We aim to use an agent-based model to accurately predict the spread of COVID-19 within multiple US state prisons.

We developed a semistochastic transmission model of COVID-19.

Five regional state-owned prisons within North Carolina.

Several thousand incarcerated individuals.

We measured (1) the observed and simulated average daily infection rate of COVID-19 for each prison studied in 30-day intervals, (2) the observed and simulated average daily recovery rate from COVID-19 for each prison studied in 30-day intervals, (3) the mean absolute percentage error (MAPE) of each prison’s summary statistics and the simulated results and (4) the parameter estimates of key predictors used in the model.

The COVID-19 pandemic disparately affected incarcerated populations in the USA, with severe morbidity and infection rates across the country. In response, many predictive models were developed to help mitigate risk. However, these models did not feature the systemic factors of prisons, such as vaccination rates, populations and capacities (to determine overcrowding) and design and were not generalisable to other prisons.

An agent-based model that used geospatial contact networks and compartmental transmission dynamics was built to create predictive microsimulations that simulated COVID-19 outbreaks within five North Carolinian regional prisons between July 2020 and June 2021. The model used the characteristics of an outbreak’s initial case size, a given facility’s capacity and its incarcerated vaccination rate as additional parameters alongside traditional susceptible-exposed-infected-recovered transmission dynamics. By fitting the model to each prison’s data using approximate Bayesian computation methods, we derived parameter estimates that reasonably modelled real-world results. These individualised estimates were then averaged to produce generalised parameter estimates for North Carolina state prisons overall.

Our model had a mean average MAPE score of 23.0 across all facilities, meaning that it reasonably forecasted facilities’ average daily positive and recovery rates of COVID-19. Our model estimated an average incarcerated vaccination rate of 54% across all prisons (with a 95% CI of ±0.12). In addition, the prisons of this study were estimated to be operating at 90% of their capacity on average (95% CI ±0.16). Given the high levels of COVID-19 observed in these prisons, which averaged over one-third positive tests on respective 1-day maxima, we conclude that vaccination levels were not sufficient in curbing COVID-19 outbreaks, and high occupancy levels likely exacerbated the spread of COVID-19 within prisons.

In addition, data gaps in facilities without recorded daily testing resulted in poor spread predictions, demonstrating how important consistent data release practices are in incarcerated settings for accurate tracking and prediction of outbreaks.

The findings of this study better quantify how spatial contact networks and facility-level characteristics unique to congregate living facilities can be used to predict infectious disease spread. Our approach also highlights the need for increased vaccination efforts and potential capacity reductions to mitigate COVID-19 transmission in prisons.

Couples diagnosed with unexplained subfertility are advised to start mild ovarian hyperstimulation and intrauterine insemination (MOH-IUI) as a primary treatment. Natural feedback mechanisms and hormone release are affected by artificially stimulated cycles and induced ovulation. Additional luteal support could positively affect progesterone patterns in the luteal phase. The LUMO study evaluates whether the addition of exogenous progesterone in the luteal phase following MOH-IUI treatment cycle will improve pregnancy and live birth rates.

A multicentre randomised, double-blind, controlled trial will be conducted in Dutch fertility clinics, academic and non-academic hospitals. There are two treatment arms: group A progesterone luteal phase support; group B placebo, without crossover. All initiated MOH-IUI cycles within 6 months after randomisation are included (study period). Participants will start study medication, applying a daily dosage of 2dd 300 mg progesterone (Utrogestan) or 2dd 300 mg placebo in vaginal capsules on the second day after the IUI procedure. Treatment is continued until the onset of menstruation, a negative pregnancy test (IUI+14 days), a miscarriage or until 7 weeks of gestation in case of a viable pregnancy. Follow-up ends at 12 months after the end of study period (18 months after study randomisation). The primary outcome is cumulative pregnancy rate, achieved within 6 months after randomisation, leading to live birth. A total of 1008 patients (504 patients in each group) will be included.

The study was approved by the Central Committee on Research Involving Human Subjects on 30 January 2023. All participating sites have the approval of the local Board of Directors to participate in the LUMO study. An informed consent form will be signed by all participants. Study results will be presented at (inter)national conferences and published in peer-reviewed journals. It is expected that the results of this trial will be used to draft national guidelines on this issue.

The study is registered in the EU CTIS trial register (2022-501534-33-00), the Dutch trial registry (registration number: LTR 24508), ClinicalTrials.gov (NCT05080569) and the WHO registry (universal trial number: U1111-1280-9461).

To explore perceptions regarding the approved and actual prescribed doses of protein kinase inhibitors (PKIs) in clinical practice in the European Union among medicine regulators and healthcare professionals (HCPs).

A qualitative descriptive study was conducted using semistructured interviews, continuing until thematic saturation was reached. Thematic analysis was undertaken using a combined deductive-inductive approach. Deductive main analytical themes were derived from the theoretical framework of questioning-based policy design, namely problem sensing, problem categorisation and problem decomposition. Subthemes were generated inductively and could coherently be situated within these main analytical themes.

Interviews were held online or in person at a location convenient for the interviewee, depending on the participant’s preference.

Seven medicine regulators involved in the regulation of cancer medicines—including PKIs—and 10 HCPs prescribing PKIs in clinical practice, from various countries within Europe, were included.

Regulators highlighted insufficient attention to optimal dose finding, yielding approved doses often based on outdated maximum tolerated dose concepts, leading to uncertainties in efficacy and safety. HCPs reported using alternative dosing strategies in clinical practice to improve tolerability and quality of life (QoL) but noted a lack of robust evidence to guide such adjustments and faced legal constraints to deviate from the approved dose. Participants emphasised the need for improved pre-approval and post-approval dose optimisation to improve safety, enhance QoL and bridge gaps between trial data and real-world patient diversity.

Collaborative efforts involving multistakeholders including HCPs, regulators, pharmaceutical companies, insurers, governments and patient representatives are essential to advance dose optimisation and improve patient-centric outcomes, with further research needed to understand these stakeholders’ perspectives.

Type 2 diabetes mellitus (T2DM) is a fast-growing chronic disease, with at least 1.3 million people diagnosed in Australia. In the Western Sydney Local Health District (WSLHD), an estimated 13.1% of all adults have T2DM. The condition significantly contributes to cardiovascular, heart and kidney diseases and causes a large disease burden. Lifestyle modifications, such as improved nutrition, increased physical activity and stress reduction, are recommended as first-line treatments for T2DM management. However, the current primary care system cannot meet the growing demands for diabetes care, necessitating the development of innovative, scalable, cost-effective solutions. Digital health technologies present a promising approach for promoting self-management in individuals with T2DM.

This cluster-randomised controlled trial aims to evaluate the feasibility and effectiveness of Gro-AUS, a localised version of the Gro Health app in Australia, to support T2DM management in Australian primary care settings. The trial will be conducted across multiple general practice clinics within the WSLHD, an area with a high prevalence of T2DM and significant cultural diversity in patient populations. Participants will be randomly assigned by clinic to either the intervention group (digital health programme) or control group (standard care). Primary outcomes include improvements in glycaemic control, cardiovascular risk factors and diabetes remission, with secondary outcomes such as weight loss, physical activity and mental well-being. Data will be collected using electronic and paper methods, with secure storage and de-identification ensuring participant privacy. The study’s mixed-method approach ensures inclusivity for patients with varying levels of digital literacy. Data will be securely stored, de-identified and used to assess the effectiveness of the intervention. Findings are expected to inform future models of diabetes care in Australia, providing evidence for the scalability of digital health technologies in chronic disease management.

This trial is by nature unblinded. The recruitment style for a stepped-wedge trial may also bias participant engagement. However, it has direct implications for clinical practice as an effectiveness implementation trial. The design also allows for a much larger sample and more statistical power to examine outcomes.

This trial has been prospectively registered with the Australian New Zealand Clinical Trials Registry. Ethical approval has been granted by the WSLHD Human Research Ethics Committee prior to data collection. Results will be disseminated through publication in a peer-reviewed medical journal and shared via the Agency for Clinical Innovation, the Primary Care Health Network and through community engagement initiatives.

ANZCTR388639.

Millions of patients receive general anaesthesia every year with either propofol total intravenous anaesthesia (TIVA) or inhaled volatile anaesthesia (INVA). It is currently unknown which of these techniques is superior in relation to patient experience, safety and clinical outcomes. The primary aims of this trial are to determine (1) whether patients undergoing (a) major inpatient surgery, (b) minor inpatient surgery or (c) outpatient surgery have a superior quality of recovery after INVA or TIVA and (2) whether TIVA confers no more than a small (0.2%) increased risk of definite intraoperative awareness than INVA.

This protocol was co-created by a diverse team, including patient partners with personal experience of TIVA or INVA. The design is a 13 000-patient, multicentre, patient-blinded, randomised, comparative effectiveness trial. Patients 18 years of age or older, undergoing elective non-cardiac surgery requiring general anaesthesia with a tracheal tube or laryngeal mask airway will be eligible. Patients will be randomised 1:1 to one of two anaesthetic approaches, TIVA or INVA, using minimisation. The primary effectiveness endpoints are Quality of Recovery-15 (QOR-15) score on postoperative day (POD) 1 in patients undergoing (1) major inpatient surgery, (2) minor inpatient surgery or (3) outpatient surgery, and the primary safety endpoint is the incidence of unintended definite intraoperative awareness with recall in all patients, assessed on POD1 or POD30. Secondary endpoints include QOR-15 score on POD0, POD2 and POD7; incidence of delirium on POD0 and POD1; functional status on POD30 and POD90; health-related quality of life on POD30, POD90, POD180 and POD365; days alive and at home at POD30; patient satisfaction with anaesthesia at POD2; respiratory failure on POD0; kidney injury on POD7; all-cause mortality at POD30 and POD90; intraoperative hypotension; moderate-to-severe intraoperative movement; unplanned hospital admission after outpatient surgery in a free-standing ambulatory surgery centre setting; propofol-related infusion syndrome and malignant hyperthermia.

This study is approved by the ethics board at Washington University, serving as the single Institutional Review Board for all participating sites. Recruitment began in September 2023. Dissemination plans include presentations at scientific conferences, scientific publications, internet-based educational materials and mass media.

NCT05991453.

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.

To estimate type 2 diabetes incidence trends by sex and socioeconomic position (SEP) and evaluate trends in SEP-related inequalities in incidence.

Ecological study using ambulatory claims data and regression-based modelling.

All 401 counties in Germany, covering the entire country.

All individuals with statutory health insurance (~85% of the population). Incident cases of type 2 diabetes were identified annually from 2014 to 2019 using the International Statistical Classification of Diseases and Related Health Problems, 10th revision codes.

Incident type 2 diabetes at the county level, adjusted for age and modelled using a mixed negative binomial regression. SEP was measured using the German Index of Socioeconomic Deprivation, and a random intercept accounted for county-level heterogeneity.

The incidence of type 2 diabetes decreased between 2014 and 2017 and plateaued thereafter. Trends were similar between sexes and deprivation levels. The greatest difference was observed between high and low deprivation, with an incidence rate ratio of 1.20 (95% CI: 1.14 to 1.27) among men and 1.21 (95% CI: 1.14 to 1.27) among women in 2014.

There was a positive trend in the decline in age-adjusted type 2 diabetes incidence between 2014 and 2019. However, social inequality persisted with deprived groups at higher risk of type 2 diabetes. The level of inequality was comparable between men and women. Continued monitoring is essential to assess whether these short-term trends persist over time.