Abstract: Background: Food insecurity is a modifiable social determinant that may accelerate cognitive decline in later life. However, longitudinal evidence is complicated by time-varying confounding, and limited research has examined whether Supplemental Nutrition Assistance Program (SNAP) participation modifies domain-specific cogni-tive outcomes. Objectives: To evaluate the longitudinal associations between food in-security and cognitive function using marginal structural models (MSMs), and to as-sess whether SNAP participation buffers these associations for total cognition, episodic memory, and mental status. Methods: Data came from 30,641 adults aged ≥50 in the 1998–2020 Health and Retirement Study, contributing 156,066 person-year observa-tions. Food insecurity and SNAP participation were assessed biennially. Stabilized in-verse probability of treatment weights accounted for time-varying socioeconomic, health, and cognitive confounding. Weighted pooled linear regression MSMs estimated the marginal effects of food insecurity, SNAP, and their interaction. Analyses were stratified by race/ethnicity. Results: In MSMs, both moderate and high food insecurity were associated with lower overall cognition (moderate: b = -0.36; high: b = -0.71; p< 0.001). Similar graded associations were observed for episodic memory (moderate: b = -0.22; high: b = -0.43; p< 0.001) and mental status (moderate: b = -0.15; high: b = -0.28); p< 0.001). SNAP participation significantly attenuated these associations, with positive interaction terms indicating substantial buffering effects. Race-stratified analyses showed consistent patterns across non-Hispanic White, non-Hispanic Black, and His-panic older adults, with particularly strong SNAP buffering among minority groups. Conclusions: The findings suggest that strengthening food assistance access may help reduce cognitive health disparities in aging populations.

Abstract: This study explores the spatial relationship between frequent physical distress (FPD) and socioeconomic as well as health-related factors across the contiguous United States. FPD, defined as having 14 or more physically unhealthy days within the past month, serves as an important measure of overall population health. While many studies have examined the causes of mental distress, research on the geographic variation and social context of physical distress remains limited. Using data from 2,673 U.S. counties, we analyzed how socioeconomic conditions and health indicators relate to FPD at both national and local levels. Ordinary Least Squares (OLS) multivariate regression model was first used to assess general associations, followed by Geographically Weighted Regression (GWR) and Multiscale Geographically Weighted Regression (MGWR) to identify spatially varying and scale-dependent relationships. Comparing the GWR and MGWR results revealed that several predictors of FPD operate at different spatial scales, reflecting local heterogeneity in health outcomes. Counties in the southeastern United States, particularly those with higher levels of socioeconomic disadvantage and poorer health conditions, showed elevated FPD rates. These findings highlight the importance of accounting for spatial context when addressing physical distress and suggest that locally tailored public health strategies may be more effective than uniform national approaches.

Abstract: According to the World Health Organization (WHO), Musculoskeletal Disorders (MSDs) are the most common occupational disease worldwide, frequently affecting agricultural workers due to the physical demands of their labor activities. In this context, a descriptive, relational, cross-sectional study was conducted in rural communities of Loja, Ecuador, with the aim of determining the prevalence of MSDs and their relationship with sociodemo-graphic and occupational factors among agricultural workers. The sample consisted of 103 farmers who completed the Standardized Nordic Questionnaire (NMQ). The results showed a high prevalence of MSDs in this population, with symptoms reported over the past 12 months, particularly in the neck, lower back, and knees. Furthermore, a statis-tically significant association was observed between the presence of MSDs and BMI. In conclusion, MSDs represent a frequent health problem among farmers, highlighting the need to implement preventive strategies and occupational health promotion programs in rural communities.

Abstract: Background: Iranian sexual and gender minorities face criminalization, surveillance, and stigma, and existing measures of belonging and resilience are fragmented.Methods: We conducted a theory-driven narrative review coupled with a structured secondary synthesis of published quantitative studies on Iranian sexual and gender minorities. We developed a five‑anchor analytic framework—Subjectivity, Groundedness, Reciprocity, Dynamism, and Self‑determination—mapped reported indicators (e.g., internalized stigma, depression, suicidality, policing/arrest risk, peer support, emotional inhibition) to anchors, extracted summary statistics as presented in primary studies, and produced anchor‑specific narrative syntheses. No new human participants were recruited.Findings: Evidence converged on compromised Subjectivity (consistent associations between internalized stigma and depression), fragile Groundedness (very high suicidal ideation in transgender samples), protective Reciprocity (peer support associated with counter‑normative action), and coercion‑shaped Dynamism (identity/expression‑linked arrest and harassment structuring visibility). Self‑determination reflected autonomy trade‑offs, including psychological costs of concealment and heightened emotional inhibition. The mapping validates and operationalizes the five‑anchor model.Conclusions: The framework offers an operational, ethically cautious template for quantifying belonging, risk, and resilience in criminalizing settings and can guide safe measurement, reporting, and service design for communities under repression and for diaspora services.

Abstract: Background: Health and social care workers (HSCWs) experience high levels of stress, burnout and emotional strain. Psychological safety is increasingly recognised as a protective factor, yet limited research has examined how individual psychological safety and team psychological safety jointly contribute to mental health outcomes in frontline care environments. Methods: A total of 821 HSCWs completed validated measures assessing individual psychological safety (NPSS), team psychological safety (TPSS), well-being, burnout and acute stress. Correlational analyses and hierarchical regression models were used to evaluate the unique and shared contributions of individual and team psychological safety to each outcome. Results: Both NPSS and TPSS were significantly associated with well-being, burnout and acute stress. Team psychological safety emerged as the strongest predictor of burnout and acute stress, accounting for substantial additional variance beyond individual psychological safety, with increases in explained variance ranging from .14 to .16. For well-being, NPSS (β = .38) and TPSS (β = .36) were both significant predictors. TPSS demonstrated large effects for burnout (β = .67) and acute stress (β = .72). Conclusions: Psychological safety plays a central role in the mental health of HSCWs. Team-based psychological safety was particularly influential in protecting against burnout and acute stress, while individual neuroceptive safety remained important for supporting overall well-being. Strengthening both individual and team-level psychological safety may enhance resilience and reduce psychological risk within health and social care settings. Implications: Incorporating NPSS and TPSS within workforce assessment may support early identification of psychological vulnerability, inform trauma-informed organisational interventions and promote more resilient, supportive workplace cultures.

Abstract:

Over the last 15 years, mixture risk assessment for food xenobiotics has evolved from conceptual discussions and simple screening tools, such as the Hazard Index (HI), towards operational, component-based and probabilistic frameworks embedded in major food-safety institutions. This review synthesizes methodological and regulatory advances in cumulative risk assessment for dietary “cocktails” of pesticides, contaminants and other xenobiotics, with a specific focus on food-relevant exposure scenarios. At the toxicological level, the field is now anchored in concentration/dose addition as the default model for similarly acting chemicals, supported by extensive experimental evidence that most environmental mixtures behave approximately dose-additively at low effect levels. Building on this paradigm, a portfolio of quantitative metrics has been developed to operationalize component-based mixture assessment: HI as a conservative screening anchor; Relative Potency Factors (RPF) and Toxic Equivalents (TEQ) to express doses within cumulative assessment groups; the Maximum Cumulative Ratio (MCR) to diagnose whether risk is dominated by one or several components; and the combined Margin of Exposure (MOET) as a point-of-departure–based integrator that avoids compounding uncertainty factors. Regulatory frameworks developed by EFSA, the U.S. EPA and FAO/WHO converge on tiered assessment schemes, biologically informed grouping of chemicals and dose addition as the default model for similarly acting substances, while differing in scope, data infrastructure and legal embedding. Implementation in food safety critically depends on robust exposure data streams. Total Diet Studies provide population-level, “as eaten” exposure estimates through harmonized food-list construction, home-style preparation and composite sampling, and are increasingly combined with conventional monitoring. In parallel, human biomonitoring quantifies internal exposure to diet-related xenobiotics such as PFAS, phthalates, bisphenols and mycotoxins, embedding mixture assessment within a dietary-exposome perspective. Across these developments, structured uncertainty analysis and decision-oriented communication have become indispensable. By integrating advances in toxicology, exposure science and regulatory practice, this review outlines a coherent, tiered and uncertainty-aware framework for assessing real-world dietary mixtures of xenobiotics, and identifies priorities for future work, including mechanistically and data-driven grouping strategies, expanded use of physiologically based pharmacokinetic modelling and refined mixture-sensitive indicators to support public-health decision-making.

Abstract: Background: Musculoskeletal disorders (MSDs) are highly prevalent among healthcare workers, particularly in nursing staff, where reported prevalence rates range from 57% to 93%. Perfusionists are highly specialized healthcare professionals responsible for operating heart-lung machines during cardiac surgery. To date, the prevalence of MSDs in this professional group has not been systematically investigated. To address this gap, perfusionists in Germany were surveyed about MSDs. Methods: The German version of the Nordic Musculoskeletal Questionnaire (NMQ) was used. Pearson correlation and chi-squared tests were applied. Results: 287 perfusionists (72 female, 215 male; age 42.6 ± 11.9 years, professional experience 13.5 ± 10.9 years) from 45 German cardiac centers participated. 86% reported MSDs, with the lower back (65.5%) and neck (58.9%) being most frequently affected, with 4.5% to 36.6% in the other body regions. Increasing age was significantly associated with higher prevalence in six body regions, more professional experience was connected in five regions. Occupational risk factors previously identified in nursing were found to be applicable in perfusion. Conclusion: This study demonstrates a high prevalence of MSDs among perfusion staff in Germany. The findings underscore the need for preventive measures, coping strategies, and further research to reduce work related musculoskeletal strain in perfusion staff.

Abstract: This is quantitative research that explored cause-and-effect predictions of mental health of college students based on advanced causal inference and machine learning classification methods. The study conducted a cross-sectional analysis of 101 university students, identifying depression, anxiety, and panic attacks prevalence based on the Student Mental Health dataset (Shariful07, 2020). This study used a two-fold analytic literature, wherein five causal inference approaches were used to predict the gender impacts on mental health outcomes adjusting for confounding factors, and three supervised learning algorithms were used to build predictive models. Findings indicated a prevalence rate of 34.7, 33.7, 32.7, and a high degree of comorbidity of mental conditions prevalence of depression, anxiety, and panic attacks respectively. Regression adjustment, Mantel-Haenszel stratification, direct standardization, propensity score, and instrumental variables all provided convergent estimates, and E-values showed that they were robust to unmeasured confounding. Machine learning models had a range of AUC-ROC of 0.52-0.71 with best results by XGBoost. The analysis of the importance of the feature revealed that marital status, age, and academic variables were the important predictors. This paper has shown that causal inference and machine learning are complementary in risk factor knowledge and prediction, respectively, and have implications in terms of early detection and intervention in university mental health services.

Abstract: This paper explores how Location-Based Games (LBGs), especially those with Augmented Reality (AR), can help people move more and spend less time just looking at screens. LBGs make walking or being active a part of the game, which could be useful for public health. I use three main sources: recent research, my own experience with LBGs, and informal interviews with adult players. I look at how well LBGs get people to move, what keeps them interested, and what problems they face. Results show that LBGs can increase motivation and activity at first, but people often lose interest over time. Safety and access are also issues. The paper ends with practical ideas to help LBGs work better and last longer.

Abstract: Background: Paramedics routinely operate in high-stress environments and endure long working hours, contributing to elevated occupational fatigue and increased injury risk. The study aimed to compare fatigue levels between injured and non-injured paramedics and examine associations between self-reported fatigue and self-reported injury among paramedics. Methods: An online survey of paramedics (n=22) assessed self-reported fatigue and injury history using the Occupational Fatigue Exhaustion Recovery Scale (OFERS) and targeted Likert-based questions, with scores transformed to quantify chronic fatigue, acute fatigue, and inter-shift recovery to identify point prevalence. Data were analysed to compare acute and chronic fatigue scores with injury status. Results: Higher chronic fatigue scores were significantly associated with self-reported injuries within the past six months. No significant differences were found in levels of acute fatigue, inter-shift recovery, or perceived fatigue on rest days between those who were and were not injured. Beliefs about fatigue-related injury risk were consistent across injured and non-injured groups. Conclusion: Chronic fatigue may be a key indicator of injury risk among paramedics. These findings highlight the need for proactive, system-level fatigue management strategies that extend beyond subjective measures and address chronic fatigue as a quantifiable operational risk.

Abstract: Neighborhood variations in depression, an important aspect of the overall mental health burden, have been linked both to environmental context (e.g. area crime, neighborhood cohesion), and to area socio-demographic composition. Previous models seeking to explain such spatial variations in mental health, such as those based on Bayesian disease mapping, follow a standard approach defined by: spatially stationary effects of area predictors; predictor effects neglecting potential spatial spillover; and a spatially structured residual to account for unmodelled spatial dependencies. In a study of depression incidence in England neighborhoods, we consider the gains from an alternative strategy, allowing nonstationary environmental impacts; spillover effects of environmental factors, and a non-stationary spatial intensity. We focus particularly on impacts of socio-behavioral environments, namely neighborhood cohesion and crime. We find these to be major influences on neighborhood depression incidence, and also find major gains in model performance by explicitly considering non-stationarity and spillovers. Allowing context heterogeneity, varying spatial intensity and spillover are shown to enhance the impacts of socio-behavioral environments on depression incidence, and such findings have broader relevance to disease mapping regression.

Abstract: Background/Objectives: Low physical activity (PA) is becoming an increasingly serious health problem among overweight school-age children. This study aimed to evaluate the influence of elevated PA during school hours on the nutritional behavior and fat mass of overweight and obese children. Methods: The study involved 11-year-old children (n=148) who were overweight and obese. In the control group, children received physical education lessons in the standard dimension (4 hours a week while the intervention group received 10 hours. Body mass index (BMI), fat mass (FM), and nutritional behavior were analyzed. Results: Compared to baseline, at the end of the intervention, the proportion of obese children increased in the control group and decreased in the intervention group. Regarding nutritional behavior, low consumption of vegetables, fruits, whole grains, poultry, and fish was observed. After a 12-month, the intervention group showed a slight decrease in the consumption of fruits, vegetables, cold cuts, fried dishes, sweets, and fast food, and an increase in the consumption of white bread, whole-grain bread, poultry, red meat, and dairy products (milk, yogurt). In the control group, children exhibited an average increase in the consumption of fruits, vegetables, poultry, red meat, sweets, and fast food Conclusions: An increase in physical activity during school hours was associated with a reduction in the number of obese children and improvements in BMI and FM. However, the intervention group demonstrated only a slight improvement in nutritional behavior.

Abstract: Volcanic eruptions can mobilize naturally occurring toxic elements such as arsenic into surrounding ecosystems, contaminating soil, water, and food webs. Despite increasing evidence of arsenic enrichment in volcanic regions, comprehensive exposure assessments that integrate dietary and drinking water pathways remain limited, particularly in post-eruption contexts where baseline data are scarce. Following the 2020 Taal Volcano eruption, this study conducted a probabilistic risk assessment to quantify aggregate exposure to inorganic arsenic (iAs) among residents of Batangas, Philippines. A Monte Carlo simulation framework (10,000 iterations) integrated post-eruption environmental data on total arsenic in soil, lake water, drinking water and clam tissues with modeled bioaccumulation and transfer factors for fish and major terrestrial crops. Two exposure scenarios, lower bound (50% iAs fraction) and upper bound (90% iAs fraction), were applied to capture uncertainty in arsenic speciation and bioavailability. Simulated iAs concentrations followed the order rice > corn > vegetables > root crops. Aggregate daily iAs doses averaged 3.0 ± 1.4 µg/kg bw/day (lower bound) and 4.0 ± 2.0 µg/kg bw/day (upper bound), with females showing slightly higher exposures and pregnant women exhibiting lower doses, likely reflecting cultural restrictions on seafood consumption during pregnancy. Sensitivity analysis identified clam intake, rice intake, and iAs in rice, clams, and drinking water as dominant determinants of total exposure. All simulated individuals exceeded the U.S. EPA non-cancer reference dose (HQ > 1) and cancer risk benchmark (10⁻⁶–10⁻⁴), indicating substantial health concern. These findings highlight the urgent need for sustained environmental monitoring, arsenic speciation analyses, biomonitoring, and public health programs to guide evidence-based management in arsenic-affected volcanic regions.

Abstract: Background: Obesity remains a major clinical and public-health challenge in the United States, contributing to rising cardiometabolic disease and long-term healthcare burden. The COVID-19 pandemic substantially altered physical activity, diet, and access to care, yet nationally representative anthropometric data from the post-pandemic period remain limited. Updated estimates are needed to characterize the current distribution of BMI and to quantify associated metabolic risk. Methods: This study analyzed NHANES 2021-2023 data using survey-weighted methods to generate nationally representative estimates of BMI, obesity prevalence, and demographic disparities. Measured height and weight were used to calculate BMI. Subgroup analyses were conducted by age, sex, and race/ethnicity. To assess clinical relevance, survey-weighted logistic regression evaluated the association between BMI category and diabetes (self-report or HbA1c ≥6.5%), adjusting for demographic and socioeconomic covariates. Results: The analytic sample included 6,337 adults. The nationally weighted mean BMI was 29.1 kg/m2, and obesity prevalence was 39.2%. Obesity was highest among adults aged 40–59 years and among non-Hispanic Black adults. In adjusted clinical models, obesity was strongly associated with diabetes (aOR 5.58; 95% CI 4.31-7.22), and overweight showed a modest elevation in odds (aOR 2.17; 95% CI 1.67-2.86). A multipanel survey-weighted BMI distribution figure illustrates overall and subgroup patterns. Conclusions: This work provides the first nationally representative post-pandemic assessment of BMI and obesity using the resumed NHANES 2021-2023 cycle, demonstrating substantial demographic disparities and clinically meaningful associations with diabetes. These findings emphasize continued population-level surveillance and highlight the need for targeted public health and clinical interventions.

Abstract: Background. Survival after childhood and adolescent and young adult (AYA) cancer has improved substantially; however, dominant survivorship models remain reac-tive—activated post-treatment and anchored to static, exposure- and organ-based screening. This design underuses the anticipatory window at diagnosis and overlooks environmental and social determinants that modulate outcomes across the life course. Methods. We narratively reviewed international frameworks (COG, IGHG, PanCare, NCCN) and synthesized evidence on environmental determinants, exposomics, toxi-cogenomics, and implementation. Building on two decades of real-world practice, we describe the evolution from the Pediatric Environmental History (PEHis) to the Ambiomic Health Compass (AHC), integrating genomic, exposomic, geospatial, clinical, and bio-monitoring layers into routine care. In this framework, survivorship is conceptualized as beginning at the time of cancer diagnosis (“day 0”). Results. PEHis operationalizes guideline-based care with structured environmental and social assessment, personalized plans, and community integration, contributing to improved survival, healthier be-haviors, reduced treatment-related mortality and stronger oncology–primary-care coor-dination. AHC extends PEHis with dynamic risk recalibration, contextual alerts, targeted biomonitoring, and toxicogenomic interpretation, enabling anticipatory decisions from day 0. The manuscript summarizes the paradigm shift (current vs. Ambiomic models), the domain-specific expansion over existing guidelines, the core clinical/system tools, and time-bound metrics (12, 24, 60 months) to support implementation and evaluation. Conclusions. Survivorship should move upstream—from late surveillance to ambiomic, exposure-aware care beginning at diagnosis. Integrating advanced exposomics, muta-tional epidemiology, and explainable analytics can reduce preventable events and chronicity, enhance equity, and align pediatric oncology with planetary health. The PEHis–AHC continuum offers a scalable blueprint for next-generation survivorship programs in Europe and beyond. Ambiomic medicine does not replace precision medi-cine—it completes and extends it by integrating exposomics, social context, and antici-patory analytics from day 0.

Abstract: Objectives: The investigators examined the prevalence of autism spectrum disorder (ASD) as related to the US military and as collected by the US Census Bureau’s National Survey of Children’s Health. Methods: Five questions asked of respondents allow for the inspection of ASD as it pertains to military insurance and active duty status (either past or present) of the parents. Results: Contemporary to the survey, Tricare (or other military insurance) covered children were 30.73% more likely to have an ASD diagnosis than their civilian counterparts. Children of mothers with any military service were 46.19% more likely to be diagnosed with ASD than children of non-military service mothers, with with the greatest portion diagnosed with severe autism (46.33% for mild ASD, 39.62% for moderate ASD, and 60.50% for severe ASD). Similarly, children of fathers with any military service were 42.74% more likely to be diagnosed with ASD than children of non-military service fathers, with increasing severity (37.37% for mild ASD, 45.64% for moderate ASD, and 82.73% for severe ASD). Conclusion: The severity of the results warrant an investigation, with all due haste, with domain-specific US military data that link mother-child and father-child medical records and include suspected or shared toxic exposure of either mother or father and their children.

Abstract: Allergic rhinitis (AR) is a widespread allergic reaction that has been shown to be impacted by the function of the immune system as well as environment and socioeconomic factors. This research is to explore the best predictive model among penalized logistic regression, random forest classifier, and XGBoost classifier, to gain insight into subjects who are susceptible to allergic rhinitis by taking advantage of the integrated data NHANES provides. The random forest model demonstrated the most stable performance. SHAP analysis provided interpretable insights at both group and individual levels, revealing that immune-related markers, including total IgE, eosinophil percentage, and the neutrophil-to-lymphocyte ratio were the strongest predictors of AR susceptibility. Environmental and socioeconomic exposures, such as cotinine levels, housing conditions, and income, also contributed substantially to the predicted risk. Overall, the findings highlighted that AR susceptibility arises from the combined influence of immunologic dysregulation and environmental stressors, underscoring the need for targeted preventive strategies.

Abstract: We present the Universal Suffering Unit (USU), a calibrated, additive unit of experienced suffering that scales with intensity, duration, and affected population, enabling aggregation across people, regions or countries, and time. We define USU as k * sum I^p * Δt, where I is a 0–10 intensity rating, p >= 1 is an exponent that modestly up-weights high intensities, and k is a calibration constant. We calibrate the unit so that a reference trajectory of renal colic (kidney-stone pain) equals 1.0 USU, propose a simple rule for co-occurring harms, and recommend reporting medians with 90% uncertainty intervals from Monte Carlo simulations. Using publicly available data, we illustrate the framework with two examples: dengue in Brazil (epidemiological weeks 1–23 of 2024) and flood-related displacement in Rio Grande do Sul, plus a year-over-year dengue comparison and a sensitivity analysis over p in {1.0, 1.25, 1.5}. These illustrations show how large numbers of moderate episodes and smaller numbers of longer, disruptive episodes can be expressed on a common experiential scale, while remaining interpretable via an anchor ladder. We discuss validation strategies, highlight ethical guardrails and misuse risks, and argue that USU is best used alongside DALY/QALY and routine operational indicators as a decision-support tool for comparing heterogeneous harms, rather than as a stand-alone welfare metric.

Abstract: Preliminary evidence suggests air pollution, particularly fine particulate matter 2.5 (PM2.5), poses a significant threat to maternal health and women of reproductive age. While emerging evidence suggests a link between air pollution and maternal anemia, the specific effect of PM2.5 exposure on hemoglobin levels among reproductive-aged women (15-49 years) remains insufficiently studied. Maternal hemoglobin decline is a known risk factor for adverse pregnancy outcomes with potentially long-term consequences. Understanding the impact of PM2.5 exposure is crucial in regions like Sub-Saharan Africa, where both anemia rates and air pollution levels are significantly elevated. This population-based study investigates the association between ambient PM2.5 concentrations and maternal hemoglobin levels across 43 Sub-Saharan African countries from 2000-2019. Using generalized linear regression models adjusted statistically significant negative association between PM2.5 exposure and hemoglobin levels were observed in Central Africa, while no significant associations were found in Eastern, Western, or Southern Africa. These results suggest that PM2.5 may be an environmental determinant of maternal anemia, with effects that vary by geography. Further research is needed in understudied regions to validate and expand on these findings.

Abstract: Anemia is a major public health concern in South Asia, a region where febrile illnesses and nutritional deficiencies are prevalent. While the environment is a key contributor to maternal anemia, there is limited availability of research on the association between ambient temperature and maternal anemia. This longitudinal population-based study with multiple covariates, utilizing population-level data, examines the relationship between climate change and maternal anemia over the period of 2010 to 2022. Using a negative binomial regression model adjusted for covariates, we modeled the region-specific (n = 8 nations) relationships between annual anemia prevalence in women of reproductive age (15-49 years old) and annual maximum temperatures. After adjustment, the statistical analysis revealed distinct regional differences, with an overall statistically significant but weak association between maternal anemia and maximum temperature. These findings underscore ambient temperature as a determinant of anemia risk in women of reproductive age in South Asia. Further research with larger datasets is needed to clarify causal mechanisms and strengthen evidence for developing climate-specific strategies to address the temperature-related anemia risk. Climate-centered approaches would reduce the large-scale impact of climate-related diseases and improve overall reproductive health.