Epidemiology and Infection

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

Background: Ebola virus disease is a life-threatening illness caused by the Ebolavirus, with symptoms manifesting two to twenty-one days after infection. Although Uganda has faced multiple Ebola outbreaks, many patients survive only to encounter persistent challenges. Therefore, this study explored the post-discharge experiences of survivors following the 2022 Ebola Virus Disease outbreak in Uganda. Methods: An exploratory qualitative study comprising of in-depth participant interviews was conducted at Mubende Regional Referral Hospital in central Uganda. Interviews were face-to-face and data were analyzed manually by inductive content analysis. Ten male and female participants were Ebola Virus Disease survivors in Mubende district who had lived in the community for at least six months post-discharge from the Ebola Treatment Unit. Results: Four themes emerged: (i) Psychosocial Burdens and Social Exclusion, (ii) Economic Hardship and Loss of Financial Stability, (iii) Chronic Physical and Health Burdens Post-Recovery and (iv) Rebuilding Lives: Psychological, Social, and Medical Pathways to Recovery. Survivors faced significant emotional burdens such as survivor guilt, grief, trauma from loss, and anxiety about transmission risks. They experienced social isolation, stigma, and discrimination, which often led to their exclusion from community activities. Financially, they struggled with debt and the loss of livelihoods, compounded by ongoing health issues. Additionally, survivors endured chronic physical complications, including pain and fatigue, which hindered their recovery. Despite these challenges, survivors sought psychological, social, and medical pathways to recovery, including confirmation of their recovery, support from family and organizations, and health maintenance practices. Supportive medical care and community assistance were crucial in their physical and emotional rehabilitation. Conclusion: Ebola Virus Disease survivors in Uganda face significant psychosocial, health, social, and economic challenges post-discharge. The findings highlight the critical need for comprehensive medical and community-based support systems to aid survivors' recovery and well-being. Further research on long-term neurological effects and community reintegration programmes is needed to inform targeted interventions that support Ebola survivors and reduce stigma and discrimination.

Introduction: Clinical students are a future health workforce, yet their roles during outbreaks of highly infectious diseases remain uncertain because of safety, training, supervision and welfare concerns. Ugandas 2022 outbreak of Ebola disease caused by Sudan ebolavirus highlighted the need to understand how clinical students perceive outbreak-related care. Aim: This study explored willingness to care for patients with Ebola virus disease among clinical students at a Ugandan medical school and examined how perceived risks, perceived benefits and support needs shaped that willingness. Methods: An exploratory descriptive qualitative study was conducted among clinical students of Makerere University in Kampala, Uganda. Fifteen undergraduate medical and nursing students in the later years of training were purposively selected. Data were collected through in-depth interviews, audio-recorded with consent, transcribed verbatim, de-identified and analysed using latent content analysis. The Health Belief Model sensitised interpretation, and reporting was strengthened using the COREQ guidance. Results: Five interrelated themes emerged, showing that willingness to care was conditional rather than simply present or absent. Students described an initial willingness grounded in professional duty, devotion to patients and the desire to save life. This willingness was restrained by perceived risks of contracting Ebola virus disease, dying, transmitting infection to family members or colleagues, emotional distress, lack of epidemic-readiness in the curriculum, inadequate preparedness and weak welfare support. Perceived benefits, including patient survival, professional learning, outbreak experience and personal fulfilment, strengthened willingness but did not override safety concerns. Students identified reliable personal protective equipment, epidemic-ready curricula, practical infection-prevention and control training, simulation, clear protocols, close supervision, psychosocial support, insurance and fair compensation as cues to action that could convert willingness into safe participation. Conclusions: Clinical students in this Ugandan teaching hospital expressed a strong sense of professional responsibility, but their willingness to participate in Ebola care was conditional upon preparedness, protection, epidemic-ready education and institutional trust. Professional duty and learning opportunities promoted participation, whereas perceived risks and inadequate support limited it. Medical education programmes and outbreak-response systems should develop ethical, supervised, competency-based student roles supported by practical curricula, reliable protective equipment and psychosocial and welfare safeguards.

Background Floods are among the most devastating natural disasters worldwide and are increasingly associated with adverse mental health outcomes, particularly Post-Traumatic Stress Disorder (PTSD). In December 2023, Hanang District in northern Tanzania experienced catastrophic mud floods that resulted in extensive loss of life, destruction of property, displacement of households, and disruption of livelihoods. While emergency humanitarian responses focused primarily on physical needs, limited evidence exists regarding the long-term psychological consequences among survivors. Therefore, this study aimed to determine the patterns of PTSD manifestations and assess cognitive factors associated with PTSD symptoms among flood victims in Hanang District, Tanzania. Methods A community-based cross-sectional study was conducted among 360 flood victims one year after the disaster. PTSD symptoms were assessed using the PTSD Checklist for DSM-5 (PCL-5). Descriptive statistics summarized PTSD severity, while chi-square tests and regression analyses examined associations between socio-demographic characteristics and PTSD manifestations. Cognitive factors were assessed based on participants' exposure to traumatic experiences and perceptions of traumatic events. Results The mean PCL-5 score was 39.2 (SD = 20.6), indicating a high burden of psychological distress. Approximately 45% of respondents had severe PTSD symptoms (PCL-5 [&ge;]45), while another substantial proportion demonstrated moderate symptom severity. PTSD manifestations varied significantly by geographical location (p < 0.001), household income (p = 0.011), and marital status (p = 0.002). Age positively predicted PTSD severity ({beta} = 0.019, p = 0.001), whereas household income negatively predicted symptom severity ({beta} = -0.297, p = 0.001). Exposure to natural disasters constituted the predominant cognitive factor, with 45% directly experiencing the flood and 38.3% witnessing the event. Exposure to secondary traumatic experiences through witnessing or learning about violent events was also common. Cognitive trauma exposure demonstrated a significant association with PTSD symptoms ({chi}2, p < 0.001). Conclusion PTSD remains highly prevalent among flood survivors in Hanang district. Both direct and indirect trauma exposure significantly contributed to PTSD manifestations. Comprehensive disaster recovery programmes should integrate trauma-focused psychological services, cognitive behavioural interventions, routine PTSD screening, and community-based psychosocial support alongside socioeconomic recovery initiatives.

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

Vaccine effectiveness (VE) estimates can vary widely between years and populations, even for the same vaccine. Estimated VE is known to be sensitive to susceptible depletion and differences in pre-vaccination infection risk between vaccinated and unvaccinated populations. However, how variation in pre-vaccination risk within and between the two groups affects VE estimates over time remains unclear. This uncertainty is especially important given negative VE estimates. We investigated the difference between estimated VE and true vaccine protection considering continuous distributions of pre-vaccination infection risk under three scenarios. When the vaccinated and unvaccinated populations differ in their mean risk, estimated VE can be higher or lower than true vaccine protection. Similar patterns arise when both populations share identical means but different risk distributions. Finally, if infection-derived immunity lasts longer than vaccine protection, annual VE estimates can vary by tens of percentage points between years despite constant true vaccine protection. These theoretical results underscore that VE studies estimate contrasting risk between vaccinated and unvaccinated individuals in a particular time and place, and VE estimates can vary counterintuitively between years and populations even with constant vaccine-induced protection. Explaining variability in estimated VE thus requires a more complete understanding of populations' distributions of infection risk.

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

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

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

Background Emergency Department (ED) crowding is an international crisis primarily driven by exit block. Point of care (POC) cardiac biomarker testing and reduced sampling intervals have been proposed to mitigate crowding by improving throughput, but whole-ED operational impacts remain poorly understood, and evaluations often rely on vulnerable observational designs. This study aimed to assess whether introducing POC high-sensitivity troponin testing and reduced sampling intervals changed whole-ED flow metrics, and to test the robustness of interrupted time series (ITS) methodology in this setting. Methods A multi-centre controlled interrupted time series (CITS) across two large urban intervention EDs and one untreated control ED in Glasgow, UK. The intervention combined whole-blood POC high-sensitivity troponin testing with a reduction in sampling intervals from 3 to 2 hours. Outcomes included daily ED admissions, mean occupancy, maximum occupancy, and mean length of stay. Analyses used a window of 120 days either side of each implementation date. Effects were evaluated using segmented ITS models, with and without controls, with permutation tests against 147 pre-intervention placebo dates. The minimum detectable effects of a similar study, applied to a national dataset, were simulated. Results Across 483,412 presentations to the intervention sites, the intervention produced no statistically significant change in any whole-ED flow metric against the untreated control at either site. Analysed alone, one intervention site appeared to show reductions in mean occupancy (-6.08, 95% CI -12.04 to -0.12) and maximum occupancy (-7.60, -14.47 to -0.73); the untreated control department produced reductions in the same direction at the same date, and both estimates attenuated to the null once the control was applied. Under a pre-specified 14-day transition specification the reductions in the untreated department reached statistical significance while those at the treated site did not. The study was limited by power due to the study window and limited control pool. Simulation demonstrated that a national dataset has the potential to provide operationally feasible and clinically important findings. Conclusion POC cardiac biomarker testing and reduced sampling intervals did not detectably improve whole-ED flow, though the design was underpowered. More importantly, uncontrolled ITS designs are highly vulnerable to confounding in complex healthcare systems; evaluations of operational interventions must utilise concurrent controls, and routinely report falsification tests.

Background. The clinical course of coronavirus disease 2019 (COVID-19), and the ability to anticipate which patients will require intensive care, were poorly characterized in Central Asia during the first pandemic wave. We aimed to describe the clinical features of hospitalized COVID-19 patients at the Tashkent State Medical University, Uzbekistan, and to identify risk factors for intensive care unit (ICU) admission. Methods. In this single-centre cross-sectional study, we reviewed the records of 2500 consecutive patients hospitalized between 11 April and 8 August 2020. Patients were grouped as asymptomatic or symptomatic, and symptomatic patients were compared by ICU versus non-ICU status. Groups were compared with chi-square or Fisher's exact and Mann-Whitney U tests. Univariable and multivariable logistic regression identified risk factors for ICU admission. Results. Of 2500 patients (median age 36 years; 60.9% male), 989 (39.6%) were asymptomatic and 1511 (60.4%) symptomatic. In total, 129 (5.2%) were admitted to the ICU and 38 (1.5%) died. ICU patients were older (median 56 vs 40.5 years) and more often had bilateral pneumonia, oxygen desaturation and cardiometabolic comorbidity. In the multivariable model (AUC 0.82), the independent predictors of ICU admission were ischemic heart disease (aOR 4.20), shortness of breath (aOR 3.22), hypertensive heart disease (aOR 2.93) and male sex (aOR 2.00). Conclusions. Older age, cardiometabolic comorbidity and respiratory compromise identified patients at high ICU risk. As one of the first clinical COVID-19 descriptions from Uzbekistan, these data provide a baseline for preparedness in Central Asia.

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

Introduction Acute lower respiratory tract disease (aLRTD) is a leading cause of hospitalisation and death, particularly in older adults and adults with comorbidities, with acute lower respiratory tract infection (aLRTI; pneumonia and non-pneumonic LRTI) being a major component. Non-pulmonary complications and functional decline after aLRTI are recognised, but their pathogen-specific burden is poorly described. We aimed to quantify renal, hepatic, thromboembolic and functional complications, and mortality, after aLRTI hospitalisation, by clinical phenotype and pathogen. Methods We conducted a cohort study of adults (>18 years) admitted with aLRTD to two hospitals in Bristol, UK (01 August 2022-31 July 2024). aLRTD was classified as pneumonia, non-pneumonic LRTI (NP-LRTI) or no diagnosis of aLRTI. Pathogens were identified from standard-of-care and research microbiology. Outcomes were acute kidney injury (AKI), acute liver dysfunction, venous thromboembolism (VTE), in-hospital falls, reduced mobility at discharge, increased care requirements, and 30-day and 1-year mortality. Analyses were descriptive. Results Among 246,797 adult admissions, 21,456 aLRTD hospitalisations were included: 10,239 (47.7%) pneumonia, 7,742 (36.1%) NP-LRTI and 3,475 (16.2%) with no evidence of aLRTI. Of 19,152 tested aLRTD admissions, 8,503 (44.4%) had a positive microbiological/virological test, yielding 9,204 pathogen detections; 1,194 (6.2%) had co-infections, and SARS-CoV-2 was most frequent, with influenza the second most common in pneumonia and NP-LRTI. Pneumonia had greater severity than NP-LRTI and no diagnosis of aLRTI (median length of stay 6 vs 4 vs 4 days; ICU admission 3.4% vs 0.7% vs 0.5%, respectively). Overall, 22.2% developed AKI, 6.1% acute liver dysfunction, 0.6% DVT and 2.4% PE; 1.8% had a fall, 11.5% reduced mobility, and 16.6% required increased care at discharge. 30-day and 1-year mortality were highest for pneumonia (14.0% and 32.0%, respectively). Pathogen-specific analyses showed longer stays and higher complications and mortality rates for SARS-CoV-2 and Streptococcus pneumoniae, and shorter stays with lower complication and mortality rates for influenza and Haemophilus influenzae. Conclusions Non-cardiovascular complications and functional decline after aLRTI were common, particularly in pneumonic and SARS-CoV-2 or pneumococcal disease. These findings support routine surveillance for renal, hepatic, thromboembolic events, early mobilisation and rehabilitation, and consideration of multi-system outcomes when evaluating public health and economic value of vaccines and therapies.

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

Background: Health and administrative professionals in tertiary hospitals face high levels of occupational stress, mental illness, and multimorbidity. The integrated measurement of these multidimensional health aspects is essential for informing effective workplace health promotion strategies. Objective: To describe the general health status of federal public hospital staff and correlate the measured health dimensions to inform institutional health promotion initiatives. Methods: A cross-sectional, online survey study was conducted at Hospital Universitario dos Servidores do Estado (HUSE) between November and December 2025. Data collection was performed via online REDCap questionnaires covering sociodemographic profiles and validated instruments (SRQ-20, MIDAS, AUDIT, WHOQOL-BREF, PHI, WHOQOL-SRPB BREF, CBI, GPAQ, and EPSO). Descriptive statistics, comparisons across employment ties (permanent vs. contracted staff), and Spearman correlation matrices were calculated. Results: Among 197 accesses, 117 completed the informed consent, and 86 finished all questionnaires. Participants were predominantly female, aged 40 to 60 years, and Christian. Screening positivity was 25% for common mental disorders, 21% for headache-related disability, and 10% for harmful drinking. Burnout scores clustered in the second quartile, while quality of life, happiness, and spirituality scores were in the upper third. Median physical activity was 670 min/week. Mental symptoms (SRQ-20), headache (MIDAS), and burnout (CBI) correlated positively with each other and negatively with quality of life, happiness, spirituality, and institutional support (EPSO). Conclusion: The set of instruments proved feasible for situational health diagnosis among hospital staff. Although the sample size was limited in this baseline wave, the initiative fostered workplace health awareness, driving concrete initiatives, including an on-site functional gym and workplace vaccination campaigns.

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

The global dissemination of antimicrobial resistance is increasingly driven by bacterial clones combining antimicrobial resistance with enhanced virulence and environmental adaptability. Escherichia coli sequence type 131 (ST131) has historically been regarded as a major disseminator of the extended-spectrum {beta}-lactamase (ESBL) blaCTX-M-15. However, the emergence of E. coli ST1193 carrying blaCTX-M-15 may represent an ongoing shift in the epidemiology of this resistance determinant. Here, we investigated the prevalence, genomic characteristics, virulence and antimicrobial resistance potential of ST1193 in comparison with ST131. A total of 1,136 E. coli isolates were recovered from touristic and non-touristic environments, hospital-associated samples, and aircraft toilets in Guadeloupe. Isolates were whole-genome sequenced and analysed for antimicrobial resistance and virulence determinants. Additionally, publicly available genomic data comprising 1,215 blaCTX-M-15-positive ST131 and ST1193 isolates were analysed to assess temporal and geographical trends. ST1193 was significantly associated with aircraft-associated samples and exhibited a higher antimicrobial resistance gene burden than ST131, while maintaining a comparable virulence factor content. Analysis of publicly available genomes revealed similar temporal emergence patterns for blaCTX-M-15-positive ST1193 and ST131, with ST1193 showing a more recent distribution and a higher number of deposited isolates in recent years, consistent with a potential ongoing clonal replacement. Comparative genomic analysis identified numerous virulence and adaptation-associated genes shared between both sequence types, while ST1193 additionally carried distinct determinants, including components of the transmissible locus of stress tolerance. Furthermore, quinolone resistance-associated mutations were strongly linked to blaCTX-M-15 carriage, particularly among ST1193 isolates. Together, these findings identify E. coli ST1193 as an emerging high-risk clone with substantial potential for blaCTX-M-15 dissemination. Its association with aircraft-associated samples further highlights the potential role of air travel in long-distance transmission and underscores the need to reconsider current surveillance strategies focused predominantly on ST131.

The 17th Ebola outbreak in the Democratic Republic of the Congo, announced on 15 May 2026, was attributed to Bundibugyo ebolavirus (BDBV). Although case isolation is the main control strategy, its effectiveness is compromised when patients escape isolation facilities before recovery. Between 14 May and 17 June 2026, 175 individuals reportedly left isolation facilities without formal discharge across Ituri Province. We assessed how this "isolation leakage" affects community transmission. We refined the SEIHFR framework to distinguish undetected community infections, detected but not-yet-isolated cases, isolated individuals, leakage, funeral-associated transmission, and removals. Using Bayesian inference, we fitted the model to daily Ituri surveillance data, escapee counts, and isolation census records. We estimated the leakage rate, reporting and detection probabilities, and the transmission rate, while fixing other parameters based on the BDBV literature. The model reproduced confirmed cases, deaths, discharges, and escapees. We estimated R_0=3.67 (95% HDI: 2.0-5.7), a leakage rate of {rho} {approx} 0.034 day^-1 (0.022-0.051), and high contact-tracing-driven detection (p_d {approx} 0.91-0.99). Leakage increased the detection-dependent reproduction number [R](p_d) from approximately 3.2 to above 5. Eliminating leakage reduced cumulative infections by about one-third, from 1,120 to 764, while the minimum detection level required for control increased from p_d [&ge;] 0.73 without leakage to p_d [&ge;] 0.87 at the fitted leakage rate. Shortening time to isolation prevented the most infections (73.4%; 59-84), followed by reducing leakage (29.7%; 14-52) and re-isolating escapees (12.6%; 6-24). Delaying leakage reduction until week 4 reduced its benefit from about 27% to below 2%. Isolation leakage represents a major transmission pathway that has until now gone largely unmeasured. While rapid initiation of isolation is highly beneficial, it cannot compensate for permeable isolation; therefore, early, community-driven efforts to control leakage, embedded within a multilayered response, are critical.

Objective. To determine the publication rate of abstracts presented at the American Diabetes Association Scientific Sessions and to evaluate the association between statistical significance of study results and subsequent publication. Research Design and Methods. We conducted a retrospective cohort study of abstracts presented at the 2018 American Diabetes Association Scientific Sessions. The primary exposure was study result category (statistically significant vs. non-statistically significant findings), and the primary outcome was publication in an indexed journal within 5 years after conference presentation. Publication status was determined through PubMed/MEDLINE and Scopus searches. Adjusted relative risks (RRs) and 95% CIs were estimated using generalized linear models with Poisson distribution and robust variance. Results. Among 541 included abstracts, 321 (59.3%) were subsequently published in indexed journals. Abstracts reporting statistically significant findings had a higher publication rate than those reporting non-statistically significant findings (61.9% vs. 42.3%; p=0.002). In the adjusted analysis, abstracts with non-statistically significant findings had a lower likelihood of publication compared with those reporting statistically significant findings (adjusted RR 0.71 [95% CI 0.55-0.93]; p=0.013). Conclusions. Approximately four in ten abstracts presented at the ADA Scientific Sessions were not published within 5 years. Abstracts reporting non-statistically significant findings had a lower likelihood of subsequent publication, suggesting persistent publication bias in diabetology research. Future initiatives promoting the interpretation of effect estimates, confidence intervals and clinical relevance, rather than statistical significance alone, may help reduce selective dissemination of evidence

Background Little evidence is available on the epidemiology of different knee injuries at a whole-of-population level. The objective of this article is to provide accurate estimates of knee injury incidence by harnessing the unique comprehensive, population-wide data of New Zealand's universal no-fault injury insurance provider, the Accident Compensation Corporation (ACC). Methods We obtained insurance claims data from ACC covering all knee injury insurance claims approved between 2015 and 2024. We calculated the number of injuries and the incidence rate per 100 000 population, by injury type, year, sex, ethnicity, and age. Results The total number of injuries increased from 184 710 (4 067 per 100 000 population) in 2015 to 244 155 (4 701 per 100 000) in 2024. The most common injuries were other/unspecified ligament sprains, contusions, and collateral ligament sprains. Ligament and cartilage injuries were more common for males than for females, while contusions were more common for females. Ligament tears and dislocations were more common in younger people (15 to 35 years of age), while cartilage injuries were more common at older ages (40 to 65 years). Discussion and Conclusions The rate of knee injuries observed in this study was higher than previously reported in other settings, probably due to broader coverage of injuries treated in primary and community care settings. A broad range of injuries were common, including those that have received less attention in the epidemiological literature to date. More research is needed on the prevention, burden, and outcomes of different knee injuries, beyond a narrow focus on cruciate ligament injuries.