BMC Infectious Diseases

Introduction: This study compared the health and economic impact of the 21-valent pneumococcal conjugate vaccine (aPCV21) vs the 20-valent pneumococcal conjugate vaccine (PCV20) on invasive pneumococcal disease (IPD) and post-meningitis sequelae (PMS) among adults aged 65 years and older in the United Kingdom (UK). Methods: A published Markov model was used to simulate a single-cohort of vaccine-naive adults who became eligible for age-based vaccination at age 65 years, with uptake occurring over time. The cohort was followed until a maximum age of 100 years. Model inputs were derived from publicly available national health data and published reports. Outcomes were discounted at 3.5% annually, and costs were adjusted to the 2024 Great Britain Pound ({pound}). Additional analyses explored the impact of changing the age-based recommendation from 65 years to 75 years. Scenarios involving instantaneous vaccine uptake rates were included as part of the sensitivity analyses. A deterministic sensitivity analysis was performed on the incremental direct medical costs between vaccinating with aPCV21 vs PCV20 under the age-based recommendation of 65 years. Results: Using an age-based recommendation of 65 years, aPCV21 led to fewer cases, deaths, and costs than PCV20. Compared with PCV20, aPCV21 averted an additional 138 IPD cases, 1 PMS case, and 22 IPD deaths, saving {pound}1.2 million in direct medical costs. Raising the age-based recommendation from 65 to 75 years and using aPCV21 (or PCV20, respectively), resulted in 120 (92) additional IPD cases, 2 (1) additional PMS case(s), 19 (14) additional IPD death(s), and an extra {pound}1.1 ({pound}0.8) million in direct medical costs. The deterministic sensitivity analysis showed that aPCV21 reduced direct medical costs across all parameter ranges. Conclusions: Compared with PCV20, aPCV21 prevented more IPD cases, IPD deaths, and direct medical costs among older adults in the UK. Changing the age-based recommendation from age 65 to 75 resulted in worse health and economic outcomes. Key words. Pneumococcal infections; pneumococcal vaccines; vaccines, conjugate; serotype; United Kingdom; health costs

Objectives: To evaluate the epidemiological evolution of unique pneumococcal serotypes covered by either PCV20 or PCV21, common serotypes by both vaccines and non-vaccine serotypes in adults. Methods: We characterized all national IPD clinical isolates from January 2009 to June 2026. Results: The highest burden of IPD was caused by serotypes shared between PCV20 and PCV21 with serotypes 3 and 8 the most prevalent in adults of all ages. During the pre-pandemic period, IPD caused by unique-PCV20 serotypes declined probably due to herd protection by paediatric vaccination with PCVs. In contrast, unique-PCV21 serotypes increased in incidence accounting for more cases than unique-PCV20 serotypes. However, after the COVID-19 pandemic, a different pattern was observed with unique-PCV20 serotypes increasing in incidence due a marked rise in IPD caused by serotypes 4 and 14 that has progressively reduced the gap in disease coverage. In adults aged 18-64 years, the fraction of disease that could be prevented with PCV20/PCV21 changed from 5% in favour of PCV21 (years 2022/2023) to 15% in favour of PCV20 (year 2026) whereas in adults [≥]65 years the vaccine contribution declined from 20% in favour of PCV21 (year 2022) to 8% in 2026. Conclusions: IPD trends in adults have changed after the COVID-19 pandemic, especially in recent years. Our study emphasizes the rapid re-emergence of infections caused by traditional PCV7 serotypes especially serotypes 4 and 14 and reinforce the idea that the use of broader PCVs must be accompanied by an increase in the uptake rates in adults of all ages.

Background: Severe malaria remains a major cause of hospitalization in malaria-endemic sub-Saharan Africa, and anemia is among its most frequent severity criteria. Data on the impact of anemia on hospital length of stay (LOS) among adults with severe malaria remain limited in Central Africa. We assessed the impact of anemia on LOS among adults hospitalized for severe malaria in Brazzaville, Republic of Congo. Methods: We conducted a cross-sectional, descriptive, and analytic study of medical records from adult patients ([&ge;]18 years) diagnosed with severe malaria (WHO criteria) and admitted to four hospitals in Brazzaville between January 2020 and December 2024. We dichotomized LOS as short (<5 days) or prolonged ([&ge;]5 days). Multivariable logistic regression, using a backward stepwise procedure, identified factors independently associated with prolonged LOS; isolated hemoglobinuria served as the reference severity category. Results: Among 523 patients hospitalized for severe malaria, 296 met the inclusion criteria. Median age was 25.5 years (IQR 20-40); 51.7% were female, and 82.1% had a low socioeconomic status. Median hemoglobin was 10.55 g/dL, and median LOS was 4 days (IQR 3-5). In multivariable analysis, prolonged LOS was independently associated with delay to consultation (aOR 1.25/day, 95% CI 1.09-1.44), isolated anemia (aOR 7.07, 1.23-40.55), convulsions (aOR 5.86, 1.81-18.97), prostration (aOR 4.42, 1.21-16.2), and impaired consciousness (aOR 4.29, 1.17-15.7). The strongest associations involved combined severity criteria: anemia with prostration (aOR 8.58), anemia with jaundice or hemoglobinuria (aOR 16.21), and impaired consciousness with anemia (aOR 33.31). Conclusions: Anemia is a major, potentially modifiable determinant of prolonged hospitalization in adults with severe malaria, with a markedly amplified effect when combined with neurologic impairment. Early anemia screening, improved access to safe blood transfusion, and reduced delays to care could shorten hospital stays and ease the burden on health systems in this setting.

Background Influenza vaccines have seen many changes over recent years in terms of both policy recommendations and product types. Immune responses and vaccine effectiveness vary by vaccine strain composition and product type. This study aimed to explore this variation on a regional level through examining spatial and temporal autocorrelations of vaccine uptake of different product types within Michigan from 2017 to 2021. Methods Influenza vaccine administration data from the 2017/18 to 2021/22 seasons were obtained from the Michigan Care Improvement Registry. Population size estimates for 2017 and 2019 were obtained from the Michigan Department of Health and Human Services. Age-standardized and age-stratified vaccination rates were computed. Disparate county-level vaccine uptake clusters were constructed using spatial autocorrelation indicators and spatiotemporal clusters were identified using scan statistics. Linear mixed effect models were used to study the impact of social vulnerability and urbanicity level on seasonal influenza vaccination rate. Results Vaccination incidence rates for adjuvanted, recombinant, and nasal sprays vaccines and for any vaccines increased significantly through the study period. County-level spatiotemporal clusters of type-specific uptake rates were identified. Most notably, we identified an emerging cluster with high uptake of adjuvanted vaccine radiating from Ottawa County, located in suburban Grand Rapids, and the disappearance of high uptake cluster for high-dose vaccine uptake in the area. Vaccination rates by any product type were found to vary seasonally and showed weak negative association with more rural regions. The overall vaccination rates appear to be lower among counties with higher social vulnerability in certain urbanicity levels, but social vulnerability and urbanicity were not significantly associated with overall vaccination rate during the study period. Conclusions Vaccine type-specific uptake was spatially and temporally diverse in Michigan between 2017 and 2022. Mid and Northern Michigan had consistently relatively lower vaccination rate compared to other regions in Michigan.

BackgroundPopulation-based spirometry data from sub-Saharan Africa remain limited. We characterised lung function, chronic obstructive pulmonary disease (COPD), lung restriction and associated factors from two Sub-Saharan African countries. MethodsWe performed spirometry using ndd Easy On-PC spirometers among adults aged [&ge;]40 years from population-based cohorts in four communities in Burkina Faso and South Africa from January 2019-April 2022. We included 1,619 participants (936 women, 683 men) with repeatable forced expiratory volume in one second (FEV1) and forced vital capacity (FVC). COPD was defined as FEV1/FVC<0.70 and lung restriction as FEV1/FVC[&ge;]0.70, with FVC<80% predicted. We used multivariable models to test factors associated with lung function measurements (FEV1, FVC and FEV1/FVC), COPD and lung restriction. ResultsMedian FEV1 and FVC ranged from 1.940-2.273L and 2.521-2.883L, respectively. COPD prevalence was 16.1% (95% CI 14.4-18.0; site range 10.8%-24.5%) and lung restriction prevalence was 21.3% (95% CI 19.4-23.4; site range 11.3%-36.2%). Prior tuberculosis was associated with higher odds of COPD (OR 2.86, 95% CI 1.75-4.59) and lower FEV1, FVC and FEV1/FVC. HIV infection was associated with higher COPD odds (OR 1.67, 95% CI 1.01-2.72) and lower FEV1/FVC. Hypertension was associated with higher lung restriction odds (OR 1.46, 95% CI 1.07-2.00) and lower FEV1 and FVC. Diabetes was associated with lower COPD odds (OR 0.50, 95% CI 0.24-0.94), lower FVC and higher FEV1/FVC. Ever-smoking and older age were associated with higher COPD odds (OR 2.21, 95% CI 1.48-3.32; OR 1.05, 95% CI 1.03-1.07, respectively); older age was associated with higher lung restriction odds (OR 1.02, 95% CI 1.00-1.04). ConclusionThis multisite study highlights substantial heterogeneity in COPD and lung restriction prevalence across African populations and identifies respiratory multimorbidity with infectious and cardiometabolic conditions. These findings provide population-based evidence to inform respiratory surveillance in sub-Saharan Africa.

Background: The relationship between recent viral infection and subsequent susceptibility to, or protection against, other viruses remains unclear, particularly across ages and symptom status. Methods: A prospective cohort study enrolled participants aged 6 months to 49 years in Washington and Oregon, USA, 2022-2024. Weekly nasal swabs were tested for SARS-CoV-2, respiratory syncytial virus (RSV), and influenza A/B; additional swabs were collected during symptomatic illness and were also tested for parainfluenza (PIV) 1-4, endemic human coronaviruses (hCOV), adenovirus, human metapneumovirus, human rhinovirus (hRV), and enteroviruses. Proportional hazards regression models were used to characterize associations between incident symptomatic infection (illness) and history of recent illness due to other viruses (within 1-7, 8-14, 15-30, 31-60, 61-90, 91-180 days), adjusted for geographic region, age, sex, daycare/school/work attendance, household size, and number of children in the household. For SARS-CoV-2, influenza, and RSV, these analyses were repeated to study incidence of infection (including asymptomatic cases). Findings: Of 3,560 participants, 2,969 (83%) had [&ge;]1 illness over 4,299 person-years. Adjusted hazard ratios (aHRs) in the short term ([&le;]2 weeks) after an illness were predominantly below 1, indicating that recent viral illness was often associated with lower risk of subsequent illness with other viruses. Across age groups, recent hRV illness was associated with lower risk of hCOV, PIV, and RSV illness (aHRs 0.07-0.27). SARS-CoV-2 illness was associated with a higher short-term risk of adenovirus and PIV illness (aHRs 3.20-3.33). In children aged 6 months-4 years, recent influenza illness was associated with higher short-term risk of SARS-CoV-2 illness. Including asymptomatic infections generally attenuated these associations. Interpretation: Some viral illnesses may alter short-term susceptibility to subsequent viral illnesses. Interactions may differ by age and symptom status. Funding: US Centers for Disease Control and Prevention; Washington State Department of Health.

Objective: To determine outcomes by referral route (any Advice and Guidance [A&G] versus elective referrals only) for patients with inflammatory bowel disease (IBD), Parkinson's disease, and systemic lupus erythematosus (SLE). Design: Population-based retrospective cohort study. Setting: Linked Clinical Practice Research Datalink (CPRD) Aurum primary care records, 2015-2022. Participants: Patients with incident diagnosis of IBD, Parkinson's disease, or SLE. Main Outcome Measures: Time from the earliest recorded A&G or elective referral to diagnosis, first recorded definitive treatment and first attended outpatient appointment. Secondary outcomes included counts of primary care consultations, prescriptions, referrals, and outpatient appointments. Propensity score weighted flexible parametric survival analyses accounted for differences between exposure groups. Results: Among 71,145 eligible patients, A&G was recorded for 1,137 (3.0%) with IBD, 971 (3.4%) with Parkinson's, and 243 (4.9%) patients with SLE. Compared with elective referral only, A&G was associated with slower time to diagnosis for IBD (adjusted median difference 15.9 weeks; hazard ratio 0.58, 95% confidence interval 0.52 to 0.66), Parkinson's disease (11.4 weeks; 0.57, 0.51 to 0.64), and SLE (6.3 weeks; 0.82, 0.73 to 0.91). However, A&G was not associated with a longer time to treatment or first attended outpatient appointment. An increase in primary care consultations following A&G was observed in patients later diagnosed with IBD and SLE. Conclusion: A&G was associated with a longer diagnostic interval than elective referral only. These findings do not establish that A&G caused diagnostic delay. A&G might be used at an earlier stage of more complex diagnostic pathways.

In England and Wales, Usutu virus (USUV) was detected in 2020 and is now established. West Nile virus (WNV) has so far only been detected in a single location, in two Aedes vexans mosquito pools sampled during 2023 surveillance. Given the growing threat to the UK from Culex-borne viruses there is a need to undertake risk assessments to contribute to preparedness and surveillance strategy for both viruses across human and animal health sectors. Here, we present the first application of a mechanistic R0-based model, incorporating temperature dependence and combining data on vector species distribution with projected future climate scenarios, to assess transmission suitability for WNV and USUV across England and Wales. Under current climate conditions, the two viruses showed comparable spatial patterns, with risk concentrated most heavily in the southeast along the Thames estuary and extending into Kent, Essex and Surrey, as well as around Cambridgeshire and adjacent counties, and across Yorkshire, Nottinghamshire and Lincolnshire. This pattern broadly corresponded with the cumulative distribution of Culex mosquito abundance established in our earlier work (Culex pipiens pipiens, Culex pipiens molestus and Culex torrentium). Both viruses were projected to show increasing transmission suitability risk over time, with substantially steeper increases under Representative Common Pathway (RCP) 8.5 then RCP 4.5. Equine WNV cases were predicted to be most likely in southeast England, in particular around Newmarket, driven by the region's dense horse population. Among the human population, London was identified as the highest-risk area, with Liverpool and Manchester also showing elevated risk levels, driven by a combination of high human and mosquito distributions. When human population distributions were age-adjusted to account for the proportion of over 60 years-olds, there was elevated risk in coastal regions, particularly in the east (Norfolk and Suffolk) and southeast (Kent) of England. The geographic distribution of positive mosquito and bird USUV cases recorded in 2023 and 2024 broadly overlapped with areas predicted to have higher USUV suitability, providing validation for the model's spatial outputs. By characterising relative spatial transmission suitability risk, this work adds to a growing understanding of where arbovirus emergence and spread are most likely across England and Wales, and can support decisions around resource allocation, targeted surveillance efforts, and public health readiness. The results also underscore the importance of incorporating climate change and vector ecology into future modelling work, given their likely influence on how arbovirus risk evolves over time.

Background/Aims: In medical practice, the rising prevalence of infections in kidney transplant recipients (KTRs) is a notable concern. However, understanding the incidence and risk factors associated with infections in KTRs remains limited. This study aims to assess the incidence and factors associated infection among KTRs. Additionally, we also investigate the median time of each infection among KTRs. Methods: A retrospective cohort study was conducted from 1 January 2018- 31 December 2022 at Thailand's tertiary hospital. We performed univariable and multivariable logistic regression analyses to identify potential risk factors related infection and investigated for the incidence and median time of each infection among KTRs. Results: A total of 179 participants were included in this study. Our findings reveal the kidney from living donor was protective factor for infection among KTRs (aOR 0.063, 95% CI 0.005-0.792; p = 0.032). Urinary tract infections (UTIs)showed as the predominant pathogenic manifestation among KTRs (43%). The median time of UTIs, cytomegalovirus (CMV) infection and pneumocystis pneumonia (PCP) were 16, 57 and 94.5 days, respectively. Conclusions: Our study establishes that receiving a kidney from a living donor is a protective factor against post-kidney transplant infections. Particularly urinary tract infections were most common of infection. Monitoring urinary tract infections post-transplantation is recommended. Moreover, awareness of UTI, CMV infection and PCP should be concerned during 2-3 months after transplantation. However, due to a small sample size; we recommend to increase the number of particular participants in further investigations.

Implementing vector control interventions against infectious diseases is operationally challenging, with delivery and population uptake taking considerable time within a community. Studies have suggested that the slow mass drug administration (MDA) of the endectocide, ivermectin, may have contributed to the lower epidemiological impact against malaria observed in recent cluster-randomised controlled trials, although evidence supporting this hypothesis is lacking. It is unclear whether interventions which have short but highly effective durations of entomological impact are more impactful than tools which kill the same number of mosquitoes but over a longer period. Transmission dynamics mathematical models can be used to explore the trade-offs between product profiles and investigate how different implementation periods could influence disease control. First, we use a simple vector-borne disease compartmental model to explore the epidemiological impact of killing the same number of mosquitoes over 10, 30 or 90 days. We find that for different mosquito-killing periods, the total number of cases averted is similar, although the observable impact may depend on when efficacy is assessed. Secondly, we adapted an existing malaria transmission model and simulated the fast and slow deployment of an effective endectocide MDA campaign in different seasonal settings. In perennial and seasonal settings and for a given coverage, slow MDA deployment averts more clinical cases of malaria than a fast synchronised distribution (completed in a single day, although the difference was modest). The benefits of the intervention depend on how well-timed a deployment is relative to the peak of the transmission season, with the efficacy of an ivermectin-like endectocide MDA being up to approximately 42% lower if started late. The work indicates that the time taken to complete MDA campaigns is unlikely to drive major differences in the effectiveness of short-lived endectocides and showcases how transmission dynamics models can be used to support epidemiological trial interpretation.

The COVID-19 pandemic substantially disrupted tuberculosis (TB) prevention, diagnosis, and treatment services worldwide. Despite increased TB notifications in Nepal during the post-pandemic recovery period, the geographic distribution and temporal clustering of notifications remain poorly understood. This study examined the spatial and temporal patterns of TB notifications across Nepal during the COVID-19 recovery period. We conducted a nationwide retrospective ecological analysis of district-level TB notifications across all 77 districts of Nepal from FY 2019/20 to FY 2023/24. Space-time, purely spatial, and purely temporal scan statistics were performed using a discrete Poisson model in SaTScan. Statistical significance was assessed using Monte Carlo simulation with 999 replications. A total of 172,155 TB cases were notified during the study period. TB notification rates increased by 51%, from 92 to 139 per 100,000 population between FY 2019/20 and FY 2023/24. Four statistically significant high-notification space-time clusters were identified (all p < 0.001). The primary cluster included Kathmandu, Lalitpur, Bhaktapur, Makwanpur, Bara, and Parsa districts during FY 2021/22-FY 2023/24 (RR = 1.60). Additional clusters were identified in western Nepal, the eastern Terai, and Rupandehi district. Purely temporal analysis showed a nationwide increase in notifications during FY 2022/23-FY 2023/24 (RR = 1.26, p = 0.001). TB notifications increased substantially during Nepals post-pandemic recovery period, with marked geographic and temporal heterogeneity. Integrating spatial and temporal surveillance may help identify areas requiring geographically targeted TB control strategies.

Background: Common variable immunodeficiency is frequently underrecognized due to its heterogeneous clinical presentation, resulting in diagnostic delays that increase disease-related complications, with each year of delay increasing mortality risk by 4% for the most common inborn error of immunity. Objective: In individuals with established common variable immunodeficiency, we sought to recognize patterns in patient diagnostic trajectories to identify opportunities for interventions to reduce time to diagnosis. Methods: We analyzed retrospective electronic health records data from 139 individuals with physician-confirmed common variable immunodeficiency across five academic health systems representing >10million patients. We evaluated diagnosing specialties and international classification of disease code trajectories in the two years prior to and at common variable immunodeficiency confirmation. Additionally, we construct specialty-specific prediction on a 1:50 case: control cohort using a gradient boosting algorithm. Results: The leading diagnoses at common variable immunodeficiency determination were respiratory (35% of individuals), and individuals with respiratory symptoms were more likely to be diagnosed by Allergy / Immunology than by other specialties (odds ratio 2.9, standard error 0.35, p-value 1.9x10-3) and had a higher frequency of visits to pulmonary disease in the two years prior to diagnosis. Individuals with neoplasms were 50% more likely than other patients (OR 2.0, SE 0.29, p-value 0.02) to receive a diagnosis at each visit, reducing their diagnostic delay by up to a year. We find that a larger ratio of number of specialties visited to the total number of visits is significantly correlated with an increased time to diagnosis. A trajectory-informed specialty-specific prediction improved detection with an area under the precision-recall curve of fifty times the value expected under the null. Conclusions: Individuals referred to Allergy / Immunology have shorter time to diagnosis, and a specialty-specific prediction could improve detection of common variable immunodeficiency. Clinical Implications: Overall, we find that phenotype-specialty interactions may inform targeted interventions to improve common variable immunodeficiency recognition across specialties, reducing diagnostic odyssey and disease-related complications.

Background: Pertussis has resurged in the post-COVID-19 era, yet diagnostic delay remains a barrier to timely treatment and transmission interruption, particularly among adults. Methods: Retrospective observational cohort study of 787 patients with confirmed pertussis from January 2016 through March 2026 across two South Carolina health systems. Electronic health record data from the 60 days preceding confirmation were used to characterize diagnostic timing (same-day vs. delayed), delay duration, and respiratory pathogen testing patterns including pertussis test modality. Results: Adults accounted for 48.4% of cases (median age 16 years [IQR 4-52]; 61.6% female). Same-day diagnosis was highest among children aged 1-4 years (75.4%) and lowest among adults aged 50 and older (approximately 50%). Among the 305 patients (38.8%) with delayed diagnosis, median delay ranged from 7-12 days in children to 22.5-26.5 days in adults aged 50 and older. Delay did not vary significantly by COVID-19 period, race, ethnicity, or insurance type. Testing breadth was greater in inpatient and emergency settings than outpatient. Pertussis testing was predominantly PCR-based in children but serologic in adults, potentially reflecting later clinical presentation relative to illness onset; however, the near-absence of PCR testing in older adults may contribute to missed diagnoses during the early illness window when PCR sensitivity is highest. Conclusions: Older adults experienced markedly delayed pertussis diagnoses compared with children. Earlier consideration of pertussis in adults with prolonged cough, including PCR testing early in illness before serologic conversion, may reduce diagnostic delay and facilitate timelier treatment and contact tracing.

Introduction Despite the significant burden of Shigellosis, routine surveillance systems in resource-limited settings lack predictive capabilities to guide proactive public health responses. Consequently, we developed and evaluated the accuracy, uncertainty and computational cost of multi-horizon forecasting models for Shigella incidence among children <5 years in rural western Kenya using 16-year longitudinal incidence time-series. Methods We estimated monthly Shigella incidence using data from three pediatric enteric studies (2008-2024), supplemented with Kenya Health Information System during inter-study gaps. Incidence estimates incorporated sampling, healthcare-seeking, stool-testing, and pathogen-specific weights, with bootstrap-derived 95% confidence intervals. Predictors included Google Trends health-related indicators, climate, temporal trends, and seasonality. Forecasts at 1-, 3-, and 6-month horizons were evaluated using rolling-origin validation. Following horizon-specific predictor selection using Boruta, 14 state-space, machine-learning, and hybrid models were compared using point-forecast and probabilistic metrics, bootstrap confidence intervals, and Model Confidence Sets. Adaptive conformal prediction recalibrated prediction intervals, while Shapley Additive exPlanations assessed predictor contributions. Results Mean monthly Shigella incidence was 2.51 per 100 child-years (standard deviation=1.85). Shigella dynamics in Kenya can be forecast beyond seasonal persistence alone for up to six months reducing absolute errors by ~ 33%. However, optimal model architectures and predictor sets shift systematically across forecasting horizons. Temporal trend and recent incidence were the dominant predictors, while predictor importance shifted with forecast horizon toward syndromic, pharmaceutical, seasonal, and lagged environmental signals. Extreme Gradient Boosting performed best at 1 month (Root mean squared error [RMSE]=1.86), remained competitive at 3 months (RMSE=1.89), whereas exponential smoothing (ETS) performed best at 6 months (RMSE=2.05). Forecast accuracy declined with increasing horizon. ETS and TBATS provided the best-calibrated prediction intervals, whereas machine-learning models generally under-covered. Model Confidence Sets demonstrated horizon-dependent model distinguishability. Conclusion Horizon-specific model selection balancing predictive accuracy, uncertainty calibration, and computational feasibility could strengthen operational early warning systems for shigellosis.

Background: Epidermal Growth Factor Receptor (EGFR) tyrosine kinase inhibitors (TKIs) are widely used for EGFR-mutated non-small cell lung cancer (NSCLC) but have distinct toxicity profiles across generations. Aims: To investigate relationships between EGFR-TKI physicochemical, pharmacokinetic and pharmacological properties and drug-specific adverse drug reactions (ADRs) using national and global pharmacovigilance data. Methods: ADRs for erlotinib, afatinib and osimertinib were extracted from the UK MHRA Yellow Card scheme and WHO VigiAccess. Disproportionality analysis used reporting odds ratios (RORs). Signals were defined as ROR >2, lower 95% confidence interval >1 and >3 cases. Drug properties were curated from ChEMBL, emc, SmPC and EMA sources. Results: VigiAccess contained 88,662 individual case safety reports (163,619 ADRs) and Yellow Card contained 752 reports (1,814 ADRs). Four System Organ Class (SOC) signals were identified in VigiAccess and three in Yellow Card, with five additional Yellow Card Preferred Term signals. Eye disorders showed the strongest consistent association with erlotinib (VigiAccess ROR 4.33; Yellow Card ROR 4.18). Osimertinib was associated with pneumonitis (ROR 7.67) and pulmonary embolism (ROR 3.94). Afatinib showed psychiatric disorder signals in both databases (ROR 3.2-3.3) and diarrhoea (ROR 2.79). Neoplasm-related signals likely reflected confounding by indication. Conclusions: EGFR-TKIs demonstrate generation-specific pharmacovigilance profiles. Integration with drug properties supports mechanistic plausibility for several signals, while the atypical afatinib psychiatric association warrants further investigation. Findings are hypothesis-generating and require confirmation in controlled observational or mechanistic studies.

Artemisinin partial resistance (ART R) associated Plasmodium falciparum Kelch13 mutations is expanding across Africa. However, their population level consequences remain poorly understood, particularly in settings of moderate to high malaria transmission. We investigated associations between Kelch13 and partner drug mutation prevalence and monthly confirmed malaria incidence across 16 Ugandan districts from 2017 to 2022, using integrated molecular, epidemiological, climatic, malaria control, and antimalarial resistance data and negative binomial regression. Combined prevalence of WHO validated and candidate Kelch13 mutations increased over the study period. Each 10 percentage point increase in Kelch13 prevalence was associated with a 13.1% higher incidence after multivariable adjustment (aIRR 1.131, 95% CI 1.090 to 1.174). Furthermore, each 10% increase in pfcrt K76T was associated with a 24% increase in malaria transmission. As expected, ITN distribution >80%, IRS coverage >90%, and higher altitude were associated with lower incidence, whereas temperatures of 25 to 30C, relative humidity of 60 to 80%, and higher precipitation were associated with higher incidence. The persistence of the Kelch13 and pfcrt K76T association after adjustment suggests that expanding ART R associated parasite populations are independently associated with malaria incidence. The relationship may be bidirectional. These findings support interpreting ART R within its ecological and intervention context and integrating molecular, epidemiological, and malaria control surveillance to safeguard antimalarial efficacy.

Importance: Coccidioidomycosis, also known as Valley fever, is a serious fungal disease caused by inhaling Coccidioides spores aerosolized from the soil. Despite rising public health concern, the distribution and incidence of coccidioidomycosis in the US remain poorly characterized as not all states are required to report cases, and case under-ascertainment is presumed to be high, particularly outside of the endemic region. Objective: To estimate monthly, county-level coccidioidomycosis incidence across the US while accounting for widespread under-ascertainment. Design, Setting, and Participants: We integrated multisource data, including human cases diagnosed in electronic health records, serological testing of dogs, and ecological and sociodemographic predictors, in a machine learning model estimating monthly, county-level coccidioidomycosis incidence. The model was trained on surveillance data from Arizona and California from 2013 to 2023, then applied to predict incidence in states across the contiguous US over the same time period. Main Outcomes and Measures: Incidence of coccidioidomycosis at the monthly county level. Results: The model predicted coccidioidomycosis incidence from 2013 to 2023 for 3,108 counties from all states in the contiguous US. The model identified 60 counties in non-reporting states where predicted incidence was [&ge;]2 cases per 100,000 population (considered moderate to high incidence). In states with mandatory reporting outside of Arizona and California, the model predicted county-level incidence rates that were 28% higher (95% bootstrap interval, 0.1%-69.8%) than those captured in national surveillance, equating to a total of 608 potentially missed cases per year (95% bootstrap interval, 340-964). Conclusions and Relevance: In this cross-sectional study, the model quantified case under-ascertainment for coccidioidomycosis relative to Arizona and California, and highlighted the potential for alternative data sources to fill in gaps in mandatory reporting. Estimating incidence while accounting for under-ascertainment and non-reporting may help efforts to improve public and physician awareness and to detect emergence in new regions.

Objective This study aimed to develop and validate a predictive model for sleep disorders in young and middle-aged adults with anemia, providing evidence for early identification, risk stratification, and individualized sleep management. Methods Data in the NHANES were analyzed and randomly divided into development and validation cohorts. LASSO regression was used to screen optimal predictors in the development cohort, followed by univariate and multivariate logistic regression to identify independent predictors and construct a nomogram. Model performance was evaluated using receiver operating characteristic curves, calibration curves, Brier scores, and decision curve analysis. Results The weighted prevalence of sleep disorders among young and middle-aged adults with anemia was 10.19%. Multivariate logistic regression identified seven independent predictors: sex, monocyte count, asthma, overweight, arthritis, congestive heart failure, and hypertension. The model showed good discrimination, with area under the curve values of 0.817 in the development cohort and 0.791 in the validation cohort. Calibration was satisfactory, with Brier scores of 0.074 and 0.075, respectively. Decision curve analysis indicated favorable clinical net benefit in both cohorts. Conclusion The proposed predictive model demonstrated good discrimination, calibration, and may help healthcare providers identify young and middle-aged adults with anemia who are at high risk for sleep disorders.

Background: Tuberculosis (TB) remains the leading infectious disease cause of mortality worldwide. A subset of individuals exposed to Mycobacterium tuberculosis (Mtb) remain TST/IGRA-negative despite sustained exposure, termed "TB resisters". The biological mechanisms underlying this resistance remain poorly understood. We applied untargeted high-resolution metabolomics to determine whether systemic metabolic profiles differ between TB resisters and matched Mtb-infected close contacts. Methods: We compared untargeted high-resolution plasma metabolomics using liquid chromatography mass spectrometry between 269 TB resisters and 269 matched Mtb-infected close contacts, enrolled across Brazil, India, and South Africa. TB resisters were defined as highly exposed close contacts who slept in the same room or spent at least 5 hours indoors per day with an infectious pulmonary TB index patient, but remained TST/IGRA-negative. Mtb-infected close contacts were those who were TST/IGRA positive. Metabolome-wide association studies (MWAS) were conducted using paired t-tests. Results: There were 1,787 features with nominal differences between TB resisters and matched Mtb-infected contacts (p < 0.05). Pathway enrichment identified fructose and mannose metabolism and bile acid biosynthesis in the overall cohort, while lipid-related pathways were enriched in Brazil. Among metabolites with confirmed chemical identities, glyceric acid concentrations were higher in TB resisters by 0.53 uM, whereas hydroxyproline was lower by 0.90 uM, butyrobetaine was lower by 0.06 uM, and homogentisate was lower by 0.0036 uM at false discovery rate of 0.2. Conclusions: Plasma metabolomic profiles differed between TB resisters and matched Mtb-infected close contacts. These findings indicate that systemic metabolic differences are associated with resistance to Mtb infection.

Understanding the relationship between weather conditions and infectious disease transmission has important public health implications; nevertheless, the functional form of this relationship often remains poorly understood. Humidity has previously been suggested to have a U-shaped effect on the transmission of respiratory pathogens, with optimal transmission occurring at both low and high levels. Here, to assess the impact of this functional form and of its potential misspecification, we first simulate transmission rates based on the U-shaped relationship and then test whether an exponential function accurately estimates these rates across a range of climates. We further use a transmission model to simulate case count data for a range of hypothetical pathogens, comparing outbreak dynamics under the U-shaped forcing function to those under the best-fit exponential approximation. In our simulations, the model fit for the exponential function is climate-dependent, performing best in very dry and very humid environments. In locations where specific humidity values centre around the vertex of the U-shaped function, the exponential model underestimates the amplitude and frequency of incidence peaks. These findings demonstrate the potential consequences of assuming an incorrect functional form for weather-infectious disease relationships and highlight the need for more careful consideration of this functional form in modelling practice.