GeoHealth

Exposure to higher levels of ambient air pollution during pregnancy has been linked to multiple adverse pregnancy outcomes. However, studies on acute exposures and reduced gestation length have reported inconsistent findings. This project aims to examine the acute association between ambient air pollution and preterm (28-36 gestational weeks) or early-term (37-38 gestational weeks) births. Daily concentrations of 12 air pollutants, based on bias-corrected numerical model outputs, were linked to vital records of singleton live preterm and early-term births from 2005-2017 in California, Florida, Georgia, Kansas, Nevada, New Jersey, North Carolina (2005-2015), and Oregon. Under a time-stratified case-crossover design, odds ratios (OR) were estimated via conditional logistic regression with adjustment for risks among ongoing pregnancies, meteorology, time trends and federal holidays. We estimated cumulative associations up to a 6-day lag using distributed lag models. Risk estimates per interquartile range (IQR) increase in exposure were pooled across states using inverse-variance weighting. Our study included 1,085,162 preterm and 3,901,185 early-term births. We observed positive associations between 0-2 day cumulate exposure to several air pollutants and early-term births, including NO2 (OR: 1.0023, 95% CI:1.0010, 1.0037 per 7.1 g/m3 increase), PM2.5 (OR=1.0022, 95% CI: 1.006, 1.0038 per 4.6 g/m3 increase), PM2.5 organic carbon (OR= 1.0026, 95% CI: 1.0013, 1.0039 per 1.7 g/m3 increase) and PM2.5 elemental carbon (OR=1.0025, 95% CI: 1.0014, 1.0035 per 0.26 g/m3 increase). Associations with preterm birth were mostly null. In conclusion, we found positive associations between short-term air pollution exposure, including PM and major PM2.5 components, and risks of early-term birth.

Background Coccidioidomycosis is an emerging fungal disease across the arid Americas and a frequent cause of community-acquired pneumonia. Understanding where Coccidioides populations originate, how they move across space, and whether they are expanding is important for interpreting changing patterns of Valley fever and anticipating future infection risk. Methods We prospectively collected and whole-genome sequenced 186 Coccidioides-positive clinical isolates submitted to a national diagnostic laboratory, and included 126 previously sequenced genomes. We applied genomic clustering, time-calibrated phylogenetic reconstruction, ancestral area reconstruction, mating-type assignment, and demographic inference to identify major populations, infer dispersal patterns, assess evidence for recombination and clonality, and reconstruct historical population dynamics. Findings We analyzed 312 genomes (139 C. immitis; 173 C. posadasii) and identified three major genetic populations within each species. C. immitis included two California-centered populations and one Pacific Northwest population, whereas C. posadasii included two Arizona-centered populations and one Texas-centered population. The most recent common ancestor was estimated at approximately 127,000 years for C. immitis and 234,000 years for C. posadasii. Most populations were not fully monophyletic, consistent with retained ancestral variation and/or ongoing gene flow. Inferred dispersal was largely asymmetric, with most movement originating from California in C. immitis and from Arizona and Texas in C. posadasii. Most populations contained both mating types, but one C. immitis population and a Brazilian subgroup of C. posadasii were clonal. All populations showed recent demographic expansion. Interpretation The evolutionary history of Coccidioides is characterized by strong geographic structure, ongoing gene flow, and recent demographic expansion. These processes are likely to influence future patterns of Valley fever endemicity and supports the use of genomic surveillance to detect shifts in disease risk as environmental conditions change.

Antimicrobial resistance (AMR) is an ever-increasing threat to population health. Industrial, environmental and societal factors are increasingly recognized as important contributors to AMR within communities. Here, we investigated the spatial distribution of AMR genes (ARGs) across Alberta, Canada and their association with socio-economic, immigration-related, and agro-industrial characteristics using municipal wastewater-based surveillance. We analyzed monthly wastewater metagenomes collected between March 2022 and March 2023 across eleven municipalities, representing 39% of Alberta's population. Integration with census data enabled multivariate analysis, revealing that municipal resistome profiles were strongly structured along income and immigration-related population gradients. ARGs spanning 14 resistance classes exhibited distinct distributional patterns across income and immigration gradients, including contrasting associations among beta-lactam, aminoglycoside, and macrolide-lincosamide-streptogramin ARGs, consistent with heterogeneous selection pressures across sub-populations. These findings demonstrate the capacity of longitudinal wastewater surveillance to identify persistent population-level resistome patterns and highlight the importance of incorporating sociodemographic context into AMR surveillance and mitigation strategies.

Objectives: Although HIV incidence has declined in some settings, the overall global burden of sexually transmitted infections remains a major public health concern. In the context of the World Health Organization's call for people-centred STI prevention and care, identifying the shared geographic pattern of multiple STIs using data-driven analysis may help detect vulnerable areas and inform integrated prevention strategies. Methods: We analysed country-level incidence counts from the Global Burden of Disease 2023 study for 204 countries and territories over 1990-2023. A Bayesian shared-component spatiotemporal model was fitted, decomposing each disease's log-rate into a shared spatial component (scaled intrinsic conditional autoregressive prior), disease-specific spatial deviations, disease-specific first-order random walk temporal effects, and five socioeconomic covariates, with a negative binomial likelihood to accommodate overdispersion. The shared spatial score - the posterior mean of the shared spatial component - was used as a continuous index of STI co-occurrence burden. Posterior exceedance probabilities quantified directional stability. External validity was assessed via Spearman correlation with the Socio-demographic Index and generalised estimating equation regression of HIV/AIDS mortality on the shared score. Results: The shared spatial score exhibited marked geographic heterogeneity. The five highest-scoring countries were Eswatini (2.25), Lesotho (2.13), Malawi (1.90), Mozambique (1.89), and South Africa (1.85), all in southern Africa. Fifty-seven countries had high directional stability (posterior exceedance probability >0.95), concentrated in sub-Saharan Africa and the Caribbean. The score correlated negatively with SDI (Spearman rho = -0.619, p = 6.4 x 10^-23) and positively with HIV/AIDS mortality (incidence rate ratio = 14.64 per standard deviation, 95% CI: 11.90-18.01). Prior sensitivity analysis confirmed near-perfect ranking stability (rho >= 0.9999). Conclusions: STI co-occurrence is geographically concentrated, with the highest shared burden in sub-Saharan Africa and persistently elevated shared spatial signals also observed in parts of mainland Southeast Asia and the Caribbean. The shared spatial score provides a data-driven tool for prioritising integrated STI screening and prevention resources across countries.

Dengue is a mosquito-borne, viral disease of increasing public health significance. Currently, most public health interventions target the vector, with efficacy dependent on timing within the season. Whilst seasonal profiles have been characterised in some endemic settings a global assessment is lacking. Here, we develop and apply a proportion-based measure of dengue seasonality to reported case time series from 1990 to 2024 across 106 countries and territories, the largest assessment of this phenomenon to date. We identify regional differences in seasonality such that every month of the year saw cases peak in at least one country or territory. Latitude was identified as influencing seasonality, with cases peaking between March and April in the southern hemisphere and July and October in the northern hemisphere. Equatorial locations displayed flat seasonality, and amplitude increased with distance from the equator. K-means clustering identified three seasonal profile types: two with pronounced seasonal outbreaks (with distinct peak timing and shape) and one with flatter, more endemic transmission. Peak month timing covaried among locations within the same seasonality cluster, with phase differences meaning that information on shifts in peak timing may be available several months in advance in some settings, of potential significance for prediction and intervention planning. Beyond aiding public health planning, identification of seasonal clusters suggests that information on dynamics in one location could be leveraged to improve forecasting power in others with similar seasonal dynamics.

Snakebite envenoming is a major neglected tropical disease disproportionately affecting rural populations in sub-Saharan Africa. In Ghana, evidence on the spatial and temporal distribution of risk remains limited, constraining targeted prevention and resource allocation. This study quantified district-level snakebite risk across Ghana, identified persistent hotspots and environmental drivers, and evaluated the relationship between snakebite burden and geographic access to treatment. Monthly district-level snakebite cases from Ghana's District Health Information Management System (2020 to 2025) were analyzed across all 261 districts using a Bayesian spatio-temporal model incorporating spatial effects, a temporal random effect, and a space-time interaction, fitted via Integrated Nested Laplace Approximation. Environmental covariates including rainfall, temperature, humidity, and NDVI quantified associations with risk. Relative risks, exceedance probabilities, Local Indicators of Spatial Association, and geographic accessibility identified priority districts. Snakebite risk showed strong spatial clustering and temporal variation. Persistent high risk districts were concentrated in Upper West (Daffiama Bussie Issa, Wa East, Wa West, Sissala East), Savannah (Bole, Gonja), North East (Mamprugu Moagduri), Western North (Bia East), Bono (Banda), Oti (Krachi Nchumuru), Western (Wassa East), and Eastern Region (Nsawam Adoagyiri, Fanteakwa North), though patterns evolved. Fanteakwa North emerged as the highest risk district nationally in 2025. Humidity and temperature were associated with increased risk, while rainfall and NDVI showed no significant effect. High risk districts often had poor treatment access, revealing inequities. This first nationwide Bayesian spatio temporal assessment provides an evidence base for surveillance, antivenom distribution, and interventions supporting WHO's 2030 snakebite reduction goals.

Larval source management (LSM) is a complementary malaria control intervention, yet evidence to guide context-specific implementation remains limited. Nigeria's recent national commitment to LSM scale-up makes the need for operational evidence particularly urgent. Informal settlements embedded within wards of differing dominant settlement archetypes may present distinct Anopheles larval habitat profiles with implications for how LSM strategies should be tailored. We evaluated Anopheles larval habitats within informal settlement areas across wards with contrasting settlement archetypes in Ibadan metropolis, Nigeria, to inform targeted larval source management. Potential breeding habitats were surveyed in dry and wet seasons within informal settlement areas across three wards -- Olopomewa, Challenge, and Agugu -- representing formal, informal, and slum settlement-dominant archetypes respectively. Habitats were characterized and assessed for Anopheles larval presence. Pareto analysis identified habitats accounting for 80% of larval abundance. Breeding habitat density per km{superscript 2} was estimated using a simulated pathway technique. Associations between mosquito dispersal scale and household malaria infections identified through Rapid Diagnostic Testing were evaluated using kernel-based distance-decay weighting. Environmental drivers of habitat suitability were modeled in MaxEnt. Of 420 potential breeding habitats identified, 31 (7.4%) contained Anopheles larvae, predominantly during the wet season (26, 83.9%). Puddles, dug wells, drainages/gutters/ditches and canals accounted for 80% of site-level larval abundance when standardized by sampling effort. Larval and breeding habitat density were highest in Agugu, the slum-dominant ward, across both seasons. Modeled mosquito dispersal scale showed best fit at 30-32m in Challenge (OR 1.41, 95% CI: 1.05-1.89) during the wet season and 16-18m in Agugu (OR 1.29, 95% CI: 1.04-1.60) during the dry season. Habitat suitability in Agugu was higher farther from large water bodies and in areas with higher population density and positive Normalized Difference Water Index values. In Challenge, suitability was higher in areas with lower nighttime light levels, positive Normalized Difference Water Index values, and negative Normalized Difference Moisture Index values. Further studies incorporating multiple wards across diverse urban settings are needed to determine whether differences in larval ecology between settlement archetypes provide a reliable basis for planning larval source management.

We develop a mathematical model of Rift Valley Fever integrating mosquito vectors, ruminants, and humans, based on an SEIR-type structure with vertical transmission in vectors. Local data from the Sudanian and especially the Sahelian zones are used to capture the impact of climatic variations on mosquito population dynamics. The mathematical analysis establishes the models positivity, determines the basic reproduction number R0, and demonstrates the local and global stability of the disease-free equilibrium. Sensitivity analysis (PRCC) highlights the most influential parameters, while the stochastic approach using a continuous-time Markov chain confirms the major role of seasonal rainfall. Numerical simulations reveal a peak in animal and human infections around the 9th month, correlating with periods of heavy rainfall. This model provides a relevant tool for surveillance and prevention within a "One Health" approach in Chad.

Background Nipah virus (NiV) is a priority zoonotic pathogen causing high-fatality outbreaks. Early NiV outbreaks in Malaysia and Singapore had limited transmission beyond spillover events. However, since 2001, NiV outbreaks with person-to-person transmission have occurred in Bangladesh and India, driven by the NiV-Bangladesh genotype and NiV-India genotype. Our study aims to estimate the reproduction number, offspring dispersion, and serial interval governing NiV transmission in Bangladesh and India during 2001-2026. Methods We conducted a systematic review of NiV outbreak investigations in Bangladesh and India, searching PubMed, Embase, Web of Science, and grey literature through 28 February 2026. Case-level offspring counts from 27 eligible sources (323 cases across 67 outbreaks) were used as input to a hierarchical Bayesian negative binomial offspring distribution model. The serial interval was estimated by parametric distribution fitting to 137 transmission pairs. Country-stratified and sensitivity analyses were performed to evaluate the robustness of estimates. Results Pooling across 67 outbreaks, we estimated a median reproduction number of 0.46 (95% CrI: 0.28-0.73), an offspring dispersion parameter of 0.07 (0.05-0.10), and a serial interval of 13.3 days (95% CI: 12.8-13.8). Country-stratified median reproduction numbers were 0.48 (0.23-0.97) for India and 0.35 (0.19-0.59) for Bangladesh, and dispersion parameters were 0.04 (0.02-0.07) and 0.11 (0.06-0.18), respectively, indicating marked overdispersion in both settings. Conclusion NiV transmission is self-limiting on average and highly overdispersed, suggesting that a disproportionate share of onward transmission arises from a small number of cases. This epidemiological profile supports targeted containment measures, including contact tracing and quarantine, for effective NiV outbreak control.

Estimating dengue force of infection (FOI) is essential for understanding transmission dynamics and targeting intervention programmes, yet surveillance data in endemic settings required for estimations are often incomplete, with varying formats. We developed a Bayesian hierarchical catalytic model that jointly fits age-stratified case data, aggregate case data, and seroprevalence surveys within a single framework, incorporating external covariates to improve parameter identifiability. Synthetic validation showed that covariates alone recovered accurate FOI point estimates even when most districts contributed only aggregate data, but did so with poorly calibrated uncertainty; anchoring the model with a single seroprevalence survey was necessary to bring credible interval coverage close to nominal. Applied to 128 districts across Java and Bali, Indonesia (2016-2024), the model revealed substantial spatial heterogeneity in FOI and reporting rates. Many districts in Java exceeded the WHO-suggested seroprevalence threshold for vaccine introduction, yet were classified as low-priority when using reported incidence as prioritisation criterion, particularly in areas with weak surveillance. Model-based seroprevalence estimation, integrating multiple data sources, offers a more consistent basis for identifying high-priority districts for vaccine introduction, and is less susceptible to surveillance bias than reported incidence.

Background. In 2026, the Democratic Republic of the Congo (DRC) experienced the largest recorded outbreak of Ebola disease caused by Bundibugyo virus, with epidemiologically linked importations and secondary transmission in Uganda. This study analysed the publicly reported trajectory and assessed the risk of introduction and onward transmission in North Africa and Europe. Methods. Public surveillance reports from the World Health Organization (WHO), European Centre for Disease Prevention and Control (ECDC), Africa CDC, national ministries of health, and peer-reviewed sources were synthesised through 15-17 July 2026. Headline counts and crude case-fatality ratios were restricted to laboratory-confirmed cases. Average notification rates were calculated from cumulative DRC counts. Exact Poisson intervals used the Garwood method, and the June-July rate ratio was estimated on the log scale. Risk was assessed across introduction likelihood, conditional onward-transmission likelihood, impact, and confidence. Results. By 15 July, the DRC had reported 2,124 confirmed cases and 828 deaths (crude confirmed-case fatality ratio, 39.0%) across 46 health zones in five provinces. Uganda had reported 20 confirmed cases and two confirmed deaths: 15 imported infections and five secondary cases, with no documented community transmission. DRC notifications averaged 47.4 per day during 1-15 July versus 35.9 per day during 2-29 June (rate ratio 1.32; counting-model 95% interval 1.20-1.46). WHO reported that more than 80% of new cases were detected outside known contact lists, while 119 confirmed healthcare-worker infections and 36 deaths had occurred. Introduction likelihood was assessed as very low to low for North Africa and very low for the general European population; delayed recognition in routine healthcare was the principal scenario for limited secondary transmission. Interpretation. Available indicators were inconsistent with effective control in eastern DRC at the data cut-off. Public reporting-date series cannot separate transmission from changing ascertainment, but they showed no sustained decline. Preparedness in North Africa and Europe should prioritise complete exposure histories, rapid isolation, validated diagnostics, protected clinical care, and contact management rather than reliance on border screening. Keywords: Bundibugyo virus; Ebola disease; outbreak surveillance; rapid risk assessment; importation; North Africa; Europe; Algeria; International Health Regulations.

Background Traumatic brain injury (TBI) affects 69 million individuals globally each year, yet care remains fragmented across complex, multi-specialty pathways and settings. Digital health technologies offer potential to bridge care gaps, particularly in resource-limited settings, yet existing frameworks do not adequately address the complexities of the TBI care pathway or the diverse global contexts in which care occurs. Methods A cross-sectional qualitative study using critical realist-informed thematic analysis was conducted with practising neurosurgeons recruited internationally via National Institute for Health and Care Global Health Research Group on Acquired Brain and Spine Injury (NIHR ABSI) collaborating centres, social media, and society newsletters. Semi-structured interviews were conducted by a single researcher (OM) via Microsoft Teams (March-July 2024), exploring technology availability, healthcare infrastructure, clinical pathways, and contextual challenges, with a systems thinking approach guiding identification of current and potential technology integration points. Fourteen neurosurgeons from twelve countries participated, representing six lower-middle, two upper-middle, and four high-income countries. Results Six inductive themes emerged: Availability, Acceptability, Applicability, Capability, Feasibility, and Possibility- forming a novel conceptual framework visualised as a hexagonal chart for guiding digital health technology design and implementation in TBI care. Marked disparities in technology availability and utilisation were identified across urban/rural settings and income levels. Conclusions This framework offers a practical, context-sensitive tool for researchers, policymakers, and clinicians developing or implementing digital health technologies in TBI care globally. Visualisation in a similar style to a radar-chart enables simultaneous consideration of factors- including digital literacy, infrastructure, and cultural attitudes- whose neglect frequently underlies implementation failures.

Background: Cardiovascular disease remains the leading cause of death in the United States, and marked geographic disparities in cardiovascular mortality persist. However, the community-level socioeconomic indicators most strongly associated with these disparities remain unclear. Community-level measures capture the social and economic conditions that influence cardiovascular health across populations and may help identify communities at greatest risk. We used the Area Health Resources File (AHRF) to identify socioeconomic measures most strongly associated with county-level cardiovascular mortality. Methods: We performed a national cross-sectional ecological analysis using the 2024-2025 Area Health Resources File (AHRF), including counties in the 50 U.S. states and the District of Columbia. The primary outcome was an AHRF-defined cardiovascular mortality composite derived from 2021-2023 National Center for Health Statistics (NCHS) mortality data. Community-level socioeconomic measures included 2023 overall, pediatric, and family childhood poverty and 2019-2023 overall, female, and White unemployment. County-level associations were evaluated using Spearman rank correlation and regional differences using the Kruskal-Wallis test. Sensitivity analyses used a partial mortality composite and Kendall {tau} correlation. Results: Among 1,982 counties, cardiovascular mortality varied significantly across U.S. Census divisions (P<0.001), with the highest population-weighted rate in the East South Central division (348.5 deaths/100,000) and the lowest in the Mountain division (233.9 deaths/100,000). Pediatric poverty demonstrated the strongest association with cardiovascular mortality ({rho}=0.612), followed by family childhood poverty ({rho}=0.603) and overall poverty ({rho}=0.524, all P<0.001). In contrast, unemployment measures were more weakly associated (overall {rho}=0.209, White {rho}=0.176, female {rho}=0.141, all P<0.001). Results were consistent in sensitivity analyses. Conclusions: County-level poverty, particularly pediatric poverty, was more strongly associated with cardiovascular mortality than unemployment across U.S. counties. These findings suggest pediatric poverty may serve as a useful community-level indicator for identifying populations at increased cardiovascular risk and prioritizing future public health interventions.

Controlled human infection models (CHIMs) are increasingly used in infectious disease research to study pathogen dynamics and evaluate interventions under controlled conditions. However, these studies are resource-intensive and involve ethical and safety constraints, making efficient study design critical. Dose-finding is a key early component in CHIMs, where the aim is to identify a challenge dose that achieves a target infection probability. Traditional rule-based designs are commonly used but can be inefficient, motivating the use of model-based adaptive approaches such as the Bayesian Continual Reassessment Method (CRM). Although CRM has been extensively studied and widely adopted in Phase I oncology trials for identifying the maximum tolerated dose of therapeutics, its application in CHIM settings remains limited, particularly when the endpoint of interest is infection. This tutorial provides step-by-step guidance for implementing a Bayesian CRM in dose-finding CHIMs, using an oropharyngeal Neisseria gonorrhoeae challenge as a motivating case study. The framework outlines key design components, including dose-grid specification, dose-response model, prior elicitation, Bayesian updating, decision rules, and stopping criteria, with particular emphasis on a clinically interpretable parameterisation. Trial operating characteristics are evaluated through simulation studies under multiple dose-response scenarios and prior-predictive analyses, and compared with a commonly used '3+3' type rule-based design. This work highlights the advantages of Bayesian model-based designs for dose-finding in CHIMs over classic rule-based designs and provides a structured, reproducible framework for implementing CRM, supporting their application in future CHIM studies.

Patients and caregivers seek informational and emotional support throughout medical care, especially when interpreting unfamiliar laboratory test results. Although resources such as patient portals and online health communities (OHCs) help address questions, gaps remain. The emergence of large language models (LLMs) offers the potential to be a complementary source of support to assist patients and caregivers in understanding and using their test results. The objective of our study is to empirically compare LLM responses to patients online questions containing their laboratory test results to responses written by peers in an OHC. We compared the 519 peer replies to 122 laboratory test-related posts from an OHC to 488 responses generated from four LLMs using mixed computational and qualitative methods. LLMs frequently provided clear explanations of medical terminology and structured interpretations of numeric results but were longer and less readable. Peers offered more personalized, context-specific emotional support. Overall, LLMs have the potential to complement peer responses in OHCs, but require greater emotional depth, reasoning transparency, and alignment with community norms.

We present FootNet, a 453-image multi-view smartphone foot dataset for binary foot segmentation, with expertannotated masks across six anatomical views (dorsal, medial, and plantar, both left and right). We benchmark four segmentation models under a controlled protocol: U-Net with a MobileNetV2 encoder achieves the best performance (IoU 0.9268, Dice 0.9608, 95 % CI [0.9209, 0.9320]); DeepLabV3 with MobileNetV3-Large scores IoU 0.8984 (Dice 0.9449); UNet++ with MobileNetV2 scores IoU 0.8913 (Dice 0.9391); and SAM ViT-B with oracle boundingbox prompt scores IoU 0.9219 on the matched 191-image subset. Bonferroni-corrected Wilcoxon signed-rank tests (k = 6 comparisons) show U-Net significantly outperforms DeepLab (p < 0.001, r = 0.638) and SAM ViT-B with oracle boundingbox (p = 0.005, r = 0.202); UNet++ does not significantly differ from DeepLab (p = 0.062). Connected-component postprocessing yields negligible benefit (mean {triangleup}IoU = +0.0003, 12 of 453 images improved). The extended dataset is available upon request

Purpose: Nonresponse to routinely collected patient-reported outcome measures (PROMs) threatens the representativeness of aggregated data. We characterized patient-, provider-, and clinic-level factors associated with PROMIS Global-10 nonresponse in routine radiation oncology care. Methods: In this retrospective cohort study, all adults seen at five Mass General Brigham radiation oncology clinics over one year were included. The primary outcome was patient-level nonresponse, defined as never completing the portal-administered Global-10 versus completing it at least once. Using iterative mixed-effects logistic regression, we modeled patient-, provider-, and clinic-level factors. Results: Among 12,214 patients, 71 providers, and five clinics, patient- and appointment-level response rates were 35.4% and 10.9%, with patient-level response ranging nearly fivefold across clinics (12.8% to 66.2%). In Model 1, male sex, lower education, not working, and recent surgery had higher odds of nonresponse, and longer time since diagnosis lower odds. After provider- and clinic-level factors were added, patient sex, education, and employment became nonsignificant, whereas recent surgery (adjusted odds ratio [aOR] 1.97) and longer time since diagnosis (aOR 0.46 for >12 months) persisted. A provider's historical collection rate was protective but attenuated at the clinic level. There, a later program launch (aOR 0.29) and higher historical collection rate (aOR 0.79) correlated with lower nonresponse, whereas academic versus community setting did not. Conclusions: Nonresponse to routinely collected PROMs is a multilevel phenomenon driven substantially by clinic-level implementation factors, not patient characteristics alone. Because response rate is only a proxy for representativeness, PROMs programs and PRO-based performance measures should prioritize representative collection over volume.

Background The 2022-2023 global mpox outbreak highlighted the critical need for robust genomic surveillance capabilities to track mpox virus (MPXV) evolution and transmission dynamics. Methods Building upon our established SARS-CoV-2 sequencing infrastructure, we implemented a Molecular Loop probe-based long-read sequencing approach using Pacific Biosciences Sequel II technology for comprehensive MPXV genomic surveillance across the United States (US). From August 2024 to June 2025, we generated 326 high-quality whole genome sequences from residual mpox-positive clinical specimens collected by Labcorp across all 10 US Department of Health and Human Services regions. Results Our analysis identified two samples containing clade Ib MPXV in January and June 2025 and captured shifting trends in clade IIb diversity, with 13 distinct lineages observed. We also identified multiple instances of large (~1.6-17.6kb) deletions proximal to the inverted terminal repeats in clade IIb genomes. APOBEC3 mutation analysis indicated substantial evidence of human-to-human transmission among both clades. Further, we observed significantly higher APOBEC3-associated SNPs per kilobase (P<0.001) in clade IIb genomic variable regions relative to their central conserved region. Our assay exhibited strong reproducibility across biological replicates from individual patients and accuracy was confirmed via parallel sequencing of select specimens by US Centers for Disease Control and Prevention (CDC) using metagenomic sequencing. We also demonstrated via custom simulation that our assay discriminates all known MPXV clades and lineages, including those we have not observed in the US. Conclusions Our integrated nationwide surveillance system facilitates real-time genomic tracking of outbreak evolution, with demonstrated capacity across SARS-CoV-2 and MPXV, positioning this platform for rapid deployment during future pathogen emergence.

Background and Aims The optimal treatment for first episodes and first recurrences of Clostridioides difficile infections (CDI) is unknown and there is emerging evidence for pulse and taper (P-T) regimens. Therefore, we sought to estimate the relative efficacy of treatment options. Methods MEDLINE and CENTRAL were searched from database inception to May 21, 2025 and unpublished conference abstracts were searched from recent infectious disease conferences. RCTs on the treatment of first episodes or first recurrences of CDI comparing fixed-dose or P-T regimens of fidaxomicin or vancomycin were included. The primary and secondary outcomes were 40- and 56-day CDI recurrence, respectively. A random-effects network meta-analysis on the risk ratio (RR) scale was conducted using a standard regimen (10-14 days) of vancomycin as the comparator. Treatments were ranked using the surface under the cumulative ranking curve (SUCRA). Results 8 RCTs were included comprising a total of 2181 patients. For 40-day recurrence, fidaxomicin P-T had the highest probability of ranking best (RR=0.10, 95%Confidence Interval [95%CI]=0.10-0.49, SUCRA=1.00), followed by vancomycin P-T (RR=0.49, 95%CI=0.32-0.76, SUCRA=0.61), fixed-dose fidaxomicin (RR=0.61, 95%CI=0.49-0.76, SUCRA=0.39), and, finally, fixed-dose of vancomycin (SUCRA=0.00). The treatments ranked in the same order for 56-day recurrence, though only 3 RCTs reported on this timepoint. Conclusion Vancomycin P-T, fidaxomicin P-T, and fixed-dose fidaxomicin were all superior to a fixed-dose vancomycin. Head-to-head comparative effectiveness RCTs are needed to quantify their relative effect sizes of and impact on long-term prevention of recurrent CDI.

Background Post-infection conditions (PICs), such as Long Covid, are associated with heterogeneous, fluctuating symptoms that profoundly affect daily functioning. Despite moderate-certainty evidence from the NIHR-funded LISTEN trial (COV-LT2-0009) that personalised self management support improves outcomes and may reduce societal and economic impacts of Long Covid, many people living with PICs still receive condition-specific services, generic advice, or stand-alone digital tools that do not address their complex needs. Aim To map care approaches in general practice and synthesise UK evidence for PIC management. Design and setting Scoping review and online survey. Method A two-phase study was conducted: (1) a scoping review of UK evidence on PIC management in general practice; and (2) a supplementary online survey of practitioners working in UK general practice to provide contextual insights. Results The scoping review identified 32 studies focused on Long Covid. One study included a comparator group (ME/CFS). Study populations were predominantly white ethnicity and female. Evidence for non-Covid PICs in UK general practice was largely absent. The supplementary survey (n=46) provided preliminary practice-level insights. Healthcare practitioners reported varied PIC presentations, diagnostic uncertainty, limited referral pathways, inequitable access, and low confidence in managing PICs. Conclusion Evidence informing PIC management in UK general practice remains predominantly Long Covid-focused and may not reflect the range of PICs encountered in practice. While survey findings are preliminary and require confirmation in larger samples, they highlight uncertainty around PIC management. Further research is needed to evaluate whether existing Long Covid pathways should be expanded or complemented by broader PIC models. Keywords general practice; Long Covid; self-management; post-viral syndromes