American Journal of Infection Control

Infectious disease forecasts can inform public health decision-making. Wastewater monitoring is a relatively new epidemiological data source with multiple potential applications, including forecasting. Incorporating wastewater data into epidemiological forecasting models is challenging, and relatively few studies have assessed whether this improves forecast performance. We present and evaluate a semi-mechanistic wastewater-informed forecasting model. The model forecasts COVID-19 hospital admissions at the state and territorial levels in the United States, based on incident hospital admissions data and, optionally, SARS-CoV-2 wastewater concentration data from multiple wastewater sampling sites. From February through April 2024, we produced real-time wastewater-informed COVID-19 forecasts using development versions of the model and submitted them to the United States COVID-19 Forecast Hub ("the Hub"). We then published an open-source R package, wwinference, that implements the model with or without wastewater as an input. Using proper scoring rules and measures of model calibration, we assess both our real-time submissions to the Hub and retrospective hypothetical forecasts from wwinference made with and without wastewater data. While the models performed similarly with and without the wastewater signal included, there was substantial heterogeneity for individual locations and dates where wastewater data meaningfully improved or degraded the models forecast performance. Compared to other models submitted to the Hub during the period spanned by our submissions, the real-time wastewater-informed version of our model ranked fourth of 10 models, with the hospital admissions-only version of our model ranking second out of 10 models. Across the 2023-2024 winter epidemic wave, retrospective forecasts from wwinference would have performed similarly with and without the wastewater signal included: fifth and fourth out of 10 models, respectively. To better understand the drivers of differential forecast performance with and without wastewater, we performed an exploratory analysis investigating the relationship between characteristics of the input data and improved and reduced performance in our model. Based on that analysis, we identify and discuss key areas for further model development. To our knowledge, this is the first work that conducts an evaluation of real-time and retrospective infectious disease forecasts across the United States both with and without wastewater data and compared to other forecasting models. Author SummaryWastewater-based epidemiology, in combination with clinical surveillance, has the potential to improve situational awareness and inform outbreak responses. We developed a model that uses data on the pathogen concentration in wastewater from one or more wastewater treatment plants in combination with hospital admissions to produce short-term forecasts of hospital admissions. We produced and submitted forecasts of 28-day ahead COVID-19 hospital admissions from this model to the U.S. COVID-19 Forecast Hub during the spring of 2024 and found that it performed well in comparison to other models during that limited time period. To assess the added value of incorporating wastewater data into the model and to investigate how it would have performed had we submitted it during the entire 2023-2024 winter epidemic wave, we performed a retrospective analysis in which we produced forecasts from the model with and without including wastewater data, using data that would have been available in real-time as of each forecast date. Both versions of the model would have been median overall performers had they been submitted to the Hub throughout the season. When comparing the models performance with and without wastewater data included, we found that overall forecast performance was very similar, with wastewater data slightly reducing overall average forecast performance. Within this result, there was significant heterogeneity, with clear instances of wastewater data improving and detracting from forecast performance. We used trends in the observed data to generate hypotheses as to the drivers of improved and reduced relative forecast performance within our model. We conclude by suggesting future work to improve the model and more broadly the application of wastewater-based epidemiology to forecasting.

BackgroundCandidemia is a rare but life-threatening bloodstream infection that remains difficult to predict using conventional risk stratification approaches, highlighting the need for improved predictive strategies. As a result, empiric antifungal therapy is often delayed even in high-risk patients. MethodsWe developed a deep learning model (PyTorch_EHR) to predict 7-day candidemia risk by using electronic health record data from two large cohorts (Houston Methodist Hospital System [HMHS] and MIMIC-IV), including adult inpatients who underwent at least one blood culture. Model performance was compared with logistic regression (LR), LightGBM, and established intensive care unit candidemia scores. We further implemented a two-step prediction framework integrating candidemia and 30-day mortality risk models to inform empiric antifungal decision-making. ResultsAmong 213,404 and 107,507 patients in the HMHS and MIMIC-IV cohorts, candidemia occurred in fewer than 1% (851 [0.4%] and 634 [0.6%], respectively). PyTorch_EHR outperformed LR, LightGBM, and existing candidemia scores, particularly in terms of area under the precision-recall curve (AUPRC) in HMHS and MIMIC-IV. By integrating 30-day mortality risk, the two-step framework identified an additional 20 and 28 candidemia cases beyond the one-step model, increasing coverage to 61% (121/199) and 46% (68/147) in HMHS and MIMIC-IV, respectively. Many patients identified by the two-step framework had high mortality yet did not receive empiric antifungal therapy (61.1% HMHS; 82.6% MIMIC-IV). ConclusionA two-step deep-learning framework integrating candidemia and mortality risk may support early identification of high-risk patients and facilitate timely empiric antifungal therapy. Prospective studies are warranted to confirm the findings.

IntroductionMusculoskeletal injuries (MSKIs) are ubiquitous in the U.S. military, especially among high-performing service members such as Marines. Given that female service members only started to be assigned to ground combat roles since December 2015, evaluation of sex on MSKI risk in ground combat occupations has not been possible until there was an ample population to study. The purpose of this population-level epidemiological study was to assess (1) if female sex was a salient risk factor for MSKI in Marines serving in different military occupations, including combat arms, and (2) the effects of integration period on MSKI risk among female Marines. Materials and MethodsA population-based epidemiological retrospective cohort study of all U.S. Marines was performed assessing female sex, occupation, and integration period on the prevalence of MSKI from 2011 through 2020. The Military Health System Data Repository was utilized to identify initial healthcare encounters for diagnosed ankle-foot, knee, lumbopelvic-hip, thoracocostal, cervicothoracic, shoulder, elbow, or wrist-hand complex injuries. Prevalence was calculated for female and male Marines in each occupational category (combat, combat support, aviators, aviation support, services) during the pre-integration (2011-2015) and post-integration (2016-2020) periods. ResultsDuring the pre-integration period, 520/1,000 female Marines (n=13,985) and 299/1,000 male Marines (n=142,158) incurred MSKIs. In the post-integration period, the prevalence increased to 565/1,000 female Marines (n=17,608) and 348/1,000 male Marines (n=161,429). In the multivariable evaluation of sex, occupation, integration period, and the interaction of sex and occupation on combined MSKIs, only female sex was a significant factor for injury (prevalence ratio [PR]=1.99), with service in ground combat and aviation occupations identified as protective factors when compared with services occupations (PR=0.69). When these same factors were evaluated for specific MSKI outcomes, female sex remained a robust factor in all lower quarter (PR=1.75-2.63) and upper quarter (PR=1.38-2.36) injuries except for shoulder injuries. Service in ground combat and aviation occupations was protective for all lower quarter injuries (PR=0.46-0.71). In the upper quarter, ground combat was protective for all injuries except for elbow injuries (PR=0.67-0.77). Serving as an aviator was a risk factor for cervicothoracic (PR=1.57) and thoracocostal (PR=1.22) injuries and a protective factor for shoulder (PR = 0.73) and wrist-hand (PR = 0.46) injuries. Adjusted risk for lumbopelvic-hip (PR=1.13), ankle-foot (PR=1.53), cervicothoracic (PR=1.19), thoracocostal (PR=1.14), and elbow (PR=1.48) injuries significantly increased during the post-integration period. There was a significant sex-by-period interaction for shoulder injuries alone, with female sex in the post-integration epoch found to be salient (PR=1.26). ConclusionsFemale sex was a salient factor for MSKI, with service in ground combat and aviation occupations identified as protective factors when compared with services occupations. In the evaluation of specific MSKIs, female sex remained a robust and significant factor in all lower quarter injuries and upper quarter injuries except for shoulder injuries. There was only a significant sex-by-period interaction for shoulder conditions, with an increased risk of these injuries in female Marines in the post-integration period.

ObjectiveThis study explored the recovery experiences of individuals who report having (largely) recovered from long covid and who attributed their improvement to mind-body approaches. Design, setting and participantsWe conducted an explorative qualitative study using purposive recruitment through social media and snowball sampling. Eighteen adult women (aged 37-62 years), who self-identified as having had long covid and having substantially recovered through mind-body approaches participated in semi-structured interviews. Data were analysed using Saunders practical thematic analysis. ResultsDespite variation in personal narratives, a common trajectory emerged: participants moved away from a biomedical explanatory model towards one centred on nervous system dysregulation. This shift, sometimes following initial scepticism, was often described as a turning point, sparking hope and motivation to engage in self-directed strategies. Recovery was not linear but an iterative process, involving cycles of practice, reflection (especially when progress stagnated) and adaptation of mind-body techniques. Over time, participants gained insights into contributing factors and, in many cases, made intentional life changes to support ongoing recovery. These patterns echo findings from previous research on mind-body approaches in chronic pain and chronic fatigue, and align with neuroscientific perspectives on symptom generation. Most participants navigated this process without formal clinical support, relying instead on online communities and actively avoiding sources of (biomedical) information that conflicted with their new understanding. ConclusionsWhile causal inferences cannot be drawn from qualitative data, this study highlights potential mechanisms that may underpin recovery for people with long covid using mind-body approaches. Further research is needed to develop structured interventions, and to evaluate their efficacy and safety. Future research should also explore how prevailing narratives within healthcare and society influence treatment engagement and recovery trajectories. STRENGTHS AND LIMITATIONS OF THIS STUDYO_LIThis is the first study exploring experiences of recovery from long covid using mind-body approaches. C_LIO_LIIn-depth, real-world accounts capture the lived-experiences over time and allow in-depth exploration if the recovery process, while the semi-structured design facilitates the emergence of themes rarely captured in clinical research. C_LIO_LIGeneralisability is limited due to self-identified long covid status, lack of formal diagnostic verification, absence of strict definitions of mind-body approaches and recovery, and a relatively homogenous sample (mostly highly educated women). C_LI

BackgroundThe COVID-19 pandemic required South African public sector HIV viral load (VL) laboratories to scale up Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) testing while maintaining essential HIV services. This placed additional pressure on diagnostic services. This dual mandate introduced significant occupational and environmental challenges (OEC) for staff that remain underexplored. ObjectiveThis study aimed to investigate the OEC and effects that staff experienced during the implementation of COVID-19 testing at public sector VL laboratories in South Africa. MethodsA quantitative, cross-sectional study utilised a census approach among technical and support staff. Data were collected via a structured REDCap questionnaire using 5-point Likert scales. Pre- and post-implementation challenges were assessed across four domains: workload, environmental conditions (space, ventilation, waste), communication, and PPE availability. Statistical analyses included the Wilcoxon Signed-Rank and Spearmans correlation tests. ResultsPerceived occupational challenges increased significantly across all domains post-implementation. Staff workload saw the highest rise (mean score 3.02 to 3.53). Adverse health effects were pervasive; 80.2% of staff reported burnout/fatigue, and 76.5% reported increased anxiety/stress. A strong positive correlation was observed between post-COVID-19 challenges and adverse mental and physical health outcomes (rho = 0.449, p < 0.001). Furthermore, 35.8% of staff considered resigning due to increased job demands. ConclusionIntegrating COVID-19 testing exacerbated systemic weaknesses, causing measurable psychological injury and threatening workforce retention. Findings suggest that the diagnostic workforce requires formal crisis surge staffing models and institutionalised mental health support to safeguard personnel and maintain essential services during future health emergencies.

BackgroundEpidemic forecasting research often assesses ensembles and their component models using probabilistic scoring rules. Quantifying how individual models affect ensemble performance is challenging, particularly across multiple targets and spatial scales. MethodsWe present Winter 2024-25 forecasts of Influenza and COVID-19 hospital admissions in England and conduct a retrospective simulation using the operational component models. Forecasts were scored using the per capita weighted interval score (pcWIS) for counts and the ranked probability score (RPS) for ordinal trend direction. We compared operational retrospective forecasts, used generalised additive models (GAMs) to estimate the expected change in score from the inclusion of a model in a sub-ensemble, and used Pareto analysis to understand which sub-ensembles were Pareto-optimal across scoring rules. ResultsNationally, the Influenza and COVID-19 operational ensembles achieved pcWIS of 5.20 x 10-7 and 3.98x 10-7, with RPS of 0.234 and 0.171 respectively. This corresponds to a 47% improvement in score versus sub-ensembles for Influenza pcWIS. However, Influenza operational ensembles were 22% worse than sub-ensembles, on average, when measured by RPS. For COVID-10, operational ensembles were 43% and 265% worse on average, than retrospective sub-ensembles by pcWIS and RPS, respectively. The sub-ensemble simulation showed individual models influenced the ensembles during different epidemic phases. The Pareto analysis demonstrated that there can be a trade-off between relative direction and absolute count score optimisation. InterpretationOur analysis shows that UKHSA forecasts were well calibrated with observations and often had comparable performance to optimal ensembles. Our GAM and Pareto analyses inform model selection for future ensembles. Author SummaryForecasts of winter hospital pressures in England are an important tool for senior healthcare leaders. It is common practice to produce a forecasting ensemble, i.e. combine the predictions of multiple models to create a single, more accurate prediction. Forecasting teams should strive to produce the best forecast possible; one tool for this is retrospective evaluation over a forecasting season using proper scoring rules to assess performance. Our forecasts are constructed of two components, an epidemic trend direction estimate as well as forecast of hospital admission numbers. There are two main challenges we address. The first is understanding at which epidemic phase different ensemble contributions are most effective, the second is the joint optimisation of an ensemble for both trend direction and admission numbers forecast. We apply these methods to a variety of ensembles (sub-ensembles) based on our own modelling suite, and compare the sub-ensembles to our operational forecasts from the Winter 2024/25 season.

BackgroundThe 2024 blood culture bottle shortage brought diagnostic resource allocation to the forefront, reflecting persistent, foundational challenges with low-value testing and empiric treatment approaches under clinical uncertainty. ObjectiveTo determine whether a machine learning approach using electronic medical record data can predict bacteremia more effectively than existing systems and practices to guide diagnostic testing and empiric treatment strategies. MethodsIn a retrospective cohort of 101,812 adult emergency department encounters (2015-2025), we first established an idealized cognitive baseline by evaluating physician and generative AI (GPT-5) application of the professional society-endorsed Fabre framework on a validation subset. We then trained an XGBoost model (Cultryx) on the full cohort to predict bacteremia, benchmarking its performance against real-world clinical heuristics (SIRS, Shapiro Rule). ResultsFor the idealized baseline, physicians applying the Fabre framework achieved 95.7% sensitivity, but GPT-5 automation failed to replicate this standard (71.6% sensitivity). In real-world benchmarking, Cultryx outperformed all clinical heuristics (AUROC 0.810). SIRS lacked specificity (41.2%), driving diagnostic overuse, while the Shapiro Rule lacked sensitivity (70.2%), missing ~30% of bacteremia cases. In contrast, when calibrated to a strict 95% sensitivity target, Cultryx achieved the highest culture volume deferral rate (26.2%, deferring ~ 15,872 bottles with predicted negative results) while maintaining a 98.9% negative predictive value. Cultryxscore, a simplified bedside tool, retained a 20.8% deferral rate. ConclusionsMachine learning provides a superior, data-driven alternative to mainstream clinical heuristics for predicting bacteremia. By maximizing culture deferment without compromising pathogen detection, Cultryx can conserve diagnostic resources, reduce unnecessary empiric antibiotic exposure, and systematically elevate patient safety. SummaryCultryx, a machine learning model for blood culture stewardship, outperforms standard clinical heuristics in predicting bacteremia. This approach could reduce culture utilization by over 26% while preserving pathogen detection, conserving diagnostic resources, reducing unnecessary antibiotic exposure, and elevating patient safety.

BackgroundPreoperative biliary stenting alters biliary colonization and may reduce the effectiveness of perioperative antibiotic prophylaxis in pancreatoduodenectomy. Although broader-spectrum regimens have been associated with improved infectious outcomes, their microbiological adequacy in routine clinical practice remains poorly defined. We therefore evaluated the real-world adequacy of a prolonged ampicillin-sulbactam protocol, its association with infectious outcomes and survival, and the potential impact of a universal piperacillin-tazobactam strategy. MethodsWe analyzed all consecutive patients who underwent elective pancreatoduodenectomy from 2002 to 2023 at our tertiary center. Demographic, operative, microbiological, and outcome data were retrieved from a prospectively maintained database. Patients were stratified by stent status. Adequacy of prophylaxis was defined as the full in vitro susceptibility of all bile isolates. The outcomes included 30-day infectious morbidity, clinically relevant POPF, PPH, DGE, reoperation, 30- and 90-day mortality and long-term survival. A coverage simulation was performed to compare ampicillin-sulbactam with a hypothetical universal piperacillin-tazobactam. Statistical methods included chi-square/Fishers exact tests, Mann-Whitney U tests, Cox models, McNemars test and Poisson regression. ResultsOf 956 patients, 424 (44%) had a biliary stent. Technical complications were comparable between groups, and rates of POPF and PPH were not increased. However, infectious morbidity was higher in stented patients, including sepsis (RR 1.62, 95% CI 1.05-2.51) and postoperative cholangitis (RR 2.20, 95% CI 1.36-3.56). Thirty- and 90-day mortality were increased (RR 2.88 and 2.73) but lost significance after adjustment. Bile cultures predominantly yielded Enterococcus and Enterobacterales with low ampicillin-sulbactam susceptibility. Overall adequacy was 21.7%. Among patients with bile cultures (n = 474), ampicillin-sulbactam covered 43.7% (207/474) versus 81.2% (385/474) with piperacillin-tazobactam; in stented patients with cultures (n = 397), coverage increased from 41.8% to 78.1%. Adequate ampicillin-sulbactam coverage was not associated with reduced infectious outcomes in Poisson models. ConclusionPreoperative stenting creates a polymicrobial, partially resistant biliary niche that ampicillin-sulbactam does not sufficiently cover. Our data shows that a piperacillin-tazobactam strategy substantially improves coverage and was therefore implemented at our center. Core message- Stented patients exhibit a distinct infectious risk profile characterized by Enterococcus-and Enterobacterales-dominated bile colonization rather than increased rates of technical complications. - In stented patients, real-world microbiological coverage of ampicillin-sulbactam was limited, and in vitro susceptibility did not independently translate into reduced postoperative infectious morbidity. - Broader prophylaxis, such as piperacillin/tazobactam, aligns with the actual flora and nearly doubles theoretical coverage, addressing the mismatch between stent-associated biofilms and narrow regimens.

Abstract This protocol is reported in accordance with the SPIRIT 2025 guidelines for clinical trial protocols. Introduction: Young children, from birth to age 5 y are particularly vulnerable to indoor air pollutants and respiratory pathogens. Portable air purifiers (or filtration) and upper-room ultraviolet germicidal irradiation (UVGI) are two widely used interventions with the potential to improve indoor air quality (IAQ) and reduce sick-related absences. However, a review of the literature revealed no real-world randomized studies evaluating their effectiveness in reducing young children's sick-related absences in early care and education (ECE) classrooms. Methods and Analysis: The OK-AIR study is a longitudinal, cluster-randomized 2x2 factorial trial conducted in Head Start centers using two implementation cohorts: Cohort 1 (five Head Start centers and 20 classrooms from 2023 to 2024) and Cohort 2 (11 centers and 59 classrooms from 2025 to 2026), with expanded inclusion of rural areas. Cohort 1 enrolled 204 children, 48 teachers and 5 site directors, and Cohort 2 enrolled 462 children, 97 teachers and 11 site directors. Within each center, four classrooms are randomized to: (1) control; (2) portable filtration; (3) upper-room ultraviolet germicidal irradiation (UVGI); or (4) both interventions. Cohort 2 was initially planned as a second factorial trial but was amended to a purifier-only design due to funding changes; details are provided in the protocol amendments section. We collect continuous IAQ data, including particulate matter (PM) with aerodynamic diameters [&le;]1 m (PM1), [&le;]2.5 m (PM2.5), [&le;]4 m (PM4), and [&le;]10 m (PM10); total volatile organic compounds (TVOCs) index; nitrogen oxides (NOx) index; carbon monoxide (CO), noise; temperature; and relative humidity, alongside daily child absences. Seasonal environmental surface swabs (dining tables and toilet flooring) are tested by Reverse-Transcriptase quantitative Polymerase Chain Reaction (RT-qPCR) for Influenza A/B, Respiratory Syncytial Virus (RSV), Human Parainfluenza Virus Type 3 (HPIV3), Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), and Norovirus. IAQ monitoring is structured across Winter, Spring, Summer, and Fall, including designated baseline/off-period weeks to characterize temporal and seasonal variability in environmental measures across classrooms and centers. Multi-informant surveys (Director, Teacher, Parent) capture contextual factors, and children's social-emotional development is assessed using teacher ratings on the Devereux Early Childhood Assessment (DECA). The primary outcome is the sick-related absence rate, analyzed as cumulative absences over the attendance year while accounting for clustering by school and classroom using generalized mixed-effects models. Secondary outcomes include children's social-emotional ratings, IAQ metrics and pathogen detection rates; analyses of IAQ incorporate time/seasonal structure, and season-stratified absenteeism analyses will be treated as secondary/exploratory refinements. An economic evaluation will estimate incremental intervention costs and cost-effectiveness/cost-benefit (such as cost per sick-related absence day averted). Ethics and Dissemination: This study was approved by the Institutional Review Board (IRB) at the University of Oklahoma. Findings will be shared through peer-reviewed publications; presentations at local, state, and national conferences; research briefs developed for lay and policy audiences; and community briefings prioritizing the participating early childhood programs and communities. ISRCTN Trial Registration: ISRCTN78764448 Disclaimer: The views expressed are those of the authors and do not reflect the official views of the Uniformed Services University or the United States Department of War. Strengths and Limitations of This Study: {middle dot} Real-world longitudinal cluster RCT: The study uses a rigorous longitudinal cluster-randomized 2x2 factorial design in real-world ECE settings. {middle dot} Combined interventions: Interventions target both air filtration and disinfection, allowing for combined and comparative evaluation. {middle dot} Objective air quality monitoring: Continuous monitoring of IAQ metrics provides objective and reliable data on environmental change. {middle dot} Environmental pathogen surveillance: qPCR on surface swabs yields an objective biological outcome to triangulate with IAQ and absences. {middle dot} Comprehensive context and child measures: Multi-method and multi-reporter data collection includes Head Start attendance records, continuous air monitoring, pathogen detection, contextual surveys completed by center directors, teachers, and parents, and standardized social-emotional assessments (DECA) completed by classroom teachers. Head Start program records providing children's longer-term health data available through Health Insurance Portability and Accountability Act (HIPAA) authorization. {middle dot} Clustered/temporal complexity: Seasonal design accounts for variation over time but may introduce complexity in modeling temporal effects. {middle dot} Practical Implications: Study findings will have practical implications for Head Start and other ECE programs striving to maximize child attendance with cost effective strategies. Keywords: Early childhood; Head Start; indoor air quality (IAQ); air purifiers; filtration; ultraviolet germicidal irradiation; cluster randomized trial; absenteeism; environmental pathogens; DECA; cost-benefit analysis

PuhrposeTo evaluate the short- and long-term cross-sectional associations between COVID-19 infection and multidimensional sleep health. MethodsData from the COVID-19 Outbreak Public Evaluation (COPE) initiative were used to examine the association between a novel multidimensional sleep health measure (COPE Multidimensional Sleep Health Scale, CMSHS) modeled from the RuSATED instrument and (1) COVID-19 infection and (2) post-acute sequelae of SARS-CoV-2 infection (PASC). ResultsData from 11,326 respondents were used for this study. The cohort was comprised of 51% women, 61% non-Hispanic White, and 17% Hispanic adults. COVID-19 infection was more prevalent among participants who had not received a booster vaccination (55.4% vs. 30.2%, p<0.001); the number of comorbid conditions was higher among those who had been infected (2.2% vs. 1.7%, p<0.001). Participants with COVID-19 infection had significantly lower CMSHS scores indicative of worse sleep health compared with uninfected participants (3.52 {+/-} 1.37 vs. 3.78 {+/-} 1.30; p < 0.001). Participants with PASC had lower CMSHS scores in comparison to those without PASC (2.72 {+/-} 1.30 vs. 3.82 {+/-} 1.28, p<0.001). In adjusted models, a progressive decline in CMSHS scores was observed over 12 months following infection (3.52 {+/-} 0.05 vs. 2.98 {+/-} 0.04; p < 0.001 for <1 month vs. 6-12 months). ConclusionCompared with uninfected individuals, multidimensional sleep health was worse among persons who had a COVID-19 infection. Individuals with PASC had greater and persistent reductions in sleep health for up to 12 months post-infection. Brief summaryO_LISeveral studies have examined the negative effects of COVID-19 on sleep, however the effects of COVID-19 infection on multidimensional sleep health remain poorly understood as do these associations over time. Using a large, population-based cohort, this study evaluates short- and long-term effects of Covid-19 infection on overall sleep health. C_LIO_LIThe study provides evidence that COVID-19 infection is associated with impairments in overall sleep health, with effects persisting up to 12 months post-infection. The findings in this study demonstrate that poor sleep health is an important long-term consequence of COVID-19 infection and emphasizes the need for sleep assessment among patients affected by COVID-19. C_LI

ObjectiveTo quantify the seasonal burden of acute respiratory viral infections among healthcare workers (HCWs), characterize virologic etiologies, and identify predictors of symptomatic illness and sick leave. MethodsWe conducted a prospective cohort study of HCWs during winter 2024-2025, with weekly surveys capturing acute respiratory infections (ARI) and sick leave. Nasal-throat multiplex PCR swabs were self-collected during symptomatic episodes. Incidence rate ratios (IRRs) for symptomatic episodes and sick days were estimated using Poisson regression; presenteeism was assessed among febrile episodes. ResultsAmong 655 HCWs, 400 (61.1%) reported [&ge;]1 symptomatic episode. Over 70,861 person-days, incidence rates were 1.34 symptomatic episodes and 0.82 sick days per 100 person-days. Among PCR-confirmed episodes (n=112), rhinovirus (45.5%) and influenza (23.2%) predominated. Female sex was associated with higher rates of symptomatic episodes (IRR 1.38, 95% CI 1.11-1.72) and sick days (IRR 2.55, 95% CI 1.62-4.00), while age >56 years was associated with lower rates of both. During febrile episodes, 38.8% (95% CI 31.5%-46.6%) reported working despite fever. ConclusionsARIs were common among HCWs and frequently resulted in sick leave, yet febrile presenteeism remained substantial, underscoring the need for strengthened respiratory virus prevention and occupational health policies.

BackgroundEmerging in January 2020, the SARS-CoV-2 pandemic quickly exposed the limitations of traditional contact tracing and overwhelmed the contact tracing efforts of US health departments. In response, Kaiser Permanente partnered with the Public Health Institute to launch the California Contact Tracing Support Initiative. This innovative, clinically integrated program aimed to link Kaiser Permanente members diagnosed at their facilities directly with contact tracing and supportive clinical care via their network. This approach promised to address key logistical and behavioral challenges hampering traditional public health agencies. This paper evaluates the programs implementation in two California counties. MethodsWe conducted a retrospective, mixed-methods process evaluation of program activities from August 2020 to June 2021, including contact tracing implementation in Fresno and San Bernardino Counties. Our methods included scoping discussions with program stakeholders, development of an epidemiological timeline and program impact model, and document review. We also conducted semi-structured interviews with program stakeholders and staff. Interviews were conducted and audio-recorded via Zoom, transcribed, and analyzed in NVivo using inductive and deductive coding with a Framework Approach. ResultsWe reviewed 474 program documents and interviewed 47 participants. Study findings highlighted difficulties in adapting program scope due to competing partner visions of program mission and collaboration. Unforeseen data demands and complex external data sharing with public health systems further complicated and delayed program implementation. ConclusionEvaluation of this contact tracing program offers key insights into public health interventions during emergencies. While the California Contact Tracing Support Initiatives integrated design showed promise, challenges arose from data systems, inter-organizational dynamics, and planning. Findings emphasize the need for clear operational steps, real-time data monitoring, defined roles, and formalized public-private partnerships in preparedness planning. These are key lessons for future complex public health interventions, especially regarding adapting programs versus maintaining fidelity amidst evolving contexts.

RationaleObstructive sleep apnea (OSA) is linked to cardiovascular, metabolic, and cognitive morbidity. Although COVID-19 has been associated with long-term respiratory and neurological sequelae, its role in precipitating new-onset OSA remains unclear. ObjectivesTo evaluate whether SARS-CoV-2 infection increases risk of developing OSA up to 4.5 years post-infection and how risk varies by hospitalization status, demographics, comorbidities, and vaccination status. MethodsThis retrospective cohort study used electronic health records from the Montefiore Health System in the Bronx. Adults tested for SARS-CoV-2 between March 1, 2020, and August 17, 2024, were classified as hospitalized COVID+, non-hospitalized COVID+, or COVID-. Patients with prior OSA or inadequate follow-up were excluded. Inverse probability weighting adjusted for demographic, clinical, socioeconomic, and vaccination covariates. New-onset OSA was assessed using weighted Cox proportional hazards models. Secondary outcomes including hypertension, myocardial infarction, heart failure, stroke, arrhythmia, pulmonary hypertension, type 2 diabetes, and obesity were evaluated with Poisson regression. Sensitivity analysis used a pre-pandemic control cohort. ResultsAmong 910,393 eligible patients, hospitalized [HR 1.41 (95% CI 1.14-1.73)] and non-hospitalized [HR 1.33 (95% CI 1.22-1.46)] COVID+ patients had higher adjusted risk of new-onset OSA versus COVID- controls. Similar findings were observed using historical controls (n=621046). After OSA onset, hospitalized COVID+ patients had higher risks of heart failure and pulmonary hypertension, while non-hospitalized COVID+ patients had higher risk of obesity vs COVID- patients. ConclusionsSARS-CoV-2 infection is independently associated with increased risk of new-onset OSA. These findings support targeted screening in post-COVID populations.

ObjectiveTo examine adherence to COVID-19 public health measures among culturally and linguistically diverse (CALD) and low socio-economic status (SES) populations in Victoria using a unique longitudinal cohort. Study DesignThe Optimise Study was a mixed-methods longitudinal cohort and social networks study (September 2020 - December 2023) assessing the impact of COVID-19 and related public health measures in Victoria, Australia. We used a serial cross-sectional design to analyse adherence to public health recommendations, restrictions, and requirements. Settings, participantsThe study examines two 28-day periods during the COVID-19 pandemic in Victoria: April 23- May 20, 2021 ( non-lockdown), and September 13-October 10, 2021 ( lockdown). We explored adherence to three categories of COVID-19 public health measures -- Recommendations (non-enforced, longer-term), Restrictions (mandated during lockdown periods), and Requirements (mandated, longer-term) -- among participants who completed questionnaires during these periods. Participants were grouped as: 1) non-CALD high SES (did not meet CALD or low-SES criteria), 2) CALD, or 3) non-CALD low-SES. Main outcome measuresPrimary outcomes were adherence to Recommendations, Restrictions, and Requirements during the two study periods. ResultsOf 782 participants recruited, 579 (75%) completed a survey or diary during at least one study period and were included in the analysis. Of these, 275 (47%) were in the non-CALD high-SES group, 114 (20%) in the CALD group, and 190 (33%) in the non-CALD low-SES group. Across all groups, risk-reduction behaviours increased during the lockdown. CALD participants showed higher adherence to some Recommendations and Restrictions compared to the other groups. Overall, 28% left home while awaiting a COVID-19 test result, commonly due to work. ConclusionsHigh adherence among CALD and non-CALD low-SES groups suggest structural barriers, rather than behavioural non-compliance, contributed to higher COVID-19 impacts, highlighting the need for tailored support. During future public health emergencies, better supports are needed for individuals working outside of home to remain in isolation while awaiting a test result. Summary box O_TEXTBOXWhat is already known about this subject? In Australia, priority populations such as culturally and linguistically diverse (CALD) and low socio-economic status (SES) groups experienced higher COVID-19 infection, mortality and a disproportionate impact from public health restrictions. What does this study add? CALD populations had an overall higher level of adherence to public health behavioural measures during both lockdown and non-lockdown periods compared to non-CALD populations. Over 25% of participants did not comply with stay-at-home requirements while awaiting a COVID-19 test result, largely due to work responsibilities. How might this impact on clinical practice? Pandemic preparedness efforts should focus on understanding the reasons for non-adherence with isolation requirements and considering tailored support during future pandemics to address the diverse C_TEXTBOX

BackgroundHospital-acquired bacterial pneumonia (HABP) and ventilator-associated bacterial pneumonia (VABP), particularly those caused by multi-drug resistant organisms (MDROs), often require newer antibiotic treatment. The efficacy and safety of newer antibiotics compared to generic antibiotics in randomized controlled trials (RCTs) have not been evaluated before. MethodsIn this systematic review, we searched RCTs in the United States National Library of Medicine (PubMed), Cochrane Central Register of Controlled Trials (CENTRAL), Scopus, Ovid MEDLINE, Clinical Trials.gov and Google Scholar databases published between 2013 and 2025. The primary efficacy endpoint was 28-day all-cause mortality. Secondary efficacy endpoints were clinical and microbiological response. Safety endpoint was nephrotoxicity. ResultsWe identified eight eligible RCTs involving 2,881 patients (1,450 patients treated with newer antibiotics and 1,431 patients treated with generic antibiotics) with HABP/VABP. The meta-analysis did not reveal any significant differences between newer and generic antibiotics for all-cause mortality at day 28 (risk ratio (RR) 0.97, 95% confidence interval (CI) 0.72-1.30), clinical response (RR 1.04, 95%CI 0.93-1.17), and microbiological response (RR 1.05, 95%CI 0.89-1.24). However, newer antibiotics showed significant lower occurrences of nephrotoxicity compared to colistin component (RR 0.30, 95%CI 0.11-0.79). In subgroup analysis, newer antibiotic regimens demonstrated significant improvement in microbiological eradication of carbapenem-resistant Gram-negative bacilli (RR 1.50, 95%CI 1.18-1.90). ConclusionsNewer antibiotics showed similar efficacy and safety in treating HABP/VABP compared to generic drugs. The superiority in microbiological eradication of carbapenem-resistant Gram-negative bacilli of newer antibiotics could suggest that future trials should be targeted for those patients to improve understanding of their therapeutic use and pathophysiology of these conditions. Key pointsNewer antibiotics, despite broader antimicrobial coverage, have not significantly outperformed generic comparators in terms of 28-day all-cause mortality, clinical, or microbiological response in patients with Gram-negative HABP/VABP. This may reflect limitations in current trial designs focused primarily on regulatory approval.

From 2021 to 2025, MRSA emerged as a major multidrug-resistant pathogen in the study area. Among 545 S. aureus isolates, 67.2% were MRSA, disproportionately affecting children under five (26.5%) and males (55.5%). Case incidence more than doubled by 2025, suggesting rising transmission or resistance. Most isolates were hospital-associated (85.2%), predominantly from outpatients (88.5%), with middle ear discharge as the main source (67%). Gentamicin showed the highest susceptibility (72.1%), while penicillin G resistance was nearly universal (96.7%). The majority (93.4%) were multidrug-resistant, with high MARI values indicating widespread and likely inappropriate antibiotic use. These findings reflect a complex interplay between pathogen behavior, antimicrobial use, and healthcare practices. Increasing MRSA burden may stem from inadequate infection control, poor stewardship, or enhanced community transmission. Incorporating molecular typing could deepen understanding of strain diversity and resistance mechanisms to guide targeted interventions

Hepatitis E virus (HEV) is considered a predominantly foodborne pathogen in developed settings. During COVID-19 lockdown periods, however, HEV concentrations in wastewater at a treatment plant in Munich, Germany decreased, suggesting that pandemic-related behaviour changes inadvertently influenced transmission. In contrast, reported cases and wastewater data from a smaller catchment showed no comparable decline. To assess whether the observed reduction is compatible with a near-exclusively foodborne infection and to reconcile the contrasting signals across surveillance modalities, we developed a stochastic, individual-level model of HEV transmission, shedding, and ascertainment in Munich. Using Approximate Bayesian Computation, we calibrated this model to wastewater and case data from 2020-2023, first separately and then jointly. Posterior parameter estimates indicated a substantial decline in transmission during lockdowns to about 35-40% of the non-lockdown level, with the 95% credible interval entirely below 1 (no change). Joint inference suggested that possible modest lockdown-associated increases in diagnosis probabilities and higher measurement variability in the smaller catchment masked this effect in clinical and small-scale wastewater data, respectively. These findings demonstrate how wastewater-based surveillance, used alongside reported cases, can enable more robust parameter inference for models of under-reported pathogens like HEV, thereby supporting informed public health risk assessments.

Mycobacterium tuberculosis (MTB) disease is a major global health threat with most tuberculosis (TB) cases occurring in low-and middle-income countries (LMIC) with limited healthcare infrastructure. Near-point-of-care testing which can be deployed at peripheral clinical settings is needed to start treatment earlier and thereby improve treatment outcomes. Here we report the development and preliminary characterization of an MTB detection assay that utilizes tongue swab or sputum specimens for The DASH(R) Rapid PCR System which employs cartridge-based automated sequence specific capture sample prep combined with dual target qPCR multicopy MTB insertion sequences IS6110 and IS1081 amplification and detection. MTB is resistant to conventional bacterial lysis techniques; therefore, we evaluated two pre-cartridge lysing techniques, mechanical lysis and sonication, and selected sonication for all subsequent studies. The DASH MTB assay demonstrated a limit of detection of 2.5 MTB cells/swab with no detection of 10 non-tuberculosis Mycobacterium strains. Clinical testing of 100 (49 positive and 51 negative) de-identified blinded sputa from South African symptomatic clinic attendees yielded an overall test sensitivity of 96% (100% for smear positive samples and 88% for smear negative samples) and specificity of 88% when compared to sputum culture. In a separate study of 110 tongue swab specimens (70 positive and 40 negative) from South African symptomatic clinic attendees, the sensitivity was 93% and the specificity was 100%. We further demonstrated that the test is compatible with peripheral LMIC settings via external battery operation and cartridge stability at 45{degrees}C for up to one year. ImportanceTuberculosis (TB) is the single most deadly infectious disease with 1.23 million deaths in 2024. Near-point-of-care testing which can be deployed at peripheral settings that lack laboratory infrastructure to deliver prompt and accurate diagnosis is needed to start treatment earlier and thereby improve treatment outcomes. In this study, we have developed an automated test to detect Mycobacterium tuberculosis (MTB), the cause of TB, from sputum and tongue swab specimens. Its high sensitivity and specificity, rapid time to result, and compatibility with environments that lack air conditioning and consistent electricity make this assay suitable for diverse clinical settings.

BackgroundRecent global outbreaks of Mpox have posed significant diagnostic challenges, particularly in resource-limited settings. Conventional diagnostic methods are often inaccessible due to cost, logistical constraints, or lack of trained personnel. These limitations highlight the urgent need for alternative, scalable diagnostic strategies. This study explored the application of machine learning (ML) classifiers trained on clinical symptom data as a rapid, cost-effective tool for Mpox detection. MethodsAn open-access dataset of clinical symptoms from suspected Mpox cases was used to train and evaluate five supervised ML algorithms: Extra Trees, Quadratic Discriminant Analysis (QDA), Decision Trees, Perceptron, and Support Vector Classifier (SVC). Prior to training, data preprocessing steps, including normalization and handling of missing values, were performed after which model training was carried out using a stratified 80:20 train-test split. Performance was assessed using accuracy, recall, area under the receiver operating characteristic curve (ROC-AUC), and F1-score metrics. Subsequently, feature importance was analyzed using permutation-based techniques to determine the contribution of each clinical symptom to model predictions. ResultsAmong the five evaluated models, SVC, QDA, and Perceptron achieved superior and identical performance metrics, with accuracy, ROC-AUC, and F1-score values of 97.7%, and a recall of 95.5%. Each of these models correctly identified 44 true positive cases with zero false positives. In addition, QDA and SVC produced the lowest number of false negatives (2) and the highest number of true negatives (42), indicating robust discriminatory power. Feature importance analysis identified skin rash as the most predictive clinical feature, with a permutation importance score of 0.12. ConclusionsThese findings demonstrate the strong potential of machine learning classifiers for detecting Mpox based on clinical features. Incorporating these models into healthcare systems could significantly enhance early case detection, improve clinical decision-making, and bolster disease surveillance. Future research should focus on prospective validation of these ML classifiers in real-world clinical environments.

ObjectivesTo quantify how urine sample type and polymicrobial context impact antimicrobial resistance (AMR) in urinary tract infections (UTIs), using routine diagnostics at scale. MethodsIn this retrospective, single-centre study, we analysed 188,687 urine cultures from the Institute of Medical Microbiology, University of Zurich, Switzerland (January 2015 to May 2023). We compared midstream urine (MU), indwelling catheter (IDC), and intermittent catheter (IMC) samples. Samples were classified as negative, bacteriuria, or UTI, by meeting a microbiological UTI threshold ([&ge;]105 CFU/mL). We compared sample types using covariate-adjusted regression and constrained ordination for community composition. In bimicrobial cultures, we assessed co-occurrence using adjusted pairwise odds ratios and degree-preserving permutation null models, supported by partner-choice analyses. AMR was modelled as acquired resistance (AR) and total resistance (TR: acquired + intrinsic) probabilities, with predictor contributions quantified using mutual information. ResultsAmong 186,819 MU, IMC, IDC samples, 56,867 met the UTI threshold. Catheter-associated UTIs (IDC and IMC) were ~60% more likely to be polymicrobial than MU samples. Community composition differed by sample type (p<0{middle dot}001). In IDC, Escherichia coli was less prevalent than in MU, but device-associated pathogens like Pseudomonas aeruginosa and Candida albicans were enriched. Most species-pairs showed no increased co-occurrence after adjusting for covariates, but a subset showed reproducible enrichment across methods (e.g., C. albicans-C. glabrata). Organism identity was the dominant determinant of AMR, with the highest mutual information across AR and TR. AR was higher in IDC for common uropathogens (e.g., E. coli). Co-isolation with hospital-associated partners (e.g., Enterococcus faecium) was associated with further AR increase. From 2015 to 2023, AR increased from ~48% to ~60%, with rising {beta}-lactam (+{beta}-lactamase inhibitor) resistance and declining fluoroquinolone resistance in Enterobacterales. ConclusionsSample type and co-isolated partners provide clinically actionable information beyond pathogen identity and could support more context-aware reporting and empiric prescribing.