American Journal of Infection Control

Point-of-care Hepatitis C Virus (HCV) RNA assays reduce diagnostic turnaround time but depend on benchtop instrumentation and continuous electricity, limiting their deployment in the harm reduction and community settings where confirmatory testing is most needed, as people who use drugs (PWUD) carry a disproportionate share of the HCV burden in the United States. This is a systemic problem in diagnostic development, where decision-making and design requirements overlook point-of-use stakeholders. Closing the gap requires integrating real-world constraints throughout design rather than validating against user needs once a product already exists. Here, we apply a human-centered design (HCD) approach to inform rigorous, stakeholder-derived design requirements, implementation considerations, and early value proposition for a novel point-of-need HCV RNA test intended for deployment in harm reduction and community settings in Indiana. To determine design specifications grounded in real-world context, our objectives were (1) identifying and characterizing context-specific experiences and barriers to HCV testing among higher-risk populations; (2) assessing the perceived benefits and acceptability of the proposed test within real-world settings across direct and indirect user groups; and (3) translating the user needs and contextual constraints into design requirements and implementation considerations that support the test's clinical, operational, and user-centered value. We conducted 18 semi-structured interviews with frontline staff and HCV testing/treatment pipeline experts (n=11) and people who get tested (n=7) across harm reduction organizations, syringe service programs, and community testing settings, analyzed using Rapid Qualitative Analysis guided by the PARRQA framework. Stakeholders responded positively to a single-encounter point-of-need RNA test, and implementation considerations, including funding restrictions, staffing structures, and diverse deployment settings, directly shaped design requirements spanning turnaround time, sample type and volume, portability, result output, target operator, and ease of use. Benchmarking these stakeholder-derived specifications against the FIND Dx HCV target product profile (TPP) showed that stakeholder input confirmed, modified, or extended several TPP criteria and introduced requirements the TPP does not address. Together, these objectives constitute an upstream, evidence-driven design process that translates contextual and stakeholder knowledge into actionable engineering requirements, highlighting the need for diverse stakeholder engagement at all stages of the design process for closing the translation gap between laboratory-validated diagnostic tools and effective point-of-need deployment.

Hydroclimate whiplash, defined as the rapid transition between unusually wet and dry conditions, is expected to intensify under climate change, yet its population health impacts remain largely unknown. Here we quantified the association between hydroclimate whiplash and mortality across the contiguous United States from 2003 to 2023 using monthly county-level mortality records, standardized precipitation evapotranspiration index data, and two-stage time-series models. We identified overall and direction-specific dry-to-wet and wet-to-dry whiplash events at seasonal and sub-annual timescales and across 5-, 10-, and 20-year recurrence intervals. More severe whiplash events were associated with higher all-cause mortality risk; 5-, 10-, and 20-year sub-annual overall whiplash events increased mortality risk over five months by 3.4%, 4.5%, and 5.7%, respectively. Elevated risks were observed across cause-specific mortality outcomes, with the strongest association for infectious diseases. We estimated that 103,471 deaths were attributable to overall whiplash during the study period. These findings identify hydroclimate whiplash as an emerging climate-related public health threat and suggest that adaptation strategies focused on single hazards may underestimate the health burden of rapid, sequential hydroclimatic extremes.

Occupational ocular injuries are a preventable yet neglected public health problem, particularly in low- and middle-income countries. Maintenance workers are exposed to diverse ocular hazards daily, yet compliance with protective measures is consistently poor. A descriptive cross-sectional study was conducted among 85 maintenance workers at the Maintenance and Essential Services Organization (MESO) of Kwame Nkrumah University of Science and Technology (KNUST), Ghana, recruited through stratified convenience sampling across seven occupational sections. A structured questionnaire assessed knowledge of ocular hazards and protective equipment, attitudes toward ocular safety, and safety practices. Data were analyzed using IBM SPSS version 26 (IBM Corp., Armonk, NY, USA); chi-square and Fishers exact tests assessed associations (p < 0.05). Participants were predominantly male (84/85, 98.8%), with a mean age of 44.5 {+/-} 10.4 years. Overall knowledge was good (mean 9.40 {+/-} 1.59 out of 11), but attitude and practice scores were average (2.78 {+/-} 0.92 and 3.27 {+/-} 0.93, respectively). Most workers correctly identified goggles and face shields as protective, but only about half recognized that ordinary sunglasses and spectacles offer inadequate protection. Although 97.6% (83/85) recognized the need for ocular protection, only 7.1% (6/85) reported consistent protective eyewear use, and fewer than half (45.9%, 39/85) had received formal ocular safety training. Routine general protective equipment use was significantly associated with ocular protection use (Fishers exact test, p = 0.011). Sand and dust particles were the leading causes of injury and only 25% (5/20) of injured workers sought formal care. Workers demonstrated good knowledge but poor attitudes and practices toward ocular safety, suggesting that knowledge alone does not translate into protective behaviour even within a relatively well-resourced institutional setting. Findings suggest that limited access to task-appropriate protective eyewear may represent an important institutional barrier. Institutional PPE supply and section-specific safety training are essential to bridge this knowledge-practice gap.

Objectives: Capturing adequate blood volume for blood cultures is critical for accurate detection of bloodstream infections. Pediatric volume targets vary by age and weight, whereas adult targets are standardized. The BD BACTEC FXI Culture System (FXI) contains an integrated calibrated load cell capable of automatically reporting blood volume measurements for each vial loaded onto the system. This study evaluated the accuracy of the FXI's blood volume measurements in simulated pediatric and adult patients. Methods: Mock pediatric and adult blood draws were performed, using bagged whole blood, to replicate real-world collection protocols. Syringe-collected blood volumes ranged from 2.0 to 15.0 mL for pediatric patients, depending on mock patient weight, and were fixed at 40.0 mL for adults. Samples were inoculated into BD BACTEC Peds Plus/F, Plus Aerobic/F, and Lytic/10 Anaerobic/F Culture Vials, with a target volume of 2.0 to 10.0 mL per bottle. Reference blood volumes were determined gravimetrically using manually obtained pre- and post-inoculation weights with a blood-specific gravity of 1.055 g/mL and were compared to the automatically measured, gravimetric-based blood volumes reported by the BACTEC FXI Culture System. Results: Automated volume estimates were accurate to a mean error of -0.03 mL per bottle (SD, 0.40 mL; n=168; 95% CI, -0.09 mL, 0.03 mL) and -0.08 mL (SD, 0.79 mL; n=72; 95% CI, -0.26 mL, 0.10 mL) when assessing total volume collected per patient. Conclusions: Our findings demonstrate that the automated system can quantify blood volumes in BACTEC culture vials and support blood volume monitoring for pediatric and adult collections. The gravimetric approach is also amenable to full automation for efficient and accurate blood volume determination.

Antimicrobial resistance is a public health challenge, driving the need for rapid, cost-effective diagnostic support tools. Artificial intelligence (AI) may enable prediction of susceptibility to untested antibiotics from known susceptibility results, but prospective clinical validation is required before routine use. We evaluated an AI-based decision support method, trained on invasive isolates from the European Surveillance System (TESSy), for prediction of antibiotic susceptibility in clinical Escherichia coli urine isolates. The evaluation included 99 E. coli isolates from urine samples with diversity in age, sex, and antibiotic susceptibility. Predictions were evaluated for 14 antibiotics using patient metadata and susceptibility results for 4-8 antibiotics as input. Prediction uncertainty was handled using conformal prediction, allowing abstention when confidence was insufficient. EUCAST disk diffusion test results were used as reference and genomic sequence data was used to explore mechanisms of the AI performance. Without conformal prediction, 84% of predictions were correct when susceptibility results of six antibiotics were used to predict susceptibility to eight additional antibiotics. Across all predictions generated using susceptibility results for six antibiotics as input, the major and very major error rates were 19% and 12%, respectively. Prediction errors varied between antibiotics and were associated with certain phenotypic and genotypic resistance patterns. Conformal prediction reduced errors but increased abstentions; at confidence levels of 90%, 95%, and 97.5%, the model abstained in 9.6%, 14%, and 22% of instances. The method showed promising performance, but its clinical use remains limited and may require diagnostic data beyond susceptibility test results and demographic variables.

Background: Children spend more than 90% of their time indoors, and early childhood education settings (ECEs) are an understudied, high-occupant-density indoor microenvironment where exposure to volatile organic compounds, particulate matter, and other toxicants has been documented. Limited knowledge exists on ECE-specific exposures affecting young children and how they compare to exposures in the home. Methods: This prospective, repeated-measures pilot study targeted enrollment of 44 preschool-aged children and 8 ECE staff across two geographically and sociodemographically distinct ECEs in metropolitan Atlanta, Georgia. Paired silicone wristbands, one home-designated and one ECE-designated, were exchanged between settings across three consecutive days and nights beginning at enrollment to characterize microenvironment-specific exposure. A single spot urine sample was also collected from each child. Continuous indoor air quality monitoring was conducted in two classrooms per site. Caregivers and ECE staff completed structured questionnaires assessing home and ECE environmental characteristics, child respiratory risk, and protocol feasibility and acceptability. Feasibility was evaluated using eight pre-specified indicators spanning recruitment and enrollment, wristband wear duration and loss by microenvironment, urine sample collection completeness, and survey completion by instrument and respondent group. Conclusion: This pilot will establish feasibility and acceptability parameters for a paired, multi-matrix silicone wristband protocol across home and ECE microenvironments. Findings will inform the design, sample size, and power calculations for a subsequent study testing indoor air interventions and pediatric respiratory outcomes in ECEs. Feasibility outcomes are reported in a companion manuscript.

Background. Direct livestock exposure is a risk factor for zoonotic influenza, including H5N1 highly pathogenic avian influenza (HPAI) A virus. But whether living in regions of high poultry and swine production intensity (PPI, SPI) increases risk of exposure to zoonotic influenza viruses independent of occupational livestock contact remains unclear. Objectives. To determine whether livestock workers and community members with no occupational livestock exposure in North Carolina, where poultry and swine production are increasingly co-located, are at higher risk of exposure to zoonotic influenza. Methods. Saliva samples from industrial livestock operation worker (ILO-W), ILO neighbor (ILO-N) and metropolitan area (Metro) households were analyzed for mucosal influenza A (H5N1, H1N1, and H3N2) hemagglutinin (HA) IgA and IgG antibodies to determine associations of PPI, SPI, exposure group, and detection of a swine-specific fecal contamination marker (Pig-2-Bac DNA) with influenza A antibody levels. Results. Residing in the highest PPI and SPI tertile was associated with significantly higher mucosal H5 and H1 HA IgA levels, including among residents without occupational livestock exposure. Households with occupational poultry or swine contact had significantly higher H5 IgA and IgG and H1 IgA levels compared to Metro households. In regression models accounting for clustering at the participant level, log10 anti-H5 HA mucosal IgA increased 0.16 (95% CI: 0.06, 0.27, p<0.005) and 0.10 (95% CI: 0.03, 0.17, p<0.005), per log10 increase in PPI and SPI, respectively, and 0.16 (95% CI: 0.03, 0.19, p<0.02) when Pig-2-Bac DNA was detected on household surfaces. Conclusions. Mucosal H5 HA IgA and IgG and H1 HA IgA were consistently elevated across different metrics of livestock exposure intensity, including residential exposure, occupational contact within a household, and a molecular marker of household swine fecal contamination in a state with intensive poultry and swine production.

Tropical cyclones are among the deadliest and costliest natural disasters in the United States, and the most intense storms are expected to become more frequent as the climate warms. Anticipating where deaths are most likely to occur is therefore central to preparedness, evacuation planning, and public health response. We modeled block-level mortality risk for twenty-four of the deadliest and costliest tropical cyclones to strike the U.S. Gulf and East Coasts, Puerto Rico, and the U.S. Virgin Islands between 1992 and 2024. For each storm, we combined NOAA hazard data (wind swaths, rainfall, and storm-surge inundation) with 2020 U.S. Census demographic and socioeconomic characteristics and the CDC/ATSDR Social Vulnerability Index for all Census blocks within 25 miles of the coast, and trained storm-specific boosted-tree models with population-standardized mortality as the outcome. Averaging block-level predictions within Saffir-Simpson categories yielded risk maps spanning tropical storms through Category 5 hurricanes. Predicted mortality risk rose with storm severity and concentrated in urban coastal communities of Puerto Rico, Louisiana, Florida, North Carolina, Virginia, Maryland, New Jersey, and New York, as well as in low-lying inlet, peninsula, and sound geographies. Large block population, non-Hispanic composition, male-dominated blocks, predominantly white blocks, and males aged 20 to 34 years ranked among the strongest predictors of mortality; patterns that likely reflect structural factors shaping exposure rather than individual susceptibility. The category-specific risk maps and an accompanying interactive dashboard provide a practical decision-support tool for emergency managers, planners, and coastal residents preparing for future storms.

Background: Autonomous ultraviolet-C (UV-C) disinfection systems are increasingly used to supplement manual environmental cleaning, yet evidence-based guidance defining pathogen-specific UV-C dose requirements across representative surfaces remains limited. Aim: To characterize operational UV-C dose requirements for clinically relevant pathogens across diverse high-touch and healthcare surfaces and determine how experimentally derived microbial inactivation can inform operational exposure parameters. Methods: SARS-CoV-2, adenovirus, Pseudomonas aeruginosa, Staphylococcus aureus, Klebsiella pneumoniae, Enterococcus faecalis, Candida auris, and Clostridioides difficile spores were exposed to defined UV-C doses on representative high-touch materials or stainless steel under standardized conditions, including a 10% fetal bovine serum organic soil challenge. Microbial inactivation was quantified by viable recovery. Dose-response analysis and operational modelling were used where supported by the experimental data. Findings: UV-C exposure significantly reduced viable recovery of all pathogens, with substantial differences in the exposure conditions associated with microbial inactivation. SARS-CoV-2 exhibited substantial inactivation at doses as low as 2.6 mJ/cm2, whereas the highest evaluated doses were 1,800 mJ/cm2 for C. difficile spores and 3600 mJ/cm2 for C. auris. For C. auris, multi-dose data estimated that approximately 1,410 mJ/cm2 was associated with a 2-log10 reference reduction, enabling distance-dependent exposure-time predictions. Conclusion: Experimentally quantified UV-C exposures produced substantial microbial inactivation across diverse pathogen classes and surfaces. Integrating delivered dose with microbial reduction provides a quantitative framework for translating laboratory efficacy into operational parameters for autonomous UV-C disinfection.

Background: Doxycycline post-exposure prophylaxis (doxy-PEP) reduces the incidence of bacterial sexually transmitted infections (STIs) among men who have sex with men and transgender women (MSMTW), but it may select for antimicrobial resistance (AMR). AMR development will depend in part how doxy-PEP changes rates of antibiotic use. Methods: We conducted a retrospective electronic medical record review of antibiotic prescriptions received by patients at the Massachusetts General Hospital Sexual Health Clinic from January 1, 2023, to December 27, 2025. Using a Bayesian negative binomial regression, we assessed the direct, indirect, and combined effects of doxy-PEP implementation on antibiotic prescription rates among doxy-PEP-eligible MSMTW who were receiving HIV pre-exposure prophylaxis. Results: Controlling for direct effects, the cohort's total antibiotic prescription rate decreased by 10% (0.90, 95% CI 0.87 - 0.94) for every 100 doxy-PEP starts. Doxy-PEP users were prescribed antibiotics at double the rate predicted in the absence of doxy-PEP implementation, and, controlling for indirect effects, received 3.40 (95% CI 2.95 - 3.91) times the antibiotic prescriptions of patients not using doxy-PEP. Doxy-PEP non-users were prescribed antibiotics at less than half the rate predicted in the absence of doxy-PEP. The full cohort's overall antibiotic prescription rate increased by 1.4 times after doxy-PEP implementation. Conclusions: Individuals who are taking doxy-PEP have higher antibiotic prescription rates, increasing selection for antibiotic-resistant bacteria in these individuals. However, doxy-PEP-driven decreases in the overall incidence of bacterial STIs have the potential to decrease selective pressure for resistant organisms in those not using doxy-PEP.

The role of bioaerosols in the transmission of pathogens and antimicrobial resistance (AMR) is of increasing clinical importance, particularly in settings housing vulnerable populations. Air filtration (e.g. HEPA filtration) and ventilation (e.g. minimum air changes per hour) measures are designed to restrict the airborne transmission of microorganisms. Despite these measures, airborne transmission remains a persistent issue in hospitals, workplaces, aged care, and schools, and is not typically assessed in routine surveillance for infection prevention. Here, we evaluated the efficacy of a high-volume air sampling approach to capture the indoor 'aerobiome', and investigated the potential for bioaerosols to mediate disease and AMR transmission in workplace and hospital settings. Our sampling approach demonstrates the benefits of simple decontamination procedures and personal protective equipment on the ability to distinguish genuine low biomass signals in air samples from blank controls, enabling reliable and sensitive microbial detection down to a limit of 69 bacterial cells/m3 of air. In a workplace bathroom setting, increased airborne biomass was strongly associated with human activity. This diminished significantly after a few hours of no activity, yet persisted in the indoor environment, with viable identical bacterial strains recovered from bioaerosols and bathroom surfaces across months of sampling. Applying our approach in a hospital ward, air samples from occupied patient rooms were not distinguishable from blank controls and contained negligible fungal and bacterial content, with only trace contributions from human occupancy. Our findings indicate that air filtration measures in this ward are effective at minimising airborne risks, but periodic testing of high-risk areas may be valuable in indoor settings with greater human traffic and may contribute key information to outbreak investigations.

Objective. Respiratory infections are the leading cause of illness at major sporting events, yet surveillance relies on athletes recognising and reporting symptoms. We evaluated whether continuous air sampling with point-of-care molecular testing could detect respiratory-virus nucleic acids in an elite team's congregate spaces during competition, and whether the resulting signals were operationally useful. Methods. We performed a prospective, descriptive environmental-surveillance study following the Canadian men's national soccer team across five host cities during the 2026 FIFA World Cup (3 June to 4 July 2026). InBio Apollo bioaerosol samplers ran continuously in up to four team-designated rooms per hotel (physiotherapy, meal, equipment, and coaches' room or hallway). Filters were changed approximately twice daily, eluted on-site, and tested with the Cepheid Xpert Xpress(R) SARS-CoV-2/Flu/RSV plus assay. A sample was considered positive if any cycle-threshold (Ct) value was reported, as less than 45, for a target. Results. Of 174 air filters, there were 13 detections of virus genetic material (9 SARS-CoV-2, 3 influenza A virus, 1 influenza B virus, 0 RSV). Detections were sparse early and clustered late in the tournament. An influenza A signal appeared the morning a player was sent home febrile, and SARS-CoV-2 signals coincided with visibly ill hotel staff, with signals falling after ill staff were excluded. Conclusion. Air sampling with point-of-care testing is feasible in the mobile environment of an elite team and can surface behavior-independent viral signals during competition that may offer opportunities for earlier precautionary actions.

Artificial intelligence (AI) has the potential to transform healthcare, with advanced multimodal approaches showing great promise in leveraging diverse health-related data. Here, we applied multimodal AI to entire electronic health record (EHR) and complete pathogen genome data to predict patient outcomes from life-threatening infection. An automated, scalable pipeline was developed for EHR data preprocessing, quality control, and standardisation. A deep learning fusion model was trained to predict in-hospital mortality, need for ICU admission, prolonged length of stay and 30-day unplanned readmission. We then developed a novel genomic large language model (gLLM) architecture to incorporate bacterial genomic features into the multimodal fusion model. The cohort comprised 2,656 bloodstream infection hospitalisations involving 2,535 patients. Deep learning fusion models using entire structured and unstructured EHR data outperformed traditional APACHE II score mortality prediction (AUROC [95% confidence intervals] 0.93 [0.92-0.94] versus 0.77 [0.77-0.78]). The model also showed strong performance for predicting the need for ICU admission (AUROC 0.978 [0.966 - 0.986]), prolonged hospital length of stay (AUROC 0.803 [0.790 - 0.812]) and unplanned readmission (AUROC 0.696 [0.690 - 0.701]). As proof of principle, incorporating entire microbial genomic features from the causative pathogen further enhanced prediction and enabled identification of key bacterial virulence pathways relevant for human disease. Multimodal AI integrating harmonised EHR and genomic data can accurately identify hospitalised patients at risk of poor outcomes. These approaches are scalable to other subspecialities of medicine.

Since the COVID-19 pandemic, wastewater-based surveillance (WBS) has emerged as a key approach to assess community-level health and the evolution of pathogens. To date, most established WBS systems focus on polymerase chain reaction (PCR) based detection and targeted sequencing of known pathogens because these approaches are well-accepted and include amplification of pathogen target sequences of interest thereby enabling lower limits of detection. Metagenomic next-generation sequencing (mNGS) is a promising approach to enable pathogen detection and surveillance beyond predefined pathogen lists, but its regular application to WBS has not been yet widely adopted because many key performance characteristics are not well-understood, including limit of detection (LOD) and false positive/negative rates. This paper describes a computational analysis to estimate the operational LOD of various sequencing approaches using a simplified model of a local wastewater (WW) system involving a military base. This paper also presents findings from two types of experiments: 1) laboratory-spiked, those for which Atlantibacter subterraneus (Asub) is introduced into real-world WW samples in a laboratory setting, and 2) system-spiked, those for which Asub is introduced at a source location of a real-world WW system. Findings indicate that mNGS detection performance varies with sequencing method and the data analysis process. In addition, findings indicate that site-specific method characterization should be used when implementing mNGS for WBS because sites can have different WW system configurations, background organisms and sequencing inhibitors.

Background To characterize Coronavirus disease 2019 (COVID-19) symptom severity, and recovery from pre-infection through one month, overall and by risk groups. Methods Symptomatic adults aged [&ge;]18 years with test-confirmed COVID-19 were enrolled from ambulatory care clinics within a national U.S. retail pharmacy network between 10/24/2024 and 08/29/2025 (NCT05160636). Adjusted mixed models for repeated measures estimated least-squares mean changes (LSE) and standard errors (SE) from pre-infection and on Days 1-7, 10, 14, and Week 4 from enrollment in composite symptom scores (sum of severity ratings (0-3) across 14 symptoms), counts of mild-to-severe, moderate-to-severe, and severe symptoms, overall and by age and clinical risk status. Effect sizes (ES) were defined as small (0.2-<0.5), medium ([&ge;]0.5), and large ([&ge;]0.8). Results The analysis included 608 adults. On Day 1, symptom severity rose sharply from pre-infection for the composite symptom score (LSE 14.2 [SE 0.3]; ES 2.22), mild-to-severe (7.6 [0.1]; 2.72), moderate-to-severe (5.0 [0.2]; 1.77); and severe (1.8 [0.1]; 0.92) (all p<0.001). By Week 4, composite score (0.7 [0.2]; 0.26), mild-to-severe (0.5 [0.1]; 0.23); moderate-to-severe symptoms (0.1 [0.1]; 0.17) and severe symptoms (0.2 [0.1]; 0.5) remained slightly above baseline (all p[&le;]0.025). Elevated severe symptom durations varied: high-risk adults (through Day 3), adults <50 years (through Day 7), and adults [&ge;]50 years (through Day 7). Conclusions COVID-19 was associated with notable acute symptoms in outpatients, followed by gradual improvement over time, although symptoms still persisted at four weeks. Improvement in severe symptoms varied by individual risk profile, reinforcing the importance risk-based follow-up and ongoing monitoring.

Acute kidney injury (AKI) is a frequent complication within intensive care units, with its sudden onset often missed. This is especially important because a timely window for intervention is required as delayed detection leads to progressively worse outcomes. Existing machine learning and deep learning models have contributed to closing this gap, but their complexity, requiring hundreds to thousands of features, and lack of generalisation pose a limitation that prevents them from being integrated into clinical workflows across different electronic health-record ecosystems. This study presents a 37-feature XGBoost model trained on the MIMIC-IV dataset with 5.4% positive cases, with hyperparameters optimised via Optuna and probabilities calibrated using isotonic regression, designed for transportability across clinical settings. Validation was conducted internally using a temporal patient-level split simulating prospective deployment, training on 2008-2016 data and testing on 2017-2022 data"External validation was performed on the eICU Collaborative Research Database, a multi-centre dataset spanning 208 US hospitals, using the trained model without retraining. SHAP TreeExplainer was used to provide feature-level explainability for individual predictions. Internal testing yielded an AUROC score of 0.794 for predicting AKI onset within a 12-24 hour window. External validation produced a 0.750 AUROC without retraining. Equitable discrimination was observed across gender, age, chronic kidney disease presence, race, and AKI stages on both datasets, with a 95% internal CI of 0.789-0.799 confirming the model's estimate stability. These results suggest that clinically useful prediction systems are achievable with substantially fewer features than current models require.

Background Carbapenem-resistant bacteria are a major global public health threat, classified as critical priority pathogens by the WHO. In Indonesia, despite a national antimicrobial resistance control programme established by the Ministry of Health in 2015, resistance rates continue to rise, including increasing carbapenem resistance among clinically important bacteria. Strengthening approaches to directly interrupt transmission is essential, yet transmission pathways remain poorly understood with limited research and policy guidance within the Indonesian context. Methods and analysis The INTERCEPT study is a UK-Indonesia multidisciplinary collaboration aiming to identify transmission routes of carbapenem-resistant bacteria across healthcare and community settings, and the mechanisms of resistance gene transfer between bacteria and mobile genetic elementss. We will conduct genomic surveillance of hospital inpatients, healthcare workers, hospital environments, and surrounding communities, including wastewater systems, combined with genomic analyses and mathematical transmission modelling. A cohort of patients with bloodstream infections will be recruited to evaluate resistant bacteria, treatment practices, and clinical outcomes. Qualitative research will explore behavioural and system-level factors influencing transmission and intervention implementation. Findings will inform stakeholder workshops to co-design context-specific interventions, with pilot intervention over 9 months with pre- and post-intervention assessment to guide scalable strategies to reduce AMR transmission. Discussion The INTERCEPT study addresses carbapenem resistance in Indonesia using an integrated approach combining microbiological surveillance, genomics, modelling, and qualitative methods. Strengths include cross-sectoral analysis (patients, workers, environment) and participatory intervention design. Limitations include geographic scope restricted to Central Java, Indonesia.

Background: General illness-related sick leave among teachers represents a relevant public health and workforce management issue. However, long-term population-based evidence describing its distribution and associated factors in Latin American urban educational systems remains limited. This study aimed to characterize the occurrence, distribution, duration, and sociodemographic, occupational, temporal, and territorial factors associated with general illness-related sick leave among public school teachers in Bogota between 2010 and 2025. Methods: A retrospective cohort study was conducted using integrated administrative databases from the Bogota District Department of Education. The primary outcome was the occurrence of at least one general illness-related sick leave episode in a teacher-month observation. Descriptive analyses were performed to characterize sociodemographic and occupational patterns. A multivariable logistic regression model was used to estimate associations. Month and year were included as temporal fixed effects to account for seasonal patterns, academic-calendar effects, pandemic-related disruption, and secular changes. Results: The cohort included 59,697 unique teachers, contributing 537,025 teacher-year observations from teachers with an active employment record between January 1, 2010, and July 31, 2025. Overall, 41.59% of teacher-year observations included at least one general illness-related sick leave episode, and 83.26% of teachers had at least one episode at any time during follow-up. Respiratory diseases accounted for the largest share of episodes, followed by musculoskeletal and infectious diseases. Mean duration varied substantially by diagnostic category, ranging from short respiratory and infectious episodes to longer absences related to neoplasms, circulatory diseases, injuries, and mental health conditions. In the multivariable teacher-month model, sick leave occurrence was associated with age, sex, occupational role, teaching area, contract type, locality, calendar month, and calendar year. Lower odds were observed among male teachers, principals, and teachers with provisional contracts, while temporal and territorial variation was observed across months, years, and localities. Conclusions: General illness-related sick leave among public school teachers in Bogota showed consistent sociodemographic, occupational, temporal, and territorial patterns. Respiratory and musculoskeletal conditions accounted for the largest share of episodes, while chronic, neoplastic, injury-related, circulatory, and mental health conditions were associated with longer durations. These findings provide population-level evidence to inform occupational health surveillance, seasonal preparedness, and workforce planning strategies within urban educational systems.

Background: Work-related musculoskeletal pain (MSP) is a prevalent concern among factory workers, impacting productivity, absenteeism, and overall quality of life. Factory workers, who perform repetitive tasks and engage in physically demanding work, are particularly susceptible to work-related MSP. Methods: This descriptive cross-sectional quantitative study included 148 factory workers (29 females and 119 males) from two factories in Kigali, surveyed between November 2023 and January 2024. Data were collected using a structured questionnaire that included demographic items, the Modified Nordic Musculoskeletal Questionnaire (M-NMQ) to assess work-related musculoskeletal pain (MSP) symptoms in nine body regions over the previous 12 months and seven days, and questions regarding interference of symptoms with normal work activities. The Coping Strategies Questionnaire (CSQ) was administered to identify coping practices among factory workers. Data analysis comprised descriptive statistics, including mean, frequency, Wilson 95% confidence intervals, and percentage values, calculated using SPSS version 25.0. Results: Participants were predominantly male (119/148, 80.4%), and 98/148 (66.2%) had worked at the factory for more than five years. Overall, 113/148 reported symptoms in at least one body region during the previous 12 months (76.4%, 95% CI 68.9-82.5). The lower back (90/148, 60.8%), shoulders (71/148, 48.0%), neck (61/148, 41.2%), and knees (58/148, 39.2%) were most frequently affected. Symptoms prevented normal work for 41/148 (27.7%, 95% CI 21.1-35.4), and 57/148 reported symptoms during the previous seven days (38.5%, 95% CI 31.1-46.5). Among 113 participants with 12-month symptoms, the most frequent Always responses were seeking medical help (78/113, 69.0%), rest and recovery (75/113, 66.4%), and medication use (59/113, 52.2%); ergonomic adjustment was least frequent (10/113, 8.8%). Conclusion: Self-reported work-related MSP symptoms were common, particularly in the lower back and shoulder, while ergonomic adjustments were uncommon. Frequent medication use highlights the need for comprehensive interventions combining ergonomic training, physiotherapy, and adequate recovery; however, these findings do not establish causation or intervention effectiveness.

Wastewater systems are increasingly recognized as important environmental reservoirs of antimicrobial resistance (AMR), acting as interfaces where resistant bacteria, antimicrobial resistance genes (ARGs), and mobile genetic elements (MGEs) converge and potentially disseminate. Wastewater samples were collected from hospital and community sites, including municipal medical centers, household wastewater outlets, and open drainage systems. Genomic DNA was extracted using the ZymoBIOMICS DNA/RNA Miniprep Kit and sequenced on the Oxford Nanopore Technologies MinION MK1D platform using the Native Barcoding Kit (SQK-NBD114.24, V14). Sequencing data were processed through a custom Snakemake workflow integrating quality control, taxonomic profiling, resistome characterization, mobilome analysis, and genome-resolved metagenomics. A total of 489 unique ARGs conferring resistance to 29 antibiotic classes were identified through metagenomic analysis. The resistome was dominated by genes conferring resistance to {beta}-lactams (including cephalosporins and carbapenems), aminoglycosides, tetracyclines, macrolides, and fluoroquinolones. Clinically important resistance determinants, including blaOXA, blaTEM, blaGES, blaCARB, cfxA, tet, qnr, sul, dfrA, erm, msrE, and aminoglycoside-modifying enzyme genes such as aac(3) and ant(3'') were detected across both hospital and community wastewater samples. Resistance mechanisms were predominantly driven by antibiotic inactivation, followed by efflux and target protection. Several priority bacterial pathogens were detected, including Escherichia coli, Klebsiella pneumoniae, Enterobacter cloacae, Pseudomonas aeruginosa, and Acinetobacter baumannii. Integration/excision elements were the predominant category of MGEs, followed by transfer-associated elements and replication/recombination/repair functions. Plasmid analysis further identified diverse incompatibility groups, predominantly IncP6, IncC, IncF, and IncR replicons, supporting the widespread occurrence of plasmid-mediated horizontal gene transfer in both settings. These findings reveal a substantial burden of clinically relevant ARGs, mobile genetic elements, and potential bacterial pathogens in hospital and community wastewater in Conakry. This study provides the first metagenomic baseline for environmental AMR surveillance in Guinea and highlights the urgent need for integrated One Health strategies to mitigate the environmental dissemination of antimicrobial resistance.