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).

Urinary tract infections (UTIs) are highly prevalent and pose a significant burden among older adults. Accurate diagnosis in this population is challenging due to the high prevalence of pre-existing lower urinary tract symptoms, inability to express symptoms and asymptomatic bacteriuria. Current diagnostic tests are unreliable, often resulting in over- and underdiagnosis. A previous pilot study proposed a higher cut-off for pyuria and identified five promising biomarkers for the diagnosis of UTIs in older adults. The UTI-GOLD study aims to validate these five new biomarkers and the higher leucocyte cut-off as a diagnostic tool for UTIs in older people in a real-world setting.

Between August 2024 and December 2027, an observational multicentre diagnostic accuracy study is being conducted across primary, secondary and tertiary healthcare facilities in the Netherlands and the UK. Adults ≥65 years with a suspected UTI will be considered eligible. Patients with pre-existing decision-making incapacity or an indwelling catheter will be excluded. UTI will be defined according to an international consensus-based reference standard. Biomarkers will be measured by liquid chromatography-mass spectrometry (neutrophil gelatinase-associated lipocalin, tissue inhibitor of metalloproteinase 2 and CXC motif chemokine ligand 9) and enzyme-linked immunosorbent assay (interleukin 6 and azurocidin). Pyuria will be quantified by automated microscopy and/or flow cytometry. Diagnostic accuracy measures will be calculated using the receiver operating characteristic curves, and sensitivity, specificity, likelihood ratios and predictive values will be reported for optimal cut-offs.

The protocol was reviewed by the local Leiden University Medical Center research committee, who declared on 15 April 2024 that the medical research involving human subject act (Dutch abbreviation: WMO) does not apply to the current study (reference number nWMODIV2_2024025). The study also received approval from the NHS Research Ethics Committee in the UK (reference number 24/LO/0649).

The study findings will be published in a peer-reviewed journal, presented at academic congresses and shared with healthcare providers.

The study was registered at clinicaltrial.gov on the 24 September 2024 with registration number: NCT06610721.

To investigate levels of recreational physical activity, screen time, sleep quality and mood in undergraduate medicine and nursing students.

Observational, cross-sectional study using an online survey administered during the academic term in 2024.

International Health Professions University in Bahrain.

279 undergraduate students from the school of medicine and school of nursing.

Physical activity levels (International Physical Activity Questionnaire-Short Form), recreational screen time (Sedentary Behaviour Questionnaire), sleep quality (Pittsburgh Sleep Quality Index) and mood (Brief Mood Introspection Scale) were measured and compared across groups, and associations between measures were assessed.

Participants reported high rates of not meeting physical activity recommendations (46.6%), high levels of recreational screen time (median=32 hours per week) and poor-quality sleep (63.1%). Males reported higher levels of physical activity, screen time and sleep quality. Higher sleep quality was observed for the school of medicine, the preclinical stage of study and participants living alone. Overweight and obese participants had significantly higher recreational screen time and more unpleasant and tired moods. Higher levels of screen time and lower sleep quality were associated with tired, unpleasant and negative moods, while not meeting physical activity recommendations was associated with poor sleep in addition to unpleasant, tired and negative moods.

Physical activity levels are positively associated with mood and sleep quality in young adult healthcare students. Recreational screen time is negatively associated with mood but has no relationship with sleep quality. Intervention programmes to increase physical activity are warranted for young adults in healthcare training.

Study data is available on reasonable request from the corresponding author.

Factors associated with multimorbidity, polypharmacy and Medication Regimen Complexity Index (MRCI) may vary across countries. However, such data are lacking in the present study setting. This study aimed to identify factors associated with multimorbidity, polypharmacy and MRCI among adults living with hypertension in public hospitals of South Gondar Zone.

Multicentred cross-sectional design

Public hospitals of Comprehensive Specialised and Primary Hospitals, Ethiopia.

Adults living with hypertension who had follow-up visits at outpatient clinics and were selected by systematic random sampling from 1 December 2021 to 28 February 2022.

Medication regimen complexity was assessed using a 65-item medication regimen complexity tool. Sociodemographic data were collected through an interview, while polypharmacy and clinical characteristics were documented using a checklist. Data were entered into SPSS V.26 and analysed using STATA V.17. A binary logistic regression model was used to determine the AOR of factors associated with multimorbidity and polypharmacy. For factors influencing MRCI, an ordinal logistic regression was used.

We found participants from Nefas Mewucha Hospital (AOR = 0.3, 95% CI 0.15 to 0.59) and Mekane Eyesus Hospital (AOR = 0.17, 95% CI 0.07 to 0.38), compared with Debre Tabor Comprehensive Specialised Hospital, polypharmacy (AOR = 5.52, 95% CI 1.49 to 20.39), medium (AOR = 19.76, 95% CI 5.86 to 66.56) and high MRCI (AOR = 120.32, 95% CI 33.12 to 437.07) were associated with multimorbidity. Multimorbidity (AOR = 25.4, 95% CI 7.48 to 86.23), controlled blood pressure (AOR = 0.43, 95% CI 0.19 to 0.92) and duration of hypertension therapy 5 years or more (AOR = 2.12, 95% CI 1.08 to 4.16) were associated with polypharmacy. Whereas controlled BP (AOR = 0.48, 95% CI 0.32 to 0.72) and multimorbidity (AOR = 14.55, 95% CI 9.00 to 23.52) were significantly associated with high MRCI. The prevalence of multimorbidity, high MRCI and polypharmacy was found in 46.1%, 35.22% and 12.29% of participants, respectively.

A considerable proportion of participants with hypertension experienced multimorbidity, polypharmacy and high medication complexity. Polypharmacy, primary hospital setting and high MRCI were independent variables associated with multimorbidity. On the other hand, multimorbidity and controlled BP were associated with polypharmacy and MRCI. Hypertension care should consider multimorbidity, polypharmacy and medication complexity.