaths during this period with data on the neighborhoods where nursing home staff reside for a sample of eighteen states, this paper finds that staff neighborhood characteristics were a large and significant predictor of COVID-19 nursing home deaths. Even after controlling for the county where a facility is located, one standard deviation increases in average staff neighborhood (Census tract) population density, public transportation use, and non-white share were associated with 1.3 (p <.001), 1.4 (p <.001), and 0.9 (p <.001) additional deaths per 100 beds, respectively. These effects are larger than all facility management or quality variables, and larger than the effect of the nursing home's own neighborhood characteristics. These results suggest COVID-19 outbreaks in staff communities can have large consequences for the facilities where they work, even in highly-rated facilities, and that disparities in nursing home outbreaks may be related to differences in the types of neighborhoods nursing home staff live in. [ABSTRACT FROM AUTHOR]

Out-of-hospital cardiac arrest (OHCA) affects over 360,000 adults in the United States each year with a 50–80% mortality prior to reaching medical care. Despite aggressive supportive care and targeted temperature management (TTM), half of adults do not live to hospital discharge and nearly one-third of survivors have significant neurologic injury. The current treatment approach following cardiac arrest resuscitation consists primarily of supportive care and possible TTM. While these current treatments are commonly used, mortality remains high, and survivors often develop lasting neurologic and cardiac sequela well after resuscitation. Hence, there is a critical need for further therapeutic development of adjunctive therapies. While select therapeutics have been experimentally investigated, one promising agent that has shown benefit is CO. While CO has traditionally been thought of as a cellular poison, there is both experimental and clinical evidence that demonstrate benefit and safety in ischemia with lower doses related to improved cardiac/neurologic outcomes. While CO is well known for its poisonous effects, CO is a generated physiologically in cells through the breakdown of heme oxygenase (HO) enzymes and has potent antioxidant and anti-inflammatory activities. While CO has been studied in myocardial infarction itself, the role of CO in cardiac arrest and post-arrest care as a therapeutic is less defined. Currently, the standard of care for post-arrest patients consists primarily of supportive care and TTM. Despite current standard of care, the neurological prognosis following cardiac arrest and return of spontaneous circulation (ROSC) remains poor with patients often left with severe disability due to brain injury primarily affecting the cortex and hippocampus. Thus, investigations of novel therapies to mitigate post-arrest injury are clearly warranted. The primary objective of this proposed study is to combine our expertise in swine models of CO and cardiac arrest for future investigations on the cellular protective effects of low dose CO. We will combine our innovative multi-modal diagnostic platform to assess cerebral metabolism and changes in mitochondrial function in swine that undergo cardiac arrest with therapeutic application of CO. [ABSTRACT FROM AUTHOR]

The goal of this study is to investigate the origin, prevalence, and evolution of the pESI megaplasmid in Salmonella isolated from animals, foods, and humans. We queried 510,097 Salmonella genomes under the National Center for Biotechnology Information (NCBI) Pathogen Detection (PD) database for the presence of potential sequences containing the pESI plasmid in animal, food, and environmental sources. The presence of the pESI megaplasmid was confirmed by using seven plasmid-specific markers (rdA, pilL, SogS, TrbA, ipf, ipr2 and IncFIB(pN55391)). The plasmid and chromosome phylogeny of these isolates was inferred from single nucleotide polymorphisms (SNPs). Our search resolved six Salmonella clusters carrying the pESI plasmid. Four were emergent Salmonella Infantis clusters, and one each belonged to serovar Senftenberg and Alachua. The Infantis cluster with a pESI plasmid carrying blaCTX-M-65 gene was the biggest of the four emergent Infantis clusters, with over 10,000 isolates. This cluster was first detected in South America and has since spread widely in United States. Over time the composition of pESI in United States has changed with the average number of resistance genes showing a decrease from 9 in 2014 to 5 in 2022, resulting from changes in gene content in two integrons present in the plasmid. A recent and emerging cluster of Senftenberg, which carries the blaCTX-M-65 gene and is primarily associated with turkey sources, was the second largest in the United States. SNP analysis showed that this cluster likely originated in North Carolina with the recent acquisition of the pESI plasmid. A single Alachua isolate from turkey was also found to carry the pESI plasmid containing blaCTX-M-65 gene. The study of the pESI plasmid, its evolution and mechanism of spread can help us in developing appropriate strategies for the prevention and further spread of this multi-drug resistant plasmid in Salmonella in poultry and humans. [ABSTRACT FROM AUTHOR]

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

Introduction: Fourteen million colonoscopies are performed annually in the United States, with the results dependent on bowel preparation before the procedure. However, suitable preparation is neglected in 20–25% of cases, resulting in increased loss of time, failure to diagnose, procedure repetition, and decreased patient satisfaction. Consequently, the current study was performed to evaluate the effect of reinforced education (RE) on the satisfaction of patients undergoing colonoscopy. Methods: This study employed an experimental research method. Participants included 83 patients referred to Ayatollah Taleghani Medical Educational Center for colonoscopy, who were divided into control and intervention groups. The control group received standard education, whereas the intervention group received instruction via media and virtually, and preparation was followed up on the day before the colonoscopy. The research tool was a demographic and satisfaction questionnaire developed by the researcher. Analyses were conducted using IBM SPSS software (v. 20). Results: Eighty-three patients, including 47 men and 36 women with an average age of 49.19 years, participated in the study. Mean (standard deviation) patient satisfaction was 11.78 (4.65) in the intervention group and 9.04 (2.95) in the control group; the independent t-test revealed a significant difference between the two groups (P<0.001). Conclusions: The study suggests that reinforced education using media and virtual means is effective in the satisfaction of patients undergoing colonoscopy. [ABSTRACT FROM AUTHOR]

The United States President's Emergency Plan for AIDS Relief (PEPFAR) has been credited with saving millions lives and helping to change the trajectory of the global human immunodeficiency virus (HIV) epidemic. This study assesses whether PEPFAR has had impacts beyond health by examining changes in five economic and educational outcomes in PEPFAR countries: the gross domestic product (GDP) per capita growth rate; the share of girls and share of boys, respectively, who are out of school; and female and male employment rates. We constructed a panel data set for 157 low- and middle-income countries between 1990 and 2018 to estimate the macroeconomic impacts of PEPFAR. Our PEPFAR group included 90 countries that had received PEPFAR support over the period. Our comparison group included 67 low- and middle-income countries that had not received any PEPFAR support or had received minimal PEPFAR support (<$1M or <$.05 per capita) between 2004 and 2018. We used differences in differences (DID) methods to estimate the program impacts on the five economic and educational outcome measures. This study finds that PEPFAR is associated with increases in the GDP per capita growth rate and educational outcomes. In some models, we find that PEPFAR is associated with reductions in male and female employment. However, these effects appear to be due to trends in the comparison group countries rather than programmatic impacts of PEPFAR. We show that these impacts are most pronounced in COP countries receiving the highest levels of PEPFAR investment. [ABSTRACT FROM AUTHOR]

Over 71 million people are infected with hepatitis C virus (HCV) worldwide, and approximately 400,000 global deaths result from complications of untreated chronic HCV. Pan-genomic direct-acting antivirals (DAAs) have recently become widely available and feature high cure rates in less than 12 weeks of treatment. The rollout of DAAs is reliant on diagnostic tests for HCV RNA to identify eligible patients with viremic HCV infections. Current PCR-based HCV RNA assays are restricted to well-resourced central laboratories, and there remains a prevailing clinical need for expanded access to decentralized HCV RNA testing to provide rapid chronic HCV diagnosis and linkage to DAAs in outpatient clinics. This paper reports a rapid, highly accurate, and minimally instrumented assay for HCV RNA detection using reverse transcription recombinase polymerase amplification (RT-RPA). The assay detects all HCV genotypes with a limit of detection of 25 copies per reaction for genotype 1, the most prevalent in the United States and worldwide. The clinical sensitivity and specificity of the RT-RPA assay were both 100% when evaluated using 78 diverse clinical serum specimens. The accuracy, short runtime, and low heating demands of RT-RPA may enable implementation in a point-of-care HCV test to expand global access to effective treatment via rapid chronic HCV diagnosis. [ABSTRACT FROM AUTHOR]

The paper investigates the factors underlying COVID-19 vaccine and booster hesitancy in the United States, and the efficacy of various incentives or disincentives to expand uptake. We use cross-sectional, national survey data on 3,497 U.S. adults collected online from September 10, 2021 to October 20, 2021 through the Qualtrics platform. Results from a multinomial logistic regression reveal that hesitancy and refusal were greatest among those who expressed a lack of trust either in government or in the vaccine's efficacy (hesitancy relative risk ratio, or RRR: 2.86, 95% CI: 2.13–3.83, p<0.001). Hesitancy and refusal were lowest among those who typically get a flu vaccine (hesitancy RRR: 0.28, 95% CI: 0.21–0.36, p<0.001; refusal RRR: 0.08, 95% CI: 0.05–0.13, p<0.001). Similar results hold for the intention to get a booster shot among the fully vaccinated. Monetary rewards (i.e., lottery ticket and gift cards) fared poorly in moving people toward vaccination. In contrast, the prospect of job loss or increased health insurance premiums was found to significantly increase vaccine uptake, by 8.7 percentage points (p<0.001) and 9.4 percentage points (p<0.001), respectively. We also show that the motivations underlying individuals' hesitancy or refusal to get vaccinated vary, which, in turn, suggests that messaging must be refined and directed accordingly. Also, moving forward, it may be fruitful to more deeply study the intriguing possibility that expanding flu vaccine uptake may also enhance willingness to vaccinate in times of pandemics. Last, disincentives such as work-based vaccination mandates that would result in job loss or higher health insurance premiums for those who refuse vaccination should be strongly considered to improve vaccine uptake in the effort to address the common good. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has presented significant public health and economic challenges worldwide. Various health and non-pharmaceutical policies have been adopted by different countries to control the spread of the virus. To shed light on the impact of vaccination and social mobilization policies during this wide-ranging crisis, this paper applies a system dynamics analysis on the effectiveness of these two types of policies on pandemic containment and the economy in the United States. Based on the simulation of different policy scenarios, the findings are expected to help decisions and mitigation efforts throughout this pandemic and beyond. [ABSTRACT FROM AUTHOR]

In light of the outbreak of COVID-19, analyzing and measuring human mobility has become increasingly important. A wide range of studies have explored spatiotemporal trends over time, examined associations with other variables, evaluated non-pharmacologic interventions (NPIs), and predicted or simulated COVID-19 spread using mobility data. Despite the benefits of publicly available mobility data, a key question remains unanswered: are models using mobility data performing equitably across demographic groups? We hypothesize that bias in the mobility data used to train the predictive models might lead to unfairly less accurate predictions for certain demographic groups. To test our hypothesis, we applied two mobility-based COVID infection prediction models at the county level in the United States using SafeGraph data, and correlated model performance with sociodemographic traits. Findings revealed that there is a systematic bias in models' performance toward certain demographic characteristics. Specifically, the models tend to favor large, highly educated, wealthy, young, and urban counties. We hypothesize that the mobility data currently used by many predictive models tends to capture less information about older, poorer, less educated and people from rural regions, which in turn negatively impacts the accuracy of the COVID-19 prediction in these areas. Ultimately, this study points to the need of improved data collection and sampling approaches that allow for an accurate representation of the mobility patterns across demographic groups. [ABSTRACT FROM AUTHOR]

Background: There is considerable geographic heterogeneity in obesity prevalence across counties in the United States. Machine learning algorithms accurately predict geographic variation in obesity prevalence, but the models are often uninterpretable and viewed as a black-box. Objective: The goal of this study is to extract knowledge from machine learning models for county-level variation in obesity prevalence. Methods: This study shows the application of explainable artificial intelligence methods to machine learning models of cross-sectional obesity prevalence data collected from 3,142 counties in the United States. County-level features from 7 broad categories: health outcomes, health behaviors, clinical care, social and economic factors, physical environment, demographics, and severe housing conditions. Explainable methods applied to random forest prediction models include feature importance, accumulated local effects, global surrogate decision tree, and local interpretable model-agnostic explanations. Results: The results show that machine learning models explained 79% of the variance in obesity prevalence, with physical inactivity, diabetes, and smoking prevalence being the most important factors in predicting obesity prevalence. Conclusions: Interpretable machine learning models of health behaviors and outcomes provide substantial insight into obesity prevalence variation across counties in the United States. [ABSTRACT FROM AUTHOR]

In an effort to gauge the global pandemic's impact on social thoughts and behavior, it is important to answer the following questions: (1) What kinds of topics are individuals and groups vocalizing in relation to the pandemic? (2) Are there any noticeable topic trends and if so how do these topics change over time and in response to major events? In this paper, through the advanced Sequential Latent Dirichlet Allocation model, we identified twelve of the most popular topics present in a Twitter dataset collected over the period spanning April 3rd to April 13th, 2020 in the United States and discussed their growth and changes over time. These topics were both robust, in that they covered specific domains, not simply events, and dynamic, in that they were able to change over time in response to rising trends in our dataset. They spanned politics, healthcare, community, and the economy, and experienced macro-level growth over time, while also exhibiting micro-level changes in topic composition. Our approach differentiated itself in both scale and scope to study the emerging topics concerning COVID-19 at a scale that few works have been able to achieve. We contributed to the cross-sectional field of urban studies and big data. Whereas we are optimistic towards the future, we also understand that this is an unprecedented time that will have lasting impacts on individuals and society at large, impacting not only the economy or geo-politics, but human behavior and psychology. Therefore, in more ways than one, this research is just beginning to scratch the surface of what will be a concerted research effort into studying the history and repercussions of COVID-19. [ABSTRACT FROM AUTHOR]

Recent years have witnessed wider prevalence of vertebral column pathologies due to lifestyle changes, sedentary behaviors, or injuries. Spondylolisthesis and scoliosis are two of the most common ailments with an incidence of 5% and 3% in the United States population, respectively. Both of these abnormalities can affect children at a young age and, if left untreated, can progress into severe pain. Moreover, severe scoliosis can even lead to lung and heart problems. Thus, early diagnosis can make it easier to apply remedies/interventions and prevent further disease progression. Current diagnosis methods are based on visual inspection by physicians of radiographs and/or calculation of certain angles (e.g., Cobb angle). Traditional artificial intelligence-based diagnosis systems utilized these parameters to perform automated classification, which enabled fast and easy diagnosis supporting tools. However, they still require the specialists to perform error-prone tedious measurements. To this end, automated measurement tools were proposed based on processing techniques of X-ray images. In this paper, we utilize advances in deep transfer learning to diagnose spondylolisthesis and scoliosis from X-ray images without the need for any measurements. We collected raw data from real X-ray images of 338 subjects (i.e., 188 scoliosis, 79 spondylolisthesis, and 71 healthy). Deep transfer learning models were developed to perform three-class classification as well as pair-wise binary classifications among the three classes. The highest mean accuracy and maximum accuracy for three-class classification was 96.73% and 98.02%, respectively. Regarding pair-wise binary classification, high accuracy values were achieved for most of the models (i.e., > 98%). These results and other performance metrics reflect a robust ability to diagnose the subjects' vertebral column disorders from standard X-ray images. The current study provides a supporting tool that can reasonably help the physicians make the correct early diagnosis with less effort and errors, and reduce the need for surgical interventions. [ABSTRACT FROM AUTHOR]

This study analyzes aggregate data on trade flows to examine the performance of bilateral free trade agreements (BFTAs) between the United States and 11 BFTA nations in a two-dimensional approach. In line with the literature, this study applies the gravity model and analyzes the effect of the treatment using Poisson pseudo-maximum likelihood (PPML) panel data from 1992 to 2017. We use the PPML as an alternative methodology to an ordinary least squares model, as it can treat zero trade values and lead to unbiased estimates and different consistencies. We consider the trade in goods but excluded services because of the different nature of trade for services. Moreover, this study highlights the quantitative performance of BFTAs without considering the industry level to compare the overall benefits for the trade flows from the U.S. as an exporter to BFTA countries and vice versa. It thus adds to the debate on the effect of FTAs on trade flows and conducts pre-FTA and post-FTA analyses to compare the volumes of exports and imports during both periods. Findings vary according to the direction of trade; notably, some trading partners increased their trading volume to 388%. In sum, this paper provides a collective current state assessment to demonstrate the most plausible reasons for the effects of the 11 BFTAs, in addition to informing policymakers on the lessons learned from each BFTA. [ABSTRACT FROM AUTHOR]

A recent paper showed that, whereas we expect pain to rise with age due to accumulated injury, physical wear and tear, and disease, the elderly in America report less pain than those in midlife. Further exploration revealed this pattern was confined to the less educated. The authors called this the 'mystery of American pain' since pain appears to rise with age in other countries irrespective of education. Revisiting this issue with the same cross-sectional data we show that what matters in explaining pain through to age 65 is whether one is working or not. The incidence of pain across the life-course is nearly identical for workers in America and elsewhere, but it is greater for non-working Americans than it is for non-workers elsewhere. As in other countries, pain is hump-shaped in age among those Americans out of work but rises a little over the life-course for those in work. Furthermore, these patterns are apparent within educational groups. We show that, if one ascribes age-specific employment rates from other OECD countries to Americans, the age profile of pain in the United States is more similar to that found elsewhere in the OECD. This is because employment rates are lower in the United States than elsewhere between ages 30 and 60: the simulation reduces the pain contribution of these non-workers to overall pain in America, so it looks somewhat similar to pain elsewhere. We conclude that what matters in explaining pain over the life-course is whether one is working or not and once that is accounted for, the patterns are consistent across the United States and the rest of the OECD. [ABSTRACT FROM AUTHOR]

Background: Virtual reality (VR) has proven effective in the treatment of specific phobias and trauma particularly when in-vivo exposure therapy might be costly (e.g. fear of flying, combat scenes). Similarly, VR has been associated with improvement of chronic pain and of acute pain during medical procedures. Despite its effectiveness as a healthcare tool, VR technology is not well-integrated into common practice. This qualitative study aims to explore the provider perception of the value of VR and identify barriers to VR implementation among healthcare providers. Methods: A 66-item self-report survey was created to examine application of VR to clinical practice, perceived value of this treatment, ease of learning the technology, billing considerations, and other obstacles. 128 providers (MDs and PhDs) who were located in the United States and had used VR as a therapeutic tool in the past year were identified through research papers, as well as user lists and news articles from VR application websites. Of the 128 providers contacted, 17% (22) completed our online self-report measure. Of these, 13% of respondents (N = 17) completed greater than 75% of the questionnaire and were considered completers. Provider responses were collected over a one-month period and qualitatively analyzed. Results: The majority of providers were from an academic institution (n = 12, 70.6%), and all providers practiced in the outpatient setting. Providers most commonly reported using VR for the treatment of acute pain and/or anxiety related to medical procedures (n = 11, 64.7%), followed by specific phobia (n = 6, 35.3%) and social phobia (n = 6, 35.3%). All providers agreed VR is a valuable tool they would recommend to colleagues. The majority (n = 15, 93.8%) believed VR helped their patients progress in treatment, compared with other methods. Providers cited the ability to individualize treatment (n = 14, 87.5%) and increase patient engagement (n = 15, 93.8%) as main benefits of VR. A minority reported negative feedback from patients about content (n = 4, 25%) or about the technology in general (n = 6, 37.5%), whereas all reported some form of positive feedback. The slight majority (n = 10, 58.8%) of providers did not find transitioning to VR difficult. Of those who did, cost was the most commonly cited barrier (n = 6). Regarding reimbursement, only 17.6% (n = 3) of providers reported the ability to bill for VR sessions. Most providers (n = 15, 88.2%) received training on their VR platform which they found beneficial. Comparing the trained and untrained groups found no significant difference in VR comfort level (p = 0.5058), the value of VR in practice (p = 0.551) or whether providers would recommend VR to others (p = 0.551), though sample sizes were small. Conclusions: In corroboration with previous research, this study demonstrates that VR is well-received by patients and providers, allowing increased patient engagement and treatment individualization. However, associated costs, including an inability to bill for this service, can present a barrier to further implementation. These findings will guide further development of virtual reality as a standardized tool in psychiatry and pain management. [ABSTRACT FROM AUTHOR]

A crucial question in the governance of infectious disease outbreaks is how to ensure that people continue to adhere to mitigation measures for the longer duration. The present paper examines this question by means of a set of cross-sectional studies conducted in the United States during the COVID-19 pandemic, in May, June, and July of 2020. Using stratified samples that mimic the demographic characteristics of the U.S. population, it seeks to understand to what extent Americans continued to adhere to social distancing measures in the period after the first lockdown ended. Moreover, it seeks to uncover which variables sustained (or undermined) adherence across this period. For this purpose, we examined a broad range of factors, relating to people's (1) knowledge and understanding of the mitigation measures, (2) perceptions of their costs and benefits, (3) perceptions of legitimacy and procedural justice, (4) personal factors, (5) social environment, and (6) practical circumstances. Our findings reveal that adherence was chiefly shaped by three major factors: respondents adhered more when they (a) had greater practical capacity to adhere, (b) morally agreed more with the measures, and (c) perceived the virus as a more severe health threat. Adherence was shaped to a lesser extent by impulsivity, knowledge of social distancing measures, opportunities for violating, personal costs, and descriptive social norms. The results also reveal, however, that adherence declined across this period, which was partly explained by changes in people's moral alignment, threat perceptions, knowledge, and perceived social norms. These findings show that adherence originates from a broad range of factors that develop dynamically across time. Practically these insights help to improve pandemic governance, as well as contributing theoretically to the study of compliance and the way that rules come to shape behavior. [ABSTRACT FROM AUTHOR]

This paper quantifies the net impact (direct and indirect effects) of the pandemic on the United States population in 2020 using three metrics: excess deaths, life expectancy, and total years of life lost. The findings indicate there were 375,235 excess deaths, with 83% attributable to direct, and 17% attributable to indirect effects of COVID-19. The decrease in life expectancy was 1.67 years, translating to a reversion of 14 years in historical life expectancy gains. Total years of life lost in 2020 was 7,362,555 across the USA (73% directly attributable, 27% indirectly attributable to COVID-19), with considerable heterogeneity at the individual state level. [ABSTRACT FROM AUTHOR]

In the United States, increasing access to the internet, the increasing costs of large-scale face-to-face data collections, and the general reluctance of the public to participate in intrusive in-person data collections all mean that new approaches to nationally representative surveys are urgently needed. The COVID-19 pandemic accelerated the need for faster, higher-quality alternatives to face-to-face data collection. These trends place a high priority on the evaluation of innovative web-based data collection methods that are convenient for the U.S. public and yield scientific information of high quality. The web mode is particularly appealing because it is relatively inexpensive, it is logistically flexible to implement, and it affords a high level of privacy and confidentiality when correctly implemented. With this study, we aimed to conduct a methodological evaluation of a sequential mixed-mode web/mail data collection protocol, including modular survey design concepts, which was implemented on a national probability sample in the U.S. in 2020–2021. We implemented randomized experiments to test theoretically-informed hypotheses that 1) the use of mail and increased incentives to follow up with households that did not respond to an invitation to complete a household screening questionnaire online would help to recruit different types of households; and 2) the use of modular survey design, which involves splitting a lengthy self-administered survey up into multiple parts that can be completed at a respondent's convenience, would improve survey completion rates. We find support for the use of mail and increased incentives to follow up with households that have not responded to a web-based screening questionnaire. We did not find support for the use of modular design in this context. Simple descriptive analyses also suggest that attempted telephone reminders may be helpful for the main survey. [ABSTRACT FROM AUTHOR]

While the early effects of the COVID-19 pandemic on the United States labor market are well-established, less is known about the long-term impact of SARS-CoV-2 infection and Long COVID on employment. To address this gap, we analyzed self-reported data from a prospective, national cohort study to estimate the effects of SARS-CoV-2 symptoms at three months post-infection on missed workdays and return to work. The analysis included 2,939 adults in the Innovative Support for Patients with SARS-CoV-2 Infections Registry (INSPIRE) study who tested positive for their initial SARS-CoV-2 infection at the time of enrollment, were employed before the pandemic, and completed a baseline and three-month electronic survey. At three months post-infection, 40.8% of participants reported at least one SARS-CoV-2 symptom and 9.6% of participants reported five or more SARS-CoV-2 symptoms. When asked about missed work due to their SARS-CoV-2 infection at three months, 7.2% of participants reported missing ≥10 workdays and 13.9% of participants reported not returning to work since their infection. At three months, participants with ≥5 symptoms had a higher adjusted odds ratio of missing ≥10 workdays (2.96, 95% CI 1.81–4.83) and not returning to work (2.44, 95% CI 1.58–3.76) compared to those with no symptoms. Prolonged SARS-CoV-2 symptoms were common, affecting 4-in-10 participants at three-months post-infection, and were associated with increased odds of work loss, most pronounced among adults with ≥5 symptoms at three months. Despite the end of the federal Public Health Emergency for COVID-19 and efforts to "return to normal", policymakers must consider the clinical and economic implications of the COVID-19 pandemic on people's employment status and work absenteeism, particularly as data characterizing the numerous health and well-being impacts of Long COVID continue to emerge. Improved understanding of risk factors for lost work time may guide efforts to support people in returning to work. [ABSTRACT FROM AUTHOR]

Terrestrial and ocean ecosystems are increasingly under threat from an array of anthropogenic pressures. And while threats mount, how people view and value nature is changing. In the United States (U.S.) in particular, there is a shift away from viewing nature as something to 'dominate,' as evidenced in the decline in hunting. However, it is unclear if or how opinions around environmental issues and conservation need might differ when comparing ocean versus terrestrial ecosystems, especially given the prevalence and continued importance of wild capture fishing in the U.S. We employed two national parallel surveys, one focused on oceans, the other land, receiving responses from nearly every state in the U.S. (N = 1,973). While we found only slight, but statistically significant more concern for ocean habitats and animals over terrestrial ecosystems, this did not translate to increased willingness to monetarily support more ocean conservation actions. Using Random Forest models, we also found the best predictor of conservation need was feeling most impacted by environmental issues personally (self and/or community), regardless of ecosystem type. In fact, land versus sea (survey) had the lowest rank in the models, underscoring the importance of general nature-based interactions. Instead, the number of outdoor recreational activities was a highly ranked variable explaining the level of reported impact to self/community, with people who participate in 2 or more activities scoring higher levels of impact, on average. Notably, people who hunt and fish, versus only do one or the other, reported higher levels of impact and participated in more activities overall, providing a more nuanced finding regarding the nature 'dominance hypothesis.' Voting, not political affiliation, was also important in explaining responses, and governmental mechanisms to fund conservation were favored over voluntary. Overall, our results add to the strong existing literature that access and connection to nature is key, but uniquely broad connection may "float all boats," especially when diversified. [ABSTRACT FROM AUTHOR]

Iron and zinc are important nutrients during infancy, particularly for infants exclusively fed human milk at the beginning of complementary feeding (CF) from 6–12 months. The 1stFoods Study examined the ingredients and nutrient contents of commercially-available infant and toddler foods (ITFs) that were sold in the US and contained meat. Company websites (n = 22) were used to create a database of commercial ITFs (n = 165) available for purchase in the US and contained at least one meat (e.g., beef, chicken, pork). Single ingredient and ready-to-serve meals (for ages ≤ 9 months) and ready-to-serve meals (for ages 10+ months) were categorized as infant and toddler products, respectively. For each product, the ingredient list, intended age/stage, serving size (g), energy (kcal), protein (g), iron (mg), and zinc (mg) per serving were recorded from product labels. Nutrient amount/100 g was calculated for each product and medians and inter-quartile ranges were calculated and compared (1) by intended age/stage of the product and (2) according to meat type. In general, toddler products contained more iron than infant products. Within infant products (n = 65), more iron was found in products containing beef relative to products with other meats, which were similar in iron content. Within toddler products (n = 38), more iron was found in products containing seafood, followed by beef, turkey, and pork. Slightly less iron was found in products with chicken. Zinc content was infrequently reported (n = 17 total products). Because many of the products assessed contained low amounts of iron and zinc, meeting the current infant and toddler requirements for iron and zinc during the CF period may be challenging if commercial ITFs containing meat are the primary source of these nutrients. [ABSTRACT FROM AUTHOR]

Stereotype threat is a well-known construct in psychology wherein individuals who belong to a negatively stereotyped demographic group underperform on cognitive or academic tasks due to the detrimental effects of a stereotype. Many psychologists have suggested that stereotype threat may be one of the reasons that some demographic groups are underrepresented in advanced academic programs and STEM fields. However, others have raised concerns about the quality of the stereotype threat research, suggesting that its apparent effects are inflated and that the phenomenon may be an illusion of questionable research practices and publication bias. The purpose of this proposed meta-analysis is to evaluate the existence of stereotype threat by (1) identifying the average effect size of stereotype threat studies in different types of studies, (2), investigating whether publication bias and p-hacking are present in the empirical research on stereotype threat, (3) testing for the influence of theoretical and methodological moderators, (4) assessing the overall quality of the research on stereotype threat, (5) and identifying the average effect in the methodologically strongest studies. This meta-analysis will be limited to studies that report data from African Americans because this population is a theoretically important group in stereotype threat research, and the size of score gaps between the African American and non-stereotyped populations in the United States should make the stereotype threat effect easiest to detect. [ABSTRACT FROM AUTHOR]

Background: Breast Cancer and cardiovascular diseases are amongst the two leading causes of mortality in the United States, and the two conditions are connected in part because of recognized cardiotoxicity of cancer treatments. The aim of this study is to investigate the predictors risk factors for thirty-day readmission in female breast cancer survivors presenting with acute heart failure. Methods: This is a retrospective cohort study of acute heart failure (AHF) hospitalization in female patients with breast cancer in 2019 using the National Readmission Database (NRD), which is the largest publicly available all-payer inpatient readmission database in the United States. Our study sample included adult female patients aged 18 years and older. The primary outcome of interest was the rate of 30- day readmission. Results: In 2019, there were 8332 total index admissions for AHF in females with breast cancer and 7776 patients were discharged alive. The mean age was 74.4 years (95% CI: 74, 74.7). The percentage of readmission at 30 days among those discharged alive was 21.8% (n = 1699). Hypertensive heart disease with chronic kidney disease accounted for the majority of readmission in AHF with breast cancer followed by sepsis, acute kidney injury, respiratory failure, pneumonia, and atrial fibrillation. Demographic factors including higher burden of comorbidities predict readmission. The total in-hospital mortality in index admission was 6.67% (n = 556) and for readmitted patients was 8.77% (n = 149). The mean length of stay for index admission was 7.5 days (95% CI: 7.25, 7.75). Conclusions: Readmission of female breast cancer survivors presenting with AHF is common and largely be attributed to high burden of comorbidities including hypertension, and chronic kidney disease. A focus on close outpatient follow-up will be beneficial in lowering readmissions. [ABSTRACT FROM AUTHOR]

Background: Approximately 20–50% of adolescent and young adult-aged childhood cancer survivors (AYA-CCS) experience sexual dysfunction (SD), although this healthcare need is widely underrecognized. Previous research from both AYA-CCS patients and their providers report that SD needs are unaddressed despite patient desires for SD discussions to be incorporated as part of their care. Patients and providers agree that standardized use of a patient-reported outcome measure may facilitate SD discussions; an SD screening approach was developed with patient and provider input. This study will measure the effectiveness of a standardized SD screening intervention and assess implementation outcomes and multilevel barriers and facilitators to guide future research. Methods: This multi-site, mixed methods, type 1 effectiveness-implementation hybrid trial will be evaluated using a pre-post design (NCT05524610). The trial will enroll 86 AYA-CCS (ages 15–39) from two cancer centers in the United States. The SD intervention consists of core fundamental functions with a "menu" of intervention options to allow for flexibility in delivery and tailoring in variable contexts. Effectiveness of the intervention on facilitating SD communication will be measured through patient surveys and clinical data; multivariable logistic regression will be used for the binary outcome of self-reported SD screening, controlling for patient-level predictors. Implementation outcomes will be assessed using mixed methods (electronic health record abstraction, patient and provider surveys, and provider interviews. Quantitative and qualitative findings will be merged using a joint display to understand factors affecting intervention success. Implications: Identification and treatment of SD in AYA-CCS is an important and challenging quality of life concern. The type 1 hybrid design will facilitate rapid translation from research to practice by testing the effects of the intervention while simultaneously identifying multilevel barriers and facilitators to real-world implementation. This approach will inform future testing and dissemination of the SD screening intervention. [ABSTRACT FROM AUTHOR]

Introduction: Physical disability represents a major burden to health and lifespan, particularly as the proportion of older adults within the United States is expected to grow. Prevention efforts for physical disability targets methods and strategies to decrease modifiable risk factors. Potential challenges for health professionals may exist in synthesizing and interpreting this broad spectrum of literature due to the discipline-specific nuance of language used, practice type or specialty, and lack of interdisciplinary resources. This scoping review will map and synthesize the literature across healthcare disciplines to identify modifiable risk factors and the evidence related to their ability to predict physical disability. We will also draw attention to the possibility of modifiable risk factors for physical disability being operationalized as pre-disability in order to strengthen primary and secondary prevention efforts. Methods: A planned search strategy using physical disability terminology will be searched in English across MEDLINE, CINAHL, Health Source, PEDro, and REHABDATA by two reviewers in line with our review objectives and inclusion criteria. Eligibility for inclusion include peer-reviewed primary research published in the English language and have established a relationship between a person-level measurable characteristic that is modifiable by changes in lifestyle behaviors and any of the commonly accepted terms used to categorize or describe physical disability. Expected results: Presentation of results will be using the PRISMA flowchart, with additional mapping and synthesis of evidence for modifiable risk factors for physical disability to clarify divergent terms used in classifying and measuring these factors and their potential for prediction of physical disability. Expected conclusion: It is anticipated that this scoping review will identify and highlight a variety of modifiable risk factors for physical disability that may aid primary and secondary prevention efforts for health professionals. [ABSTRACT FROM AUTHOR]

Healthcare associated infections (HAIs) are costly but preventable. A limited understanding of the effects of environmental cleaning on the riskiest HAI associated pathogens is a current challenge in HAI prevention. This project aimed to quantify the effects of terminal hospital cleaning practices on HAI pathogens via environmental sampling in three hospitals located throughout the United States. Surfaces were swabbed from 36 occupied patient rooms with a laboratory-confirmed, hospital- or community-acquired infection of at least one of the four pathogens of interest (i.e., Acinetobacter baumannii (A. baumannii), methicillin resistant Staphylococcus aureus (MRSA), vancomycin resistant Enterococcus faecalis/faecium (VRE), and Clostridioides difficile (C. difficile)). Six nonporous, high touch surfaces (i.e., chair handrail, bed handrail, nurse call button, desk surface, bathroom counter near the sink, and a grab bar near the toilet) were sampled in each room for Adenosine Triphosphate (ATP) and the four pathogens of interest before and after terminal cleaning. The four pathogens of interest were detected on surfaces before and after terminal cleaning, but their levels were generally reduced. Overall, C. difficile was confirmed on the desk (n = 2), while MRSA (n = 24) and VRE (n = 25) were confirmed on all surface types before terminal cleaning. After cleaning, only MRSA (n = 6) on bed handrail, chair handrail, and nurse call button and VRE (n = 5) on bathroom sink, bed handrail, nurse call button, toilet grab bar, and C. difficile (n = 1) were confirmed. At 2 of the 3 hospitals, pathogens were generally reduced by >99% during terminal cleaning. One hospital showed that VRE increased after terminal cleaning, MRSA was reduced by 73% on the nurse call button, and VRE was reduced by only 50% on the bathroom sink. ATP detections did not correlate with any pathogen concentration. This study highlights the importance of terminal cleaning and indicates room for improvement in cleaning practices to reduce surface contamination throughout hospital rooms. [ABSTRACT FROM AUTHOR]

In the absence of a single comprehensive systematic review of Rational Emotive Behaviour Therapy interventions across all settings, we reviewed the methodological quality, effectiveness and efficacy of Rational Emotive Behaviour Therapy interventions on irrational/rational beliefs. We explored the impact of Rational Emotive Behaviour Therapy on wider outcomes (e.g., mental health) and identified the characteristics of successful interventions. PsycARTICLES, PsycINFO, Scopus, SPORTDiscus, and PubMed were systematically searched up to December 2023 with 162 Rational Emotive Behaviour Therapy intervention studies identified which included a validated measure of irrational/rational beliefs. Where possible, effect size for irrational/rational belief change was reported and data was analysed through a qualitative approach. Using the Mixed Methods Appraisal tool, methodological quality within the Sport and Exercise domain was assessed as good, whilst all other domains were considered low in quality, with insufficient detail provided on intervention characteristics and delivery. Most studies were conducted in the United States, within the Education domain, and assessed irrational beliefs in non-clinical adult samples. Overall, studies reported significant reductions in irrational beliefs, increases in rational beliefs and improvements in mental health outcomes (e.g., depression). More successful interventions were delivered by trained Rational Emotive Behaviour Therapy practitioners, adopted the ABC framework and were longer in duration. We highlight the importance of designing and conducting rigorous future Rational Emotive Behaviour Therapy research to generate clearer insights as to its impact on irrational/rational beliefs and mental health outcomes. [ABSTRACT FROM AUTHOR]

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

Background: Lower income is associated with high incident cardiovascular disease (CVD) and mortality. CVD is an important cause of morbidity and mortality in cancer survivors. However, there is limited research on the association between income, CVD, and mortality in this population. Methods: This study utilized nationally representative data from the National Health and Nutrition Examination Survey (NHANES), a cross-sectional survey evaluating the health and nutritional status of the US population. Our study included NHANES participants aged ≥20 years from 2003–2014, who self-reported a history of cancer. We evaluated the association between income level, prevalence of CVD, and all-cause mortality. All-cause mortality data was obtained through public use mortality files. Income level was assessed by poverty-income ratio (PIR) that was calculated by dividing family (or individual) income by poverty guideline. We used multivariable-adjusted Cox proportional hazard models through a backward elimination method to evaluate associations between PIR, CVD, and all-cause mortality in cancer survivors. Results: This cohort included 2,464 cancer survivors with a mean age of 62 (42% male) years. Compared with individuals with a higher PIR tertiles, those in the lowest PIR tertile had a higher rate of pre-existing CVD and post-acquired CVD. In participants with post-acquired CVD, the lowest PIR tertile had over two-fold increased risk mortality (Hazard Ratio (HR) = 2.17; 95% CI: 1.27–3.71) when compared to the highest PIR tertile. Additionally, we found that PIR was as strong a predictor of mortality in cancer survivors as CVD. In patients with no CVD, the lowest PIR tertile continued to have almost a two-fold increased risk of mortality (HR = 1.72; 95% CI: 1.69–4.35) when compared to a reference of the highest PIR tertile. Conclusions: In this large national study of cancer survivors, low PIR is associated with a higher prevalence of CVD. Low PIR is also associated with an increased risk of mortality in cancer survivors, showing a comparable impact to that of pre-existing and post-acquired CVD. Urgent public health resources are needed to further study and improve screening and access to care in this high-risk population. [ABSTRACT FROM AUTHOR]

Background: Obesity is associated with locality and alcohol use; however, less is known about how the interaction of these two factors may compound the risk of obesity among adolescents. Objectives: This study examines the relationship between alcohol use and obesity among adolescents from rural and urban areas in the United States. Methods: Data came from a sample of American adolescents aged 12–17 years from the National Survey on Drug Use and Health (2015–2019; n = 39,489). Obesity was regressed on age, sex, race/ethnicity, income, cigarette smoking, locality, and alcohol use, with an interaction term to examine locality x alcohol use. Predicted probabilities were plotted to assess the interaction. Results: Compared to adolescents from urban areas, those from rural areas had 1.35 times higher odds of being obese (95% CI 1.25, 1.47). Predicted probabilities indicated that the probability of being obese was higher for rural adolescents at lower levels of drinking, up to about 40 drinks in the past 12 months. Conclusions: Findings suggest rural-urban differences at the intersection of alcohol use and obesity could depend on the frequency of use, but overall adolescents from rural areas may be more at risk. [ABSTRACT FROM AUTHOR]

Robust testing capacity was necessary for public health agencies to respond to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the coronavirus disease 19 (COVID-19) pandemic. As the nation faced the need for robust testing capacity, it became necessary to use all possible resources. In many cases, veterinary diagnostic laboratories rose to meet this demand because these facilities routinely perform high throughput diagnostic testing of large animal populations and are typically familiar with pathogens of high pandemic concern. In this study, we evaluated the impact of veterinary diagnostic laboratories in the United States on SARS-CoV-2 testing. Results of surveys, semi-structured interviews, and analysis of publicly available information showed that veterinary diagnostic laboratories had a substantial impact on human health through population-level testing in the COVID-19 response, supporting timely and informed public health interventions. This success was not without significant hurdles, as many participating veterinary diagnostic laboratories experienced restriction in their response due to difficulties obtaining the Clinical Laboratory Improvement Amendments (CLIA) certification required to conduct human diagnostic testing. Our results point out the importance of reducing hurdles before the next major public health emergency to enhance access to testing resources overall and to ultimately improve population health. [ABSTRACT FROM AUTHOR]

Although average body size in the U.S. has increased in recent decades, stigma directed at individuals with higher weight has not diminished. In this study, we explored this phenomenon by investigating the relationship between people's perceived social norms regarding higher weight and their reported levels of weight bias (i.e., anti-fat attitudes). Our predictions for perceived social norms drew on the concepts of intergroup contact and ingroup favoritism, which were also probed in this study. We hypothesized that both greater descriptive norms and more favorable injunctive norms regarding higher weight would be associated with lower reported weight bias. Individuals' quantity and quality of social contact with people with higher weight were also predicted to be associated with lower weight bias. Finally, we predicted that individuals who perceived themselves as heavier would display ingroup favoritism (i.e., report less weight bias). Participants (N = 272) from the United States completed a set of online questionnaires about their perceived social norms, social contact with people with higher weight, and explicit weight bias. We found support for each of these pre-registered predictions (ps < 0.03), and post hoc analyses revealed that quality, but not quantity, of social contact with individuals with higher weight was an important predictor of lower weight bias. Together, these findings provide insight into the social psychology of weight bias and help to lay a theoretical foundation for future efforts to reduce weight stigma. [ABSTRACT FROM AUTHOR]

Background: Emerging evidence suggests that there is an increase in healthcare utilization (HCU) in patients due to Coronavirus Disease 2019 (COVID-19). We investigated the change in HCU pre and post hospitalization among patients discharged home from COVID-19 hospitalization for up to 9 months of follow up. Study design and methods: This retrospective study from a United States cohort used Optum® de-identified Clinformatics Data Mart; it included adults discharged home post hospitalization with primary diagnosis of COVID-19 between April 2020 and March 2021. We evaluated HCU of patients 9 months pre and post -discharge from index hospitalization. We defined HCU as emergency department (ED), inpatient, outpatient (office), rehabilitation/skilled nursing facility (SNF), telemedicine visits, and length of stay, expressed as number of visits per 10,000 person-days. Results: We identified 63,161 patients discharged home after COVID-19 hospitalization. The cohort of patients was mostly white (58.8%) and women (53.7%), with mean age 72.4 (SD± 12) years. These patients were significantly more likely to have increased HCU in the 9 months post hospitalization compared to the 9 months prior. Patients had a 47%, 67%, 65%, and 51% increased risk of ED (rate ratio 1.47; 95% CI 1.45–1.49; p <.0001), rehabilitation (rate ratio 1.67; 95% CI 1.61–1.73; p <.0001), office (rate ratio1.65; 95% CI 1.64–1.65; p <.0001), and telemedicine visits (rate ratio 1.5; 95% CI 1.48–1.54; p <.0001), respectively. We also found significantly different rates of HCU for women compared to men (women have higher risk of ED, rehabilitation, and telemedicine visits but a lower risk of inpatient visits, length of stay, and office visits than men) and for patients who received care in the intensive care unit (ICU) vs those who did not (ICU patients had increased risk of ED, inpatient, office, and telemedicine visits and longer length of stay but a lower risk of rehabilitation visits). Outpatient (office) visits were the highest healthcare service utilized post discharge (64.5% increase). Finally, the risk of having an outpatient visit to any of the specialties studied significantly increased post discharge. Interestingly, the risk of requiring a visit to pulmonary medicine was the highest amongst the specialties studied (rate ratio 3.35, 95% CI 3.26–3.45, p <.0001). Conclusion: HCU was higher after index hospitalization compared to 9 months prior among patients discharged home post-COVID-19 hospitalization. The increases in HCU may be driven by those patients who received care in the ICU. [ABSTRACT FROM AUTHOR]

Fire suppression has negatively impacted thousands of acres of private and public lands in the United States. As a case study, the New Jersey Pine Barrens (NJPB) are a disturbance driven ecosystem that is experiencing serious ecological implications due to a loss of traditional forest thinning activities such as harvesting for forest products or thinning for wildfire fuel-load reduction measures coupled with a long-standing philosophy of fire suppression and dormant-season prescribed burning. Dense closed-canopy forest conditions, dissimilar to historic open-canopy forests of the NJPB, have reduced abundance and diversity of certain flora and fauna, including regionally imperiled breeding birds. In recent years, active forest stewardship (e.g., thinning, clear-cutting, and burning) has occurred on private and some public lands within the NJPB; however, the impact of such management on breeding birds is unclear due to a paucity of research on this subject within the NJPB. During 2012, 2013, 2016, and 2017, we conducted repeat-visit point counts (n = 1,800) for breeding songbirds across 75 control and 75 treatment sites within the NJPB to assess the influence of forest structure at three strata levels (groundcover, midstory profile, and canopy) on breeding bird communities. Specifically, we constructed a hierarchical community abundance model within a Bayesian framework for Bird Conservation Region (BCR) 30 priority upland birds (n = 12) within three species suites: Forested Upland, Scrub-Shrub (or Young Forest), and Grassland. At the community level, we found a negative relationship between bird abundance and live tree basal area. At the BCR 30 suite level, we found no relationship between Forested Upland suite-level abundance and any of the measured covariates; however, we found a negative relationship between percentage of woody groundcover and Scrub-Shrub suite-level abundance, and negative relationship between horizontal visual obstruction at 2 m above ground level and Grassland suite-level abundance. Furthermore, the two latter species suites exhibited a strong negative relationship with basal area. We recommend active forest stewardship that specifically targets opening the canopy to achieve basal areas between ~0–15 m2/ha via selective thinning, shelter cutting, and small-scale clear cutting. Mechanical treatment and prescribed burning would produce such conditions and have the added benefit of reducing fuel loads across this ~4,500 km2 landscape as well as assisting in carbon defense strategies for the region. [ABSTRACT FROM AUTHOR]

Amblyomma americanum, a known vector of multiple tick-borne pathogens, has expanded its geographic distribution across the United States in the past decades. Tick microbiomes may play a role shaping their host's life history and vectorial capacity. Bacterial communities associated with A. americanum may reflect, or enable, geographic expansion and studying the microbiota will improve understanding of tick-borne disease ecology. We examined the microbiota structure of 189 adult ticks collected in four regions encompassing their historical and current geographic distribution. Both geographic region of origin and sex were significant predictors of alpha diversity. As in other tick models, within-sample diversity was low and uneven given the presence of dominant endosymbionts. Beta diversity analyses revealed that bacterial profiles of ticks of both sexes collected in the West were significantly different from those of the Historic range. Biomarkers were identified for all regions except the historical range. In addition, Bray-Curtis dissimilarities overall increased with distance between sites. Relative quantification of ecological processes showed that, for females and males, respectively, drift and dispersal limitation were the primary drivers of community assembly. Collectively, our findings highlight how microbiota structural variance discriminates the western-expanded populations of A. americanum ticks from the Historical range. Spatial autocorrelation, and particularly the detection of non-selective ecological processes, are indicative of geographic isolation. We also found that prevalence of Ehrlichia chaffeensis, E. ewingii, and Anaplasma phagocytophilum ranged from 3.40–5.11% and did not significantly differ by region. Rickettsia rickettsii was absent from our samples. Our conclusions demonstrate the value of synergistic analysis of biogeographic and microbial ecology data in investigating range expansion in A. americanum and potentially other tick vectors as well. [ABSTRACT FROM AUTHOR]

To examine the association between recent experiences of discrimination and depressive symptom presentation and severity among a U.S. sample of older Black and African American adults. A cross-sectional survey of 124 Black and African American adults aged 50 and older in the United States was conducted assessing interpersonal discrimination and depressive symptoms. The Perceived Ethnic Discrimination Questionnaire assessed four forms of interpersonal discrimination. A measure of heightened vigilance to bias assessed anticipatory coping with discrimination experiences. Past-month affective and somatic symptoms of depression were assessed using the Depressive and Somatic Symptoms Scale. All forms of interpersonal racial discrimination were positively associated with greater affective symptom severity. Being avoided, devalued, and threatened or actively physically harmed were associated with greater somatic symptom severity. Vigilant coping was positively associated with affective symptom severity but not somatic symptom severity. Racial discrimination is linked to depression severity among older Black and African American and varies by symptom. This study helps inform work on processes linking discrimination with poorer psychological outcomes and will allow for more effective interventions and prevention efforts that are tailored to older minority populations. [ABSTRACT FROM AUTHOR]

Prior research suggests that members of terrorist groups prioritize forming network ties based on trust to improve their organizational and operational security. The homophily principle, which postulates that individuals tend to form relationships based on shared characteristics, can be a key mechanism through which people identify trustworthy associates. Next to homophily, the mechanism of establishing interconnected relationships through transitivity is also well-known to serve this purpose and shape community structures in social networks. We analyze the community structures of the Islamist co-offending network in the United States, which is highly violent, to assess whether homophily and transitivity determine which extremists form co-offending ties. We rely on a new database on the individual attributes and the co-offending relationships of 494 Islamist offenders radicalized in the United States between 1993 and 2020. Using community detection algorithms, we show that the US Islamist co-offending network is highly clustered, modular, and includes many small but only a few large communities. Furthermore, results from exponential random graph modeling show that transitive relationships as well as spatial proximity, ideological affiliation, and shared socio-cultural characteristics drive co-offending among US Islamist extremists. Overall, these findings demonstrate that the processes of homophily and transitivity shape violent social networks. [ABSTRACT FROM AUTHOR]

The number of refugees globally grew to 35.3 million in 2022, and many refugees are exposed to various health risks along their migration journey. As a result, they may arrive in host communities with numerous health issues, including communicable diseases and chronic and mental health conditions. Navigating the healthcare system in a host country proves to be a significant challenge for them, leading to delayed care. This qualitative study explored the convolute healthcare needs of refugees in the United States by soliciting insights from stakeholders involved in refugee resettlement and healthcare. In-depth interviews were conducted with fifteen stakeholders who work closely with refugees, including healthcare providers, cultural/clinical health navigators supporting refugees, staff from refugee resettlement agencies and governmental entities, and researchers studying refugee health. Following informed consent, interviews were audio-recorded, transcribed verbatim, and imported into MAXQDA 2022 (VERBI Software) for thematic analysis. The results revealed key themes, including the heterogeneity of refugee populations, limited awareness of preventive healthcare, high prevalence and suboptimal management of chronic conditions, complexity of the healthcare system, lack of follow-up, and language barriers. Further research is warranted concerning the long-term health of refugee populations in the United States. Additionally, more tailored programs involving peer educators are recommended to support refugee communities in navigating the complex healthcare system in the host country. [ABSTRACT FROM AUTHOR]

Extended-spectrum beta-lactamase (ESBL) Escherichia coli (E. coli) is an emerging pathogen of high concern given its resistance to extended-spectrum cephalosporins. Broiler chicken, which is the number one consumed meat in the United States and worldwide, can be a reservoir of ESBL E. coli. Backyard poultry ownership is on the rise in the United States, yet there is little research investigating prevalence of ESBL E. coli in this setting. This study aims to identify the prevalence and antimicrobial resistance profiles (phenotypically and genotypically) of ESBL E. coli in some backyard and commercial broiler farms in the U.S. For this study ten backyard and ten commercial farms were visited at three time-points across flock production. Fecal (n = 10), litter/compost (n = 5), soil (n = 5), and swabs of feeders and waterers (n = 6) were collected at each visit and processed for E. coli. Assessment of ESBL phenotype was determined through using disk diffusion with 3rd generation cephalosporins, cefotaxime and ceftazidime, and that with clavulanic acid. Broth microdilution and whole genome sequencing were used to investigate both phenotypic and genotypic resistance profiles, respectively. ESBL E. coli was more prevalent in backyard farms with 12.95% of samples testing positive whereas 0.77% of commercial farm samples were positive. All isolates contained a blaCTX-M gene, the dominant variant being blaCTX-M-1, and its presence was entirely due to plasmids. Our study confirms concerns of growing resistance to fourth generation cephalosporin, cefepime, as roughly half (51.4%) of all isolates were found to be susceptible dose-dependent and few were resistant. Resistance to non-beta lactams, gentamicin and ciprofloxacin, was also detected in our samples. Our study identifies prevalence of blaCTX-M type ESBL E. coli in U.S. backyard broiler farms, emphasizing the need for interventions for food and production safety. [ABSTRACT FROM AUTHOR]

Acoustic surveys of bat echolocation calls are an important management tool for determining presence and probable absence of threatened and endangered bat species. In the northeastern United States, software programs such as Bat Call Identification (BCID), Kaleidoscope Pro (KPro), and Sonobat can automatically classify ultrasonic detector sound files, yet the programs' accuracy in correctly classifying calls to species has not been independently assessed. We used 1,500 full-spectrum reference calls with known identities for nine northeastern United States bat species to test the accuracy of these programs using calculations of Positive Predictive Value (PPV), Negative Predictive Value (NPV), Sensitivity (SN), Specificity (SP), Overall Accuracy, and No Information Rate. We found that BCID performed less accurately than other programs, likely because it only operates on zero-crossing data and may be less accurate for recordings converted from full-spectrum to zero-crossing. NPV and SP values were high across all species categories for SonoBat and KPro, indicating these programs' success at avoiding false positives. However, PPV and SN values were relatively low, particularly for individual Myotis species, indicating these programs are prone to false negatives. SonoBat and KPro performed better when distinguishing Myotis species from non-Myotis species. We expect less accuracy from these programs for acoustic recordings collected under normal working conditions, and caution that a bat acoustic expert should verify automatically classified files when making species-specific regulatory or conservation decisions. [ABSTRACT FROM AUTHOR]