Documenting evidence on global health strategies and programmes that provide safeguards for vulnerable populations and strengthen overall pandemic preparedness is essential. This study aimed to identify factors associated with adherence to COVID-19 mitigation measures, COVID-19-related symptoms and testing, as well as pandemic-related income loss among internally displaced persons (IDPs) in urban and remote areas of Burkina Faso, Niger and Mali.

This cross-sectional study used fixed-site respondent-driven sampling (RDS).

Primary care settings across six urban and remote locations in Burkina Faso, Mali and Niger.

4144 internally displaced adults, who had been forced from their homes within 5 years of the survey, participated in the study. The survey was conducted between August and October 2021 in two selected locations in three countries: Kaya (n=700) and Ouahigouya (n=715) in Burkina Faso; Bamako (n=707) and Ménaka (n=700) in Mali; and Niamey (n=733), and Diffa (n=589) in Niger. Participants were included if they were born in the study countries, displaced due to conflict, violence or disaster, aged 18 years or older, and living or working in the study site for at least 1 month.

The primary outcomes measured were adherence to COVID-19 mitigation measures, presence of COVID-19 symptoms, COVID-19 testing and vaccination rates and pandemic-related income loss.

Among 4144 IDPs surveyed across 6 sites in Burkina Faso, Mali and Niger, over half (52%) reported experiencing at least one COVID-19 symptom in the preceding 2 weeks. However, 8% had ever been tested for COVID-19, and fewer than 5% had received a vaccine in all sites except Diffa, where 54% reported vaccination. While willingness to be vaccinated was high (ranging from 56.6% in Bamako to 89.5% in Niamey), access remained limited. Compliance with public health measures varied; for example, 41.7% of IDPs were able to maintain physical distance from non-household members, and just 60.2% reported wearing a mask. Chronic health conditions were consistently associated with higher odds of COVID-19 symptoms (Ménaka OR: 14.65; 95% CI: 7.36 to 29.17). Economic vulnerability was widespread, with more than half of IDPs in Bamako (58.1%) and Niamey (66.4%) reporting income loss due to the pandemic, and average monthly income declining by over 50% in most sites. IDPs in urban areas generally reported greater exposure to COVID-19 risk factors, while those in remote settings reported lower adherence and poorer access to basic preventive measures.

This is the first known RDS study to explore the impact of the COVID-19 pandemic on IDPs. Findings suggest that IDPs in urban areas may face heightened risks of exposure and infection, underscoring the need to prioritise them in public health efforts. Low testing and vaccination rates and significant income loss call for advocacy and economic relief to address these vulnerabilities. Future pandemic responses should integrate health interventions with targeted support, especially mitigating income loss to bolster IDPs’ resilience.

Early childhood development (ECD) lays the foundation for lifelong health, academic success and social well-being, yet over 250 million children in low- and middle-income countries are at risk of not reaching their developmental potential. Traditional measures fail to fully capture the risks associated with a child’s development outcomes. Artificial intelligence techniques, particularly machine learning (ML), offer an innovative approach by analysing complex datasets to detect subtle developmental patterns.

To map the existing literature on the use of ML in ECD research, including its geographical distribution, to identify research gaps and inform future directions. The review focuses on applied ML techniques, data types, feature sets, outcomes, data splitting and validation strategies, model performance, model explainability, key themes, clinical relevance and reported limitations.

Scoping review using the Arksey and O‘Malley framework with enhancements by Levac et al.

A systematic search was conducted on 16 June 2024 across PubMed, Web of Science, IEEE Xplore and PsycINFO, supplemented by grey literature (OpenGrey) and reference hand-searching. No publication date limits were applied.

Included studies applied ML or its variants (eg, deep learning (DL), natural language processing) to developmental outcomes in children aged 0–8 years. Studies were in English and addressed cognitive, language, motor or social-emotional development. Excluded were studies focusing on robotics; neurodevelopmental disorders such as autism spectrum disorder, attention-deficit/hyperactivity disorder and communication disorders; disease or medical conditions; and review articles.

Three reviewers independently extracted data using a structured MS Excel template, covering study ML techniques, data types, feature sets, outcomes, outcome measures, data splitting and validation strategies, model performance, model explainability, key themes, clinical relevance and limitations. A narrative synthesis was conducted, supported by descriptive statistics and visualisations.

Of the 759 articles retrieved, 27 met the inclusion criteria. Most studies (78%) originated from high-income countries, with none from sub-Saharan Africa. Supervised ML classifiers (40.7%) and DL techniques (22.2%) were the most used approaches. Cognitive development was the most frequently targeted outcome (33.3%), often measured using the Bayley Scales of Infant and Toddler Development-III (33.3%). Data types varied, with image, video and sensor-based data being most prevalent. Key predictive features were grouped into six categories: brain features; anthropometric and clinical/biological markers; socio-demographic and environmental factors; medical history and nutritional indicators; linguistic and expressive features; and motor indicators. Most studies (74.1%) focused solely on prediction, with the majority conducting predictions at age 2 years and above. Only 41% of studies employed explainability methods, and validation strategies varied widely. Few studies (7.4%) conducted external validation, and only one had progressed to a clinical trial. Common limitations included small sample sizes, lack of external validation and imbalanced datasets.

There is growing interest in using ML for ECD research, but current research lacks geographical diversity, external validation, explainability and practical implementation. Future work should focus on developing inclusive, interpretable and externally validated models that are integrated into real-world implementation.

We aimed to determine the prevalence of hospital discharge communication problems in adults of 10 high-income nations and the associated factors.

Secondary analysis of cross-sectional survey data.

2023 Commonwealth Fund International Health Policy Survey for Adults, including data from residents of Australia, Canada, France, Germany, the Netherlands, New Zealand, Sweden, Switzerland, the UK and the USA.

3763 survey respondents aged 18 and older who reported hospitalisation at least one time in the past 2 years.

Our primary outcome measure is poor discharge communication (PDC), which is a composite variable comprising three questions regarding the provision of written information, follow-up arrangement and discussion of medications at time of discharge.

The overall PDC rate was 17.1%, with the highest in Germany (19.7%) and the lowest in the Netherlands (9.2%). No follow-up arrangement was the most commonly reported problem (22.8%). Respondents who concerned about social service needs and mental health issues were more likely to report PDC.

Providers should consider factors which impact PDC at hospital discharge and tailor communication appropriately. Hospitals, communities and countries should work towards policies that address underlying issues related to social determinants of health, including support for lower-income patients, improved treatment access for patients with physical and mental health conditions, and food and housing stability.

To identify and contextualise evidence-based strategies for implementing deprescribing practices at different levels of healthcare in Brazil, through the development of an evidence brief for policy that includes stakeholder deliberation and considers barriers, facilitators and equity aspects.

This protocol outlines the development of an evidence brief for policy using a mixed-methods design. It involves synthesising evidence for health policies by integrating global research and local evidence through three stages: stakeholder exchange, evidence brief development and external endorsement. The Supporting Policy-Relevant Reviews and Trials tools for evidence-informed health policies will guide both the synthesis of strategies and the facilitation of deliberative dialogues. The synthesis will encompass evidence from systematic reviews and meta-analysis on deprescribing strategies across healthcare levels, focusing on effectiveness, harms, costs, perceptions, barriers, facilitators and equity. Studies proposing strategies not yet implemented will be excluded. Study selection and data extraction will be conducted independently and in duplicate. The methodological quality of included studies will be assessed using the A Measurement Tool for Assessing the Methodological Quality of Systematic Reviews-2 criteria. Synthesised evidence will be used to develop evidence-based strategies, which will then be presented in deliberative dialogues for endorsement by stakeholders and adaptation to the Brazilian context. Endorsement rates will be classified as high, moderate or low based on predefined criteria.

This study was approved by the University of Sorocaba Research Ethics Committee (certificate 82098324.7.0000.5500). Informed consent will be obtained from all participants. Findings will be disseminated through peer-reviewed publications and conference presentations.

CRD42024548845.

The tobacco and nicotine industry fuels tobacco-related addiction, disease and death. Indigenous peoples experience a disproportionate burden of commercial tobacco-related morbidity and mortality. Over the past two decades, significant progress has been made in reducing smoking prevalence among Indigenous peoples; however, smoking remains a leading contributor to the burden of death and disease. This review will summarise evidence on commercial tobacco resistance and/or eradication strategies, including policy reforms, in relation to Indigenous peoples across Oceania, the Pacific Islands and North America.

This review will follow guidelines from the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews and will be conducted in accordance with the Joanna Briggs Institute (JBI) methodology for scoping reviews. This review will consider academic and grey literature published since 1 January 2000. The following electronic databases will be searched for relevant primary research articles and commentaries: PubMed, Scopus, Informit, Web of Science and PsycINFO. Additional searches will be conducted in ProQuest to identify relevant grey literature. Papers will be screened by two reviewers to determine eligibility, followed by full-text data extraction. Findings will be synthesised descriptively for each review question and by region. Studies included in the review will be assessed against criteria for Indigenous engagement in research.

This protocol was led by Indigenous interests, needs and rights of Indigenous peoples, consistent with the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP), the WHO’s Framework Convention on Tobacco Control and ethical practice. This review was conceptualised with Indigenous leadership and through engagement, including but not limited to the Indigenous lived experience of the authors (MK, E-ST, HC, PNH, PH, SAM, AW, SW and RM). This review supports the global goal of eradicating commercial tobacco-related harms – reframing commercial tobacco use as a structurally imposed harm sustained by colonial and commercial forces rather than personal choice. Findings from this review will be shared with Indigenous partners and communities who requested this work and will be submitted for peer-reviewed publication.

Open Science Framework https://osf.io/wxqcb

Congenital cytomegalovirus (cCMV) is an important cause of long-term childhood disability. In Australia, the identification and treatment practices and the long-term clinical and neurodevelopmental outcomes of children with cCMV are unknown. The Australasian cCMV Register (ACMVR) is a longitudinal register and resource for research that aims to describe and explore, in Australian children with cCMV: (1) their clinical characteristics over time, (2) antiviral therapy use/prescribing up to 1 year of age and (3) risk factors and potential avenues for prevention of adverse sequelae of the virus.

Children

Ethics and governance approvals, study database and a steering group have been established. Data collection is active in five sites across Australia.

The ACMVR will inform our understanding of the long-term outcomes for children with cCMV in Australia and provide a sampling frame and resource for recruitment in future clinical and epidemiological research to inform practice and policy. New opportunities for the establishment of additional study sites and collaborations with Australian maternity and fetal medicine researchers and with cCMV registries in other countries are currently being explored.

This longitudinal study aimed to document shifts in specialty preferences, career pathways and intended practice locations among medical students following the implementation of structured career initiatives during the 2023–2024 academic year.

A longitudinal observational survey study.

A private, not-for-profit institution, VinUniversity in Hanoi, Vietnam during the 2023–2024 academic year.

All year 2, year 3 and year 4 medical students (n=144 eligible), of whom 105 (73%) completed both baseline and follow-up surveys.

Structured career counselling initiatives introduced at the start of the academic year, including academic mentoring, clinical mentoring, hands-on clinical exposure in year 4 and multiple career counselling activities.

The primary outcome was change in specialty preference over time, measured by students’ self-reported first-choice specialty at baseline and follow-up. Secondary outcomes included shifts in factors influencing career decisions (eg, personal interest, income and family expectations), intended practice location (domestic or international) and preferred career pathways (residency, Specialist Level I, master’s degree or direct workforce entry).

Personal interest remained the strongest influence on specialty choice from baseline to follow-up (mean scores 4.27 vs 4.36 on a 5-point scale). A notable decrease occurred in the importance of income (3.82 to 3.22; p

Noticeable shifts in specialty preferences and career pathways were observed after a series of career initiatives were implemented. Although these trends coincided with the new programmes, further qualitative research is needed to elucidate how and why these career initiatives may have influenced decision-making. Informed by these findings, medical educators can refine interventions to support students’ evolving preferences and ultimately strengthen healthcare workforce distribution.