American Journal of Infection Control

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.

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.

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. 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.

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.

Motivation: With the scaling frequency and intensity of extreme heat events across the globe, it is critical for public institutions to develop early detection systems and continuous monitoring of these events and their impacts. In Brazil, Rio de Janeiro was the first city to publish its heat protocol, with the Rio Heat Dashboard as a central component of this system. Implementation: The dashboard was implemented using R/Shiny and integrates climatic and health data from multiple sources. General features: The application comprises real-time heat exposure monitoring and automatic alert level classification, which is monitored daily by multiple municipal actors and supports activation of actions specified in the heat protocol. It also features a health impact module, which lists each heat event and its impact on mortality, and primary care and emergency visits. Availability: The source for full reproducibility is available through https://github.com/joaohmorais/RioHeatDashboard.

Background: Refractory and macrolide-resistant Mycoplasma pneumoniae pneumonia (MPP) has emerged as a major challenge in pediatric respiratory medicine, amplified by the post-2023 resurgence. However, a systematic overview of the research landscape specific to treatment-refractory and drugresistant disease in children remains lacking. Methods: Research articles and reviews on pediatric refractory or macrolide-resistant MPP published between 2000 and 2025 were retrieved from OpenAlex using Boolean searches. After screening, 2,286 records were quantitatively analyzed for annual output, contributing countries/institutions, thematic clusters, and citation-burst dynamics using Python. Results: Annual publications grew exponentially, with a pronounced surge after 2023 (n=378 in 2025). China produced the highest volume (45.1%) but recorded fewer citations per publication than the US, Japan, and Canada. The literature resolved into four clusters: macrolide resistance/molecular basis, epidemiology, etiology/co-infection, and refractory disease management. Burst analysis showed an evolution from earlier fronts like 23S rRNA mutations and azithromycin to recent emerging trends like pandemic-related co-circulation, genotype surveillance, and co-infection. Conclusions: Research on pediatric refractory and resistant MPP is expanding rapidly, shifting in emphasis from etiologic descriptions toward resistance mechanisms and clinical management. Standardizing the treatment of macrolide-unresponsive disease and post-pandemic epidemiological surveillance represent the principal directions for future work. Keywords: Mycoplasma pneumoniae; children; macrolide resistance; refractory pneumonia; bibliometric analysis; research trends

Forecasting influenza hospitalizations informs public health preparedness, yet questions remain about which types of forecasts best guide action. We evaluate categorical trend forecasts, which communicate probabilities of upcoming increases or decreases in epidemic trajectories, submitted to CDC's FluSight Forecasting Challenge between Fall-2024 and Spring-2026. Teams submitted probability distributions over five categories describing direction and magnitude of week-over-week changes in laboratory-confirmed influenza hospital admissions. We assessed performance using Ranked Probability Skill Score, Brier Skill Score, and measures of forecast-observation agreement. Most models outperformed an equal-probability baseline; the FluSight ensemble ranked among the top three in the 2024-25 and 2025-26 seasons. Forecasts were most accurate during stable periods and least during periods of rapid change, with most models underestimating observed trends. Conclusions were robust to choice of scoring metric and reference model. These results support categorical trend ensembles as an approach to communicating infectious disease forecasts that may inform public health decision-making.

Background The COVID-19 pandemic substantially altered respiratory pathogen circulation worldwide. However, longitudinal analyses of changes in respiratory pathogen ecology across the pandemic and post-pandemic periods remain limited. Methods We analyzed 19,968 respiratory samples tested with the BioFire(R) FilmArray(R) Respiratory Panel at a hospital in Tokyo, Japan, between January 2020 and March 2026. We evaluated temporal changes in pathogen circulation, age-specific epidemiology, co-detection patterns, pairwise pathogen associations, and clinical parameters. Results At least one respiratory pathogen was detected in 27.8% of tests. Respiratory pathogens resurged asynchronously following the relaxation of COVID-19-related public health measures. Influenza virus circulation remained markedly suppressed until late 2022 before re-emerging in successive large seasonal epidemics, whereas other pathogens, including RSV, human metapneumovirus, and Mycoplasma pneumoniae, exhibited distinct resurgence patterns. Pathogen distributions also varied by age. Human rhinovirus/enterovirus remained predominant among young children, whereas SARS-CoV-2 predominated among older adults. Co-detection occurred in 14.0% of positive specimens and was significantly more frequent in younger patients. Pairwise analysis identified both positive and negative pathogen associations; however, the patterns varied across age groups and study periods. Conclusions Respiratory pathogen circulation changed substantially during the transition from the COVID-19 pandemic to the post-pandemic period, with pathogen-specific, age- and period-dependent patterns. Continued surveillance is warranted to determine how respiratory pathogen circulation will evolve and to inform infection control strategies in the post-pandemic era.

Background. A primary constraint on the capacity of EMS programs to meet industry demand is psychomotor instruction and verification, requiring direct observation of each student by a qualified evaluator. Whether AI video analysis can relieve it is untested; none has been applied to EMS skill examination or compared with human examiners. Objective. To quantify human EMS evaluator inter-rater reliability and evaluate an AI video-analysis platform against it. Methods. In a prospective, fully crossed study, five certified EMS evaluators and an AI platform independently scored identical video-recorded EMT performances of cervical collar application (n=15), bag-valve-mask (BVM) ventilation (n=14), and medical assessment (n=15) on dichotomous checklists with critical-failure criteria. Agreement was assessed at item, score, and decision levels using Fleiss' kappa, Krippendorff's alpha, Gwet's AC1, and ICC(2,1)/ICC(2,k). Results. Human item agreement was moderate (kappa 0.409 to 0.467), as was single-rater reliability (ICC(2,1) 0.539 to 0.694), against good panel reliability (ICC(2,k) 0.854 to 0.919). Recorded pass/fail agreement was fair (kappa 0.297 to 0.388) and critical-failure agreement near zero for two skills (kappa 0.028, 0.119). AI alignment tracked rubric observability rather than task complexity: r = 0.857 (collar, exceeding every human), -0.173 (BVM), 0.664 (medical), and it was most lenient on two skills. Conclusions. Human evaluators are an imperfect standard, especially on critical failures. The AI was a legitimate additional rater where checklist items were discrete and visually verifiable, but not where credit required judging continuous quantities such as ventilation rate, volume, or suction duration. Defensible uses are formative and archival, not summative. These results reflect an early, non-specialist configuration: a baseline, not a limit.

Mosquito-borne diseases pose a growing public health challenge as climate change reshapes vector population dynamics. West Nile virus (WNV), transmitted between birds and Culex mosquitoes, disproportionately affects Maricopa County, Arizona, one of the nation's highest-burden counties, yet whether models that include weather and avian dynamics improve forecast accuracy remains unclear. Using a 15-year weekly time series of mosquito abundance, mosquito infection prevalence, and human cases, we developed four mechanistic model configurations of varying complexity, from mosquito-human dynamics alone to full models incorporating avian dynamics and weather forcing. We fitted each model to the weekly-observed data, generated probabilistic 1- and 2-week-ahead forecast horizons, and evaluated forecasts against a historical baseline. All configurations fit the data equally regardless of weather or avian dynamics. However, models incorporating both birds and weather created more accurate forecasts of mosquito abundance and mosquito infection prevalence, and all configurations outperformed the baseline for forecasting human cases. Forecast accuracy was highest in summer and fall, and ensemble aggregation sometimes outperformed every individual model, stabilizing predictions across the 15-year record. These findings indicate that avian and weather dynamics are most critical for predicting mosquito-specific data, positioning this framework as a scalable tool for public health planning for WNV surveillance under climate change.

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.

Purpose: Lung cancer screening (LCS) with low-dose computed tomography (LDCT) reduces lung cancer mortality, yet screening participation remains low. We evaluated whether a brief informational video nudge delivered immediately before a scheduled clinical encounter increased LCS ordering and baseline LCS completion. Patients and Methods: We conducted a randomized feasibility trial within Kaiser Permanente Colorado from March through October 2025. LCS-eligible patients with an upcoming primary care or pulmonology appointment were assigned to intervention or usual care based on birth month. Intervention patients were split into two group, a group who received the LCS informational video nudge via text message within 24 hours of an eligible appointment; and second group who received the text plus a QR code video link during appointment rooming. Outcomes included LCS orders, baseline LCS-LDCT completion, and video engagement. Multivariable logistic regression was used to evaluate factors associated with LCS ordering. Results: Among 1,093 patients, 549 were assigned to intervention and 544 to usual care. Intervention patients were more likely to receive an LCS order within 1 day of their appointment (22.6% vs 16.4%; p=.010) and any time during follow-up (32.6% vs 24.1%; p=.002). Baseline LCS-LDCT completion was 51% higher in the intervention group, although the difference was not statistically significant (8.6% vs 5.7%; p=.078). Among the intervention group, 93 individuals (17%) viewed the video, generating 114 total views, and viewers watched an average of 79% of the video. Most views (82.5%) occurred through text-message delivery rather than QR codes. Conclusion: A brief, low-burden LCS informational video delivered immediately before a clinical encounter and integrated into existing workflows significantly increased LCS ordering and was associated with higher screening completion. Timely, scalable digital nudges may provide an effective strategy for improving LCS participation. Based on the observed effectiveness, feasibility, and efficiency of the intervention, KPCO incorporated the behavioral nudge into standard clinical care in February 2026.

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.

To evaluate the cost-utility and 5-year budget impact of first-line olaparib plus abiraterone versus abiraterone alone for metastatic castration-resistant prostate cancer (mCRPC) in China after the eleventh round of volume-based procurement (VBP). The intention-to-treat (ITT) population was assigned primary decision-analytic weight; the prespecified BRCA1/2-mutated (BRCAm) subgroup was a supporting analysis.

Background: In Tanzania, over 10,000 women aged 15-44 are diagnosed with cervical cancer annually, and more than 6525 die. The majority are diagnosed at an advanced stage, which contributes to delays in treatment enrollment and illness complications. Multiple factors can affect womens willingness and ability to access cervical pre-cancer screening. Purpose: To explore the barriers and facilitators to attending cervical pre-cancer screening among women living in a resource-constrained area of Southern Tanzania Methods: A qualitative study of 17 focus group discussions and 12 in-depth interviews was conducted from December 2023 to July 2024. Data collection took place in the community and health centers of a town and the surrounding rural areas in Kilombero district. The study used purposive sampling to recruit 112 women and 23 men aged 18- 50+ years. Results: Fear of death emerged as a central theme in the data, acting as both a barrier to and a facilitator of womens screening behavior. Women linked death with the screening procedure, receiving results and undergoing treatment. Participants mistrusted the speculum itself and linked it to pain, vaginal infection, and infertility. Conversely, fear of physical and social death motivated other women to attend the screening to learn about their health and prevent the consequences of a positive diagnosis. Public trust in the healthcare system and the role of health information sources were also identified as influencing women's decisions to undergo or not undergo screening. Conclusion: Our findings showed that cervical cancer screening uptake is influenced by fear of death and other co-factors. To improve early cervical cancer screening, programs and policy interventions are needed to raise awareness of the disease while also addressing womens specific concerns. Also, strengthening structural dimensions such as the availability of the healthcare workforce, healthcare facilities, and ensuring equitable service availability are essential to reducing cervical cancer complications and avoidable mortality.

Background Global labor migration from LMIC to higher-income destinations has expanded rapidly, placing increasing pressure on destination-country health. Existing research on cross-border migrant workers has focused largely on occupational health, general healthcare utilization, and disease-specific risks, while there is considerably less evidence on their sexual and reproductive health. This study contributes to this understudied field by examining the policy and health-system factors that shape the sexual and reproductive health services for migrant workers in Taiwan. Methods A qualitative study was conducted in Taiwan between November 2025 and August 2026. 22 stakeholders were purposively recruited from academia, healthcare, nongovernmental organizations, government, labor brokerage, and employers. Data were collected through semi-structured interviews and small focus groups. Interviews were conducted in Mandarin Chinese, transcribed verbatim, and translated into English. Data were analyzed using framework analysis combining deductive coding based on the AAAQ framework with inductive coding of implementation and contextual themes. Results Gaps were identified across all four AAAQ dimensions. Participants described limited migrant-responsive SRH programming; physical, financial, administrative, social, and information barriers; shortcomings in linguistic and cultural responsiveness; and weaknesses in interpretation, coordination, and continuity of care, despite generally favorable views of Taiwan's clinical quality. Conclusions Our findings show that broad insurance coverage and strong clinical capacity do not by themselves ensure the realization of migrant workers' SRHR. In Taiwan, rights were mediated through labor brokerage, gendered live-in work arrangements, and fragmented governance across health, labor, immigration, and social-welfare systems. Improving migrant SRHR therefore requires stronger implementation of existing protections, reduced dependence on informal intermediaries, and more integrated institutional responsibility for cross-sector migrant health needs.

Background and Objectives: Recurrence following burr-hole drainage of chronic subdural haematoma (cSDH) occurs in 10-25% of cases, sustained by neovascularisation of the subdural neomembrane supplied by the middle meningeal artery (MMA). MMA embolisation reduces recurrence; whether incidental burr-hole intersection of MMA branches during drainage confers similar benefit is unknown. Methods: We performed a multicentre retrospective cohort study of consecutive adults undergoing burr-hole drainage for cSDH at two UK tertiary neurosurgical centres. Postoperative thin-slice CT was used to classify burr-hole intersection of the underlying MMA groove (no hit, distal-branch hit or main-branch hit) and measure perpendicular burr-hole-to-MMA-groove distance. Co-primary outcomes were radiological recurrence and recurrence requiring intervention. Patient-clustered multivariable logistic regression adjusted for prespecified clinical covariates and treating site. Results: 227 patients (284 operated hemispheres) were included. Radiological recurrence decreased from 34.4% with no branch hit to 22.9% with main-branch intersection, with the gradient confined predominantly to unilateral cSDH. Main-branch intersection was associated with lower adjusted odds of radiological recurrence in unilateral cSDH (adjusted OR 0.30, 95% CI 0.11- 0.81; P = .018), with a similar but non-significant association in the overall cohort (adjusted OR 0.53, 95% CI 0.26-1.07; P = .075). Burr-hole-to-MMA-groove distance demonstrated a more consistent association: in the overall cohort, each 5-mm increase independently increased the odds of radiological recurrence (adjusted OR 1.38, 95% CI 1.04-1.82; P = .025). In unilateral cSDH, each 5-mm increase was independently associated with both radiological recurrence (adjusted OR 1.45, 95% CI 1.03-2.04; P = .034) and recurrence requiring intervention (adjusted OR 1.52, 95% CI 1.05-2.20; P = .027). Conclusion: Main-branch intersection of the middle meningeal artery during routine burr-hole surgery is associated with lower recurrence of unilateral cSDH, while the accompanying burr-hole-to-MMA-groove distance gradient provides biologically plausible support for a dose-response relationship. Together, these findings provide mechanistic rationale for prospective evaluation of intentional neuronavigation-guided MMA targeting (BURR-MMA; NCT07549893).