Environment International

Background: Endocrine-disrupting chemicals (EDCs) in consumer products are ubiquitously detected in human biospecimens, yet most epidemiological studies examine single chemicals rather than real-world co-exposures. We evaluated associations between a mixture of seven urinary chemical biomarkers and systemic inflammation. Methods: Survey-weighted log-log regression models adjusted for age, sex, race/ethnicity, poverty-income ratio, and survey cycle were conducted with Benjamini-Hochberg FDR correction (primary analysis, N=4,864). A sensitivity analysis additionally adjusted for body mass index and smoking status (N=4,494). Results: In the primary analysis, 5 of 7 chemicals showed significant associations after FDR correction: ethylparaben ({beta} = -0.056, FDR P < .001), propylparaben ({beta} = -0.026, FDR P = .007), bisphenol A ({beta} = +0.052, FDR P = .005), monoethyl phthalate ({beta} = +0.043, FDR P = .002), and monocyclohexyl phthalate ({beta} = +0.215, FDR P = .007). The WQS mixture index was significantly associated with CRP ({beta} = +0.056, 95% CI [0.031, 0.081], P < .001), with monocyclohexyl phthalate carrying the largest mixture weight (0.342). In the BMI- and smoking-adjusted sensitivity analysis, associations attenuated to null for all chemicals, though MCP preserved direction ({beta} = +0.129) and the WQS mixture direction was maintained ({beta} = +0.018). Two multiple imputation sensitivity analyses confirmed that monocyclohexyl phthalate was the only chemical to maintain a positive direction across all four analytical specifications (primary complete-case, BMI-adjusted complete-case, primary-aligned imputation, and BMI-adjusted imputation), reaching statistical significance in three of four specifications and providing convergent evidence of a robust MCP-inflammation association. Conclusions: The chemical mixture showed a significant collective association with systemic inflammation, consistent with a cumulative pro-inflammatory burden from co-exposure to multiple consumer product chemicals. These findings suggest that regulatory approaches should shift from single-chemical to mixture-based risk assessment frameworks for consumer product safety.

Background: In the absence of high-resolution response data, exposure-response modelling often relies on aggregated low-frequency exposure data, leading to loss of high-resolution information. Mixed Data Sampling (MIDAS) from econometrics offers an alternative but is limited due to its inability to make high-resolution predictions, inflexible likelihoods and penalised nonlinear functions, and limited visualization options. We propose a mixed-frequency Distributed Lag Non-linear Model (mf-DLNM) which can eliminate the need to aggregate exposure data in environmental epidemiology and provide high resolution predictions for time series studies. Methods: We evaluated the inference and predictive performance of the mf-DLNM. To evaluate its ability to estimate exposure-response relationships, we applied mf-DLNM and same-frequency (sf)-DLNM using data from the West Midlands, UK. Additionally, we compared the predictive performance of mf-DLNM with sf-DLNM and MIDAS across nine regions of England. As MIDAS cannot predict at the resolution of the predictor (daily), we compared the predictive performance of mf-DLNM and MIDAS at weekly resolution. To test the model's ability to predict high temporal resolution risk (daily), we compared sf-DLNM (with access to daily mortality counts) with mf-DLNM (with access only to weekly mortality counts). Results: In the West Midlands example, mf-DLNM performed comparably to sf-DLNM in estimating daily risk of temperature on respiratory mortality. Furthermore, mf-DLNM and MIDAS exhibited similar performance for weekly predictions. For high-resolution predictions, mf-DLNM and sf-DLNM showed nearly similar performance, despite mf-DLNM having access only to low-resolution response data. Conclusion: This mixed-frequency approach in environmental epidemiology overcomes the limitations of predicting health risks using aggregated exposure data and provides estimates of high-resolution outcomes in the absence of high-frequency health outcome datasets.

Mass gatherings pose significant public health risks by facilitating the spread of infectious diseases. While wastewater-based surveillance (WBS) has been widely used to monitor pathogens in high-income settings, its use as a practical, multi-pathogen surveillance tool during mass gatherings in low- and middle-income countries remains limited. This study aimed to assess the operational feasibility, epidemiological significance, and public health utility of multi-pathogen WBS during the African Nations Championship (CHAN) football tournament in Uganda. Wastewater surveillance was conducted at Mandela National Stadium during eight match days in August 2025. Moore swabs were deployed at 38 manholes receiving wastewater from different toilet facilities across the stadium to capture representative wastewater samples. Samples were processed using Nanotrap(R) microbiome virus particles to concentrate pathogens, followed by nucleic acid extraction. Samples were analyzed for multiple enteric and respiratory pathogens, including Mpox, using quantitative PCR (qPCR). Descriptive analyses were performed to characterize pathogen detection patterns, positivity rates, and temporal distribution across surveillance sites. A total of 304 wastewater samples were collected and analyzed, of which 259 (85.2%) tested positive for at least one pathogen. Multiple pathogens were consistently detected across sampling days, with enteric pathogens predominating, particularly Shigella spp. (53.6%), Rotavirus A (35.9%) and Enterovirus (32.2%). The mpox virus was also detected in a notable proportion of samples (28.6%) across several sampling days. Respiratory pathogens, including SARS-CoV-2 (11.8%) and Influenza B (8.2%), were identified intermittently at lower frequencies. Pathogen diversity varied over time, with up to eight pathogens detected on a single day, and co-detection of multiple pathogens observed in the majority of positive samples. Cq value distributions further demonstrated variability in detected signal patterns across pathogens. Surveillance findings informed real-time public health interventions, including sanitation reinforcement, intensified hygiene promotion, environmental disinfection, and targeted risk communication, strengthened syndromic surveillance with on-site triage, and targeted environmental health assessments of food handling and wastewater infrastructure. These findings demonstrate the operational feasibility and public health utility of integrating multi-pathogen wastewater-based surveillance into mass-gathering preparedness and response frameworks in low-resource settings. By capturing diverse pathogen signals and informing targeted interventions during the CHAN football tournament, WBS can provide actionable population-level insights that can support outbreak preparedness and response. Scaling WBS within national preparedness systems could strengthen epidemic intelligence, enhance early warning capacity, and support data-driven public health decision-making during future mass gatherings and emerging infectious disease threats.

Background. Tobacco smoke exposure, quantified by serum cotinine, is associated with cardiovascular, metabolic, and sleep-related health risks. The relationship between biomarker-verified tobacco smoke exposure and objectively measured, free-living wrist-worn ambient light patterns has not been examined in a nationally representative U.S. adult sample. Methods. We analyzed NHANES 2011-2014 cross-sectional data from 6,937 adults aged >20 years with valid serum cotinine and wrist-worn Physical Activity Monitor (PAM) ambient light data. Seven light outcomes were modeled using survey-weighted linear regression with log2(cotinine+1) as the continuous exposure across four covariate adjustment levels. Benjamini-Hochberg false discovery rate (FDR) correction was applied across the 7 outcomes within each model. Results. In Model 2 (adjusted for age, sex, race/ethnicity, education, poverty-income ratio, BMI, and survey cycle; N = 6,350), higher serum cotinine was associated with significantly higher nighttime light (beta = +0.024, 95% CI: 0.010, 0.038; p-FDR = 0.014) and lower evening light (beta = -0.031, 95% CI: -0.055, -0.008; p-FDR = 0.042). In exploratory behavioral models without alcohol (Model 3a; N = 5,766), both nighttime and evening associations remained FDR-significant. After additional adjustment for alcohol, which substantially reduced the sample due to 37.6% missingness (Model 3b; N = 3,866), the nighttime association attenuated below the FDR threshold, while the evening association remained FDR-significant. Categorical analyses showed progressively higher nighttime light across cotinine groups, and a hypothesis-generating sex interaction was identified (p-interaction = 0.001). Conclusions. Higher serum cotinine concentrations were associated with higher nighttime and lower evening ambient light after sociodemographic adjustment. Attenuation after behavioral adjustment and the cross-sectional design preclude causal inference. Longitudinal studies with formal mediation analyses are needed to clarify the temporal ordering and mechanisms linking tobacco smoke exposure, smoking-related behaviors, and personal light-dark cycle patterns.

To understand the utility of healthcare facility-level wastewater surveillance (WWS) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), it is important to correlate wastewater SARS-CoV-2 RNA detection with the number of clinical infections. WWS for SARS-CoV-2 was performed at three skilled nursing facilities (SNFs) over 25 weeks. Electronegative membrane filtration (enMF) and Nanotrap(R) Magnetic Virus Particles (NP) virus concentration methods were compared. Extracts were tested by droplet digital polymerase chain reaction. Spearman's correlations ({rho}) between wastewater virus RNA concentrations and infection counts were calculated. From split wastewater samples, enMF recovered higher SARS-CoV-2 RNA concentrations than NP. Combining data from all facilities, the median concentrations were 53.0 versus 38.6 gc/100 mL for enMF and NP, respectively (p=0.001). Using enMF, correlations were moderate to strong at SNF A ({rho} ranged 0.67 to 0.86, all p-values <0.001). Weak to moderate correlations can be explained by the sampled manhole not representing the entire facility (SNF B, {rho} ranged 0.47 to 0.72, p-values ranged <0.001 to 0.12) and longitudinal data gaps from summer heat and equipment maintenance (SNF C, {rho} ranged 0.14 to 0.59, p-values ranged 0.52 to <0.01). WWS can be a valuable tool for tracking dynamics of SARS-CoV-2 infections in healthcare facilities.

To explore the safety of combined use of lidocaine/prilocaine aerosol and condoms of different materials, this study conducted compatibility tests between them. By observing changes in various physical properties of condom materials after exposure to the aerosol, the compatibility of different polymer materials with the aerosol was analyzed.The results showed that within 15 minutes of exposure to the aerosol, there was no significant difference in all physical properties of natural rubber latex condoms compared with the blank control group (P>0.05), indicating they can be used together. In contrast, obvious changes in physical properties of polyurethane condoms occurred within 5 minutes of exposure (P<0.05), and their performances failed to meet industrial application standards, so combined use is strictly prohibited.This study clarifies the compatibility differences between two mainstream condom materials and lidocaine/prilocaine aerosol, providing experimental evidence and theoretical references for rational matching in clinical and daily use as well as avoiding potential safety risks.

Background Cardiovascular-kidney-metabolic (CKM) syndrome integrates adiposity, metabolic risk, kidney dysfunction, and cardiovascular disease in a prevention-oriented framework. National estimates across 1999-2023 NHANES and future burden remain limited. Methods We analyzed US adults aged 20 years from 11 NHANES cycles, 1999-2000 through August 2021-August 2023. CKM stage 0-4 was assigned using harmonized examination, laboratory, medication, and questionnaire data. Prevalence was survey-weighted and standardized to the 2010 US Census adult population. Decade trends used survey-weighted logistic regression adjusted for age, sex, and race and ethnicity. Exploratory 2040 and 2050 projections combined NHANES prevalence models with US Census projections under population-aging-only, trend-continuation, and risk-improvement scenarios. Results Among 62,890 eligible adults, 62,888 had sufficient CKM data. In 2021-2023, age-standardized prevalence was 87.9% (95% CI, 86.5%-89.4%) for CKM stage 1 and 62.0% (95% CI, 60.1%-63.8%) for stages 2-4. Stage 2 accounted for 50.1% (95% CI, 48.2%-51.9%) and stages 3-4 for 11.9% (95% CI, 11.0%-12.7%). From 1999-2000 to 2021-2023, any CKM increased by 4.6 percentage points (95% CI, 2.4 to 6.9; P<0.001), whereas stages 2-4 changed by 2.1 percentage points (95% CI, 5.1 to 0.8; P=0.156). In adjusted decade models, any CKM increased (OR, 1.28; 95% CI, 1.19-1.38; P<0.001), while stages 2-4 showed no significant linear trend (OR, 0.95; 95% CI, 0.89-1.01; P=0.084). Excess adiposity and diabetes increased, dyslipidemia declined, and hypertension, chronic kidney disease, and clinical cardiovascular disease were stable. With population aging alone, projected stages 2-4 burden rose from 164.8 million adults in 2023 to 193.7 million in 2050; under risk improvement, it was 147.7 million. Conclusions CKM syndrome remained highly prevalent among US adults. Although later stages did not increase significantly, population aging may expand the absolute care burden unless broad risk improvement occurs.

While microbiome is increasingly recognized as crucial for human health, translating this knowledge into effective healthcare and preventive strategies remains challenging. Many studies focus on identifying changes in microbiome composition associated with disease and evaluating the potential of such disease-associated microbial profiles as biomarkers for disease diagnosis. Under the hypothesis that microbiome dysbiosis may reflect physiological alterations present long before disease onset, in this work, we analyse the potential of disease-specific microbial signatures not as a diagnostic tool when the disease is already present, but as a means of health assessment in the general population. Moreover, instead of trying to define a single health measure, we believe it is necessary to consider several ways in which the microbiome departs from health, according to different disease-related physiological changes. To evaluate our assumptions, we designed a two-stage study: the identification of disease-specific microbial signatures (discovery stage) and, subsequently, the study of their distribution in the general population to assess associations with general health (external validation stage). Specifically, in the discovery phase we characterized 16 disease-specific bacterial signatures from large public microbiome data using a compositional data analysis methodology. In the second phase, we quantified these microbial signatures in the Lifelines-DMP cohort, a large population-based cohort, and evaluated their association with self-reported health status. Results indicate that most disease-specific microbial signatures associate with health status, supporting our assumption that microbial composition can capture physiological alterations before disease onset, and highlighting the importance of considering multiple ways in which microbiome departs from a healthy state. These findings reaffirm the potential of microbial information as an additional tool in preventive medicine.

While significant progress has been made in perinatal outcomes over recent decades in low- and middle-income countries (LMICs), maternal and newborn quality improvement initiatives often fail to account for the spatial conditions in which they are implemented. Health systems are increasingly deploying evidence-based care models into built environments that are not optimally structured to meet the needs of its patient population. As the principal users, patients and health care workers can offer pragmatic insights about improving these structural designs. Our objective was to gather insights from patients, providers, and companions about how the physical design of their health facilities influenced their experience receiving or delivering perinatal care. We conducted a prospective observational study using a human-centred design (HCD) approach to analyse perceptions of the quality of perinatal care across two low resource settings: Ethiopia and Bangladesh. Using engagement and assessment tools, we conducted interviews, focus groups, facility walk-throughs, co-design workshops, and infrastructural assessments with patients, companions, providers, and Ministry of Health representatives. Descriptive statistics and thematic analysis were used to identify key learnings and develop recommendations. Across both countries, participants identified the need for facility layouts that better support privacy, mobility during labour, alternative birth positions, companion involvement, cultural and religious practices, sanitation, and provider visibility. Based on these insights, we developed six recommendations to better align health facility infrastructure with maternal and newborn care delivery needs. Our findings suggest that investments in health facility infrastructure may improve care experiences and help enable respectful, safe, and evidence-based maternal and newborn care. Alongside targeted spatial improvements, government authorities responsible for health facility planning should incorporate participatory design processes to ensure infrastructure reflects the needs of patients, companions, and providers and supports high-quality care delivery.

Mosquito-borne diseases continue to pose significant public health challenges worldwide, particularly in densely populated regions of South Asia and parts of North America experiencing increasing vector prevalence due to climate and environmental changes. Commercial mosquito repellents are widely used as a primary preventive measure; however, their efficacy, safety, and public health impacts vary depending on formulation, active ingredients, environmental conditions, and user practices. This study presents a comparative evaluation of commonly used mosquito repellent products in South Asia and North America, including coils, vaporizers, sprays, creams, and Natural repellents. The research aims to assess repellent efficacy against major mosquito vectors, evaluate potential health and respiratory effects associated with prolonged exposure, and analyze consumer awareness and usage patterns across different regions. Laboratory-based efficacy testing and field observations were conducted to compare protection duration, repellency rate, and environmental performance under varying climatic conditions. Safety assessments included analysis of chemical composition, indoor air quality impact, and reported adverse health symptoms among users. The findings indicate significant differences in effectiveness and safety profiles among product categories and geographical regions. Synthetic repellents generally demonstrated higher repellency duration, while herbal formulations showed improved safety and environmental compatibility. The study highlights the importance of standardized evaluation protocols, regulatory oversight, and public awareness in promoting safe and effective mosquito control strategies. These findings may support policymakers, healthcare professionals, and manufacturers in improving mosquito repellent technologies and reducing the burden of mosquito-borne diseases globally.

Introduction: Blackpool, England's most deprived local authority, has the highest drug-related death rate in the country. People in police custody with problem substance use are a key Core20PLUS5 inclusion-health group, yet referral from the police into structured drug and alcohol treatment is fragmented and relies heavily on self-report. We evaluated the current police-to-treatment route in Blackpool and designed an evidence-informed unified pathway. Materials and Methods: A mixed-methods service evaluation and pathway-design project was conducted during a six-month General Practice / Public Health rotation. Routinely collected referral data from Horizon (the local specialist drug and alcohol service) covering the 47-month period from December 2019 to October 2023 were analysed. Findings were triangulated with national policy, the Project ADDER and Liaison and Diversion evaluations, and the international evidence on police-led pre-arrest diversion. Results: Of 5,900 total referrals into Horizon over 47 months, only 269 (4.56%) originated from the police. Police referrals accounted for fewer than 5% of monthly referrals in 30 of 47 months, for 5 to 9.9% in 16 months, and for >/= 10% in only one month (10.8%, December 2022). Blackpool recorded 76 drug-misuse deaths in 2019-21 (19.4 per 100,000, approximately four times the England rate). A six-step unified pathway is proposed: Initiate Referral (opt-out, from ADDER Police and Liaison and Diversion); Initial Assessment; Tailored Treatment Plan; Continuous Support; Collaboration and Monitoring; and Evaluation and Adjustment. Conclusions: Police contact is markedly under-used as a gateway to treatment despite Blackpool having the highest drug-related mortality in England. An opt-out, multi-agency pathway anchored in Core20PLUS5 has the potential to narrow the treatment gap, reduce re-offending, and address the structural health inequalities that drive premature mortality.

Essential medicines are one of the cornerstones of financial protection and health equity. The REMEDIAR Program is an initiative of the Argentine Ministry of Health aimed at ensuring free access to essential medicines for the uninsured at the point of care in primary healthcare centers (PHC). This study analyzes the financing, procurement, and distribution of this program over two decades (2002 to 2024). It evaluates how the program's capacity to navigate economic and political challenges ensured an uninterrupted supply of essential drugs at the primary healthcare level in a federal country where health services are devolved to provinces. We adopted a mixed-methods approach to examine the duality between international concessional loans and domestic treasury funding. Findings reveal that while international financing enhanced predictability and efficiency, reducing procurement timelines from 458 to 235 days, it also constrained domestic planning through external conditionalities. Conversely, while national centralized procurement achieved superior price efficiency and lower dispersion, it faced rigidities in adapting to local needs. Territorial distribution analysis confirms that REMEDIAR reduced access barriers for vulnerable households without formal insurance. However, the program entered a stabilization phase, failing to consolidate robust coordination with subnational policies, becoming entrenched in its own operational logic. The study concludes that program effectiveness depends not only on resource volume but on management quality. To guarantee long-term sustainability, transition to national financing requires profound institutional redesign. This must integrate operational capacities with federal coordination and domestic regulations, ensuring that the primary healthcare supply chain remains resilient to macroeconomic volatility and political shifts, aligned with sub-national strategies.

During the COVID-19 pandemic, limited testing capacity and reporting delays complicated epidemic surveillance and decision-making in Mexico. We calibrated \textit{covidestim}, a Bayesian nowcasting model, to estimate the total SARS-CoV-2 infections from reported cases and deaths using Mexican surveillance data. Disease-progression distribution priors were calibrated using Mexico City records and validated through comparisons with national seroprevalence surveys, hospitalization data, and annual reported severe-case rates across all states. Using the reconstructed estimates of active infections, we implemented an event-based risk framework that quantifies the probability of encountering at least one infectious individual in gatherings of different sizes. This probability was subsequently translated into a four-level epidemiological traffic-light indicator and computed at both state and municipality levels. The resulting estimates revealed substantial spatial heterogeneity that is obscured by state-level aggregation, particularly in states with marked differences between urban and rural municipalities. To evaluate consistency with public-health indicators, we compared the proposed risk classification with the official Mexican epidemiological traffic-light system, considering interpretable gathering sizes relevant to public-health decision making. Weekly reports derived from this framework were delivered to policymakers in the State of Queretaro in Mexico, as an anticipation tool for school reopening and public-space management. This demonstrates that this Bayesian reconstruction of infections combined with event-based risk metrics can provide an interpretable and generalizable municipality-level complement to routine surveillance systems, particularly in regions with limited testing capacity and heterogeneous local transmission dynamics.

Background: Pregnancy loss has important implications for womens health. Although maternal age is a well-established risk factor, the contribution of routinely measured cardiometabolic and behavioral markers at population-scale remains incompletely characterized. Objective: To examine associations between cardiometabolic, nutritional, and behavioral risk markers and pregnancy loss among U.S. women of reproductive age. Methods: We conducted a cross-sectional analysis of 4,842 U.S. women aged 20-44 years with [&ge;]1 pregnancy using the National Health and Nutrition Examination Survey data (2013-2023). Pregnancy loss was defined as [&ge;]1 prior miscarriages. Exposures included body mass index, smoking exposure (cotinine), lipid biomarkers, vitamin D and folate, and a composite cardiometabolic-nutritional risk score. Survey-weighted logistic regression estimated adjusted odds ratios (aORs) and 95% confidence intervals, with bootstrap resampling for predictor robustness. Results: The weighted prevalence of pregnancy loss was 23%. Higher odds of pregnancy loss were associated with increasing age (aOR per year=1.02; 95% CI: 1.00-1.04), Non-Hispanic Black race (aOR=1.32; 95% CI: 1.00-1.74), overweight (aOR=1.56; 95% CI: 1.16-2.11), obesity (aOR=2.06; 95% CI: 1.39-3.05), and smoking (aOR=1.58; 95% CI: 1.19-2.10). Adverse lipid profiles, particularly elevated triglycerides (aOR=1.83; 95% CI: 1.16-2.90) and high low-density lipoprotein (aOR=2.97; 95% CI: 1.45-6.61), were independently associated with pregnancy loss. Vitamin D/folate were not stable predictors. Higher composite cardiometabolic-nutritional risk scores were observed among women with pregnancy loss (P=0.026). Conclusion: Pregnancy loss clustered with adverse cardiometabolic and behavioral risk markers in a nationally representative population. These findings highlight pregnancy loss as a marker of broader metabolic vulnerability supporting the need for longitudinal studies and cardiometabolic profiling to inform preconception care and risk stratification.

The human small intestine (SI) plays a central role in nutrient processing, host-microbe interactions, and immune regulation, yet remains poorly characterized due to the lack of minimally disruptive sampling methods. Here, we present a protocol for deploying, recovering, and analyzing samples collected using an ingestible device that enables multi-region, lumen-targeted SI sampling during normal digestion. The device incorporates a ~30-cm collapsible tube wound into pH- or time-responsive layers that sequentially unfurl in situ, typically capturing three spatially ordered samples with high yield and reliable retrieval. This protocol outlines study design, participant handling, device recovery, contamination control, and standardized workflows for analyses, including cell quantification, culturomics, sequencing, and metabolomics. We further describe benchmarking approaches for evaluating spatial resolution and strategies for assay prioritization when sample volume is limiting. By reducing participant burden and facilitating integration with stool, saliva, and clinical metadata, this approach enables longitudinal and large-cohort studies linking SI microbial ecology and host physiology to human health.

This cross-sectional study aimed to examine the prevalence of alcohol use and its sociodemographic correlates among adults with cardiovascular disease (CVD). We analyzed data from two large US cohorts: the All of Us Research Program (2017-2023) and the National Health and Nutrition Examination Survey (NHANES, 1999-2016). Both CVD diagnosis and past-year alcohol consumption were self-reported. Risky drinking was defined as exceeding moderate drinking or binge drinking (All of Us), or moderate/heavy drinking (NHANES). Multivariable logistic regression was used to exam associations with sociodemographic and lifestyle factors. Among 32,788 current drinkers with CVD in the All of Us cohort, 15% exceeded moderate drinking thresholds and 26% reported binge drinking. Older age, female sex, and higher socioeconomic status were inversely associated with risky drinking, while smoking was positively associated. In NHANES, moderate drinking rose from 47.3% to 57.2% and heavy drinking from 6.7% to 7.2%. Moderate/heavy drinking was positively associated with age <65 but inversely with age [&ge;]65. Higher education and income were linked to moderate drinking, while current smoking was strongly associated with heavy drinking. These results highlight the need to integrate holistic screening for alcohol use, tobacco use, and social context into routine cardiovascular care.

Women's disproportionate responsibility for unpaid domestic and care work, including water collection, remains a barrier to gender equality globally and may constrain women's ability to engage in income-generating activities. We compared women's and men's time use in rural Kenya and Honduras and assessed whether women's time spent on water collection and income-generating activities differed between communities that had or had not received an improved water source from World Vision. We also examined the measurement of time-use agency among women and men. In-person surveys were conducted in July-August 2024 with 95 participants (48 women, 47 men) in six Kenyan communities and 102 participants (53 women, 49 men) in six Honduran communities. Surveys included a 24-hour time-use recall module and items on time-use agency. Analyses compared time use by gender and by community intervention status (improved vs. not yet improved water supply), and confirmatory factor analysis assessed the validity of the time-use agency measure. Women in both study sites spent substantially more time than men on unpaid domestic and care work activities, including cooking, cleaning, laundry, and caregiving. In Kenya, women also spent significantly more time collecting water. Men spent more time sleeping (Kenya), on paid work (Honduras), unpaid agricultural work (both settings), and traveling (both settings). Across both countries, there were no significant differences between intervention and comparison communities in women's time spent on water collection or income-generating activities. In Kenya, most respondents reported high influence over their time, and six items showed strong validity for measuring instrumental time-use agency. Women's time burdens remained high even in communities that had received improved water sources, including at the household level. Our results suggest that more transformative water infrastructure, combined with interventions that address gendered social norms, may be needed to meaningfully reduce women's domestic work burden and support their economic empowerment.

Introduction: Women aged 45-49 occupy a heterogeneous late-reproductive-life stage, but population research often treats them as a uniform group. This study examined correlates of Demographic and Health Survey (DHS)-defined infecund/menopausal status among Nigerian women aged 45-49. Methods: This cross-sectional secondary analysis used the 2024 Nigeria Demographic and Health Survey Women Recode dataset. Weighted descriptive statistics summarised reproductive exposure status among 3,237 women. Out of these, 3,110 women classified as either fecund or infecund/menopausal were subjected to Survey-adjusted Chi-square tests and Binary Logistic regression at p<0.05, where pregnant and postpartum amenorrhoeic women were excluded. Results: More than half of women were classified as infecund/menopausal (54.1%), while 41.5% were fecund; 3.2% were postpartum amenorrhoeic, and 1.3% were pregnant. Findings indicated that currently married/cohabiting women (AOR=4.87; 95% CI: 2.24-10.56) and formerly married women (AOR=8.30; 95% CI: 3.69-18.66) had higher odds of infecund/menopausal classification than women never in a union. Secondary education, higher education, middle-to-richest wealth quintiles, and five or more children ever born were associated with lower odds, while Northern minority ethnicity was associated with higher odds. Adding the current contraceptive method attenuated several education, wealth and parity associations; modern-method and traditional-method users had markedly lower odds than non-users. Conclusion: Late-reproductive-life exposure status among Nigerian women aged 45-49 is socially patterned, with union status showing the most stable association. DHS-defined infecund/menopausal status is a demographic exposure category rather than clinically confirmed menopause. It is therefore concluded that the cross-sectional associations should not be interpreted causally.

Childhood diarrheal disease remains a leading cause of morbidity and mortality among children under five years in sub-Saharan Africa, particularly in settings affected by inadequate sanitation, climate variability, malnutrition, and limited healthcare access. Conventional forecasting approaches are often constrained by sparse surveillance data, weak spatial representation, and limited incorporation of mechanistic disease dynamics. This study presents a Physics-Informed Multimodal Artificial Intelligence Digital Twin framework that integrates Physics-Informed Neural Networks, Graph Neural Networks, diffusion-reaction epidemiological modeling, multimodal fusion learning, and Digital Twin simulation to estimate and predict childhood diarrheal disease burden in Kenya, Somaliland, and Zimbabwe. Using public epidemiological, environmental, climate, sanitation, and synthetic proof-of-concept datasets, the framework modeled temporal disease dynamics, spatial transmission, pathogen-attributed burden, and outbreak trajectories while enforcing epidemiological consistency through physics-informed optimization. Results demonstrated robust forecasting performance, enhanced spatial transmission modeling, uncertainty-aware predictions, and realistic outbreak simulations across the three countries. Rotavirus, Shigella, and Cryptosporidium were identified as major contributors to modeled mortality burden, while unsafe water exposure, poor sanitation, malnutrition, and climate-sensitive transmission substantially increased disease risk. Compared with a Bayesian baseline model, the multimodal framework achieved superior nonlinear risk characterization, geospatial learning, and temporal prediction. These findings highlight the potential of scientific machine learning and digital twin systems for infectious disease surveillance, outbreak forecasting, climate-health analytics, and evidence-based public health decision-making in low-resource African settings. Keywords: Physics-Informed Neural Networks, Graph Neural Networks, Digital Twin, Childhood Diarrheal Disease, Epidemiology, Kenya, Somaliland, Zimbabwe, Scientific Machine Learning, Spatial Epidemiology, Multimodal Fusion

Background and Aims: MASLD has become the most prevalent chronic liver disease globally. Although MVPA and plasma fatty acids have been individually studied in relation to metabolic health, their independent and combined associations with MASLD incidence remain unclear. We aimed to investigate these associations. Methods: This study included 51,717 UK Biobank participants free of liver disease at baseline, with MVPA measured using wrist-worn accelerometers and plasma fatty acids quantified via NMR. Multivariable-adjusted Cox models and restricted cubic splines were used. Results: Over a median follow-up of 7.8 years, 472 incident cases were identified. In fully adjusted models, meeting recommended MVPA levels together with higher n-6 PUFA concentrations was associated with a 71% lower risk (HR 0.29, 95% CI 0.18-0.45). The MVPA-MASLD association was nonlinear, with risk reduction plateauing at approximately 189 minutes per week. Higher n-6 PUFA was associated with reduced risk, whereas n-3 PUFA showed no significant association. Conclusions: These findings suggest that behavioral and metabolic factors may jointly influence MASLD risk. Further studies in diverse populations are needed to confirm these associations.