GeoHealth

Background: Climate mitigation policies can lower air pollutant concentrations and deliver substantial health co-benefits. The French Ecological Transition Agency (ADEME) proposed four contrasting Transitions 2050 net-zero scenarios. We quantified mortality, morbidity, and health-economic co-benefits from projected PM2.5 and NO2 reductions across all four scenarios in continental France. Methods: Emission projections were input to the CHIMERE chemistry-transport model to estimate PM2.5 and NO2 concentrations for 2030 and 2050. Health impacts were assessed using disease-specific cessation-lag assumptions relative to 2019, covering premature mortality, morbidity, DALYs, and economic benefits across nine outcomes (hypertension, lung cancer, ischaemic heart disease, stroke, COPD, type-2 diabetes, acute lower respiratory infections, and asthma in children and adults). Findings: Population exposure is projected to decline by about 40% for PM2.5 and 70% for NO2 by 2050, with health gains remaining substantial and broadly equivalent across all four scenarios and modest differences between sufficiency-oriented and technology-driven pathways. Under delayed-impact assumptions, avoided premature deaths ranged from 21,300 to 22,100 for PM2.5 and 24,500 to 26,200 for NO2. Morbidity and disability-adjusted life year (DALY) reductions, as well as economic savings, spanned similarly; total avoided morbidity cases were 84,000-88,000, direct medical cost reductions were e1.0-1.1 billion/year, and intangible cost savings of e41-43 billion and e36-39 billion, respectively. Interpretation: Health co-benefits are substantial, consistent across contrasting scenarios, and increase markedly from 2030 to 2050. Explicitly incorporating these co-benefits into climate policy appraisals may strengthen the case for ambitious mitigation and improve decision-maker acceptability.

Background Childhood overweight and obesity represent major public health challenges, shaped by socio-economic and environmental factors. This study investigates the mediating and moderating role of urban environmental exposures in socio-economic disparities in childhood excess weight. Methods Data was drawn from a population-based sample of children (2-9 years) and adolescents (10-17 years) living in Geneva, Switzerland. Parents reported household financial situation and children's height and weight, from which excess weight (i.e. overweight or obesity) was derived. Residential exposures to air pollution (PM2.5, NO2), noise (daytime, nighttime), and neighborhood greenness (green areas, canopy coverage) were estimated based on geocoded residential addresses. The association between household financial situation and excess weight was evaluated, as well as the mediating and moderating roles of urban environmental exposures. Results The analysis included 1006 children and 1154 adolescents. Among children, an average-to-poor household financial situation was associated with higher odds of excess weight in children (adjusted odds ratio [aOR]: 1.79, 95% confidence interval [CI]: 1.13; 2.84). Higher noise exposure was associated with excess weight (daytime: aOR: 1.40, 95% CI: 1.10; 1.77, nighttime: aOR: 1.37, 95% CI: 1.08; 1.74), while the association with PM2.5 appeared stronger among socio-economically disadvantaged children, though the interaction did not reach statistical significance (financial situation x PM2.5 interaction: aOR: 1.59, 95% CI: 0.98; 2.59). No significant associations were observed among adolescents. Conclusion These findings highlight the joint influence of social and environmental inequalities on childhood excess weight and stress the need to address these interconnected determinants to design equitable, targeted public health interventions.

Introduction Air pollution is the largest environmental risk to human health. Children are disproportionately affected by air pollution and their exposure is amplified during physical activity. Observed concentrations of nitrogen dioxide in 1 in 4 London school playground exceeds the European limit, but the health impacts of air pollution exposure in London school playgrounds remain unexplored. Our study aims to assess and compare the acute changes in lung function and airway inflammation of primary school-aged children exercising in school playgrounds. Methods and analysis 330 children aged 8 to 11 years from ten London schools will be recruited to complete 90 minutes of physical activity and 90 minutes of rest in their school playground in a randomised crossover design. Pre-, post-, and 24-hour post-exposure oscillometry measurements will be performed with airway resistance at 5 Hz (R5) the primary physiological outcome. Nasal lavage samples will be collected pre-exposure and 24-hour post-exposure for analysis of inflammatory, oxidative, and vascular biomarkers, with IL-6 as the primary biological outcome. Mixed-effects regression models will examine associations between estimated pollutant exposures, exercise and physiological responses.

Background: Dengue is a major public health problem in Brazil, and Minas Gerais is one of the states with the highest burden. In January 2019, the Brumadinho dam collapse released about 12 million cubic meters of iron ore tailings into the Paraopeba River basin, causing environmental disturbance that could plausibly affect vector habitats and dengue transmission. We evaluated the spatiotemporal dynamics of dengue in Minas Gerais from 2014 to 2023 and tested whether the disaster was associated with changes in affected municipalities. Methods: We performed an ecological spatiotemporal analysis using dengue notifications from SINAN for all municipalities in Minas Gerais (2014-2023). Municipalities were classified as Paraopeba basin, regional controls, or state controls. Temporal similarity was assessed using Pearson correlation-based hierarchical clustering and non-metric multidimensional scaling (NMDS). Sources of variation were examined with PERMANOVA and principal component analysis (PCA). A linear mixed-effects model with municipality as a random effect was used to test changes after 2019, with pre/post contrasts estimated from marginal means. Results: Dengue showed strong temporal synchrony across the state, with major epidemic peaks in 2015-2016, 2019, and 2023. Health region explained 31.5% of the variation in temporal incidence profiles (p = 0.001), whereas Paraopeba basin status explained no significant variation (p = 0.998). No temporal cluster was enriched for municipalities in the Paraopeba basin. PCA identified 2023, 2019, and 2016 as the main years driving variability. In the mixed model, year was significant (p < 0.001), but Paraopeba basin status and its interaction with time were not. Incidence increased significantly after 2019 in non-exposed municipalities (p < 0.001), but not in basin municipalities (p = 0.088). Conclusions: Dengue dynamics in Minas Gerais were driven mainly by regional and state-wide epidemic processes, with no significant independent effect of the Brumadinho dam collapse on notified dengue patterns.

INTRODUCTION: Alzheimers disease and related dementias (AD/ADRD) arise from cumulative environmental, social, behavioral, and biological influences across the life course. The neural exposome framework conceptualizes how exogenous, behavioral, and endogenous factors interact to shape brain health; however, its application to preclinical AD/ADRD research, particularly in rural populations, remains limited. METHODS: We developed and piloted a community-embedded, decentralized research model to operationalize the neural exposome framework among cognitively unimpaired adults aged 45+ in two rural Midwestern U.S. communities, integrating environmental, social, behavioral, geospatial, and biological measures to evaluate exposure-related neurobiological and cognitive vulnerability. RESULTS: This approach demonstrated high feasibility and acceptability, achieving strong recruitment, retention, data completeness, and multidomain biomarker collection in rural community-based settings DISCUSSION: Pilot findings support the feasibility of neural exposome-informed research in rural U.S. communities and highlight its potential to advance prevention-oriented research on brain health and AD/ADRD.

Introduction Air pollution is a leading driver of cardiovascular disease with a growing body of literature implicating this in worse glucose homeostasis. Increases in fine particulate matter air pollution (PM2.5) are associated with increased blood glucose and hemoglobin A1c across the glycemic spectrum from normoglycemia to prediabetes to all forms of diabetes. Despite strong evidence for positive associations of PM2.5 with dysglycemia, it remains unknown if reducing air pollution exposure through air filtration can effect improvements in glucose. This study aims to test the hypothesis that short-term, in-home air pollution reduction using high efficiency particulate air (HEPA) filtration will improve blood sugar in adults with prediabetes. Methods and analysis This trial is a randomized, double-blind, sham-controlled trial of the effects of lowering air pollution exposure using HEPA filtration on cardiometabolic health in adults with prediabetes living in the New York City area. Participants will be randomly assigned to use bedroom air cleaners, or sham air cleaners, while measuring PM2.5 continuously for 1 month. The primary outcomes will be continuous glucose monitoring metrics measured before and after HEPA air filtration. Exploratory outcomes will include insulin resistance measures, serum biomarkers and transcriptomics measured before and after HEPA intervention. We will quantify effects of HEPA filtration with models using treatment arm (true versus sham filtration) as the independent variable. Secondary analyses will model continuous measures of PM2.5 as the independent variable. Ethics and Dissemination This study has undergone peer review; and the work was supported by Grant 2023-0214 from the Doris Duke Foundation, who had no other role in study design or implementation. The study was registered in ClinicalTrials.gov (NCT05994937) prior to recruitment. Clinical Trials Clinical Trials NCT05994937; https://clinicaltrials.gov/study/NCT05994937

Background: The 2026 FIFA World Cup may bring over one million visitors to North America from around the globe to participate in mass gathering events. The nature of the event and recent news have raised concerns for some that the tournament could lead to infectious disease outbreaks or fuel existing epidemics. Objective: To systematically assess the infectious disease threat posed to the United States by the tournament. Design: A multi-institutional team evaluated pathogen-specific risk across three dimensions: importation, outbreak potential, and impact to identify a priority pathogen list. A systematic screening protocol ensured common criteria and that pathogen information was collected when necessary to inform inclusion. Results: Increased risk from the World Cup is near zero for 63 of 77 evaluated pathogens. Pathogens were predominantly excluded as threats due to low excess importation risk and low outbreak potential if introduced. The remaining priority pathogens fall into five categories: (a) mosquito borne pathogens with the potential for sustained transmission in some host cities, (b) seasonal respiratory viruses, (c) chronic infections with high prevalence outside the United States, (d) pathogens present in the United States with likely increased transmission at World Cup activities, and (e) high-consequence infectious threats. Limitations: Data availability is variable across diseases. Impact calculations may not reflect actual costs to host cities. Disease incidence in World Cup travelers may differ from national incidence rates. Conclusion: While infectious disease outbreaks at the 2026 FIFA World Cup are possible, in an already highly connected world where large gatherings are frequent, the elevated risk from the tournament is not as extreme as it first may seem.

Background. On 8 May 2023 the Japanese Ministry of Health, Labour and Welfare reclassified COVID-19 under the Infectious Disease Control Law from a designated infectious disease (with case-by-case reporting requirements comparable to those of a Category-2 disease) to a Category-5 ("Class-5") notifiable disease, joining the same category as seasonal influenza and most other endemic respiratory infections. Under this regime, COVID-19 case counts are reported weekly from a nationwide network of sentinel medical facilities (initially approximately 5,000, reduced to approximately 3,000 following an April 2025 surveillance reform), and individual case reporting is no longer required. We aimed to characterize the spatial topology of COVID-19 epidemics under this sentinel-surveillance regime and to detect, in a data-driven manner, any structural change in epidemic dynamics over this period. Methods. We analyzed weekly per-sentinel-facility COVID-19 case counts in all 47 prefectures of Japan from 2023-W17 to 2026-W19 (159 weeks). For each week we computed the Shannon pseudo-entropy S of the prefecture-share distribution and global, local, and time-lagged Moran's I across a 92-edge contiguity-based adjacency matrix. To identify any structural change in a data-driven manner, we adopted a two-stage approach motivated by an empirical regularity established in Section 3: we first verified the wave-amplitude-invariant entropy ceiling (S_max >= 3.80 in all five pre-transition waves), then restricted change-point detection to the weeks after S(t) last attained this ceiling, applying PELT, CUSUM, and Bai-Perron sup-F within this restricted region. Seasonal structure was characterized by truncated Fourier regression with first-order autoregressive errors (Cochrane-Orcutt) over harmonic orders K = 1 to 6; between-period comparisons used moving block bootstrap as the principal inferential statistic. Results. The five epidemic waves during 2023-2025 followed a stereotyped spatial template in which S(t) traced a characteristic U-shape around each peak, with a wave-amplitude-invariant entropy ceiling reaching on average 99.4% of the theoretical maximum ln 47 (range 3.820-3.836, SD 0.006). The last week in which S(t) attained this entropy ceiling was 2025-W42. Restricting change-point detection to the 29 subsequent weeks, PELT and CUSUM localised the structural break to late 2025: PELT identified 2025-W48 (robust across penalty values >= sigma^2*ln(n) and across entropy-ceiling thresholds 3.78-3.82) and CUSUM peaked at 2025-W50 (p < 0.0001), placing the break within a two-week window centred on late November 2025. Bai-Perron sup-F peaked later at 2026-W02 (p = 0.062, with reduced power on n = 29). We adopted 2025-W48 as the principal change-point, defining 135 pre-transition weeks and 24 post-transition weeks. Two anti-phase spatial modes were identified in the pre-transition record: a summer-onset Okinawa-seeded Kyushu cascade (Mode A; annual peak epi week 26) and a winter-onset Tohoku-centred connected-cluster mode (Mode B; annual peak epi week 51), approximately 25 epi weeks out of phase. After the regime transition, this ceiling was not attained, and the spatial-persistence ratio I(tau = 8 wk)/I(0) shifted from a highly variable distribution centred near 0.27 (pre-transition, 125 weeks) to a tightly clustered distribution around 0.89 (post-transition, 24 weeks); the mean difference was 0.62 (95% bootstrap CI 0.32 to 0.90; moving block bootstrap p < 0.0001 across block lengths 1-12). The principal finding remained significant under autoregressive-augmented null models and was robust to adjacency-matrix choice, the April 2025 surveillance reform, harmonic order K = 1 to 6, and Okinawa exclusion. Conclusions. Data-driven analysis of 159 weeks of Japanese sentinel surveillance identifies a candidate spatial-persistence regime transition emerging in late November 2025, in which the spatial structure of weekly case shares persists for at least 8 weeks rather than dissipating as in pre-transition. The transition coincides with loss of the wave-amplitude-invariant entropy ceiling and with absence of the Mode A signature through the observed post-transition period. The recent uptick in Okinawa case shares (continuing through 2026-W19) leaves open whether the Mode A signature is structurally suppressed or merely deferred; observation through summer 2026 is required to distinguish a sustained shift from a transient anomaly.

Hybrid mechanistic-statistical models offer interpretability and adaptability for short-term seasonal epidemic forecasting, but it remains unclear whether their accuracy depends more on increased biological complexity or on the assimilation of richer data. Using eight retrospective influenza seasons in North Carolina, we evaluate whether training on historical data and assimilating auxiliary emergency department (ED) visit data improves four-week-ahead hospital admission forecasts more than adding biological complexity (multi-subtype structure and cross-season immunity). Hierarchical Bayesian training on historical data improves accuracy by 22.4 % (95 % CI: 16.4-28.1 %), and inclusion of ED visit data yields a further 5.3 % (95 % CI: 3.0-7.6 %) improvement, whereas added biological complexity produces diminishing or null gains. We further observe a substitution effect in which ED visit data partially compensates for omitted biological structure. We deployed a simplified model variant in the 2025-2026 CDC FluSight Challenge and ranked among the top ensemble performers, supporting the robustness of Bayesian hierarchical training in real time. Together, these findings indicate that short-term forecast accuracy is driven more by historical learning and assimilating auxiliary signals than by biological fidelity, with implications for how forecasting systems should balance mechanistic complexity.

This work focuses on the global and partial identification problem for fractional differential equations. We provide a general numerical procedure based on global and local optimization algorithms with two refinements for biological systems that ensure solution positivity and homogeneous parameter units. The method is applied to a new fractional model of Dengue outbreak called the Fractional Homogeneous Nishiura (FHN) model, calibrated using data of newly infected people in Cape Verde. We show that our identification method yields a better fit between data and model solutions than previous approaches and that our FHN model captures the dynamics of Dengue more closely than existing systems.

INTRODUCTION: Alzheimers disease (AD) is a multifactorial disorder, yet current research largely focuses on downstream biomarkers with limited attention to environmental contributors. Experimental studies suggest that per and polyfluoroalkyl substances (PFAS) may contribute to neuroimmune and neurodegenerative pathways relevant to AD. OBJECTIVE: To examine associations between PFAS exposure and neuroimmune and AD related plasma biomarkers in cognitively unimpaired rural adults. METHODS: In a cross sectional pilot study (n=48), serum concentrations of 33 PFAS were measured, including four legacy compounds (PFOS, PFHxS, PFOA, PFNA). Plasma neuroimmune related (ITGB2, SMOC1, TREM2, GFAP) and AD related biomarkers (Ab42/40, ptau217) were detected using proteomic analysis. RESULTS: PFOS showed moderate associations with ITGB2, SMOC1, and Ab42/40 in unadjusted analyses, which attenuated after adjustment for age. PFOA and PFNA demonstrated consistent inverse associations with TREM2 before and after adjustment. DISCUSSION: Findings suggest possible compound specific PFAS associations with immune and amyloid related biomarkers, supporting further investigation in longitudinal and PFAS mixture based studies.

Lead is a toxic metal ubiquitous in our environment. While dramatic reductions in lead sources have paralleled equivalent decreases in lead-poisoning rates, chronic lead exposure remains a critical public health concern. Childhood lead exposure (at its lowest levels) is liked to changes in cognitive development but less is known about lead's effects on children's brain structure, especially as a result of in utero exposure. We measured prenatal and early-postnatal lead exposure in shed deciduous teeth of 448 9- and 10-year-old children (from 20 United States cities) and linked those lead levels to childhood brain structure, cognition/behavior, and neighborhood- and family-level socioeconomic characteristics. Here we show negative associations between tooth-lead levels and the thickness of the brain's cortex, particularly in regions linked to language processing. With increasing tooth-lead levels, children of lower-income (versus higher-income) families showed steeper declines in receptive vocabulary. Caregiver-reported behavioral problems exhibited similar associations. With in utero exposure linked to adverse neurodevelopmental outcomes (well before lead exposure and its risks are evaluated by healthcare professionals), prenatal screening of maternal lead levels/exposure, coupled with recommended strategies to reduce its placental transmission, may help reduce lead's effects on future generations.

Sex work is diverse and includes a broad range of people and settings. Over the last thirty years, a large proportion of public health emergencies of international concern (PHEIC) have involved infections transmitted through sexual or close contact and in sexual networks (WHO 2024). Sex workers can face increased disadvantage in relation to these public health emergencies. Given the significant health inequalities sex workers can face, they should be eligible to receive targeted and tailored health support to reduce health protection risks (Hester 2019; Jeal and Salisbury 2004a). However, they are often not explicitly eligible for targeted and tailored support due to a lack of information on incidence, prevalence of disease, and even more basic data such as reliable estimates of the number of sex workers in the UK. Accordingly, the aim of this paper is to determine a population size estimate, with uncertainty, that is more robust than those currently available. In this study, we apply Bayesian Evidence Synthesis to bring together historic estimation efforts with recent ONS National Population Estimates and Genito-Urinary Medicine Clinics Attendance Data (GUMCAD) from the UK Health Security Agency (UKHSA). A key feature of our model is the embedding of uncertainty from each input study in model priors, hence propagating it through to our final estimate. The Bayesian evidence synthesis model estimated a total of 84,000 sex workers in the United Kingdom (95% credible interval: 49,000-130,000), representing 0.121% of the current UK population.

Background Rabies is a zoonotic neglected public health problem associated with animal bites, especially domestic carnivores claiming 59,000 deaths annually predominantly in developing countries of Africa and Asia. There is a high risk of exposure among rural communities endemic with animal rabies where adoption of prevention strategies is minimal. This study determined the prevalence of suspected rabies exposure, associated factors, and delayed post-exposure care-seeking among animal-bite human cases in Busia district, Uganda. Methods: This was a cross-sectional study that involved 332 consecutively sampled animal bite human cases that occurred within the period 2023 to 2024. Data for the bite cases from records were collected using a data abstraction tool. In addition, interviewer-administered semi-structured questionnaires were used to collect data on sociodemographic, animal-related and environmental characteristics. Approximate bite locations were collected using Global Positioning System (GPS) coordinates via Kobo collect. Analysis was carried out in STATA 17 using mixed effects modified Poisson regression for factors associated with suspected rabies exposure. Results: The median age of the bite cases was 18 (IQR: 9-36) with the male gender predominantly affected. The prevalence of suspected rabies exposure was 53.6% (95% Confidence interval - CI: 46.8-60.3). Factors associated were urban versus (vs) rural residence (adjusted prevalence ratio-aPR: 1.04, 95%CI: 1.00-1.08), being bitten by a stray animal (aPR: 1.28, 95% CI: 1.22-1.35) and wild animal (aPR: 1.22, 95% CI: 1.14-1.30) vs domestic animal, vaccination status of the biting animal i.e. vaccinated vs unvaccinated (aPR: 0.76, 95% CI: 0.69-0.85), provoked vs unprovoked bites (aPR: 0.82, 95% CI: 0.79-0.86), and distance to nearest river ([&ge;]5km) vs <5km (aPR: 0.93, 95% CI: 0.87-0.99). The prevalence of delayed post-exposure seeking was 23.0% (95% CI: 16.5-31.1) among the suspected rabies exposures. Conclusion: The study reveals a high prevalence of suspected rabies exposure. Factors associated are multidimensional i.e. are of human, animal and environmental origin. The one health paradigm should be emphasized during routine surveillance of rabies-related cases. The study observed that 1 in 5 bite cases delayed to seek care post bite exposure. We recommend collaborations between sectors, routine vaccination and awareness campaigns, and monitoring of wild carnivore populations and environmental dynamics in rabies-related surveillance.

Background Oropouche virus (OROV) is an emerging vector-borne virus with rapidly expanding geographic range, increasing case counts, and growing evidence of severe outcomes including neuroinvasive disease and vertical transmission. Because OROV infection presents with nonspecific febrile illness that overlaps clinically with other viruses including dengue, zika, and chikungunya, accurate molecular diagnostics are essential for patient care and surveillance. Yet existing assays rely on single genomic targets and are vulnerable to detection failure as the virus evolves and reassorts. Methodology/Principal Findings To support diagnostic capacity, we developed and clinically validated a multiplexed qPCR assay targeting three regions of the OROV S segment, incorporating redundancy to preserve sensitivity across viral diversity while enabling robust clinical interpretation. The multiplex also includes an assay targeting RNaseP as an internal sample control to ensure adequate sample processing. We evaluated assay performance using both historical and contemporary OROV strains and validated the assay on contrived serum, plasma, and cerebrospinal fluid samples, assessing linearity, limit of detection (LOD), accuracy, specificity, precision, and sample stability. The assay met or exceeded all predefined acceptance criteria for clinical testing and achieved an LOD as low as 6 copies per reaction for contemporary outbreak strains. We further implemented a logic-based interpretation matrix that reduced false-positive risk while maintaining sensitivity near the analytical LOD. Conclusions/Significance Our assay sensitively and specifically detects OROV RNA in serum, plasma, and cerebrospinal fluid while incorporating safeguards against viral evolution and reassortment. The assay has been approved for use by CLIA at Nexus Medical Labs in 49 U.S. states, expanding access to timely OROV diagnostics in the United States and providing a durable framework for molecular detection of reassorting, rapidly evolving viruses as OROV continues to spread into new regions.

Background: Mixture epidemiology deploys sophisticated estimators, Bayesian kernel machine regression with causal mediation analysis (BKMR-CMA), quantile G-computation (QGC), and parametric G-computation, alongside conventional regression. Comparative evaluations have assumed additive, non-mediated data-generating processes, leaving conditions under which estimator choice determines causal validity uncharacterized. Methods: We developed a simulation framework using military-relevant exposure distributions (metals, per- and polyfluoroalkyl substances [PFAS], polychlorinated biphenyls [PCBs]) and allostatic load (AL) across three deployment tiers, with parameters drawn from military occupational health and contamination literature. Four data-generating processes were specified as directed acyclic graphs: direct effects with confounding (M1), full mediation through AL (M2), synergistic AL-exposure interaction (M3), and collider structure (M4). We evaluated ordinary least squares (OLS), QGC, G-computation, and BKMR-CMA on bias, root mean squared error, and 95% confidence interval coverage across 500 Monte Carlo replications at n = 500 and n = 1,000. Results: No estimator dominated across all mechanisms. Under M1, OLS and G-computation produced near-identical modest positive bias; BKMR-CMA achieved lower root mean squared error through kernel shrinkage. Under M2, BKMR-CMA exhibited severe positive bias for AL (mean bias = +0.579 SD units; coverage = 32.8%). Under M3, BKMR-CMA was the only estimator achieving nominal 95% coverage for AL (95.2%), while regression-based approaches fell to 83.6%. Under M4, G-computation produced persistent bias and near-zero coverage for lead, reflecting structural non-identification. Conclusions: Estimator validity is fundamentally mechanism-dependent. Researchers should base estimator choice on explicit causal assumptions about whether AL functions as confounder, mediator, moderator, or collider, particularly in military and occupational cohorts. We provide a mechanism-to-estimator mapping for applied researchers.

While SARS-CoV-2 and influenza continue to place a significant burden on population health, within-household differences in decisions towards vaccination and seeking care across these two pathogens, and across sociodemographic groups, remain largely unexplored. By conducting a household-level survey in Illinois, we found that many individuals made inconsistent decisions about vaccination: among all adults, 29% were vaccinated for only one of COVID-19 or influenza, and among those with children in the home, 39% lived with a child whose influenza or COVID-19 vaccination status differed from their own. A higher proportion of adults were vaccinated against COVID-19 compared to influenza, while the opposite was true for those younger than 18 years old. These differences hold even when accounting for disparities in coverage by age, race/ethnicity, political affiliation, and socioeconomic status. While vaccinated individuals consistently reported wanting to protect themselves or others, those who declined vaccination reported highly heterogeneous reasons ranging from resource constraints to distrust or misconceptions about vaccination. These differences are even more pronounced for COVID-19, with larger partisan gaps and higher refusal driven by safety concerns, lack of trust, or religious reasons than those who decide not to get the influenza vaccine. In contrast to vaccination, the decision to seek medical care when sick showed opposite sociodemographic trends, that are likely attributable to illness severity. Our findings highlight that closing gaps in COVID-19 and influenza vaccination coverage will require an integrative strategy that accounts for diverse motivations, fears, and barriers to access, while addressing social inequalities common to both diseases.

Successive dengue virus (DENV) outbreaks can progressively reshape population immunity influencing disease expression and diagnostic performance. Objectives The aim was to evaluate the impact of secondary infections across sequential outbreaks on clinical severity, serotype dynamics and diagnostic concordance. Methods This retrospective study analyzed 976 febrile-stage samples from three sequential outbreaks in Misiones, Argentina. For serotyping and clinical analyses, 869 viremic samples confirmed by at least one direct method were included (2016: n=512; 2019: n=148; 2024: n=209). Additionally, 318 samples, including 107 non-viremic cases, were used to compare NS1 rapid diagnostic tests (NS1 Ag) and RT-PCR. Viral serotyping and clinical and laboratory markers of disease severity were evaluated. Results Secondary infections increased from 31.05% (2016) to 43.24% (2019) and 53.87% (2024) (p<0.0010). Serotype distribution shifted from DENV-1 predominance in 2016 (95.12%), DENV-1/DENV-4 co-circulation in 2019 (60.71%/39.29%), and DENV-2 predominance in 2024 (97.60%). Secondary infections were associated with more severe disease manifestations, particularly in 2024, with higher hematocrit (p=0.0120) and hemoglobin (p=0.0080), lower white blood cells (p=0.020) and platelet counts (p=0.0030), and elevated AST (p=0.0007) and ALT (p=0.0130). Concordance between NS1 Ag and RT-PCR was lower in secondary infections (k=0.457 vs k=0.759, p=0.0013). Conclusions The rising frequency of secondary infections may affect both clinical severity and diagnostic performance during outbreaks. The clinical impact was more evident in 2024, likely associated with the introduction of a new serotype. These findings highlight the need for optimized surveillance and diagnostic strategies to improve case detection and patient management during epidemics.

Large epidemics of invasive meningococcal disease are rare in temperate regions. Here, we analyse administrative data on the largely forgotten epidemic of bacterial meningococcal meningitis that occurred in Glasgow in 1907, probably the largest on record in the UK. The epidemic, predominantly confined to the city, killed around 1,000 people, had a case fatality rate of nearly 70%, and hit infants and young children the hardest. We show the rapid rise and fall in cases and the spatial distribution of incidence and mortality rates within the city. We find that within-household overcrowding was a key driver of incidence whereas between-household geographic proximity was not. We also find that the spatial distribution of disease risk during the epidemic persisted in the post-epidemic period and during a later outbreak. The findings suggest that interventions should prioritise populations in areas that have experienced higher incidence rates to mitigate the risk of future outbreaks.

Healthcare waste (HCW) management is a critical determinant of occupational safety, infection control, and environmental protection, particularly in low- and middle-income settings. Using the knowledge-attitude-practice (KAP) framework, this study assessed cognitive, behavioral, and institutional dimensions of HCW management among healthcare workers in urban Bangladesh. A cross-sectional survey was conducted among 342 cleaners and nurses in hospitals in the Chattogram Metropolitan Area (CMA) and Cumilla City Corporation (CuCC). Marked disparities were observed across professional groups. Training coverage was significantly lower among nurses than cleaners in CMA (22.5% vs. 48.7%; p = 0.002), whereas in CuCC nurses showed higher coverage (69.0% vs. 52.3%; p < 0.01). Knowledge of color-coded waste segregation was generally inadequate, with only 39.3% of CMA cleaners correctly identifying pharmaceutical waste bins compared with 60.0% of nurses (p < 0.01); CuCC nurses demonstrated substantially higher awareness (82.8%). Attitudinal indicators favored nurses, with strong hygiene and environmental risk awareness (95-100%) compared with cleaners (66-87.3%; p < 0.001). Despite this, compliance with segregation practices remained low across both sites (<30%). Several institutional support indicators were more favorable among nurses, particularly in CuCC. These findings indicate a significant knowledge-practice gap, emphasizing that effective HCW management requires not only training but also strengthened institutional structures and enforcement mechanisms to reduce public health and environmental risks.