GeoHealth

Military aviation training noise remains understudied despite its widespread impacts across urban, rural, and wilderness areas. The predominance of low-frequency noise and repetitive training can create pervasive noise pollution, yet past research often fails to capture the full range of health and quality-of-life effects. This study analyzed two complaint datasets related to Whidbey Island Naval Air Station noise: U.S. Navy records (2017-2020) and Quiet Skies Over San Juan County data (2021-2023). We analyzed and mapped sentiment intensity from noise complaints relative to modeled annual noise exposure, developed a typology to classify impacts, and modeled the environmental and operational factors influencing complaints. Findings revealed widespread negative sentiment and anger, often beyond the bounds of estimated noise contours, suggesting that annual cumulative noise models inadequately estimate community impacts. Complaints consistently highlighted sleep disturbance, hearing and health concerns, and compromised home environments due to shaking, vibration, and disruption of daily life. Residents also reported significant social, recreational, and work disruptions, along with feelings of fear, helplessness, and concern for children's well-being. The number of complaints were strongly associated with training schedules, with late-night sessions being the strongest predictor. A delayed response pattern suggests residents reach a frustration threshold before filing complaints. Overall, our findings demonstrate persistent negative sentiment and diverse impacts from military aviation noise. Results highlight the need for improved noise metrics, modeling and operational adjustments to mitigate the most disruptive effects.

Wastewater surveillance (WWS) is established as a vital tool for monitoring polio and SARS-CoV-2 with potential to improve surveillance for many other infectious diseases. This study evaluated the feasibility of detecting measles virus (MeV) RNA in wastewater as part of a national WS preparedness trial in Brisbane, Australia, from March to June 2025. Composite and passive sampling methods were employed in parallel at three wastewater treatment plants serving populations between 230,000 and 584,000. Nucleic acids were extracted and analyzed using RT-qPCR targeting MeV N and M genes to distinguish wild-type and vaccine strains. MeV RNA were detected in both 24-hour composite and passive samples on May 26 to 27, 2025 from the largest catchment of 584,000 which also included an international airport. No measles cases were reported in this city or region within 4 weeks of the WS detections. These were confirmed as vaccine-derived measles virus (MeVV) strain via specific RT-qPCR assay. Extraction recoveries varied (11.5% to 70.5%), with passive sampling showing higher efficiency. This is the first report of use of passive samples for detection of MeV. These findings are consistent with other studies reporting WWS results of both MeVV genotype A and wild type genotype B and/or D. It demonstrates the potential for sensitive MeV WWS with rapid differentiation of MeVV from wild type MeV shedding, including in airport transport hubs and with different sample types. Use of WWS could strengthen measles surveillance by enabling rapid detection of MeV RNA and supporting outbreak preparedness and response. This requires optimised methods which are specific to or differentiate wild-type MeV from MeVV. Furthermore, the successful detection of MeV using passive sampling in this study highlights its potential for deployment in diverse global contexts which may include non-sewered settings.

Benzalkonium chloride (BAC) is widely used as a disinfectant in cleaning products and is frequently detected in indoor dust. In this study, we assessed dust samples, along with information on cleaning product use, from 24 pregnant participants. Dust samples were analyzed for BAC concentration and microbial tolerance. Different chain lengths of BAC (C12, C14, and C16) were quantified using LC-MS/MS, and bacterial isolates were tested for BAC tolerance using minimum inhibitory concentration (MIC) assays. BAC was ubiquitously detected, with C12 and C14 being dominant. Higher BAC concentrations were associated with reported disinfectant use and increased microbial tolerance. These findings suggest that indoor antimicrobial use may promote microbial resistance, highlighting potential exposure risks in indoor environments and the need for further investigation into health and ecological impacts.

Respiratory infections caused by bacterial pathogens like Mycobacterium tuberculosis and Bordetella pertussis have increased since the COVID 19 pandemic, yet clinical surveillance of both suffers from underreporting and delayed diagnoses. Wastewater monitoring is a valuable public health surveillance tool that can help fill gaps in clinical data yet has rarely been applied to respiratory bacterial pathogens despite evidence of bacterial shedding via excretion types that enter wastewater. In this study, we investigated the possibility for wastewater monitoring of two bacterial respiratory diseases, tuberculosis and pertussis, using two case studies of wastewater monitoring for M. tuberculosis and B. pertussis. We retrospectively measured concentrations of these pathogens in wastewater samples collected longitudinally from communities with and without known outbreaks of these diseases. We designed and validated a novel B. pertussis specific assay for the NAD(P) gene; B. pertussis nucleic acids were detected sporadically in wastewater during an identified outbreak. We used a highly specific, established assay for M. tuberculosis nucleic acids, and found low concentrations of the marker in wastewater that were lag-correlated with clinical incidence rates 5 weeks later. Findings support the potential of wastewater monitoring for M. tuberculosis and B. pertussis to enable identification of communities with outbreaks of tuberculosis and pertussis and provide early warning for tuberculosis.

Infectious disease surveillance systems in tropical countries show that respiratory disease incidence generally manifests as year-round activity with weak fluctuations and irregular seasonality. Previously, using a ten-year time series of influenza-like illness (ILI) collected from outpatient clinics in Ho Chi Minh City (HCMC), Vietnam, we found a combination of nonannual and annual signals driving these dynamics, but with unknown mechanisms. In this study, we use seven stochastic dynamical models incorporating humidity, temperature, and school term to investigate plausible mechanisms behind these annual and nonannual incidence trends. We use iterated filtering to fit the models and evaluate the models by comparing how well they replicate the combination of annual and nonannual signals. We find that a model including specific humidity, temperature, and school term best fits our observed data from HCMC and partially reproduces the irregular seasonality. The estimated effects from specific humidity and temperature on transmission are nonlinearly negative but weak. School dismissal is associated with decreased transmission, but also with low magnitude. Under these weak external drivers, we hypothesize that stochasticity makes a strong sub-annual cycle more likely to be observed in ILI disease dynamics. Our study shows a possible mechanism for respiratory disease dynamics in the tropics. When the external drivers are weak, the seasonality of respiratory disease dynamics is prone to the influence of stochasticity.

Aims Indoor air pollution resulting from combustion of unclean cooking fuels has been linked to adverse health outcomes, but evidence regarding its association with mental health in low- and middle-income countries remains limited. We investigated the association between household use of unclean cooking fuels, as a proxy for indoor air pollution, and depression symptoms among adults aged 45 years and older in India, and assessed effect modification by age, sex, caste, and rural/urban residence. Methods We conducted a cross-sectional analysis of the first wave (2017-2018) of data from the Longitudinal Aging Study in India (LASI), a nationally representative survey of adults aged [≥]45 years. Cooking fuel type was classified as clean or unclean, and depression symptoms were assessed using the 10-item Centre for Epidemiologic Studies Depression (CES-D-10) scale. We used logistic regression to estimate odds ratios for depression symptoms, and linear regression to compare mean CES-D-10 scores by cooking fuel type, adjusting for sociodemographic and housing characteristics. Results We included 62,650 respondents. Median age was 57 years (IQR: 50-65), 46.7% were women, 47.6% reported using unclean cooking fuels, and 27.6% screened positive on the CES-D-10. After adjusting for sociodemographic and housing characteristics, use of unclean cooking fuels was associated with higher odds of screening positive on the CES-D-10 (aOR: 1.08; 95% CI: 1.02, 1.15), and higher mean CES-D-10 scores (adjusted mean difference: 0.34; 95% CI: 0.24, 0.44). The association was more pronounced among individuals living in urban areas (aOR: 1.36; 95% CI: 1.21, 1.53). Conclusion Use of unclean cooking fuels was associated with depression symptoms among older adults in India, and especially among those living in urban areas.

This study evaluates the feasibility of implementing artificial intelligence (AI)-driven disease surveillance systems at Julius Nyerere International Airport (JNIA) in Tanzania, a key hub for regional and international travel. Through a mixed-methods approach combining qualitative interviews and quantitative surveys, the research assesses the infrastructure, human resource capacity, and regulatory frameworks necessary for AI integration. Findings indicate that while Port Health Officers are strongly optimistic about AIs potential to enhance disease detection, the airport faces significant barriers, including outdated infrastructure, insufficient technical resources, and a lack of trained personnel. Ethical and privacy concerns, particularly surrounding data security, also emerged as key challenges, compounded by limited public awareness and the socio-cultural acceptability of AI systems. Furthermore, the study identifies gaps in national policies and inter-agency coordination that hinder the effective implementation of AI technologies. The research concludes that while current conditions render AI adoption infeasible, strategic investments in infrastructure, workforce training, and policy development could pave the way for future integration, enhancing public health surveillance at JNIA and potentially other airports in low- and middle-income countries. This study contributes critical insights into the barriers and opportunities for AI-driven disease surveillance in low-resource settings, specifically focusing on a high-priority transit point, international airports. It emphasizes the importance of region-specific solutions to enhance health security in East Africa and supports the broader global health agenda by advocating for international collaboration and the development of scalable disease surveillance systems. Future research should explore pilot AI implementations at other airports to evaluate real-world challenges and refine AI systems for broader applicability, including cost-effectiveness analyses and integration of public perspectives on AI.

BackgroundIn long-term care facilities (LTCFs), close-contact identification often relies on staff recall and monitoring records because residents may be unable to self-report reliably. How these different record-generation processes relate to proximity-based sensor measurements in routine LTCF workflow remain unclear, and how such differences may influence contact-based decision-making in outbreak response is not well understood. MethodsWe conducted a five-day observational study in a Japanese LTCF using ultra-wideband (UWB) indoor positioning. Twenty-seven participants wore UWB tags, including 16 residents and 11 staff members; 10 staff members completed questionnaires. We compared UWB-derived proximity with questionnaire-derived contacts from staff self-report and monitoring-based proxy records, and assessed directional discrepancies under multiple distance-time thresholds. ResultsQuestionnaire-based records and UWB-derived proximity showed different patterns of discrepancy across contact types. Within this facility, resident-related monitoring-based proxy records showed relatively small directional discrepancies, whereas staff self-reports tended to identify additional resident-staff contacts under the baseline threshold ([≤]1.0 m for [≥]15 min). Several alternative thresholds were associated with discrepancies closer to zero than the baseline, although the apparent ranking varied by summary metric. ConclusionsIn this single-facility observational study, different contact-list generation processes were associated with different patterns of discrepancy relative to a proximity-based operational measure. These findings support interpretation in terms of workflow-specific contact-list generation rather than a single universally optimal threshold and may help inform facility-level review of contact identification practices in LTCFs. These findings support aligning contact identification strategies with facility-specific workflows to improve the feasibility and effectiveness of IPC practices in LTCFs.

Background. Young sexual and gender minorities of color face compound health risks shaped by interlocking systems of racism, cisgenderism, and class inequality. Spatial health research documents that place shapes health, but existing methods cannot specify the mechanisms through which spatial configurations produce different health outcomes for differently positioned people. This gap prevents targeted intervention. ObjectiveTo develop and pilot test the Spatial Intersectionality Health Framework (SIHF), which specifies three mechanisms through which space produces intersectional health inequities: Layered (multiple oppressive systems activating simultaneously), Positional (the same space producing different health pathways by intersectional position), and Conditional (nominally protective spaces carrying hidden costs for specific positions). We also introduce and validate Intersectional Geographically-Explicit Ecological Momentary Assessment (IGEMA) as the methodology operationalizing SIHF across three data levels. MethodsThe GeoSense study enrolled 32 young sexual and gender minorities of color (ages 18-29) in New York City. IGEMA was implemented across three integrated levels: (1) GPS mobility tracking via participants personal smartphones, linked to census tract structural exposure indices across n=19 participants; (2) ecological momentary assessment of intersectional discrimination with multilevel modeling of mood, stress, and sleep outcomes; and (3) map-guided qualitative interviews with SIHF mechanism coding and intercoder reliability assessment across 92 coded records from 18 participants. This study was conducted as the pilot for NIH R01HL169503. ResultsAll three SIHF mechanisms were empirically detectable. A compound structural gendered racism index outperformed every single-axis alternative in predicting daily mood (b=-0.048, p=.001) and stress (b=0.121, p<.001). The Positional mechanism accounted for 71% of coded harm experiences. Intercoder reliability for mechanism assignment reached kappa=0.824 at Stage 2 reconciliation. Daily intersectional discrimination predicted greater sleep disturbance (b=1.308, p=.004). ConclusionsSIHF and IGEMA together provide an empirically testable framework for specifying how space produces intersectional health inequities. Mechanism specification, not spatial location alone, is the condition for designing research and intervention that reaches the source of harm for multiply marginalized populations.

BackgroundInsecticides remain the cornerstone of mosquito vector control for malaria, dengue, and other mosquito-borne diseases, yet global patterns of deployment and their socioeconomic and environmental drivers are poorly characterized. Understanding where and why insecticides are used is essential for better targeting control efforts and ensuring they are effective, equitable, and efficient. MethodsWe analyzed annual country-level insecticide-use data from 122 countries (1990-2019), reported as standard spray coverage for insecticide-treated nets (ITNs), residual spraying (RS), spatial spraying (SS), and larviciding (LA). Generalized linear mixed models and hurdle models quantified associations between deployment and disease incidence, human development index (HDI), human population density, temperature, and precipitation. Models were evaluated using repeated cross-validation and applied to generate downscaled predictions of insecticide use at subnational administrative region level 2 (ADM2) globally. FindingsInsecticide deployment increased with malaria and dengue incidence, but this response was substantially stronger in higher-HDI countries, indicating that deployment depends on socioeconomic capacity as well as disease burden that leads to weaker scaling in lower-resource settings. Intervention types exhibited distinct patterns; ITN use tracked malaria burden, whereas infrastructure-intensive approaches (e.g., RS and SS) were concentrated in higher-HDI settings and increased with Aedes-borne disease incidence. Downscaled ADM2-level maps uncovered substantial within-country heterogeneity that is obscured at the national scale, highlighting regions where predicted deployment remains low relative to disease risk across sub-Saharan Africa, South Asia, and parts of Latin America. InterpretationGlobal insecticide deployment reflects not only epidemiological need but also economic and logistical capacity, creating mismatches between risk and control. High-resolution mapping can support more equitable allocation of interventions, guide insecticide resistance stewardship, and improve strategic planning as climate and urbanization reshape mosquito-borne disease risk.

Zoos may serve as sentinel sites for zoonotic vector-borne diseases. West Nile virus (WNV) and Usutu virus (USUV) are closely related orthoflaviviruses transmitted between Culex mosquitoes and a bird reservoir. Both viruses can also infect mammals, including humans, where they may cause symptoms and, more rarely, hospitalization and death. However, serological cross-reactivity between WNV and USUV complicates their differential diagnosis. Here, we aimed to reconstruct the dynamics of emergence of WNV in a zoo located in a newly affected area in Europe, using ELISA and Virus Neutralization Test (VNT) serological analysis of 1707 animal sera collected between 2015 and 2024. Combining this data in a model accounting for cross-reactivity with USUV, we estimated yearly forces of infection (FOI) by both viruses, and thus found that WNV likely circulated in the area one year prior to the first cases reported to the passive surveillance system. Our results also showed that, in the zoo, mammals and reptiles had a lower risk of infection than birds (relative risk of 0.14 [0.05; 0.28]), and that the exposure of birds to water (aquatic lifestyle or proximity to stagnant water) affected the risk. Finally, we estimated diagnosis parameters, including the sensitivity of the VNT (80.4% [76.5%; 84.3%]), the expected VNT titer value, and the level of serological cross-reactivity between viruses during the VNT. To conclude, our modelling framework allowed to disentangle the co-circulation of two closely related viruses, a crucial point in ensuring the reliable sentinel surveillance of these vector-borne zoonotic pathogens.

BackgroundSchistosomiasis remains a major public health challenge in sub-Saharan Africa. Recent World Health Organization (WHO) guidance calls for community-wide treatment and fine-scale data to optimise preventive chemotherapy (PC) strategies, yet the practical implications for resource allocation by health ministries are unclear. MethodsWe analysed epidemiological and cost data from Ethiopia and Zimbabwe to compare survey designs and five implementation scenarios. Scenarios varied by data source, administrative unit of implementation, WHO guidance on PC strategies. Outcomes were target population, praziquantel needs, and delivery costs. ResultsGeostatistical surveys reduced sample size by up to 90% and survey costs by [&ge;]72% compared with a design-based approach, while increasing spatial coverage. Applying updated WHO guidance expanded eligibility to pre-school-aged children and adults, and in one scenario increased treatment needs by 72% in Ethiopia and 262% in Zimbabwe. Correspondingly, praziquantel requirements and delivery costs were driven primarily by expanded age eligibility rather than geographic coverage. ConclusionsGeostatistical surveys provide substantial efficiency gains for impact assessments, enabling cost-efficient, granular targeting. However, implementing 2022 WHO guidance was the dominant driver of increases in programme scope and resource needs, underscoring the importance of accurate fine-scale data to guide efficient planning and budgeting toward elimination goals. Author summarySchistosomiasis control programmes are required to use finer-scale data and updated World Health Organization (WHO) guidance to decide where and how often to deliver praziquantel. We analysed national schistosomiasis data and programme costs from Ethiopia and Zimbabwe to compare different approaches to impact assessment surveys and to estimate how treatment needs change under alternative decision rules.We found that model-based geostatistical surveys can reduce the number of people that need to be sampled and the cost of surveys while providing more detailed information for planning at sub-district level. However, when we applied the 2022 WHO schistosomiasis guidance, expanded eligibility (including adults and pre-school-age children and a lower threshold for community-wide treatment) substantially increased the number of people needing treatment. In our scenarios, expanded eligibility drove much larger increases in praziquantel requirements and delivery costs than changes in geographic coverage. Our findings help health ministries to anticipate the operational and budget implications of updated guidance and highlight why accurate fine-scale data are essential for equitable and realistic planning toward elimination.

Infectious disease dynamics are strongly shaped by human mobility, social structure, and heterogeneous contact patterns, yet many epidemic models do not jointly capture these features. This study develops a spatial metapopulation epidemic model incorporating recurrent group-switch interactions to represent real-world transmission processes. Building on the Movement-Interaction-Return framework, the model integrates household structure, age-stratified contacts, and mobility between locations within a single SEIR framework. Using UK demographic, mobility, and social contact data, the model quantifies how within- and between-group interactions, mobility rates, and location connectivity influence epidemic spread. Both deterministic and stochastic simulations are implemented to analyse outbreak dynamics, variability, and fade-out probabilities for COVID-19-like and Ebola-like infections. Results shows that highly connected locations drive faster transmission, earlier epidemic peaks, and greater difficulty in containment, whereas larger but less connected locations tend to produce slower, more localised outbreaks despite their population size. Comparative analysis reveals that COVID-19-like infections spread rapidly and remain difficult to control even under interventions, while Ebola-like infections exhibit slower dynamics and are more effectively contained, particularly under targeted measures. Non-pharmaceutical interventions, particularly widespread closures, substantially reduce infections, hospitalisations, and deaths, although effectiveness depends on timing and pathogen characteristics. These findings highlight the importance of integrating mobility, clustering, and demographic heterogeneity to inform targeted and effective epidemic control strategies.

The global ambition to end the human immunodeficiency virus (HIV) epidemic requires understanding which system-level policy levers, enacted under the framework of Universal Health Coverage (UHC), are most effective in achieving both transmission reduction and diagnostic coverage. This study addresses an important evidence gap by quantifying the within-country association between measurable UHC policy indicators and the estimated rate of new HIV infections across nine Southeast Asian countries between 2013 and 2022. Employing a Fixed-Effects panel data methodology, the analysis controls for time-invariant national heterogeneity, ensuring reliable estimates of policy impact. We found that marginal changes in total current health expenditure (CHE) as a percentage of gross domestic product (GDP) were not statistically significantly associated with changes in HIV incidence. However, increases in the UHC Infectious Disease Service Coverage Index were statistically significantly associated with concurrent reductions in HIV incidence (p < 0.001), suggesting the efficacy of targeted service implementation as the principal driver of curbing new HIV infections. In addition, the UHC Reproductive, Maternal, Newborn, and Child Health Service Coverage Index exhibited a statistically significant positive association with changes in HIV incidence (p < 0.01), which is interpreted as a vital surveillance artefact resulting from expanded detection and reporting of previously undiagnosed HIV cases. Furthermore, out-of-pocket (OOP) health expenditure as a percentage of CHE showed a counter-intuitive negative association with changes in HIV incidence (p < 0.01), suggesting this metric primarily shows ongoing indirect cost burdens on the established patient cohort, or, alternatively, presents a diagnostic access barrier that results in lower case finding. These findings suggest that policymakers should prioritise investment in targeted infectious disease service efficacy over aggregate fiscal commitment and utilise integrated sexual health platforms for strengthened HIV surveillance and case identification.

BackgroundEvidence on the long-term impact of urban green and blue spaces (UGBS) interventions remains limited. This study is a 15-year evaluation of an urban greenway development in Belfast (United Kingdom), assessing the potential effects of this UGBS intervention on physical activity (PA), mental wellbeing and co-benefits. MethodsUsing quasi-experimental design, a repeated cross-sectional survey was conducted in 2010 (baseline), 2017 (post-opening) and 2023 (long-term follow-up) with about 1,200 adults participated each wave. Outcomes included PA, mental wellbeing, general health, quality of life, social capital and environmental perception. Multilevel mixed-effect regressions were performed to examine within-group changes at long-term follow-up. Difference-in-differences analysis investigated the between-group changes that might be attributed to the greenway. Additional comparative analyses included distance-decay analysis and comparison with population trends in Northern Ireland. ResultsAt six years after completion, the greenway intervention appears to buffer a decline in duration of PA - mainly from moderate-intensity activity (decline lower by 118.6 min/week, 95%CI: 3.9-232.2) but with no significant impact on the proportion of the population meeting the recommended PA level. The intervention is associated with a smaller decline in self-rated health (4.98 units; 95%CI: 0.62-9.34) relative to control group. Intervention association with mental wellbeing was positive but not significant (p=0.30). The greenway also showed positive effects on social capital and environmental perceptions, with impacts most evident in improving safety and trust in the local area. ConclusionThis study provides evidence to support the public health impact of UGBS and its long-term health and social benefits.

BackgroundThe rapid growth of the worlds urban population has contributed to the expansion of informal urban settlements in many cities across the world. In these settings, lack of safe sanitation combined with high population density and poverty contributes to heightened health risks for often vulnerable populations. The aim of this study was to evaluate the effect of a shared, onsite sanitation intervention on the nutritional status of children in Maputo, Mozambique. MethodsThe Maputo Sanitation (MapSan) trial was a controlled before-and-after study to evaluate the effect of a shared, onsite sanitation intervention on child health in Maputo, Mozambique. Here, we report the effects on childhood stunting, wasting and underweight, and height-for-age, weight-for-height and weight-for-age z-scores. Children were enrolled aged 1-48 months at baseline and outcomes were measured before and 12 and 24 months after the intervention, with concurrent measurement among children in a comparable control arm. The primary analysis was intention-to-treat. The trial was registered at ClinicalTrials.gov, number NCT02362932. ResultsWe enrolled 757 and 852 children in the intervention and control groups respectively. There was no evidence for an effect of the intervention on any outcome at 12 or 24 months of follow-up except for wasting where there was very weak evidence for an effect (adjusted prevalence ratio: 0.497; 95% CI: 0.22-1.11; p=0.09). In two exploratory analyses - one including only those children born into compounds post-intervention and a second excluding children in control compounds which had independently improved their sanitation facilities during follow-up - we found that stunting increased in the intervention group whilst wasting decreased. ConclusionsThis study contributes to the growing evidence on the role of sanitation in shaping child health outcomes in informal urban settlements. We found no evidence for an effect on stunting and weak evidence for an effect on wasting. More research is needed to understand how sanitation can reduce childhood undernutrition in complex urban environments.

Loneliness--the perceived discrepancy between desired and actual social connection--is a common and aversive psychological state associated with a range of adverse health outcomes. Several theoretical models suggest that these associations may operate partly through health behaviors. In this preregistered study, we used data from the All of Us Research Program to evaluate associations of loneliness and functional rurality (FR), a study-specific contextual index of reduced neighborhood accessibility, with Fitbit-derived physical activity and sleep outcomes. Final samples included 16,912 participants for physical activity analyses and 13,937 for sleep analyses. In adjusted models, higher FR was associated with greater loneliness ({beta} = 0.061, 95% CI [0.045, 0.077], p = 9.63 x 10-14). FR and loneliness were independently associated with fewer daily steps and lower moderate-to-vigorous physical activity. Loneliness was also associated with shorter sleep duration, greater sleep duration variability, higher odds of short sleep, and higher odds of low sleep efficiency. FR was not associated with sleep duration or sleep duration variability but showed a small positive association with mean sleep efficiency and lower odds of low sleep efficiency. Interaction analyses provided little evidence that FR modified the associations of loneliness with most outcomes, although the FR x loneliness interaction was significant for sleep duration variability, indicating that loneliness was more strongly associated with irregular sleep duration in higher-FR contexts. Sensitivity analyses using stricter valid-day thresholds, winsorization, quartile-based exposure coding, and a backward 30-day window yielded directionally similar findings. These results suggest that FR and loneliness are independently associated with lower physical activity, whereas loneliness shows a more consistent relationship with adverse sleep patterns.

Mycetoma is a neglected tropical disease caused by various bacterial and fungal pathogens that has a significant health impact across a broad geographically defined "mycetoma belt" spanning South America, Africa and Asia. Histologically, mycetoma is characterised by invasive and destructive granuloma development in the skin, deep tissues and bone, leading to tissue destruction, deformities and high morbidity. The presence of macroscopic, highly compacted pathogen microcolonies, or "grains," is a key diagnostic feature, and the formation of grains supports pathogen persistence and disease chronicity. However, there is a paucity of information on immune responses in mycetoma patients and on the relative importance of phylogeny and/or grains in establishing the local immune landscape. Here, we used spatial proteomics to examine the distribution of 43 immune-related proteins in surgical biopsies from 11 patients with mycetoma of bacterial (Actinomycetoma; Actinomadura pelletierii and Streptomyces somaliensis; n=6) and fungal (Eumycetoma; Madurella mycetomatis; n=5) origin. Using mixed-effects modelling, an exploratory analysis across species and pathogen classes revealed few significant differences in immune marker expression. In contrast, and independently of pathogen class, the cellular infiltrate closest to grain boundaries had higher per-cell expression of CD66b+, ARG1, and VISTA. The preferential accumulation of CD66b+ARG1+VISTA+ cells at grain boundaries was confirmed by quantitative immunofluorescence analysis. Hence, the local tissue microenvironment surrounding the mycetoma grain represents a specialised immunosuppressive niche, with parallels to the tumour microenvironment.

We used 2492 whole genome sequences from Laos to investigate the molecular epidemiology of SARS-CoV-2 from 2021 through 2024, covering the major waves of COVID-19 disease in Laos including time periods of travel restrictions and after relaxation of travel across international borders. We identify successive waves of COVID-19 caused by shifts in the dominant lineage, beginning with the Alpha variant in April 2021 and continuing through the Delta and Omicron variants. We quantify a shift from a small number of viral introductions responsible for widespread transmission in early waves to a larger number of introductions for each variant after travel restrictions were lifted, and identify potential routes of introduction into the country. Our study underscores the importance of genomic surveillance to public health responses to characterize viral transmission dynamics during pandemics.

BackgroundDelayed tuberculosis (TB) treatment remains a major challenge to TB control and is associated with increased mortality, drug resistance, and onward transmission. Food insecurity may contribute to delayed TB treatment through economic, physical, and psychosocial pathways. Depression and anxiety are also associated with delayed TB treatment and may mediate the relationship between food insecurity and delayed TB treatment. This study examined the association between food insecurity and delayed TB treatment initiation and assessed the mediation roles of depression and anxiety for this relationship among people newly diagnosed with TB. MethodsWe recruited 180 participants newly diagnosed with TB in Gaborone, Botswana. Food insecurity, depression, and anxiety were measured using the Household Food Insecurity Access Scale, PHQ-9, and Zung Self-Rating Anxiety Scale, respectively. Delayed TB treatment was defined as > 2 months since first TB symptoms. Logistic regression was used to examine the association between food insecurity and delayed TB treatment. Causal mediation analysis was conducted to assess the mediating roles of depression and anxiety. ResultsAmong the 180 participants, 45 (25%) experienced delayed TB treatment initiation. Participants with delayed TB treatment had slightly higher median scores for food insecurity (2 vs. 1, p = 0.11), depression (9 vs. 6, p = 0.001), and anxiety (37 vs. 34, p = 0.05). There was insufficient evidence of an overall association between food insecurity and delayed TB treatment initiation (OR = 1.04, 95% CI 0.98-1.11, p = 0.20). Mediation analysis found insufficient evidence of total and direct effects through depression and anxiety. However, there was evidence of significant indirect effect through depression (OR = 1.04, 95% CI 1.01-1.08, p < 0.001) and a borderline indirect effect through anxiety (OR = 1.02, 95% CI 1.00-1.04, p = 0.05). ConclusionMediation analysis revealed associations between food insecurity and delayed TB treatment initiation mediated by depression and anxiety which were not evident in total effects analysis. These findings highlight the importance of considering both socioeconomic and psychological factors in addressing delayed TB treatment. Further studies are needed to confirm these pathways.