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.

The objective of the scoping review was to systematise the existing knowledge about skill mix changes among the healthcare workforce during the COVID-19 pandemic.

Scoping review according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Review.

Five databases including CINAHL Ultimate, Web of Science, Medline, Embase and Scopus were searched in August 2024.

The review encompassed original research studies published from January 2020 to August 2024, on the skill mix of healthcare workers during the COVID-19 pandemic. Quantitative and qualitative studies were included without geographical or linguistic restrictions.

Data were independently extracted by two researchers, capturing details such as publication year, study title, country, target population, study purpose and methodology, sample size, analysed variables, results and recommendations.

A total of 13 563 records were identified in the databases of which 3962 remained for abstract review. 32 articles were included in the final analysis. 17 of the 32 papers were from Western and Southern Europe. The healthcare professions which were described in the studies were physicians, nurses, midwives, paramedics, pharmacists, physiotherapists, occupational therapists and medical assistants, of which the majority of the studies were conducted among nurses (n=16), pharmacists (n=11) and physicians (n=6). Most studies (n=9) concerned the adding of new tasks/roles and reallocating tasks in combination with teamwork (n=8). Research covered a range of topics, including psychological aspects of work, patient safety, work reorganisation, training and collaboration. Many studies focused on the challenges related to skill mix, such as the blurring of responsibilities and role ambiguity.

The research summarised in this review demonstrates the impact of implementing skill mix changes on healthcare workers during the COVID-19 pandemic, particularly in the area of mental health. The research highlights the importance of adaptation in response to pressures among healthcare professions and the entire system. Further research is needed to examine the long-term impact of skill mix on healthcare workers across regions and professions in crisis situations.

This study aimed to assess the clinical severity and risk factors of diabetic ketoacidosis (DKA) at type 1 diabetes (T1D) diagnosis in children under 18 years in Greater Poland from 2006 to 2023, including temporal trends and the impact of COVID-19.

A retrospective cross-sectional study.

Greater Poland Province, Poland.

The study cohort comprised 2432 European Caucasian children (boys: 1335) aged 0–18 years with newly diagnosed T1D admitted to one hospital between 2006 and 2023.

DKA and its severity were classified according to the International Society for Pediatric and Adolescent Diabetes criteria. The multivariable analysis assessed the following risk factors for DKA at T1D diagnosis: age, sex, seasonality and the presence of T1D autoantibodies. Poisson regression models with a log link were used to assess the impact of the COVID-19 pandemic on monthly DKA cases at T1D onset, including time, pandemic period and their interaction as predictors.

DKA was diagnosed in 51.4% (1248) of newly diagnosed T1D patients, with 24.9% classified as mild, 14.4% as moderate and 12.1% as severe. Modest sex-related differences were observed, with DKA at T1D onset slightly more common in males than females (52.8% vs 47.2%). However, when comparing the DKA and non-DKA groups, a higher proportion of females presented with DKA (47.2%) than those without DKA (42.9%) (p=0.034). Children aged 0–2 years showed the highest DKA prevalence at T1D onset (76.4%), with a significant proportion experiencing severe DKA (33.6%). Factors like age, sex, season, glycaemia, glycated haemoglobin and autoantibodies did not independently predict DKA risk. The COVID-19 pandemic did not affect DKA rates at diagnosis.

The frequency of DKA is high, and its severity is substantial among children with newly diagnosed T1D in Greater Poland. Children aged 0–2 years are at the greatest risk of severe DKA at onset, underscoring the need for earlier recognition and intervention in this age group. Our findings emphasise the critical importance of increased awareness, education, point-of-care glucose testing, and targeted strategies such as T1D screening programmes to reduce the occurrence of DKA.

Marginalised populations—such as racialised groups, low-income individuals, newcomers and those in rural areas—disproportionately experience severe diabetes-related complications, including diabetic foot ulcers, retinopathy and amputations, due to systemic inequities and limited access to care. Although community-based programmes address cultural and accessibility barriers, their isolation from mainstream healthcare systems leads to fragmented care and missed opportunities for early intervention.

Artificial intelligence (AI)-powered technologies can enhance accessibility and personalisation, particularly for underserved populations. However, integrating AI into community settings remains underexplored, with socioethical concerns around inclusion, diversity, equity and accessibility requiring urgent attention.

This realist review aims to examine how, why and under what circumstances AI applications can be effectively integrated into community-based diabetic care for marginalised populations. The review will develop a programme theory to guide ethical, inclusive and effective AI implementation to ensure AI-driven innovations address health disparities and promote culturally sensitive, accessible care for all.

Using the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) extension for Reviews guidelines, this realist review will systematically search MEDLINE, Embase, CINAHL, Cochrane library, Google Scholar and Scopus, alongside grey literature. A two-stage screening process will identify eligible studies, and data extraction will use a developed tool. Synthesis will employ realist logic, analysing relationships between contexts (eg, organisational capacity), mechanisms (eg, AI functionalities) and outcomes (eg, reduced disparities).

Ethics approval is not required for conducting this realist review. Ethics approval will be obtained from the University of Toronto; however, following the completion of the realist review for patients and community members’ engagement to support knowledge mobilisation and dissemination to ensure practical application and reciprocity.

This protocol was registered at PROSPERO (CRD42025636284).

Migrants and refugees with low language proficiency (LLP) in the dominant language of their host country have a higher risk of suffering from certain mental health disorders compared with non-migrant populations. They are also more likely to experience a lack of access to mental healthcare due to language-related and culture-related barriers. As part of the MentalHealth4All project, a digital multilingual communication and information platform was developed to promote access to mental healthcare for LLP migrants and refugees across Europe. This paper describes the study protocol for evaluating the platform in practice, among both health and/or social care providers (HSCPs) and LLP migrants and refugees.

We will conduct a pretest–post-test cross-national survey study to evaluate the platform’s effect evaluation (primary objective) and process evaluation (secondary objective). The primary outcomes (measured at T0, T2 and T3) are four dimensions of access to mental healthcare services: availability, approachability, acceptability and appropriateness of mental healthcare. Secondary outcomes (measured at T2) are: actual usage of the platform (ie, tracking data), perceived ease of use, usefulness of content, comprehensibility of information, attractiveness of content and emotional support. Participants will be recruited from nine European countries: Belgium, Germany, Italy, Lithuania, the Netherlands, Poland, Slovakia, Spain and the UK. Using convenience sampling through professional networks/organisations and key figures, we aim to include at least 52 HSCPs (ie, 6–10 per country) and 260 LLP migrants (ie, 30–35 per country). After completing a pretest questionnaire (T0), participants will be requested to use the platform, and HSCPs will participate in an additional personalised training (T1). Next, participants will fill out a post-test questionnaire (T2) and will be requested to participate in a second post-test questionnaire (T3, about 6–8 weeks after T2) to answer additional questions on their experiences through a brief phone interview (T3 is optional for migrants/refugees).

For all nine countries, the ethical review board of the participating university (hospital) has assessed and approved the protocol. If successful, the MentalHealth4All platform will be made publicly available to help improve access to mental healthcare services, as well as HSCPs’ cultural competencies in delivering such services, for any LLP migrants and refugees across Europe (and beyond). Findings will also be disseminated through peer-reviewed journals and conferences.

The ‘MHealth4All project’ was prospectively registered on Open Science Framework, DOI: 10.17605/OSF.IO/U4XSM.