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BACKGROUND: Firefighter health is of utmost importance both to first responders and the individuals they serve daily. The impact of shift schedule on firefighter health remains poorly understood. OBJECTIVE: The purpose of this study was to examine differences in health and performance outcomes in firefighters across two different shift schedules. METHODS: Firefighter (N = 1995) body composition, muscular strength, endurance, flexibility, and cardiorespiratory endurance were assessed and described. A Mann-Whitney U test was conducted to compare differences between shift schedules (48/96 and 4's and 6's). RESULTS: There was a statistically significant difference in BMI (U = 70115, z=–1.988, p =.047), BF% (U = 67341, z=–2.779, p =.005), and trunk flexion score (U = 81362, z = 1.979, p =.048) favoring the 48/96 shift schedule. CONCLUSION: Firefighters following a 48/96 shift schedule exhibited improved body composition and fitness performance in comparison to peers who followed a 4's and 6's shift schedule. Findings may guide regional firefighter scheduling to optimize health and performance. [ABSTRACT FROM AUTHOR]

Background and purpose The purpose of this study was to collect information regarding healthcare worker (HCW) vaccination policies in Utah family practice clinics. Methods The study was conducted in Utah family practice clinics in the most densely populated counties in the state and was a cross-sectional descriptive design. Data were collected from 91 family practice clinic managers. Descriptive statistics were performed, as well as a content analysis for open-ended items. Conclusions HCWs are employed in environments where infectious diseases can be easily spread from person to person, thus, vaccinations can be instrumental in protecting the health of HCWs and patients alike. In Utah, 56.8% of family practice clinics had either no vaccination policy for HCWs or had a policy with no consequences for noncompliance. Utah family practice clinics need to implement changes to create and maintain HCW vaccination policies. Implications for practice Nurse practitioners can be leaders and change agents by working with their county and state health departments to create state-wide policies that mirror the position statements from the American Nurses Association and the American Association of Nurse Practitioners. [ABSTRACT FROM AUTHOR]

Introduction: Postmarketing drug safety surveillance research has focused on the product-patient interaction as the primary source of variability in clinical outcomes. However, the inherent complexity of pharmaceutical manufacturing and distribution, especially of biologic drugs, also underscores the importance of risks related to variability in manufacturing and supply chain conditions that could potentially impact clinical outcomes. We propose a data-driven signal detection method called HMMScan to monitor for manufacturing lot-dependent changes in adverse event (AE) rates, and herein apply it to a biologic drug., Methods: The HMMScan method chooses the best-fitting candidate from a family of probabilistic Hidden Markov Models to detect temporal correlations in per lot AE rates that could signal clinically relevant variability in manufacturing and supply chain conditions. Additionally, HMMScan indicates the particular lots most likely to be related to risky states of the manufacturing or supply chain condition. The HMMScan method was validated on extensive simulated data and applied to three actual lot sequences of a major biologic drug by combining lot metadata from the manufacturer with AE reports from the US FDA Adverse Event Reporting System (FAERS)., Results: Extensive method validation on simulated data indicated that HMMScan is able to correctly detect the presence or absence of variable manufacturing and supply chain conditions for contiguous sequences of 100 lots or more when changes in these conditions have a meaningful impact on AE rates. Applying the HMMScan method to FAERS data, two of the three actual lot sequences examined exhibited evidence of potential manufacturing or supply chain-related variability., Conclusions: HMMScan could be utilized by both manufacturers and regulators to automate lot variability monitoring and inform targeted root-cause analysis. Broad application of HMMScan would rely on a well-developed data input pipeline. The proposed method is implemented in an open-source GitHub repository., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The US Department of Agriculture's Economic Research Service leads the federal government in data development and research on food security in US households. Nutrition security is an emerging concept that, although closely related, is distinct from food security. No standard conceptualization or measure of nutrition security currently exists. We review the existing research on nutrition security and how it is informed by the more robust literature on food security and diet quality. Based on this review, we propose a conceptual framework for understanding nutrition security and its relationship to food security. We identify two constructs (healthy diets and nutritional status) and multiple subconstructs that form the basis of nutrition security. The proposed framework and corresponding constructs are intended to provide ( a ) understanding of how nutrition security arises and how it differs from food security, ( b ) background on why assessment and monitoring of nutrition security is important, and ( c ) guidance for a research agenda that will further clarify the meaning of nutrition security and its measurement.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

The COVID-19 pandemic resulted in widespread school closures, reducing access to school meals for millions of students previously participating in the US Department of Agriculture (USDA) National School Lunch Program (NSLP). School-prepared meals are, on average, more nutritious than home-prepared meals. In the absence of recent data measuring changes in children's diets during the pandemic, this article aims to provide conservative, back-of-the-envelope estimates of the nutritional impacts of the pandemic for school-aged children in the United States. We used administrative data from the USDA on the number of NSLP lunches served in 2019 and 2020 and nationally representative data from the USDA School Nutrition and Meal Cost Study on the quality of school-prepared and home-prepared lunches. We estimate changes in lunchtime calories and nutrients consumed by NSLP participants from March to November 2020, compared to the same months in 2019. We estimate that an NSLP participant receiving no school meals would increase their caloric consumption by 640 calories per week and reduce their consumption of nutrients such as calcium and vitamin D. Because 27 to 78 million fewer lunches were served per week in March-November 2020 compared to the previous year, nationally, students may have consumed 3 to 10 billion additional calories per week. As students return to school, it is vital to increase school meal participation and update nutrition policies to address potentially widening nutrition disparities.

Food insecurity is associated with increased risk of chronic disease and poor dietary intake. The United States charitable food system, a complex network of food banks, pantries and congregate meal sites, provides food for millions of low-income households each year. Food banks and pantries play a critical role in supporting food security and are an important contributor to dietary intake for its clients. In recent years, there has been an increased focus on sourcing and supplying more nutritious foods within the charitable food system. Despite this, there is a lack of alignment in how the charitable food system defines and tracks the nutritional quality of food.In 2019, Healthy Eating Research convened a panel of nutrition, charitable food system and food policy experts to create a set of evidence-based nutrition standards. Standards were developed based on a review of the literature and existing nutrition ranking systems, while also considering the operational needs and capacity of the charitable food system. The panel provided recommendations for eleven distinct food categories: fruits and vegetables, grains, protein, dairy, non-dairy alternatives, beverages, mixed dishes, processed and packaged snacks, desserts, condiments and cooking staples, and other miscellaneous items. Products are ranked into three tiers, choose often (green), choose sometimes (yellow) or choose rarely (red), based on designated saturated fat, sodium, and added sugar thresholds. This paper outlines the expert panel's approach and summarizes the barriers and opportunities for implementing these standards across the charitable food system., (© 2022. The Author(s).)

In 2019, the National School Lunch Program and School Breakfast Program served approximately 15 million breakfasts and 30 million lunches daily at low or no cost to students.Access to these meals has been disrupted as a result of long-term school closures related to the COVID-19 pandemic, potentially decreasing both student nutrient intake and household food security. By the week of March 23, 2020, all states had mandated statewide school closures as a result of the pandemic, and the number of weekly missed breakfasts and lunches served at school reached a peak of approximately 169.6 million; this weekly estimate remained steady through the final week of April.We highlight strategies that states and school districts are using to replace these missed meals, including a case study from Maryland and the US Department of Agriculture waivers that, in many cases, have introduced flexibility to allow for innovation. Also, we explore lessons learned from the pandemic with the goal of informing and strengthening future school nutrition policies for out-of-school time, such as over the summer.

Objective: Inform coronavirus disease 2019 (COVID-19) infection prevention measures by identifying and assessing risk and possible vectors of infection in nursing homes (NHs) using a machine-learning approach., Design: This retrospective cohort study used a gradient boosting algorithm to evaluate risk of COVID-19 infection (ie, presence of at least 1 confirmed COVID-19 resident) in NHs., Setting and Participants: The model was trained on outcomes from 1146 NHs in Massachusetts, Georgia, and New Jersey, reporting COVID-19 case data on April 20, 2020. Risk indices generated from the model using data from May 4 were prospectively validated against outcomes reported on May 11 from 1021 NHs in California., Methods: Model features, pertaining to facility and community characteristics, were obtained from a self-constructed dataset based on multiple public and private sources. The model was assessed via out-of-sample area under the receiver operating characteristic curve (AUC), sensitivity, and specificity in the training (via 10-fold cross-validation) and validation datasets., Results: The mean AUC, sensitivity, and specificity of the model over 10-fold cross-validation were 0.729 [95% confidence interval (CI) 0.690‒0.767], 0.670 (95% CI 0.477‒0.862), and 0.611 (95% CI 0.412‒0.809), respectively. Prospective out-of-sample validation yielded similar performance measures (AUC 0.721; sensitivity 0.622; specificity 0.713). The strongest predictors of COVID-19 infection were identified as the NH's county's infection rate and the number of separate units in the NH; other predictors included the county's population density, historical Centers of Medicare and Medicaid Services cited health deficiencies, and the NH's resident density (in persons per 1000 square feet). In addition, the NH's historical percentage of non-Hispanic white residents was identified as a protective factor., Conclusions and Implications: A machine-learning model can help quantify and predict NH infection risk. The identified risk factors support the early identification and management of presymptomatic and asymptomatic individuals (eg, staff) entering the NH from the surrounding community and the development of financially sustainable staff testing initiatives in preventing COVID-19 infection., (Copyright © 2020 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.)

Despite research on the causes of populism and on the narratives of populist leaders, there is little empirical work on the relationship between populist attitudes and behavior, notably including criminal behavior. Our overarching concern is the recurrent social volatility of metaphorical populist themes that are central to impactful political messaging. Drawing on a national United States survey conducted around the 2016 election, we use multilevel models to show that the politically charged exclusionary boundaries of "America First" populism are behaviorally connected to increased odds of having been arrested. We argue that the rapid redrawing of social boundaries that make up populist attitudes is closely connected with the effects of economic and political frustrations during times of rapid social change. In the process, we develop a behavioral analysis of the social volatility of the recurrent populist movement in America., Competing Interests: The authors declare no competing interest.

Purpose: The impact of non-clinical transfer delay (TD) from the ICU to a general care unit on the progress of the patient's care is unknown. We measured the association between TD and: (1) the patient's subsequent hospital length of stay (LOS); (2) the timing of care decisions that would advance patient care., Methods: This was a single center retrospective study in the United States of patients admitted to the surgical and neurosurgical ICUs during 2013 and 2015. The primary outcome was hospital LOS after transfer request. The secondary outcome was the timing of provider orders representing care decisions (milestones) that would advance the patient's care. Patient, surgery, and bed covariates were accounted for in a multivariate regression and propensity matching analysis., Results: Out of the cohort of 4,926 patients, 1,717 met inclusion criteria. 670 (39%) experienced ≥12 hours of TD. For each day of TD, there was an average increase of 0.70 days in LOS (P < 0.001). The last milestone occurred on average 0.35 days later (P < 0.001). Propensity matching analyses were confirmatory (P < 0.001, P < 0.001)., Conclusions: TD is associated with longer LOS and delays in milestone clinical decisions that progress care. Eliminating delays in milestones could mitigate TD's impact on LOS., (Copyright © 2018 Elsevier Inc. All rights reserved.)