Encephalitis is brain parenchyma inflammation, frequently resulting in seizures which worsens outcomes. Early anti-seizure medication could improve outcomes but requires identifying patients at greatest risk of acute seizures. The SEIZURE (SEIZUre Risk in Encephalitis) score was developed in UK cohorts to stratify patients by acute seizure risk. A ‘basic score’ used Glasgow Coma Scale (GCS), fever and age; the ‘advanced score’ added aetiology. This study aimed to evaluate the score internationally to determine its global applicability.

Patients were retrospectively analysed regionally, and by country, in this international evaluation study. Univariate analysis was conducted between patients who did and did not have inpatient seizures, followed by multivariable logistic regression, hierarchical clustering and analysis of the area under the receiver operating curves (AUROC) with 95% CIs.

2032 patients across 13 countries were identified, among whom 1324 were included in SEIZURE score calculations and 970 were included in regression modelling. The involved countries comprised 19 organisations spanning all WHO regions.

The primary outcome was measuring inpatient seizure rates.

Autoantibody-associated encephalitis, low GCS and presenting with a seizure were frequently associated with inpatient seizures; fever showed no association. Globally, the score had limited discriminatory ability (basic AUROC 0.58 (95% CI 0.55 to 0.62), advanced AUROC 0.63 (95% CI 0.60 to 0.66)). The scoring system performed acceptably in western Europe, excluding Spain, with the best performance in Portugal (basic AUROC 0.82 (95% CI 0.69 to 0.94), advanced AUROC 0.83 (95% CI 0.72 to 0.95)).

The SEIZURE score performed best in several countries in Western Europe but performed poorly elsewhere, partly due to differing and unknown aetiologies. In most regions, the score did not reach a threshold to be clinically useful. The Western European results could aid in designing clinical trials assessing primary anti-seizure prophylaxis in encephalitis following further prospective trials. Beyond Western Europe, there is a need for tailored, localised scoring systems and future large-scale prospective studies with optimised aetiological testing to accurately identify high-risk patients.

In Bangladesh, evidence on the long-term trajectory of adolescents' sexual and reproductive health (SRH) remains limited, largely due to the lack of longitudinal data to assess the changes over time. To address this gap, the Advancing Sexual and Reproductive Health and Rights (AdSEARCH) project of International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b) set up an adolescent cohort study aimed at documenting changes in SRH knowledge, attitudes and practices, and identifying the factors affecting these changes. This article presents the baseline sociodemographic and SRH characteristics of this cohort as a pathway for future analyses.

This cohort study included 2713 adolescents from the Baliakandi Health and Demographic Surveillance System run by icddr,b. The cohort covered three age groups from girls and boys, giving a total of five cohorts: girls aged 12, 14 and 16 years; and boys aged 14 and 16 years. A total of seven rounds of data had been collected at 4-month intervals over 2-years follow-up period.

The majority of adolescents were attending school (90%), and school dropouts were higher among boys. Around 17% of the respondents were involved in income-generating activities, which were mostly boys. Among girls, the mean age of menarche was 12.2 years. Overall, 6% of adolescents had major depressive disorder, with prevalence increasing with age. Gender differences were evident regarding knowledge about conception and contraception. Egalitarian attitudes towards social norms and gender roles were found higher among girls (52%) compared to boys (11%). The majority of adolescents reported experiencing social/verbal bullying (43%), followed by physical violence (38%) and cyberbullying (4%).

This article presents the baseline findings only. A series of papers is in the pipeline for submission to different peer-reviewed journals. The findings from this study will be used to support data-driven policy formulation for future adolescent health programmes.

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.

The COVID-19 pandemic’s unprecedented nature has exposed significant vulnerabilities in most public health systems and highlighted the importance of coordinated responses across various levels of government. A global debate emerged on the types of health measures necessary to curb the rapid spread of contagious and/or lethal diseases. However, some of these measures involved restricting individual rights, raising significant ethical, legal and public health questions. The protocol of this systematic review aims to address a critical gap in the literature by analysing how Public Health Surveillance services worldwide implemented compulsory right-restricting measures during the COVID-19 pandemic, and what impacts these measures had on public health outcomes and individual rights.

This protocol focuses on studies about right-restricting measures enacted by Public Health Surveillance services during the COVID-19 pandemic. It will be unrestrictive as to period (starting in 2019, when the outbreak was identified), language or publication status in a preliminary stage. It will include only peer-reviewed publications, discarding opinion articles, editorials, conference papers and non-peer-reviewed publications. Considering the PICo strategy, the research question of this systematic review can be formulated as follows: Problem—right-restricting measures enacted by Public Health Surveillance services; Interest—implementation modalities and impacts on individual rights and public health outcomes; Context—COVID-19 pandemic. This protocol will use the following databases: Pubmed, Cochrane/CENTRAL, Embase, Scopus and Web of Science. Considering the various measures that may have been adopted, the following categories of analysis will be used: (i) Public Health Surveillance as a field, (ii) the various specific areas of Health Surveillance, (iii) law enforcement, (iv) right-restricting measures and consent, (v) interactions between right-restricting measures and routine Public Health Surveillance functions, (vi) differences between countries and (vii) Health Surveillance lessons learnt from the COVID-19 pandemic. These categories are not strictly mutually exclusive; however, each study will be assigned to the category most aligned with its primary focus. To ensure the validity and reliability of findings, each study will have its risk of bias assessed at both the study and outcome levels.

Patients and the public were not involved in the design, conduct, reporting or dissemination plans of this systematic review. The results will be presented in one or more articles to be submitted to scientific journals and may also be presented at scientific conferences and to public policy makers.

This systematic review protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) on 20 November 2024 (registration number CRD42024613039).