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Introduction: Burn-related fatalities pose a significant global public health challenge, with a substantial impact on the elderly population. This study examines two decades of burn-related mortality data in the United States, aiming to understand the trends, disparities, and contributing factors among adults aged 65 and older., Objectives: The primary objectives of this study are to (1) analyze the trends in burn-related mortality rates among older adults, (2) investigate disparities based on gender, race and geographic regions, and (3) identify comorbidities and complications associated with burn-related deaths in this demographic., Methods: Data were obtained from the Centers for Disease Control and Prevention (CDC) using the National Center for Health Statistics database. The study cohort consists of individuals aged 65 and older who experienced burn-related deaths between 1999 and 2020. Various demographic variables, including age, sex, race/ethnicity, and location of death, were considered. The study also examined urban-rural classifications and regional differences. Mortality rates were calculated and adjusted for age. Joinpoint regression analysis was employed to assess trends in age-adjusted mortality rates over time. Modes of death and common comorbidities and complications were analyzed., Results: Between 1999 and 2020, a total of 96,498 older adults succumbed to burn injuries in the United States. Analysis revealed a concerning increase in burn-related mortality rates from 2012 onwards. Demographic disparities were evident, with older men consistently exhibiting higher mortality rates compared to women. Racial disparities were observed, with Black individuals experiencing the highest mortality burden. Geographic analysis indicated elevated mortality rates in Western states and rural areas. Accidents emerged as the leading cause of death, with ischemic heart disease and hypertensive diseases being prevalent comorbidities. Complications, with septicemia being the most common, contribute significantly to mortality., Conclusion: Our analysis of 20 years of burn-related mortality data from the CDC reveals alarming trends in the United States. Unlike global trends, mortality rates have stagnated from 1999 to 2020, indicating a persistent public health challenge. Black individuals aged over 65 bear the brunt of burn-related mortality, facing the highest age-adjusted rates among all racial groups. Regional disparities are stark, with states in the top 90 % exhibiting significantly higher age-adjusted mortality rates compared to those in the bottom 10 %. Moreover, rural areas consistently report higher mortality rates than urban areas. Ischemic heart disease, hypertensive diseases, and other heart-related conditions emerge as prevalent comorbidities. To effectively reduce burn-related injuries and fatalities, targeted public health policies are imperative. These interventions must prioritize high-risk populations and adopt culturally sensitive approaches to promote safety. Additionally, enhancing access to healthcare and fire safety education is vital for mitigating the burden of burn-related mortality among the elderly population., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd and International Society of Burns Injuries. All rights reserved.)

Objectives: Veteran homelessness has declined in the past decade, but the proportion of unsheltered homeless veterans has increased. We identified characteristics of unsheltered homelessness in a large contemporary veteran cohort and examined outpatient and inpatient encounters before and after intake to US Department of Veterans Affairs (VA) homeless programs., Methods: National data from the Homeless Operations Management Evaluation System (HOMES) database and the Corporate Data Warehouse were analyzed on 191 204 veterans experiencing housing instability from January 2018 through December 2021. We used hierarchical multivariate logistic regressions to model associations between sheltered status and veteran correlates. Repeated-measures analysis of variance assessed changes in care utilization after intake in homeless programs., Results: Age <50 years (odds ratio [OR] = 1.3; 95% CI, 1.2-1.4), Hispanic ethnicity (OR = 1.2; 95% CI, 1.1-1.3), some college education (OR = 1.1; 95% CI, 1.0-1.1), and a bachelor's degree (OR = 1.2; 95% CI, 1.1-1.2) were associated with veteran unsheltered homelessness. Unsheltered veterans were more likely to have a VA service-connected disability (OR = 1.4; 95% CI, 1.4-1.5), military sexual trauma (OR = 1.1; 95% CI, 1.0-1.1), and/or combat exposure (OR = 1.1; 95% CI, 1.0-1.1). Unsheltered and sheltered homeless veterans had an increase in outpatient encounters and a decrease in inpatient care after intake to the VA homeless program., Conclusions: Contemporary unsheltered homeless veterans are younger and Hispanic with some college education. Innovative public health approaches that better engage and reduce barriers to entry need to be tested for a diverse unsheltered homeless population., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Responding to measles outbreaks in the United States puts a considerable strain on public health resources, and limited research exists about the effectiveness of containment strategies. In this paper we quantify the impact of isolation, contact tracing, and exclusion in reducing transmission during a measles outbreak in an under-vaccinated community. [ABSTRACT FROM AUTHOR]

Accurate predictive modeling of pandemics is essential for optimally distributing biomedical resources and setting policy. Dozens of case prediction models have been proposed but their accuracy over time and by model type remains unclear. In this study, we systematically analyze all US CDC COVID-19 forecasting models, by first categorizing them and then calculating their mean absolute percent error, both wave-wise and on the complete timeline. We compare their estimates to government-reported case numbers, one another, as well as two baseline models wherein case counts remain static or follow a simple linear trend. The comparison reveals that around two-thirds of models fail to outperform a simple static case baseline and one-third fail to outperform a simple linear trend forecast. A wave-by-wave comparison of models revealed that no overall modeling approach was superior to others, including ensemble models and errors in modeling have increased over time during the pandemic. This study raises concerns about hosting these models on official public platforms of health organizations including the US CDC which risks giving them an official imprimatur and when utilized to formulate policy. By offering a universal evaluation method for pandemic forecasting models, we expect this study to serve as the starting point for the development of more accurate models., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Chharia, Jeevan, Jha, Liu, Berman and Glorioso.)

This comprehensive study delves into the epidemiological landscape of Pulmonary Heart Disease (PHD) mortality in the United States from 1999 to 2020, leveraging the extensive CDC WONDER database. PHD encompasses conditions affecting the right side of the heart due to lung disorders or elevated pressure in the pulmonary arteries, including pulmonary hypertension, pulmonary embolism, and chronic thromboembolic pulmonary hypertension (CTEPH). Analyzing data from death certificates, demographic characteristics, and geographical segmentation, significant trends emerge. The age-adjusted mortality rates (AAMRs) for PHD-related deaths show a fluctuating pattern, initially decreasing from 1999 to 2006, followed by a steady increase until 2020. Male patients consistently exhibit higher AAMRs than females, with notable disparities observed among racial/ethnic groups and geographic regions. Non-hispanic (NH) Black or African American individuals, residents of specific states like Colorado and the District of Columbia, and those in the Midwest region demonstrate elevated AAMRs. Furthermore, nonmetropolitan areas consistently manifest higher AAMRs than metropolitan areas. These findings underscore the urgent need for intensified prevention and treatment strategies to address the rising mortality associated with PHD, particularly among vulnerable populations. Insights from this study offer valuable guidance for public health initiatives aimed at reducing PHD-related mortality and improving outcomes nationwide., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

During the 1918 influenza pandemic, the U.S., unlike Europe, put considerable effort into public health interventions. There was also more geographic variation in the autumn wave of the pandemic in the U.S. compared with Europe, with some cities seeing only a single large peak in mortality and others seeing double-peaked epidemics. Here we examine whether differences in the public health measures adopted by different cities can explain the variation in epidemic patterns and overall mortality observed. We show that city-specific per-capita excess mortality in 1918 was significantly correlated with 1917 per-capita mortality, indicating some intrinsic variation in overall mortality, perhaps related to sociodemographic factors. In the subset of 23 cities for which we had partial data on the timing of interventions, an even stronger correlation was found between excess mortality and how early in the epidemic interventions were introduced. We then fitted an epidemic model to weekly mortality in 16 cities with nearly complete intervention-timing data and estimated the impact of interventions. The model reproduced the observed epidemic patterns well. In line with theoretical arguments, we found the time-limited interventions used reduced total mortality only moderately (perhaps 10–30%), and that the impact was often very limited because of interventions being introduced too late and lifted too early. San Francisco, St. Louis, Milwaukee, and Kansas City had the most effective interventions, reducing transmission rates by up to 30–50%. Our analysis also suggests that individuals reactively reduced their contact rates in response to high levels of mortality during the pandemic. [ABSTRACT FROM AUTHOR]

The Intervention Wheel is a population-based practice model that encompasses three levels of practice (community, systems, and individual/family) and 17 public health interventions. Each intervention and practice level contributes to improving population health. The Intervention Wheel, previously known as the Public Health Intervention Model, was originally introduced in 1998 by the Minnesota Department of Health, Section of Public Health Nursing. The model has been widely disseminated and used throughout the United States since that time. The evidence supporting the Intervention Wheel was recently subjected to a rigorous critique by regional and national experts. This critical process, which involved hundreds of public health nurses, resulted in a more robust Intervention Wheel and established the validity of the model. The critique also produced basic steps and best practices for each of the 17 interventions. Part I describes the Intervention Wheel, defines population-based practice, and details the recommended modifications and validation process. Part II provides examples of the innovative ways that the Intervention Wheel is being used in public health/public health nursing practice, education, and administration. The two articles provide a foundation and vision for population-based public health nursing practice and direction for improving population health. [ABSTRACT FROM AUTHOR]

The Intervention Wheel is a population-based practice model that encompasses three levels of practice (community, systems, and individual/family) and 17 public health interventions. Each intervention and practice level contributes to improving population health. The Intervention Wheel, previously known as the Public Health Intervention Model, was originally introduced in 1998 by the Minnesota Department of Health, Section of Public Health Nursing (PHN). The model has been widely disseminated and used throughout the United States since that time. The evidence supporting the Intervention Wheel was recently subjected to a rigorous critique by regional and national experts. This critical process, which involved hundreds of public health nurses, resulted in a more robust Intervention Wheel and established the validity of the model. The critique also produced basic steps and best practices for each of the 17 interventions. Part I describes the Intervention Wheel, defines population-based practice, and details the recommended modifications and validation process. Part II provides examples of the innovative ways that the Intervention Wheel is being used in public health/PHN practice, education, and administration. The two articles provide a foundation and vision for population-based PHN practice and direction for improving population health. [ABSTRACT FROM AUTHOR]

Although public health has made substantial advances in closing the health disparity gap, Black Americans still experience inequalities and inequities. Several theoretical frameworks have been used to develop public health interventions for Black American health; yet the existing paradigms do not fully account for the ontology, epistemology, or axiology of Black American populations. The Black Lives Matter (BLM) movement provides a basis for understanding the constructs that may contribute to Black American health. By drawing from the 13 BLM principles, this paper presents an alternative approach for developing, implementing, and evaluating public health interventions for Black populations in the USA. Furthermore, the approach may inform future public health research and policies to reduce health disparities within and across Black populations in the USA., (© 2020. The Author(s).)

Background: Identifying hotspots in a pandemic is essential for early containment. In the context of the rapid global dissemination of the Covid-19 pandemic, describing viral infection rates in relation to international air travel early during the pandemic can help inform future public health policy. The objective of this study is to determine whether proximity to an international airport predicted higher infection rates during the early phase of the Covid-19 pandemic in the United States (US)., Methods: In this cross-sectional study, the authors examined the incidence of Covid-19 in areas near US international airports in the first weeks after detection of Covid-19 in all 50 states, using publicly available county-level incidence of Covid-19 data. They performed a multiple regression to determine the relative effects of population density and air traffic in the Counties Containing Airports (CCA) and the number of Covid-19 cases, and determined the odds of Covid-19 in CCA compared to the rest of the state., Results: Multiple regression analysis revealed that air traffic was significantly correlated with Covid-19 cases during the initial phase of pandemic while population density was not significantly correlated. Three weeks into the pandemic, the pooled odds of Covid-19 cases in CCA was 2.66 (95% CI [2.64, 2.68], p < 0.0001)., Conclusions: The counties in the US containing international airports represented initial hotspots for Covid-19 transmission. Early public health containment efforts focused on these areas may help mitigate disease transmission during future similar novel respiratory virus epidemics., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Competing Interests: Declaration of Competing Interests We have no conflicts of interest to declare.