Environmental Health Perspectives

Abstract This protocol is reported in accordance with the SPIRIT 2025 guidelines for clinical trial protocols. Introduction: Young children, from birth to age 5 y are particularly vulnerable to indoor air pollutants and respiratory pathogens. Portable air purifiers (or filtration) and upper-room ultraviolet germicidal irradiation (UVGI) are two widely used interventions with the potential to improve indoor air quality (IAQ) and reduce sick-related absences. However, a review of the literature revealed no real-world randomized studies evaluating their effectiveness in reducing young children's sick-related absences in early care and education (ECE) classrooms. Methods and Analysis: The OK-AIR study is a longitudinal, cluster-randomized 2x2 factorial trial conducted in Head Start centers using two implementation cohorts: Cohort 1 (five Head Start centers and 20 classrooms from 2023 to 2024) and Cohort 2 (11 centers and 59 classrooms from 2025 to 2026), with expanded inclusion of rural areas. Cohort 1 enrolled 204 children, 48 teachers and 5 site directors, and Cohort 2 enrolled 462 children, 97 teachers and 11 site directors. Within each center, four classrooms are randomized to: (1) control; (2) portable filtration; (3) upper-room ultraviolet germicidal irradiation (UVGI); or (4) both interventions. Cohort 2 was initially planned as a second factorial trial but was amended to a purifier-only design due to funding changes; details are provided in the protocol amendments section. We collect continuous IAQ data, including particulate matter (PM) with aerodynamic diameters [≤]1 m (PM1), [≤]2.5 m (PM2.5), [≤]4 m (PM4), and [≤]10 m (PM10); total volatile organic compounds (TVOCs) index; nitrogen oxides (NOx) index; carbon monoxide (CO), noise; temperature; and relative humidity, alongside daily child absences. Seasonal environmental surface swabs (dining tables and toilet flooring) are tested by Reverse-Transcriptase quantitative Polymerase Chain Reaction (RT-qPCR) for Influenza A/B, Respiratory Syncytial Virus (RSV), Human Parainfluenza Virus Type 3 (HPIV3), Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), and Norovirus. IAQ monitoring is structured across Winter, Spring, Summer, and Fall, including designated baseline/off-period weeks to characterize temporal and seasonal variability in environmental measures across classrooms and centers. Multi-informant surveys (Director, Teacher, Parent) capture contextual factors, and children's social-emotional development is assessed using teacher ratings on the Devereux Early Childhood Assessment (DECA). The primary outcome is the sick-related absence rate, analyzed as cumulative absences over the attendance year while accounting for clustering by school and classroom using generalized mixed-effects models. Secondary outcomes include children's social-emotional ratings, IAQ metrics and pathogen detection rates; analyses of IAQ incorporate time/seasonal structure, and season-stratified absenteeism analyses will be treated as secondary/exploratory refinements. An economic evaluation will estimate incremental intervention costs and cost-effectiveness/cost-benefit (such as cost per sick-related absence day averted). Ethics and Dissemination: This study was approved by the Institutional Review Board (IRB) at the University of Oklahoma. Findings will be shared through peer-reviewed publications; presentations at local, state, and national conferences; research briefs developed for lay and policy audiences; and community briefings prioritizing the participating early childhood programs and communities. ISRCTN Trial Registration: ISRCTN78764448 Disclaimer: The views expressed are those of the authors and do not reflect the official views of the Uniformed Services University or the United States Department of War. Strengths and Limitations of This Study: {middle dot} Real-world longitudinal cluster RCT: The study uses a rigorous longitudinal cluster-randomized 2x2 factorial design in real-world ECE settings. {middle dot} Combined interventions: Interventions target both air filtration and disinfection, allowing for combined and comparative evaluation. {middle dot} Objective air quality monitoring: Continuous monitoring of IAQ metrics provides objective and reliable data on environmental change. {middle dot} Environmental pathogen surveillance: qPCR on surface swabs yields an objective biological outcome to triangulate with IAQ and absences. {middle dot} Comprehensive context and child measures: Multi-method and multi-reporter data collection includes Head Start attendance records, continuous air monitoring, pathogen detection, contextual surveys completed by center directors, teachers, and parents, and standardized social-emotional assessments (DECA) completed by classroom teachers. Head Start program records providing children's longer-term health data available through Health Insurance Portability and Accountability Act (HIPAA) authorization. {middle dot} Clustered/temporal complexity: Seasonal design accounts for variation over time but may introduce complexity in modeling temporal effects. {middle dot} Practical Implications: Study findings will have practical implications for Head Start and other ECE programs striving to maximize child attendance with cost effective strategies. Keywords: Early childhood; Head Start; indoor air quality (IAQ); air purifiers; filtration; ultraviolet germicidal irradiation; cluster randomized trial; absenteeism; environmental pathogens; DECA; cost-benefit analysis

The co-occurrence of per- and polyfluoroalkyl substances (PFAS) and volatile organic compounds (VOCs) in industrial environments poses complex toxicological risks that standard additive models fail to capture. This study elucidates a novel "metabolic blockade" mechanism wherein PFAS competitively inhibits the renal excretion of VOC metabolites, thereby amplifying neurotoxic burdens. Utilizing a Double Machine Learning (DML) framework on data from National Health and Nutrition Examination Survey (2005-2020), we analyzed a final intersectional cohort of 1,975 participants. We identified a robust inhibition of VOC metabolite clearance by serum PFAS. Specifically, PFNA significantly suppressed the excretion of the benzene metabolite URXPMA (Causal {beta}TMLE = -0.219, p < 0.001), with efficacy dependent on perfluorinated chain length. Molecular docking simulations revealed the biophysical basis of this antagonism: long-chain PFNA exhibited superior binding affinity to the Organic Anion Transporter 1 (OAT1) ({Delta}G = -6.333 kcal/mol) compared to native VOC metabolites ({Delta}G = -4.957 kcal/mol), confirming high-affinity competitive inhibition at the renal interface. In a neurocognitive sub-cohort (N = 1,200), this interference translated into functional synergism; high-PFNA exposure magnified VOC-associated cognitive impairment by 1.5-fold and significantly exacerbated the negative association between VOC burden and processing speed ({beta}int = -0.263, p = 0.004). These findings define PFAS as a "metabolic amplifier" of co-contaminant toxicity, necessitating a paradigm shift toward mixture-based hazardous material regulations that account for transporter-level interactions.

BackgroundAir pollution is a potentially modifiable risk factor for dementia with a population attributable risk fraction of 3%. Little is known about the causal mechanisms behind the association, so we aimed to investigate this. MethodsData from the UK Biobank were used to investigate the association between six measures of air pollution (NO2, NOx, PM2{middle dot}5-10, PM2{middle dot}5, PM2{middle dot}5 absorbance and PM10) and dementia incidence. Indirect pathways through four mediators (cardiovascular conditions, mental health treatment, insufficient exercise and social isolation) were explored. Logistic regression was used to model the associations between air pollution, mediators and dementia. Casual mediation analysis implemented using the g-formula was used to investigate the joint indirect effect through the mediators. FindingsExposure to the highest quintile of PM2{middle dot}5 (Rte:1{middle dot}14, 95% CI:1{middle dot}06-1{middle dot}23), NOx (Rte:1{middle dot}11, 95% CI:1{middle dot}03-1{middle dot}20) or NO2 (Rte:1{middle dot}08, 95% CI:0{middle dot}99-1{middle dot}16), compared to the lowest quintile, was associated with higher dementia risk. Most of the observed association resulted from the direct effect of air pollution, consisting of pathways not captured through considered mediators. Amongst those in the highest PM2{middle dot}5 quintile, jointly intervening on the four mediators would result in a 1% reduction in risk of dementia (Rpnie:1{middle dot}01, 95% CI: 1{middle dot}01-1{middle dot}02). The randomised pure natural indirect effect was similar for NO2 (Rpnie:1{middle dot}01, 95% CI: 1{middle dot}00-1{middle dot}01) and NOx (Rpnie:1{middle dot}01, 95% CI: 1{middle dot}01-1{middle dot}02). InterpretationMost of the association between dementia and PM2{middle dot}5, NO2 and NOx occurs through the direct effect of air pollution, or other unmeasured mediators, and not pathways through these four mediators. FundingMedical Research Council (Grant MR/W006774/1).

BackgroundDengue outbreaks have become a severe threat to Bangladesh as the infections and mortality numbers are skyrocketing in recent years. Favorable environmental and anthropogenic conditions have established the capital of Bangladesh, Dhaka city as the epicenter of dengue outbreak. Studies have showed that climate change induced extreme weather events are exacerbating Aedes mosquito breeding and dengue virus transmission conditions. Methodology/Principal FindingsIn this study, short-term (0-6 weeks) associations of maximum temperature and heatwave days on dengue cases in Dhaka city were examined through Distributed Lag Non-linear Model (DLNM) methodology for weekly measurement of 2016-2024, taking into account relative humidity, cumulative rainfall, seasonality and hospital closure effect. Two separate negative binomial models were constructed. The maximum temperature model rendered an overall inverted U-shaped association, where the maximum temperature range of 31.5-33.2{degrees}C showed a sustained elevated dengue risk, with highest risk estimate at 33.2{degrees}C [relative risk (RR): 1.186, 95% CI: 1.002, 1.403]. Whereas, results of weekly heatwave days showed an overall protective effect (RR<1) for dengue cases. The lowest risk of infection was found at 3 heatwave days per week, with RR 0.275 (95% CI: 0.178, 0.423). Multiple sensitivity analyses were conducted for both models to evaluate their robustness. Lastly, the optimized models were analyzed under three distinct sub-periods, to capture the association of exposure variables with predominant circulating serotypes. Conclusions/SignificanceThe findings of the study aim to support public health policymakers and healthcare authorities in designing and implementing effective vector control interventions under emerging climatic emergencies. Author SummaryDengue disease is one of the most buringing issue in Bangladesh in recent years. This vector-borne disease is inherently influenced by climatic variables, i.e., temperature, rainfall, humidity, etc. Moreover, these relations are complex and non-linearly associated. Due to shift in climatic conditions, the occurance of extreme weather events are becoming frequent, with increased magnitude and longer duration. In this study, the nonlinear and delayed association of dengue infections due to the exposure of extreme temperature events were assessed in climate-change vulnerable Dhaka city. To do this, a statistical method was used, called distributed lag nonlinear methodology (DLNM). The results showed that dengue infections had an inverted U-shaped (parabolic) relationship with maximum temperature, while compared to mean maximum temperature, and a suppressive association with heatwaves relative to days without heatwaves. The findings aim to work as an early warning system, and support to policymakes and healthcare authorities to tackle the dengue surge in the changing climate.

Ambient air pollution has been associated with increased incidence of chronic disease and is estimated to contribute towards 4.2 million early deaths annually. Whilst the health impacts are well described, less is understood about the underlying biological mechanisms, particularly when considering the co-occurrence of multiple pollutants. Using an atmospheric chemistry transportation model (EMEP4UK), we generate pre-baseline sampling pollution exposure estimates for eight pollutants in Generation Scotland (N = 22,071, recruited between 2006 - 2011). Cox-proportional hazard models reveal associations between pollution exposure and all-cause dementia (PM2.5) and myocardial infarction (NO3_Coarse) over 18 years of follow-up. We perform Bayesian multivariate epigenome-wide (N = 18,512, Illumina EPIC v.1) and proteomic (N = 15,314, 133 mass-spectrometry proteins) association studies, revealing 11 pollutant-methylation associations and 140 pollutant-protein associations. We identify positive associations between exposure (PM2.5 and NO3_Fine) and epigenetic age-acceleration (PhenoAge epigenetic clock). Furthermore, we explore the development of pollutant EpiScores, assessing these in holdout and independent test sets. Our results enhance knowledge of molecular correlates of air pollution exposure, whilst providing further evidence of contributions of air pollutants to chronic disease.

Accurate quantification of individual exposure to air pollutants remains a major challenge in environmental health, as fixed-site monitoring fails to account for mobility, indoor environments, and physiological variability. We deployed TracMyAir, a smartphone-based digital health platform designed to generate time-resolved, personalized exposure and inhaled dose estimates for PM2.5 and ozone under real-world conditions. In an exploratory study of 18 adults contributing more than 1,500 participant-hours, the platform integrated smartphone geolocation, regulatory (AirNow) and community-based (PurpleAir) air quality data, building infiltration modeling, microenvironment classification, and wearable-derived physical activity metrics to compute eight tiers of hourly exposure estimates, culminating in individualized inhaled dose. Hourly dose estimates derived from smartphone-and smartwatch-based step counts were concordant (Spearman correlation p=0.97-0.98), while heart rate-based estimates yielded greater variability and higher mean values (p=0.82-0.92). Exposure explained 51-73% of variance in inhaled dose of PM2.5 and 68-84% of ozone, suggesting that physiological-based modeling approaches improve hyperlocal estimates of personal pollutant burden. Substantial inter-and intra-individual variability reflect dynamic microenvironmental transitions and activity patterns. Modeled doses based on regulatory and community sensor networks were strongly correlated (R=0.84), with community sensors located closer to participants on average, supporting the feasibility of integrating dense, low-cost monitoring networks. No consistent association was observed between outdoor pollutant levels and neighborhood socioeconomic status in this cohort. These findings demonstrate the feasibility of a scalable, smartphone-centered digital health approach for hyperlocal exposure and inhaled dose modeling. By leveraging ubiquitous consumer devices and existing air quality networks, TracMyAir enables personalized environmental exposure assessment with potential applications in epidemiology, population health, and precision environmental medicine.

Food costs are more significantly impacted by climate change as countries grow. It is well known that climate change has an impact on the productivity of most agricultural goods, but it is unclear how specifically it will affect food costs. The present research explores how the North Atlantic Oscillation (NAO) index, a widely used climate indicator, affects food prices around the world. This is achieved by applying a robust bivariate Hurst exponent (robust bHe). The research creates a color map of this coefficient using a window-sliding technique over various intervals of time, displaying an illustration that changes overtime. Additionally, the NAO index and global food prices are examined for causal connections using variable-lag transfer entropy using a window-sliding technique. The results show that notable rises in a number of international food prices for long as well as short periods are associated with significant increases in the NAO index. Furthermore, the causative function of the NAO index in influencing global food costs is confirmed by variable-lag transfer entropy. Is highly recommended as it directly connects the research to actionable outcomes for policymakers and the overarching goal of sustainability and food security. This study provides the first direct evidence of a robust, long-range cross-correlation and causal link between the North Atlantic Oscillation (NAO) index and key global food prices. It introduces a novel, robust methodological framework to visualize this time-varying relationship, offering a critical tool for policymakers and forecasting models.

Wastewater is increasingly being recognized as an important data stream that can contribute to infectious disease surveillance and forecasting. With this recognition, a growing number of statistical inference approaches are being developed to use wastewater data to provide quantitative insights into epidemiological dynamics. However, few existing approaches have allowed for systematic integration of data streams for inference, for example by combining case incidence data and/or serological data with wastewater data. Furthermore, only a subset of existing approaches have been able to handle missing data without imputation and to handle datasets with different sampling times or intervals. Here, we develop a statistically rigorous, yet lightweight, approach to infer and forecast time-varying effective reproduction numbers (Rt values) using longitudinal wastewater virus concentrations either alone or jointly with additional data streams including case incidence data and serological data. Our approach relies on a state-space modeling approach for inference and forecasting, within the context of a simple bootstrap particle filter. We first describe the structure of our underlying disease transmission process model as well as our observation models. Using a mock dataset, we then show that Rt can be accurately estimated by interfacing this model with case incidence data, wastewater data, or a combination of these two data streams using the bootstrap particle filter. Of note, we show that these data streams alone do not allow for reconstruction of underlying infection dynamics due to structural parameter unidentifiability. We then apply our particle filter to a previously analyzed SARS-CoV-2 dataset from Zurich that includes case data and wastewater data. Our analyses of these real-world datasets indicate that incorporation of process noise (in the form of environmental stochasticity) into the state space model greatly improves our ability to reconstruct the latent variables of the model. We further show that underlying infection dynamics can be made identifiable through the incorporation of serological data and that the bootstrap particle filter can be used to make forecasts of Rt, case incidence, and wastewater virus concentrations. We hope that the inference approach presented here will lead to greater reliance on wastewater data for disease surveillance and forecasting that will aid public health practitioners in responding to infectious disease threats.

Infectious disease forecasts can inform public health decision-making. Wastewater monitoring is a relatively new epidemiological data source with multiple potential applications, including forecasting. Incorporating wastewater data into epidemiological forecasting models is challenging, and relatively few studies have assessed whether this improves forecast performance. We present and evaluate a semi-mechanistic wastewater-informed forecasting model. The model forecasts COVID-19 hospital admissions at the state and territorial levels in the United States, based on incident hospital admissions data and, optionally, SARS-CoV-2 wastewater concentration data from multiple wastewater sampling sites. From February through April 2024, we produced real-time wastewater-informed COVID-19 forecasts using development versions of the model and submitted them to the United States COVID-19 Forecast Hub ("the Hub"). We then published an open-source R package, wwinference, that implements the model with or without wastewater as an input. Using proper scoring rules and measures of model calibration, we assess both our real-time submissions to the Hub and retrospective hypothetical forecasts from wwinference made with and without wastewater data. While the models performed similarly with and without the wastewater signal included, there was substantial heterogeneity for individual locations and dates where wastewater data meaningfully improved or degraded the models forecast performance. Compared to other models submitted to the Hub during the period spanned by our submissions, the real-time wastewater-informed version of our model ranked fourth of 10 models, with the hospital admissions-only version of our model ranking second out of 10 models. Across the 2023-2024 winter epidemic wave, retrospective forecasts from wwinference would have performed similarly with and without the wastewater signal included: fifth and fourth out of 10 models, respectively. To better understand the drivers of differential forecast performance with and without wastewater, we performed an exploratory analysis investigating the relationship between characteristics of the input data and improved and reduced performance in our model. Based on that analysis, we identify and discuss key areas for further model development. To our knowledge, this is the first work that conducts an evaluation of real-time and retrospective infectious disease forecasts across the United States both with and without wastewater data and compared to other forecasting models. Author SummaryWastewater-based epidemiology, in combination with clinical surveillance, has the potential to improve situational awareness and inform outbreak responses. We developed a model that uses data on the pathogen concentration in wastewater from one or more wastewater treatment plants in combination with hospital admissions to produce short-term forecasts of hospital admissions. We produced and submitted forecasts of 28-day ahead COVID-19 hospital admissions from this model to the U.S. COVID-19 Forecast Hub during the spring of 2024 and found that it performed well in comparison to other models during that limited time period. To assess the added value of incorporating wastewater data into the model and to investigate how it would have performed had we submitted it during the entire 2023-2024 winter epidemic wave, we performed a retrospective analysis in which we produced forecasts from the model with and without including wastewater data, using data that would have been available in real-time as of each forecast date. Both versions of the model would have been median overall performers had they been submitted to the Hub throughout the season. When comparing the models performance with and without wastewater data included, we found that overall forecast performance was very similar, with wastewater data slightly reducing overall average forecast performance. Within this result, there was significant heterogeneity, with clear instances of wastewater data improving and detracting from forecast performance. We used trends in the observed data to generate hypotheses as to the drivers of improved and reduced relative forecast performance within our model. We conclude by suggesting future work to improve the model and more broadly the application of wastewater-based epidemiology to forecasting.

Measles outbreaks have surged globally in recent years, but current surveillance systems have limited capacity to monitor measles virus (MeV) transmission and evolution at population scale. Although MeV can be detected in wastewater, the public health potential of wastewater genomic surveillance for MeV remains largely unexplored. Here, we deploy sensitive, low-cost MeV wastewater genomic surveillance combining virus concentration, whole-genome amplicon sequencing, and bioinformatic analysis alongside routine clinical genomic surveillance during the 2024-25 outbreak in South Africa. Integrated phylogenetic analyses of wastewater and clinical MeV genomes revealed previously undetected interprovincial spread and transmission links not captured by standard N450 sequencing. Our findings demonstrate that wastewater-integrated whole-genome surveillance expands the coverage and resolution of routine MeV monitoring and provides a scalable tool to advance measles control and elimination efforts.

IntroductionAtmospheric conditions under climate change increase pressure on healthcare systems. Especially, the intensive care units (ICU) are vulnerable due to low buffer capacity and high utilization rates. MethodsDaily ICU cases from 2009 to 2023 were derived from the German statutory health insurance data of eleven regional AOK insurances. Cases were stratified by age and sex. Generalized additive models were used to investigate the associations between daily ICU cases and lagged atmospheric variables. Thirteen intensive care relevant diseases were analyzed using disease-specific predictor sets. Analyses were conducted for regions derived from a human-biometeorological characterization of Germany. Model performance was assessed using (weighted) explained deviance. ResultsOver the 15-year study period, 9,970,548 ICU patients were recorded (44% women), 74.3% aged [&ge;]60 years. Trauma was the most common ICU-related disease, followed by non-ST elevation myocardial infarction (NSTEMI), pneumonia and ischemic stroke. ICU demand was most sensitive (p [&le;] 0.05) to pressure-related factors, thermo-physiological parameters and ozone concentration. In terms of sex-age differences, atmospheric factors affected men more frequently, while women were more impacted by cold weather and particulate matter (PM10). Heat was more relevant for patients aged [&ge;]60 years. The NSTEMI model in Central Eastern Germany performed best (weighted explained deviance of 49.3%). In males [&ge;]60 years, heatwaves were associated with a reduced risk of ICU cases (Relative Risk = 0.94, 95%-Confidence Interval 0.89 to 0.99). ConclusionThe study identified key atmospheric factors for ICU, enabling the German healthcare system to prepare better for short-term impacts of meteorological and air quality factors. KEY MESSAGESWhat is already known on this topic: O_LIThe atmospheric changes have a direct impact on public health and the inpatient care, particularly in intensive care units. C_LIO_LIConsequently, there is a necessity to investigate the influence of atmospheric factors on intensive care in order to prepare the healthcare system for the new circumstances. C_LI What this study adds: O_LIThe study provides evidence that atmospheric factors influence the intensive care in Germany and describes age and sex-specific aspects. C_LIO_LIThe results offer valuable insights into how different atmospheric factors affect the demand for intensive care in hospitals. C_LI How this study might affect research, practice or policy: O_LIThe study enables the German healthcare system to better prepare for short-term effects of atmospheric factors, and structural or resource-related adjustments could be made in hospitals to anticipate for short-term fluctuations in intensive care demand. C_LI

ObjectiveTo characterize associations between tattooing and health status. MethodsWe used data from [~]27,000 respondents to the 2020-2022 Utah Behavioral Risk Factor Surveillance System (BRFSS). Multivariable Poisson regression was used to calculate prevalence ratios (PR) and 95% confidence intervals (CI) associating ever receiving a tattoo with physical/mental health status. ResultsIn this cross-sectional study, ever receiving a tattoo was associated with self-reported "poorer" vs. "excellent" overall health, particularly among women (PR=3.08 [95% CI: 2.26- 4.21]). Tattooing was also associated with obesity (women, PR=1.40 [95% CI: 1.22-1.61]; men, PR=1.21 [95% CI: 1.04-1.40]) and chronic pain (women, PR=1.59 [95% CI: 1.43-1.77]; men, PR=1.55 [95% CI: 1.37-1.76]). Tattooed individuals were more likely to have been diagnosed with a depressive disorder (women, PR=1.64 [95% CI: 1.53-1.75]; men, PR=1.55 [95% CI: 1.39-1.73]) and to have had six or more teeth removed, vs. none (women, PR=2.18 [95% CI: 1.61-2.96]; men, PR=2.88 [95% CI: 2.10-3.95]). ConclusionsPublic health entities may consider partnering with tattoo studios and conventions to provide information about nutrition, exercise, dental care, mental health resources, and health screenings.

IntroductionMusculoskeletal injuries (MSKIs) are ubiquitous in the U.S. military, especially among high-performing service members such as Marines. Given that female service members only started to be assigned to ground combat roles since December 2015, evaluation of sex on MSKI risk in ground combat occupations has not been possible until there was an ample population to study. The purpose of this population-level epidemiological study was to assess (1) if female sex was a salient risk factor for MSKI in Marines serving in different military occupations, including combat arms, and (2) the effects of integration period on MSKI risk among female Marines. Materials and MethodsA population-based epidemiological retrospective cohort study of all U.S. Marines was performed assessing female sex, occupation, and integration period on the prevalence of MSKI from 2011 through 2020. The Military Health System Data Repository was utilized to identify initial healthcare encounters for diagnosed ankle-foot, knee, lumbopelvic-hip, thoracocostal, cervicothoracic, shoulder, elbow, or wrist-hand complex injuries. Prevalence was calculated for female and male Marines in each occupational category (combat, combat support, aviators, aviation support, services) during the pre-integration (2011-2015) and post-integration (2016-2020) periods. ResultsDuring the pre-integration period, 520/1,000 female Marines (n=13,985) and 299/1,000 male Marines (n=142,158) incurred MSKIs. In the post-integration period, the prevalence increased to 565/1,000 female Marines (n=17,608) and 348/1,000 male Marines (n=161,429). In the multivariable evaluation of sex, occupation, integration period, and the interaction of sex and occupation on combined MSKIs, only female sex was a significant factor for injury (prevalence ratio [PR]=1.99), with service in ground combat and aviation occupations identified as protective factors when compared with services occupations (PR=0.69). When these same factors were evaluated for specific MSKI outcomes, female sex remained a robust factor in all lower quarter (PR=1.75-2.63) and upper quarter (PR=1.38-2.36) injuries except for shoulder injuries. Service in ground combat and aviation occupations was protective for all lower quarter injuries (PR=0.46-0.71). In the upper quarter, ground combat was protective for all injuries except for elbow injuries (PR=0.67-0.77). Serving as an aviator was a risk factor for cervicothoracic (PR=1.57) and thoracocostal (PR=1.22) injuries and a protective factor for shoulder (PR = 0.73) and wrist-hand (PR = 0.46) injuries. Adjusted risk for lumbopelvic-hip (PR=1.13), ankle-foot (PR=1.53), cervicothoracic (PR=1.19), thoracocostal (PR=1.14), and elbow (PR=1.48) injuries significantly increased during the post-integration period. There was a significant sex-by-period interaction for shoulder injuries alone, with female sex in the post-integration epoch found to be salient (PR=1.26). ConclusionsFemale sex was a salient factor for MSKI, with service in ground combat and aviation occupations identified as protective factors when compared with services occupations. In the evaluation of specific MSKIs, female sex remained a robust and significant factor in all lower quarter injuries and upper quarter injuries except for shoulder injuries. There was only a significant sex-by-period interaction for shoulder conditions, with an increased risk of these injuries in female Marines in the post-integration period.

Incidence of norovirus has strong seasonality in temperate and continental climates. Many studies have examined its association with climate variables, but evidence remains disparate. We address this gap by performing a systematic review to summarise and interpret the strength and directionality of associations between climate variables and norovirus incidence. Embase, Scopus, Web of Science and PubMed databases were screened for peer-reviewed studies on 2nd of December 2024. Articles were included if they described any climate or meteorological variable, in a categorical or numerical format, relative to a measurement of norovirus incidence risk in a human population, or prevalence or survivability outside the human host. Bias was assessed using a modified Critical Appraisal Skills Programme checklist. If dispersion of the effect in a human population was provided, the mean size was calculated using inverse variance weighting. The effect size outside the host was summarised as D-values, representing the time required to achieve a 90% reduction in the detected amount of virus. A total 139 studies were included. Predictors of risk were ambient and water temperature, relative and absolute humidity, anomalies of ambient temperature and precipitation, atmospheric and vapour pressure. High heterogeneity in direction and size of effects was observed due to regional differences in the factors driving norovirus seasonality and differences in outcome and exposure definitions. Our review suggests that the sensitivity of norovirus to individual climate variables is region and time specific, reflecting geographical differences in the relative importance of norovirus transmission via environmental pathways versus human-to-human contact. Plain Language SummaryNorovirus, a gastrointestinal virus, has a higher number of cases during specific months of the year. Regions with similar types of climate appear to have similar time periods when the increase in the number of infections occurs, which has been linked to norovirus case numbers being correlated to individual climate variables, such as temperature or rainfall. To understand how these associations compare globally and what are their potential explanations, we screened four major scientific databases, namely Embase, Scopus, Web of Science and PubMed. After the selection process, a total 139 peer-reviewed studies were included in this study. We found that ambient and water temperature, relative and absolute humidity, anomalies of ambient temperature and precipitation, atmospheric and vapour pressure were predictors of an increase in norovirus cases. However, the strength and direction of the relationships differed from region to region. A potential explanation is that geographies also differ in how important individual routes are for the transmission of norovirus, specifically via the environment as opposed to direct human-to-human contact, whereas climate is likely to have a greater influence on the former. Key pointsO_LIThe strength and direction of associations between climate variables and norovirus incidence varies by region and time period C_LIO_LIThe strength of associations vary across the transmission routes of norovirus, e.g., environmental versus human-to-human contact C_LIO_LIClimate variables impact norovirus survival and dissemination outside the host, which may inform models of environmental virus transmission C_LI

IntroductionObesity and related health conditions are unevenly distributed across neighborhoods, often co-occuring with multiple health challenges and socioeconomic disadvantages. Using an ecosyndemic framework, which integrates ecological and social dimensions that contribute to the clustering of health problems, this study examines how adverse obesity-related health outcomes spatially cluster in relation to obesogenic environments and socioeconomic position (SEP) across Dutch neighborhoods. MethodsNationwide neighborhood-level data on health outcomes, obesogenic environmental exposures (food environment, walkability, drivability, bikeability, sports facilities), and SEP were combined for all inhabited Dutch administrative neighborhoods in 2016 (N=12,420). Cluster analysis was used to identify distinct neighborhood profiles and descriptive statistics to characterize each cluster, with spatial patterns visualized using an interactive heatmap and principal component plots. ResultsFive neighborhood clusters were identified. The Ecosyndemic cluster (N=1,070 neighborhoods) exhibited the highest burden of obesity (17% [IQR 16;19), chronic diseases (36% [IQR 33;38%) and risk of anxiety/depression (55% [IQR 51;58]), unhealthy food environments and low SEP. In contrast, the Privileged cluster (N=6,425) had more favorable health outcomes and living conditions, including lower obesity prevalence (12% [IQR 11;14]). The Psychosocial Vulnerability cluster (N=991) was notable for elevated risk of anxiety/depression (47% [IQR 43;51]) combined with relatively low obesity (11% [IQR 8;12]). The Syndemic cluster (N=1,836; obesity 15% [IQR 14;17]) and Towards Privileged cluster (N=2,098; obesity 12% [IQR 10;13]) represented intermediate profiles. ConclusionObesity and related health issues frequently cluster with unfavorable environment and SEP at the neighborhood level. The ecosyndemic framework offers a novel approach for identifying high-risk areas and supports targeted, social and place-based interventions.

PurposeWe introduce PRE-CISE, a pre-calibration workflow that integrates coverage analysis, local sensitivity, and collinearity diagnostics to streamline model calibration and transparently address nonidentifiability. We demonstrate the benefits of PRE-CISE using a four-state Sick-Sicker Markov testbed and a COVID-19 case study. MethodsPRE-CISE begins with a coverage analysis to verify that model outputs generated with parameter sets drawn from their prior distribution span calibration targets, followed by local sensitivities to quantify the influence of parameters on model outputs, guiding the resizing of the prior distribution bounds to improve coverage. Identifiability is then assessed via collinearity analysis; large indices indicate practical nonidentifiability. For the testbed model, we calibrated 3 parameters to survival, prevalence, and the proportion of Sick to Sicker at 10, 20, and 30 years. For the COVID-19 model, we calibrated 11 parameters to match daily confirmed incident cases. Bayesian calibration was conducted on both analyses. ResultsCoverage analyses flagged initial misfits; local sensitivities identified the Sick-to-Sicker transition probability has a greater effect on model outputs, and resizing its prior distribution bounds improved coverage. Collinearity analyses showed that combining multiple calibration targets across time points enabled recovery of all three parameters. In the COVID-19 model, local sensitivity analyses prioritized time-varying detection rates and contact-reduction effects, reducing the search space, thereby improving calibration efficiency. Daily incident case calibration targets yielded collinearity indices below practical thresholds (e.g., < 15) for all parameter combinations, whereas weekly calibration targets were larger and closer to the cutoff. ConclusionsPRE-CISE provides a practical, transparent pathway that helps modelers refine prior distribution bounds and calibration targets before intensive calibration, improving uncertainty reporting and strengthening the reliability of model-based health policy analyses.

BackgroundSecondhand smoke (SHS) exposure remains a major public health concern among adolescents, particularly in low- and middle-income countries. Evidence from Zambia is limited, despite increasing tobacco use and existing tobacco control policies. This study examined the prevalence and correlates of SHS exposure among adolescents in Zambia. MethodsWe analyzed data from the 2021 Zambia Global Youth Tobacco Survey (GYTS), a nationally representative, school-based survey. The sample included 6,499 adolescents aged 11-17 years enrolled in grades 7-9. The primary outcome was any SHS exposure, defined as exposure to tobacco smoke at home, school, enclosed public places, or outdoor public places. Weighted prevalence estimates were calculated, and multivariable logistic regression was used to identify factors associated with SHS exposure, adjusting for demographic, social, environmental, and socioeconomic variables. ResultsOverall, 66.0% of adolescents reported exposure to SHS. Adolescents living with a parent or guardian who smoked had nearly three times higher odds of SHS exposure (adjusted odds ratio [AOR] = 2.76; 95% CI: 2.12-3.62; p < 0.001). Having friends who smoked tobacco (AOR = 1.86; 95% CI: 1.52-2.30; p < 0.001) and seeing teachers smoking at school (AOR = 1.88; 95% CI: 1.40-2.56; p < 0.001) were also significant predictors. Media exposure was important: seeing people use tobacco on television (AOR = 1.88; 95% CI: 1.63-2.17; p < 0.001) and exposure to tobacco advertisements (AOR = 1.38; 95% CI: 1.14-1.67; p = 0.001) increased odds of SHS exposure. Adolescents who had smoked cigarettes had higher odds of exposure (AOR = 2.80; 95% CI: 1.70-4.67; p < 0.001), as did those intending to use tobacco in the next five years (AOR = 1.64; 95% CI: 1.21-2.24; p = 0.002). Age, sex, and grade level were not independently associated with SHS exposure. ConclusionsSHS exposure among adolescents in Zambia is widespread and is largely driven by household smoking, peer influence, school environments, and media exposure. Strengthening enforcement of smoke-free policies, promoting smoke-free homes, and addressing social and media influences are critical to reducing adolescent SHS exposure.

BackgroundAcute respiratory infection (ARI) remains a leading cause of morbidity and mortality among children under five in Nigeria. Although polluting cooking fuels are widely considered a key risk factor, their effects may be shaped by broader socioeconomic and geographic conditions. This study examined both individual and structural determinants of ARI and assessed how these factors intersect to pattern risk. MethodsWe analysed data from 28,728 children under five in the 2024 Nigeria Demographic and Health Survey. Three ARI definitions were applied. Survey-weighted quasibinomial logistic regression estimated associations between ARI and cooking fuel type, child age and sex, household wealth quintile, residence type, geopolitical zone, and parental education. To examine intersectional patterning, we conducted a Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA), constructing strata defined by combinations of cooking fuel, wealth, residence, and geopolitical zone. The intraclass correlation coefficient (ICC) quantified between-strata variance. ResultsStrict ARI prevalence was 1.9%, and 8.3% of children had broader respiratory symptoms. In unadjusted analyses, polluting fuel use was associated with higher odds of respiratory symptoms (OR 1.85, 95% CI 1.43-2.39). After adjustment, this association was substantially attenuated, indicating confounding by structural factors. Child age was the most consistent predictor: children aged 24-59 months had about half the odds of strict ARI compared with infants (aOR 0.53, 95% CI 0.41-0.68). Geopolitical zone showed the strongest overall association. MAIHDA revealed that 9% of total ARI variance lay between intersectional strata (ICC = 0.09), and this variance was not explained by child age or sex. The population-attributable fraction for polluting fuel declined from 41.4% to 12.4% after adjustment. ConclusionsARI risk among Nigerian children is shaped more by structural and geographic inequalities than by household fuel use alone. Equity-focused, subnational policies addressing intersecting socioeconomic and regional disadvantage are needed to reduce the ARI burden.

Antimicrobial resistance (AMR) is a growing problem, with annual deaths set to pass 10 million by 2050 if current trends continue. Wastewater surveillance has been proposed as a strategy to understand population-level resistance, and water reclamation facilities (WRFs) have been identified as a control point for environmental dissemination of resistant bacteria. Understanding dynamics of AMR across WRFs requires advanced molecular tools that elucidate host bacteria, especially for mobile resistance carried on plasmids. To that end, influent, activated sludge, and effluent were collected from three WRFs in North Carolina, Texas, and California during three weeks of Spring 2024. Samples were analyzed using Hi-C proximity ligation sequencing to identify the AMR host range for chromosomal and plasmid-based resistance. A total of 1,868 hits for 244 unique resistance genes were observed, with seven resistance genes identified in all samples. Resistance genes were more likely to be carried on a microbial plasmid in influent, but more likely to be in a chromosome in activated sludge. Seventeen total microbial hosts for resistance genes were identified in effluent, suggesting WRF effluents may be sources of resistant bacteria to receiving surface waters. A high proportion of all identified host relationships were confined to just four bacterial families. Hi-C contact mapping is a critical tool to more fully describe the AMR host range in complex matrices, particularly for plasmid-based resistance genes. ImportanceAntimicrobial resistance (AMR) threatens modern medicine. Water reclamation facilities receive a complex mixture of antibiotics and rely on active microbial communities for treatment, thereby acting as critical systems to prevent environmental spread of resistance. However, AMR dynamics are difficult to discern in complex wastewater environments due to antibiotic resistance genes (ARGs) being frequently carried on mobile pieces of DNA that are difficult to link to specific bacteria using conventional shotgun sequencing. Novel proximity ligation sample preparation techniques like Hi-C physically link co-located sequences of DNA before shotgun sequencing. This allows sequencing to elucidate the bacterial hosts for both stable and mobile ARGs. In the current study, Hi-C sequencing was carried out on influent, activated sludge, and effluent collected from water reclamation facilities in California, Texas, and North Carolina to assess the resistome host range across treatment. 5 Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/26346186v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@1e4620eorg.highwire.dtl.DTLVardef@e1c3a7org.highwire.dtl.DTLVardef@1f40964org.highwire.dtl.DTLVardef@94b886_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundProlonged wakefulness, restricted sleep, and circadian factors can impact driving performance and road safety. Currently, there are no effective objective roadside tests to detect the state of drivers sleepiness during or prior to driving, or predict future driving impairment risk. This paper reports on an extended wakefulness protocol used to determine if a portable virtual reality device to administer vestibular-ocular motor function (VOM) tests can effectively detect 1) drivers state of sleepiness during or just prior to driving, and 2) predict trait sleepiness and future driving risk. MethodsFifty healthy adults with regular sleep within 9pm to 8am were recruited for an experimental laboratory procedure which involved two phases: an initial overnight sleep study, and a subsequent period of extended wakefulness lasting ~29 hours. During the wakefulness phase, participants undertook neurobehavioural testing, a simulated driving test, and repeat assessments of VOM to establish if ocular markers can predict sleepiness state and sleepiness-related performance impairments (Trial registry ACTRN12621001610820). DiscussionThis protocol outlined a study that aimed to establish the sensitivity of VOM test the effects of extended wakefulness and circadian phase on driver state and trait sleepiness and subsequent sleepiness-related driving impairment. Furthermore, the protocol aims to define the best VOM predictors to identify driver sleepiness state (road side testing and pre-drive assessments) and sleepiness trait (predicting future driving risk) to establish proof of concept for its potential application as a roadside, pre-drive and general sleepiness related fitness to drive test.