International Journal of Hygiene and Environmental Health

Wastewater-based epidemiology (WBE) has emerged as a powerful population-level surveillance tool, but its coverage is structurally concentrated in in-network urban areas, potentially leaving rural populations underrepresented. Routine human movement between sewered (in-network) and unsewered (off-network) areas may, however, cause wastewater treatment plant (WWTP) measurements to reflect infectious disease dynamics beyond sewer boundaries. We evaluated this hypothesis using daily clinical COVID-19 testing data (January 2021-April 2022) across New York State excluding New York City (NYC). We disaggregated weekly cases and tests into in-network (WWTP catchment area) and off-network (outside WWTP catchment area) components applied to two geographic frameworks: administrative counties (N = 53 mixed-coverage) and mobility-defined communities identified through Walktrap community detection applied to census tract-level movement networks (N = 32 mixed-coverage). In/off-network COVID-19 trends were strongly correlated under both frameworks. County-level statewide aggregate correlations were high (incidence r = 0.994, positivity r = 0.996), as were individual county correlations (median r = 0.909 and 0.932, respectively). Mobility-defined community-level statewide correlations were similarly strong (r = 0.990 and 0.992), with comparable unit-level medians (r = 0.877 and 0.894). The mobility-defined community framework provided better population balance between in-network and off-network strata (87.5% vs. 69.8% in balanced range) and a higher floor on representativeness (minimum r = 0.440 vs. 0.177). Population size was the dominant predictor of in-network/off-network alignment at both scales; wastewater infrastructure density and off-network signal variability provided additional explanatory power at the mobility-defined community level. WWTPs broadly represent COVID-19 dynamics in surrounding off-network populations, supporting their use as sentinel surveillance sites. Representativeness weakens in smaller, more rural communities, and mobility-defined communities provide a complementary framework for identifying where this occurs.

Background: Population-based biomonitoring of contemporary-use pesticides remains limited in the United States, particularly in rural agricultural regions, and few studies have repeated measurements within the same individuals over time. Methods: We analyzed 28 urinary pesticide-related biomarkers among 600 adults from the population-based Survey of the Health of Wisconsin with archived urine collected during 2008-2016; 296 participants provided repeat urine and updated exposure information in 2025. Detection frequencies, co-detection, and within-person detection patterns were characterized. Generalized estimating equations were used for stacked, repeated-measures analyses of factors associated with detection of aminomethylphosphonic acid (AMPA), glyphosate, 2,4-dichlorophenoxyacetic acid (2,4-D), and any of these three. Prospective-only analyses evaluated more detailed agricultural and recent exposure measures. Results: Glyphosate, AMPA, and 2,4-D were detected in 7.7%, 6.2%, and 4.3% of retrospective specimens and 5.4%, 3.1%, and 4.1% of prospective specimens, respectively. Co-detection and persistent detection across the 9 to 17-year interval was rare. In repeated-measures models, greater fruit and vegetable intake, older age, and male sex were associated with higher 2,4-D detection. Lower household income was associated with lower AMPA detection, while afternoon/evening collection was associated with higher AMPA detection. In prospective analyses, working on field-crop agricultural land showed the strongest agricultural associations, particularly for 2,4-D and detection of any of the three pesticides. Associations were not seen with self-reported conventional versus organic produce consumption. Conclusions: Urinary pesticide detections were generally infrequent in this Wisconsin population. Diet and direct agricultural activities may be more informative exposure pathways than residing near cropland or private well drinking-water characteristics.

Background: Children spend more than 90% of their time indoors, and early childhood education settings (ECEs) are an understudied, high-occupant-density indoor microenvironment where exposure to volatile organic compounds, particulate matter, and other toxicants has been documented. Limited knowledge exists on ECE-specific exposures affecting young children and how they compare to exposures in the home. Methods: This prospective, repeated-measures pilot study targeted enrollment of 44 preschool-aged children and 8 ECE staff across two geographically and sociodemographically distinct ECEs in metropolitan Atlanta, Georgia. Paired silicone wristbands, one home-designated and one ECE-designated, were exchanged between settings across three consecutive days and nights beginning at enrollment to characterize microenvironment-specific exposure. A single spot urine sample was also collected from each child. Continuous indoor air quality monitoring was conducted in two classrooms per site. Caregivers and ECE staff completed structured questionnaires assessing home and ECE environmental characteristics, child respiratory risk, and protocol feasibility and acceptability. Feasibility was evaluated using eight pre-specified indicators spanning recruitment and enrollment, wristband wear duration and loss by microenvironment, urine sample collection completeness, and survey completion by instrument and respondent group. Conclusion: This pilot will establish feasibility and acceptability parameters for a paired, multi-matrix silicone wristband protocol across home and ECE microenvironments. Findings will inform the design, sample size, and power calculations for a subsequent study testing indoor air interventions and pediatric respiratory outcomes in ECEs. Feasibility outcomes are reported in a companion manuscript.

Urban air pollution remains a significant public health concern, contributing to premature deaths and adverse health outcomes. However, there is little causal research evaluating the effectiveness of policies designed to improve air quality. This study assesses the impact of all three stages of London's Ultra Low Emission Zone (ULEZ) on air pollution, via PM2.5 levels, and respiratory health, via prescription records for bronchodilator and respiratory corticosteroid medications. Analyses are at general practice level, using a generalised synthetic control method to estimate causal impacts. Stage 1 was associated with a statistically significant but negligible 0.77% reduction in PM2.5 levels, with no corresponding change in prescribing. Stage 2 produced a paradoxical 2.69% increase in PM2.5, alongside a 4.44% decrease in inhaled corticosteroid quantity but a 12.51% increase in average daily quantity (ADQ) usage, suggesting a worsening of disease severity among existing patients. Stage 3 yielded a 2.69% PM2.5 reduction and a modest 2.18% decrease in bronchodilator ADQ usage. Spillover effects beyond the ULEZ boundary were statistically significant, but negligible. We find overall that the ULEZ had minimal effects on both air quality and respiratory prescribing across all three stages. These findings provide new insights into the effectiveness of ULEZ policies in reducing air pollution and its associated health impacts, suggesting the zone's effects are considerably smaller than previously reported, and that integration with broader policy measures may be necessary to achieve meaningful public health gains.

Background Childhood respiratory mortality in China has fallen by over 90% in three decades alongside sustained national warming, yet national long-run evidence on temperature and child respiratory mortality is lacking. Methods We linked Global Burden of Disease (GBD) 2021 mortality estimates for China - lower respiratory infections (LRI), ages 0-19, and asthma, ages 0-24, 1990-2021 - with C-LSAT 0.5 deg gridded temperature data (1990-2019), aggregated nationally and to five climate zones. Four annual indicators (mean temperature, diurnal temperature range, seasonal amplitude, interannual variability) entered regressions of log mortality rates with Newey-West standard errors. A bootstrapped (500 resamples) quadratic model probed the minimum mortality temperature (MMT), with PM2.5-adjusted analyses and future-exposure, permutation, and detrended falsification tests. Results LRI deaths fell by 96.3% (330,194 in 1990 to 12,098 in 2021; 95% uncertainty interval 9,669-14,891) and asthma deaths by 94.9% (3,287 to 167), while mean temperature rose 0.364 deg C per decade and diurnal temperature range narrowed 0.092 deg C per decade. Baseline coefficients were large (mean temperature -1.696, SE 0.174; diurnal temperature range +2.408, SE 0.336; seasonal amplitude -0.162, SE 0.082; interannual variability +2.924, SE 1.514, per 1 deg C in log rate), but the future-exposure test failed and detrending nullified every coefficient: the associations are trend-level, and short-cycle causal effects are not identifiable. Nor was the national MMT identifiable - observed temperature support spans only 6.66-8.13 deg C, and the nominal turning point of 35.84 deg C is an extrapolation artifact (quadratic term p = 0.963). Within the observed range, warming and declining mortality moved in the same direction. Conclusions The 96% decline in childhood respiratory mortality cannot be attributed to warming. China sits on the low-temperature side of the optimum, and the marginal direction of future warming requires stronger designs to establish. The falsification framework offers a discipline for climate-health inference in China.

Occupational ocular injuries are a preventable yet neglected public health problem, particularly in low- and middle-income countries. Maintenance workers are exposed to diverse ocular hazards daily, yet compliance with protective measures is consistently poor. A descriptive cross-sectional study was conducted among 85 maintenance workers at the Maintenance and Essential Services Organization (MESO) of Kwame Nkrumah University of Science and Technology (KNUST), Ghana, recruited through stratified convenience sampling across seven occupational sections. A structured questionnaire assessed knowledge of ocular hazards and protective equipment, attitudes toward ocular safety, and safety practices. Data were analyzed using IBM SPSS version 26 (IBM Corp., Armonk, NY, USA); chi-square and Fishers exact tests assessed associations (p < 0.05). Participants were predominantly male (84/85, 98.8%), with a mean age of 44.5 {+/-} 10.4 years. Overall knowledge was good (mean 9.40 {+/-} 1.59 out of 11), but attitude and practice scores were average (2.78 {+/-} 0.92 and 3.27 {+/-} 0.93, respectively). Most workers correctly identified goggles and face shields as protective, but only about half recognized that ordinary sunglasses and spectacles offer inadequate protection. Although 97.6% (83/85) recognized the need for ocular protection, only 7.1% (6/85) reported consistent protective eyewear use, and fewer than half (45.9%, 39/85) had received formal ocular safety training. Routine general protective equipment use was significantly associated with ocular protection use (Fishers exact test, p = 0.011). Sand and dust particles were the leading causes of injury and only 25% (5/20) of injured workers sought formal care. Workers demonstrated good knowledge but poor attitudes and practices toward ocular safety, suggesting that knowledge alone does not translate into protective behaviour even within a relatively well-resourced institutional setting. Findings suggest that limited access to task-appropriate protective eyewear may represent an important institutional barrier. Institutional PPE supply and section-specific safety training are essential to bridge this knowledge-practice gap.

Background Rural-urban disparities in SARS-CoV-2 infection epidemiology remain poorly quantified and understood in Zambia despite differences in healthcare access, services and preventive interventions. This study examined the geographical distribution and associated factors of SARS-CoV-2 cases across selected rural and urban districts of Zambia. Methods A convergent mixed-methods study comprised of quantitative survey and qualitative interviews was conducted in; Ndola (Urban), Kafue (Peri-urban) and Lufwanyama (Rural). The proximate determinant framework guided variable selection and interpretation. Quantitative combined (Hospital-surveillance data with community survey), while qualitative included In-depth interviews. Participants were sampled using multistage sampling technique. Quantitative data were analysed using STATA version 17, while qualitative data were analysed thematically. Findings were integrated through triangulation. Results A total of 528 participants were included, with a median age 31 years (15-71). Overall SARS-CoV-2 positivity was 12.6%, varying across rural (16.5%), peri-urban (14.9%), and urban (9.9%) settings, though residence was not associated with infection (P<0.132). Participants aged [&ge;]49 years had significantly higher odds of infection (aOR=8.78; 95% CI:1.15-66.99), whereas secondary education (aOR=0.37; 95% CI:0.16-0.86) and hospital-based testing (aOR=0.37; 95% CI:0.15-0.92) were associated with lower odds of infection. Vaccine uptake was highest in urban areas but was not independently associated with infection. Qualitative findings revealed marked rural-urban differences in perceived susceptibility, testing access, vaccine decision-making, and adherence to preventive measures, explaining several quantitative observations. Conclusion SARS-CoV-2 infection across rural and urban settings in Zambia was influenced by demographic, behavioral, and health-system factors rather than geographic residence alone. These findings highlight the need for context-specific prevention strategies, equitable access to testing, strengthened community surveillance, and targeted risk communication to improve preparedness and response for future respiratory disease outbreaks.

Motivation: With the scaling frequency and intensity of extreme heat events across the globe, it is critical for public institutions to develop early detection systems and continuous monitoring of these events and their impacts. In Brazil, Rio de Janeiro was the first city to publish its heat protocol, with the Rio Heat Dashboard as a central component of this system. Implementation: The dashboard was implemented using R/Shiny and integrates climatic and health data from multiple sources. General features: The application comprises real-time heat exposure monitoring and automatic alert level classification, which is monitored daily by multiple municipal actors and supports activation of actions specified in the heat protocol. It also features a health impact module, which lists each heat event and its impact on mortality, and primary care and emergency visits. Availability: The source for full reproducibility is available through https://github.com/joaohmorais/RioHeatDashboard.

Communities exposed to flooding can experience markedly different mental health outcomes, yet conventional resilience indicators capture only part of the social and contextual conditions that may explain this variation. This study develops a multilevel and predictive framework for examining community resilience and depressive symptoms following flood exposure in Indonesia. Data were drawn from 20,303 respondents aged 15 years and older nested within 312 communities in the Indonesia Family Life Survey (IFLS-5). Depressive symptoms were assessed using the 10-item Centre for Epidemiologic Studies Depression Scale (CES-D-10), with Rasch Partial Credit Model calibration used to examine measurement properties. Bayesian multilevel models quantified between-community heterogeneity and assessed how far observable structural resources accounted for this variation. Community resilience was represented through two complementary constructs: structural resilience, based on observable socioeconomic and social-capital resources, and Latent Community Protective Capacity (LCPC), a model-derived proxy for residual contextual variation in depressive-symptom risk. Approximately 6 percent of variation was attributable to between-community differences, while observable structural resources explained only part of this heterogeneity. Structural resilience and LCPC were weakly correlated (r = 0.155). Moderation analyses provided no clear evidence that structural resilience altered the flood-depression association, while LCPC showed a directionally consistent but uncertain buffering pattern. Predictive models incorporating community-level information improved discrimination, with the best-performing model reaching an ROC-AUC of approximately 0.71. The findings suggest that observable resource-based indices provide an incomplete account of community-level mental health vulnerability and that residual contextual measures may provide complementary information, while requiring cautious interpretation and independent validation.

Background: Early trophic feeding promotes gut maturation, feeding tolerance, and growth in preterm neonates. However, delays remain common despite recommendations for initiation within 24 hours of birth, especially in resource-limited settings. Evidence on feeding initiation timing and predictors among Ethiopian preterm neonates is limited. Objective: To determine time to trophic feeding initiation and identify predictors among preterm neonates admitted to Adama Hospital Medical College, Ethiopia. Methods: A hospital-based retrospective cohort study was performed on 436 randomly chosen preterm neonates admitted to NICU. Data extraction was performed using a structured checklist. Time to trophic feeding initiation was analyzed using Kaplan-Meier estimates, log-rank tests, and bivariable and multivariable Cox regression models . Adjusted hazard ratios with 95% CIs were reported. Results:The sample comprised 416 preterm neonates, of whom 311 (74.8%) started trophic feeding during follow-up, and 105 (25.2%) were censored. The rate of initiation of trophic feeding was 1.92 per 100 person-hours (95% CI 1.72 to 2.15). Median time to initiation was 42 hours (interquartile range 24 to 50). Independent predictors of feeding initiation were determined by multivariable analysis and included gestational age, birth weight, maternal anaemia, respiratory distress syndrome and necrotising enterocolitis. Neonates born at 34-36 weeks had earlier initiation than those born at <34 weeks (AHR 1.39; 95 % CI 1.09 to 1.78). Similarly, neonates with a birth weight of [&ge;]1500 g had an earlier initiation than those with a birth weight of <1500 g (AHR 1.41; 95% CI 1.04 to 1.91). Delayed initiation was associated with maternal anaemia (AHR 0.70; 95% CI 0.51-0.95), respiratory distress syndrome (AHR 0.67; 95% CI 0.51-0.88) and necrotising enterocolitis (AHR 0.48; 95% CI 0.33-0.69). Conclusions: Delayed trophic feeding remains common among preterm neonates. Standardized feeding protocols, strengthened maternal care, and individualized nutrition strategies are needed to improve neonatal outcomes in study area.

Background Dengue is a major public health challenge, and predictive models are crucial for early warning systems. However, many current modeling practices rely exclusively on climatic factors or employ complex algorithms that lack the interpretability needed for informed public health decision-making. To address these shortcomings, we developed and validated a multidimensional, interpretable statistical model to predict monthly dengue incidence. Methodology/Principal Findings We used a Generalized Linear Mixed Model (GLMM) with a Negative Binomial distribution to analyze 14 years (2010-2023) of spatiotemporal data from 37 municipalities in Huila, Colombia, an endemic region. The model integrates non-linear and lagged effects of climatic, demographic, and socioeconomic factors. The final model underwent rigorous external validation on an independent test set (2021-2023). Our model demonstrated high predictive discrimination (R2 = 0.743, Spearman's {rho} = 0.657), accurately capturing the timing of epidemic outbreaks. Key findings include the identification of an optimal thermal window for transmission at 27-28{degrees}C, a threshold effect for precipitation above 800 mm, and a saturation dynamic in outbreak autocorrelation. Conclusions/Significance This mechanistically-informed statistical approach provides a robust and transparent tool for epidemiological surveillance, successfully balancing high predictive performance with the explanatory power needed for effective, data-driven public health interventions.

Background: Case fatality reflects the quality and timeliness of clinical care for leptospirosis, yet no study has examined it at a national level in the Dominican Republic (DR), where leptospirosis is endemic. We describe the case fatality rate (CFR) of leptospirosis in the DR between 2012 and 2026 and identify associations with mortality. Methods: We conducted an analytical cross-sectional study, using national surveillance records merged with a discharge-condition extract via a composite key. We calculated CFR with Wilson 95% confidence intervals among 5,412 valid cases (suspected, probable, or confirmed) reported from January 2012 through June 2026. We compared proportions with Pearson's chi-square test, assessed annual trend with ordinary least squares linear regression, and fitted multivariable logistic regression models to account for confounding. Results: Overall CFR was 8.5% (460/5,412), with no significant annual trend (p=0.372). CFR was higher in men than women (p < 0.001) and increased significantly with age (p < 0.001). Male gender (OR: 1.79; 95%CI: 1.18-2.73) and pre-existing comorbidity (OR: 1.76; 95% CI: 1.21- 2.57) were independent predictors of death. Clinical complications were the strongest predictor in the adjusted model (OR: 3.16; 95%CI: 2.17-4.61), attenuating the gender effect. CFR varied widely by province (2.43-22.22%) and correlated negatively with incidence at the province level (p = 0.066). Conclusions: Leptospirosis case fatality is concentrated among men, people with comorbidity, and those who develop clinical complications. These national, long-term findings can help prioritize clinical and surveillance resources as extreme weather events are expected to intensify across the Caribbean.

Micronutrient deficiencies are prevalent in Madagascar, where diets rely heavily on starchy staples and access to animal-source foods is limited. Small dried fish (SDF) are widely available, yet their nutritional value and health risks remain poorly documented. We combined market surveys, taxonomic identification, and micronutrient and heavy metal analyses of nine SDF types collected along National Road 7. The samples encompassed 33 fish families, were dominated by small pelagic species (Clupeidae and Engraulidae), and were appreciated by consumers. A daily portion (5 g for infants; 10 g for young children and women of childbearing age) contributed substantially to Recommended Nutrient Intakes (RNIs). Across samples and groups, SDF were rich (>30% of RNI) in selenium and, for infants and young children, in calcium. All samples were a source of (>15% of RNI), or rich in, phosphorus, whereas iron contributions were more variable but often substantial. Several samples exceeded 100% of RNIs for selenium, calcium, iron, or manganese in infants and young children, and some were also sources of magnesium and, less frequently, zinc. Vitamin A was absent from sun-dried samples but detected in a smoked freshwater type. Heavy metal concentrations varied markedly, and portions of several types led to estimated exposures to inorganic arsenic or cadmium exceeding reference values, whereas freshwater species and some pelagic types showed a more favorable nutrition-risk balance. Overall, SDF are affordable, nutrient-dense foods with strong potential to alleviate micronutrient deficiencies in Madagascar, while highlighting the need for type-specific guidance to balance nutritional benefits and contamination risks.

Purpose: This study assesses the association between colorectal cancer (CRC) screening and a validated, housing-based measure of individual-level socioeconomic status (SES, called HOUSES hereafter) within rural communities and determines whether HOUSES-integrated geospatial analysis can be used to tailor interventions. Methods: We used CRC screening data from a subset of Mayo Clinic Midwest patients living in cities without ready access to routine care in the Mayo Clinic Health System in 2019 to represent rural communities. At the individual level, we assessed the association between CRC screening rates and the HOUSES index, adjusting for age, sex, race/ethnicity, comorbidity, distance from home address to clinic, and area deprivation index, using a multilevel mixed-effects logistic regression model. Additionally, we conducted geospatial analysis to examine the correlation between hotspots of 1) lower CRC screening rates and 2) lower SES of the subject population (HOUSES quartile 1). Findings: Among 34,489 individuals (median age 64.0 years, 52.4% female), those with the lowest SES (HOUSES Q1) had 37% lower odds of being CRC screening adherent than those with the highest SES (HOUSES Q4) (adj. OR [95% CI]: 0.63 [0.58-0.69]). In the 14 identified HOUSES Q1 hotspots, there was a significant correlation in counts of HOUSES Q1 and low CRC screening (correlation coefficient=0.81). Conclusion: Lower SES was significantly associated with lower CRC screening among rural populations. HOUSES-enabled geospatial analysis identified geographic hotspots with lower CRC screening rates for targeted interventions to address disparities in CRC screening in rural communities. HOUSES may be a useful digital tool for cancer preventive care and research.

Background Severe acute respiratory infections (SARI) strain healthcare systems. Sentinel surveillance remains central to SARI monitoring, but routinely collected hospital discharge data offer a scalable, population-wide complement. In Finland, national registers now enable register-based surveillance, yet SARI case definitions remain unevaluated. Aim To evaluate whether routinely collected electronic health records can support register-based SARI surveillance and establish a national case definition. Methods We conducted a retrospective register-based study linking inpatient discharge data from the Finnish Care Register for Health Care (Hilmo) and laboratory-confirmed pathogen notifications from the National Infectious Diseases Register (NIDR). Admissions were aggregated into hospitalisation episodes using generic and pathogen-specific respiratory ICD-10 codes and linked to laboratory-confirmed respiratory pathogens within an admission-centred window. We assessed the impact of diagnostic coding position, laboratory linkage windows and alternative case definitions on age distribution, seasonality and epidemic trend detection. Results We included 145,435 respiratory hospitalisation episodes. Laboratory confirmations clustered around admission, and a -7-to-+3-day window was selected; 51,498 (35.4%) had a linked laboratory confirmation. Specific primary-position diagnoses preserved clear seasonality and age distributions consistent with SARI epidemiology, whereas secondary-position diagnoses showed attenuated seasonality. A combined case definition incorporating specific primary diagnoses and laboratory-supported syndromic episodes produced stable epidemic curves while improving sensitivity over laboratory confirmation alone. Conclusion National discharge and laboratory registers can support robust SARI surveillance in Finland when case definitions are carefully designed. A combined register-based definition balances specificity, sensitivity and feasibility, complementing sentinel surveillance and integrated respiratory monitoring. Keywords Severe acute respiratory infection (SARI); surveillance; electronic health records; ICD-10; case definition; Finland

Background Childhood respiratory infectious deaths are partitioned across four Global Burden of Disease cause modules-26 etiological attributions within lower respiratory infections, tuberculosis, COVID-19, and whooping cough-never jointly reported. Whether the structure of this combined mortality spectrum has changed over time, and with what implications for intervention design, has not been quantified. We assembled and analyzed the integrated spectrum for children and adolescents aged 0-19 years, 1990-2023. Methods We integrated Global Burden of Disease Study 2023 (release v8352) estimates into a 29-node spectrum-26 lower respiratory infection etiologies plus tuberculosis, COVID-19, and pertussis-globally and across seven super-regions, with uncertainty propagated by summing bounds. We computed Shannon diversity, Herfindahl concentration, and effective cause counts; phenotyped pandemic-window collapse and rebound per cause; linked pathogen shares to WHO/UNICEF vaccine coverage; and mapped geographic concentration in sub-Saharan Africa and South Asia. Reporting follows GATHER. Results In 2023 the 29 causes jointly accounted for 965,330 deaths (95% uncertainty interval [UI] 680,096-1,342,437). Shannon diversity rose from 2.336 to 2.711 (+16.1%) between 1990 and 2023; the effective number of causes nearly doubled (5.57 to 9.94), inversely coupled to total deaths (Spearman rho = -0.997). Whooping cough ranked second (112,954 deaths; 95% UI 64,576-185,708; 11.7%) and showed the spectrum's only rebound above 100% (-57.4% collapse, +111.0% rebound). Tuberculosis ranked third (87,764; 57,779-124,912; 9.1%) with the highest concentration in sub-Saharan Africa and South Asia (87.1%). COVID-19 entered at rank five (52,899; 47,275-59,183; 5.5%). Nineteen of 29 causes exceeded the poverty-lock threshold (>80.59% of deaths in sub-Saharan Africa plus South Asia). Conclusions Childhood respiratory infectious mortality has become more diverse and more concentrated in poverty as it has declined. Single-pathogen interventions now address a shrinking share; the spectrum's structure argues for platform interventions-oxygen, antimicrobial access, referral-tailored jointly by age and geography, implying that pathogen-specific strategies alone cannot finish the remaining mortality agenda.

In primary healthcare centers lacking advanced imaging, community-based active tuberculosis (TB) case finding often relies on basic symptom screening. This approach often misses cases and leads to the inefficient allocation of rapid molecular testing (RMT). We aimed to develop and internally validate a simple clinical triage scorecard to improve TB detection and guide RMT use in resource-constrained settings. We conducted a retrospective cross-sectional study of 15,137 adults ([&ge;]18 years) evaluated within the Zero TB Yogyakarta program (2020-2025). Participants with complete clinical assessments and confirmatory GeneXpert results were included. Using multivariable logistic regression, we identified independent clinical predictors, which were subsequently transformed into an integer-based point scorecard. Model performance was evaluated via discrimination and calibration, utilizing bootstrap resampling (1,000 iterations) for internal validation. Among the 15,137 participants, 251 (1.7%) were GeneXpert-positive. The final multivariable model identified eight independent predictors: age, male sex, body mass index, prolonged cough, hemoptysis, unexplained weight loss, TB contact history, and diabetes mellitus. The model demonstrated strong predictive accuracy, with an optimism-adjusted AUROC of 0.836 and good calibration. When translated to the integer scorecard and compared directly to standard national symptom screening, the scorecard performed (AUROC 0.81 vs. 0.73; p<0.001). At a high sensitivity cut off score of [&ge;] 0, the tool achieved 93.63% sensitivity and 41.33% specificity. This point-of-care clinical scorecard provides higher diagnostic accuracy than standard symptom screening algorithms. By offering flexible operational thresholds, it empowers local health programs to dynamically balance the urgency of case detection with available diagnostic capacity, optimizing GeneXpert allocation where advanced radiological imaging is unavailable.

Abstract Preventable neonatal deaths remain a major public health problem in Zimbabwe, where near-universal antenatal and facility-delivery coverage coexist with a rising neonatal mortality rate. This study evaluated whether institutionalising three core "vital signs" of the community health system (a trained village health worker (VHW) workforce, functional community governance structures, and modified women's and men's participatory learning and action groups) reduces preventable neonatal deaths in Mashonaland West Province. An embedded QUAN (qual) mixed-methods design was used, with a two-arm, parallel-group cluster-randomised controlled trial as the dominant strand. Fifty-two ward-level clusters were randomised 1:1 to the institutionalised community health system package or to standard Ministry of Health and Child Care community services, and 984 pregnant women were enrolled between 1 September 2020 and 31 October 2021, with each mother-infant pair followed to 28 days after delivery, yielding 973 mother-infant pairs for intention-to-treat analysis. The primary outcome was neonatal death within 28 days of life, expressed per 1,000 live births. The primary analysis used a three-level mixed-effects log-binomial regression model with cluster and community-health-worker random intercepts, adjusted for pre-specified covariates. Supervised machine-learning classifiers with leave-one-cluster-out cross-validation, Cox proportional-hazards regression, and multilevel logistic models were fitted as supplementary analyses. An embedded longitudinal process evaluation used key informant interviews and focus group discussions, which were analysed thematically and integrated with the quantitative findings. The neonatal mortality rate was 44.8 per 1,000 live births in the intervention arm versus 110.1 per 1,000 in the control arm. The adjusted risk ratio for neonatal death was 0.43 (95% CI 0.26-0.70; p < 0.001), a 57% relative reduction, with a number needed to treat of 16 mother-infant pairs (95% CI 11-29). Low birthweight (<2,500 g), birth interval under two years, and low community women's literacy were the strongest risk factors, while trained VHWs, functional community governance, early antenatal care, and sustained participatory group attendance were independently protective. The women's and men's groups were protective in a dose-dependent manner, becoming significant at four or more cycles (about 14 meetings) (adjusted odds ratio 0.71; 95% CI 0.60-0.85; p = 0.001). A random forest classifier discriminated against neonatal deaths with a cross-validated area under the curve of 0.842 and a sensitivity of 0.912. Qualitative findings converged with the trial results, identifying male engagement, earlier care-seeking, danger-sign literacy, social-network activation, and community death audits as the behavioural and structural mechanisms of change. Institutionalising the community health system package (trained VHWs, functional governance, early antenatal engagement, and sustained participatory groups) was associated with a substantial reduction in preventable neonatal deaths. The findings suggest that in high-coverage, high-mortality settings, the binding constraint is structural rather than clinical, and that scaling functional community governance and workforce infrastructure in the most disadvantaged communities may accelerate progress toward neonatal survival targets. The principal limitations are a one-year follow-up period, the rarity of neonatal death, and concurrent national programming that only partially reached the control clusters. Trial registration: Pan African Clinical Trials Registry, PACTR202607591142118 (https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=PACTR202607591142118); registered retrospectively on 7 July 2026.

Mosquito-borne diseases pose a growing public health challenge as climate change reshapes vector population dynamics. West Nile virus (WNV), transmitted between birds and Culex mosquitoes, disproportionately affects Maricopa County, Arizona, one of the nation's highest-burden counties, yet whether models that include weather and avian dynamics improve forecast accuracy remains unclear. Using a 15-year weekly time series of mosquito abundance, mosquito infection prevalence, and human cases, we developed four mechanistic model configurations of varying complexity, from mosquito-human dynamics alone to full models incorporating avian dynamics and weather forcing. We fitted each model to the weekly-observed data, generated probabilistic 1- and 2-week-ahead forecast horizons, and evaluated forecasts against a historical baseline. All configurations fit the data equally regardless of weather or avian dynamics. However, models incorporating both birds and weather created more accurate forecasts of mosquito abundance and mosquito infection prevalence, and all configurations outperformed the baseline for forecasting human cases. Forecast accuracy was highest in summer and fall, and ensemble aggregation sometimes outperformed every individual model, stabilizing predictions across the 15-year record. These findings indicate that avian and weather dynamics are most critical for predicting mosquito-specific data, positioning this framework as a scalable tool for public health planning for WNV surveillance under climate change.

Background Carbapenem-resistant bacteria are a major global public health threat, classified as critical priority pathogens by the WHO. In Indonesia, despite a national antimicrobial resistance control programme established by the Ministry of Health in 2015, resistance rates continue to rise, including increasing carbapenem resistance among clinically important bacteria. Strengthening approaches to directly interrupt transmission is essential, yet transmission pathways remain poorly understood with limited research and policy guidance within the Indonesian context. Methods and analysis The INTERCEPT study is a UK-Indonesia multidisciplinary collaboration aiming to identify transmission routes of carbapenem-resistant bacteria across healthcare and community settings, and the mechanisms of resistance gene transfer between bacteria and mobile genetic elementss. We will conduct genomic surveillance of hospital inpatients, healthcare workers, hospital environments, and surrounding communities, including wastewater systems, combined with genomic analyses and mathematical transmission modelling. A cohort of patients with bloodstream infections will be recruited to evaluate resistant bacteria, treatment practices, and clinical outcomes. Qualitative research will explore behavioural and system-level factors influencing transmission and intervention implementation. Findings will inform stakeholder workshops to co-design context-specific interventions, with pilot intervention over 9 months with pre- and post-intervention assessment to guide scalable strategies to reduce AMR transmission. Discussion The INTERCEPT study addresses carbapenem resistance in Indonesia using an integrated approach combining microbiological surveillance, genomics, modelling, and qualitative methods. Strengths include cross-sectoral analysis (patients, workers, environment) and participatory intervention design. Limitations include geographic scope restricted to Central Java, Indonesia.