Effective community-based disease management is essential for public health. In low- and middle-income countries, sustainable strategies for timely diagnosis and treatment are a research priority. This study aims to assess the feasibility of a non-invasive saliva self-sampling method, paired with digitally linked molecular point-of-care diagnostics, for detecting respiratory infections among paediatric patients in the Tshwane District, South Africa.

A field study will be conducted at Steve Biko Academic Hospital to compare saliva collection using the CandyCollect lollipop device and standard mouth swabs. The spiral groove of the lollipop device captures pathogens, which are stored in DNA/RNA preservation media and later analysed using quantitative PCR and commercially available rapid antigen tests. The multiplex respiratory pathogen panel, based on TaqMan real-time PCR technology, targets key paediatric pathogens including Streptococcus pneumoniae, Haemophilus influenzae, Mycoplasma pneumoniae, respiratory syncytial virus (RSV) and influenza A/B. Nucleic acids will be extracted using standard viral extraction kits and analysed following manufacturer protocols. Internal controls will be included in each qPCR run, and samples with CT values below defined thresholds will be considered positive. Rapid antigen tests will detect common pathogens such as influenza A/B, RSV and SARS-CoV-2 for comparative analysis. User experience and acceptability will be assessed via child-friendly and caregiver surveys following sample collection. The study will be implemented in two phases: diagnostic performance evaluation and user feedback assessment. The protocol is aligned with the Standard Protocol Items: Recommendations for Interventional Trials 2013 checklist.

Ethical approval has been granted by the University of Pretoria (509/2023) and the Gauteng Department of Health (GP_202406_032). The study is registered in the Pan African Clinical Trial Registry (PACTR202411743094783). Findings will be disseminated through peer-reviewed journals, conferences and stakeholder briefings. The study complies with South Africa’s Protection of Personal Information Act. Data collection is scheduled from November 2024 to February 2025, with project completion expected within 1 year.

Pan African Clinical Trial Registry (PACTR202411743094783).

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.

Many patients with tuberculosis (TB) suffer from a huge economic burden, even though TB services are often provided free of charge at the point of care. Costs can create significant barriers, hindering patients’ access to TB treatment. These costs include direct medical costs (such as consultation fees), direct non-medical costs (such as transportation costs) and indirect costs (such as wages foregone). This systematic review aims to synthesise the best available evidence on economic evaluations of patient-cost studies on self-administered treatment (SAT) for drug-sensitive TB compared with facility-based directly observed treatment, short-course (FB DOTS), globally.

We will conduct a systematic review following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols guidelines and search PubMed, Academic Search Complete, Scopus, CINAHL Plus (EBSCO) and Google Scholar for articles published up to 2025, without date restrictions. Eligible studies must be full or partial (cost analyses without effectiveness data) economic evaluations conducted globally, comparing SAT to FB DOTS regarding TB patient costs. Grey literature will be included. Exclusion criteria include studies not reporting patient costs between SAT and FB DOTS, and non-economic evaluations (non-original research). Two independent reviewers will conduct the screening, data extraction and quality assessment. A quality assessment will be performed using the Consolidated Health Economic Evaluation Reporting Standards statement, the Consensus on Health Economic Criteria checklist and the ROBINS-I tool.

Ethics approval is not required for this systematic review because it does not use individual patient data. Instead, we will use publicly available economic evaluation research studies. Findings will be presented at international and national conferences and published in open-access, peer-reviewed journals.

CRD42024591221.

Most research on the relationship between diabetes and cognitive health has used data from high-income countries. This study described this relationship in India, the world’s most populous country.

Cross-sectional analysis of the baseline wave of the nationally representative Longitudinal Ageing Study in India, conducted from 2017 to 2019.

All 36 Indian states and union territories.

57 905 adults aged 45 years or older.

Scaled cognitive scores (mean of 0 and SD of 1) and cognitive impairment defined as a cognitive score 1.5 SD or below the age-matched and education-matched mean. Diabetes was defined as a self-report of a prior diabetes diagnosis made by a health professional or having a measured haemoglobin A1c ≥6.5%.

In age-adjusted and sex-adjusted models, people with diabetes had cognitive scores that were 0.24 SD higher (95% CI 0.22 to 0.26) and had a 1.2% (95% CI 0.6% to 1.7%) lower prevalence of cognitive impairment than people without diabetes. Differences persisted even when adjusting for demographic, socioeconomic and geographical characteristics. Rural versus urban residence modified the relationships of diabetes with cognitive score (p=0.001) and cognitive impairment (p=0.003). In fully adjusted models, rural respondents with diabetes had 0.05 SD (95% CI 0.03 to 0.07) greater cognitive scores and 1.6% (95% CI 0.9% to 2.4%) lower prevalence of cognitive impairment than those without diabetes. In urban areas, respondents with and without diabetes had similar cognitive scores and prevalence of cognitive impairment.

Middle-aged and older adults with diabetes living in India had better cognitive health than those without diabetes. Rural versus urban area of residence modified this relationship. Urban–rural differences, the nutrition transition and social conditions likely influenced the cross-sectional relationship between diabetes and cognitive health in India, leading to different associations than reported in other countries.