In rural sub-Saharan Africa (sSA), the burden of antimicrobial resistance (AMR) remains high. As AMR continues to rise, there is a strong need for practical, implementable surveillance to monitor and mitigate risks, as well as inform timely, evidence-based clinical decision-making. Emerging evidence points to possible community-level drivers, such as transmission between human, animal and environmental reservoirs as contributing factors, yet microbiological surveillance or opportunities for wastewater-based surveillance are often limited and insufficient in these settings. Therefore, alternative sustainable and affordable approaches are needed. We intend to build on the demonstrated potential of metagenomic profiling of pooled faecal material, which accurately predicted population-level AMR prevalence in invasive Enterobacterales infections.

We aim to validate this metagenomic pooled approach on additional populations, and to evaluate whether AMR patterns could be similarly predicted from surveillance of community One Health reservoirs. We will assemble existing data from hospital-based microbiology diagnostic laboratories in rural Burkina Faso and Kenya, and determine to what extent community-level metagenomic data, and/or faecal material of patients on hospital admission, can predict AMR in clinical isolates. We will perform community-level surveys in eight clusters per country, randomly selecting 15 households per cluster. We will systematically sample suspected environmental AMR exposure sites in and around households (soil, drinking water, latrines, chicken faeces) and collect data on community-level antibiotic use, hygiene practices, contact with domestic animals and sanitary facilities. Samples and data will be collected twice: during the dry and during the rainy season.

In addition to evaluating the accuracy of predicting resistance in clinical isolates, we will quantify community-level exposure risks. We will conduct metagenomic profiling on pooled DNA extracts from human stool samples (hospital and community-level) and from household environments. Bayesian statistical models will quantify relationships between AMR gene abundance in the environment and in human stool, and invasive bacteria identified among clinical patients, accounting for geography and seasonality. A cost-utility analysis will determine under what circumstances the use of pooled metagenomic data to inform empirical antibiotic policies would represent an efficient use of resources.

The proposed surveillance protocol is developed in partnership with local communities and local and international researchers and has received ethical approval in Kenya and Burkina Faso. It will assess whether intermittent, pooled-sample metagenomics provides a viable, low-cost and practical approach for population-level AMR surveillance in settings that—like many in rural sSA—lack systematic microbiological diagnostics and where sewage systems for wastewater-based surveillance are absent. By providing an alternative to routine microbiological-based surveillance where this proves challenging to implement, this approach may help improve treatment outcomes, contribute to equity and public health. Findings will be disseminated through peer-reviewed publications and academic conferences and will contribute to the recently proposed WHO AMR surveillance strategy, which combines survey-based approaches with routine AMR surveillance.

To translate and validate the HLS-Child-Q15, a relatively short questionnaire for assessing health literacy in children originally validated in German, into English to make it accessible to a large population of English-speaking children.

We translated the HLS-Child-Q15 into English following established methods, including forward and backward translation, using multiple translators. We incorporated clinician and consumer input into the translation process. We conducted a qualitative pre-test to assess comprehension and a validation to assess psychometric properties and test-retest reliability.

Perth Children’s Hospital, Perth, Western Australia

We recruited English-speaking children aged 8 to 15 years.

Qualitative analysis of pre-test interviews, Cronbach’s α coefficient for internal consistency and intraclass correlation coefficient for test-retest reliability.

The translation process yielded an acceptable translation. A qualitative pre-test conducted with 10 children demonstrated good comprehension of questionnaire items and resulted in small changes to increase item clarity. Validation with 207 participants demonstrated that questionnaire output score increased with age, school year, self-efficacy score, parental educational level and home literacy environment score. Internal consistency was assessed, with a Cronbach’s α coefficient of 0.854 (95% CI 0.812 to 0.887). Test-retest reliability was moderate, with an intraclass correlation coefficient of 0.612 (95% CI 0.402 to 0.761).

The translated HLS-Child-Q15 was well understood by children. Validation of the translated questionnaire demonstrated adequate psychometric properties, consistent with the original German questionnaire. The translated HLS-Child-Q15 is suitable for use with English-speaking children.

Data are available on reasonable request and in compliance with institutional ethics and governance requirements.

ACTRN12622001499774

To explore perceptions of digitalisation and patient safety from the view of the German general public and related sociodemographic factors.

Cross-sectional survey.

A nationwide survey was undertaken in 2024, using data from the Techniker Krankenkasse (TK) Monitor of Patient Safety. The TK Monitor of Patient Safety is an annual survey of the population on the state of patient safety in medical care.

1000 German adults (18 years and older).

Ordinal logistic regression analyses were performed to investigate the associations among sociodemographic factors (age, gender, education and household income) and perceptions on digitalisation and patient safety.

The majority of respondents expected benefits from digital applications in healthcare. Over half of the respondents (58%) believed that artificial intelligence (AI) can help reduce complications and errors, while 49% of the respondents believed that the use of AI poses serious new risks for the healthcare sector. The results showed that sociodemographic variables are important factors influencing patient safety perceptions of digitalisation and AI. Female, older, less educated and/or lower-income individuals were less likely to perceive benefits from digital care applications and AI.

In our study, the German public appears to view digital technologies and AI as tools both for improving patient safety and as potential risk factors. Our findings also highlight the importance of analysing sociodemographic factors to identify specific disparities in how different groups are affected by digitalisation. Such analysis is essential for developing targeted strategies that mitigate current patient safety risks, ensuring that digital health solutions are equitable and safe across all demographic groups.

Air pollution is a significant global health concern, with studies from the USA and Europe linking long-term exposure to respiratory issues and poor school attendance in children. While Indian cities experience much higher pollution levels, the impact on lung development in Indian children remains unclear. This study aims to assess the burden of impaired lung function in Indian children and identify key factors contributing to pollution-induced lung injury.

This longitudinal, prospective cohort study is conducted in four cities categorised by particulate matter 2.5 (PM_2.5) levels: ‘very high’ (Delhi), ‘high’ (Mumbai, Bangalore) and ‘moderate’ (Mysore). A total of 4000 participants (1000 from each city) will be included in the study. Participants will complete a structured questionnaire covering sociodemographics, asthma and allergy history (International Study of Asthma and Allergies in Childhood core questionnaire), dietary intake (24-hour recall and Food Frequency Questionnaire), Physical Activity-C Questionnaire and air pollution exposure. Spirometry and Forced Oscillation Technique will be used to assess lung function. Blood samples will be collected for identification of biomarkers to predict lung impairment. After quality checks, data will be compiled, summarising pulmonary function parameters alongside covariates and confounders. Analysis of Variance (ANOVA) will assess between-city and within-city differences in lung function.

We anticipate a higher prevalence of reduced lung function in children residing in cities with very high and high PM_2.5 levels compared with the moderately polluted city. Findings from this study could establish normal age-appropriate lung function reference values for Indian urban children, aiding in clinical diagnosis.If a reliable biomarker for identifying children at risk of lung impairment is available, it could serve as an early predictor of poor lung health in asymptomatic children.

The approval from individual site institutional review board (IRB) is obtained prior to initiation of the study from institutional ethics committee, St. John’s Medical College and Hospital, Bangalore; institutional ethics committee, JSS Medical College, Mysore; institute ethics committee, Indian Institute of Technology Bombay and institute ethics committee, All India Institute of Medical Sciences. Findings from this study will be disseminated through conference presentations, peer-reviewed publications and establishment of normal age-appropriate lung function reference values for children living in urban India.

Sepsis is a major cause of death both globally and in the United States. Early identification and treatment of sepsis are crucial for improving patient outcomes. International guidelines recommend hospital sepsis screening programmes, which are commonly implemented in the electronic health record (EHR) as an interruptive sepsis screening alert based on systemic inflammatory response syndrome (SIRS) criteria. Despite widespread use, it is unknown whether these sepsis screening and alert tools improve the delivery of high-quality sepsis care.

The Sepsis Electronic Prompting for Timely Intervention and Care (SEPTIC) master protocol will study two distinct populations in separate trials: emergency department (ED) patients (SEPTIC-ED) and inpatients (SEPTIC-IP). The SEPTIC trials are pragmatic, multicentre, blinded, randomised controlled trials, with equal allocation to compare four SIRS-based sepsis screening alert groups: no alerts (control), nurse alerts only, prescribing clinician alerts only, or nurse and prescribing clinician alerts. Randomisation will be at the patient level. SEPTIC will be performed at eight acute-care hospitals in the greater New York City area and enrol patients at least 18 years old. The primary outcome is the percentage of patients with completion of a modified Surviving Sepsis Campaign (SSC) hour-1 bundle within 3 hours of the first SIRS alert. Secondary outcomes include time from first alert to completion of a modified SSC hour-1 bundle, time from first alert to individual bundle component order and completion, intensive care unit (ICU) transfer, hospital discharge disposition, inpatient mortality at 90 days, positive blood cultures (bacteraemia), adverse antibiotic events, sepsis diagnoses and septic shock diagnoses.

Ethics approval was obtained from the Columbia University Institutional Review Board (IRB) serving as a single IRB. Results will be disseminated in peer-reviewed journal(s), scientific meeting(s) and via social media.

ClinicalTrials.gov: NCT06117605 and NCT06117618.