Women with gestational diabetes mellitus (GDM) are at seven-fold to ten-fold increased risk of type 2 diabetes mellitus (T2DM) when compared with those who experience a normoglycaemic pregnancy, and the cumulative incidence increases with the time of follow-up post birth. This protocol outlines the development and validation of a risk prediction model assessing the 5-year and 10-year risk of T2DM in women with a prior GDM diagnosis.

Data from all birth mothers and registered births in Victoria and South Australia, retrospectively linked to national diabetes data and pathology laboratory data from 2008 to 2021, will be used for model development and validation of GDM to T2DM conversion. Candidate predictors will be selected considering existing literature, clinical significance and statistical association, including age, body mass index, parity, ethnicity, history of recurrent GDM, family history of T2DM and antenatal and postnatal glucose levels. Traditional statistical methods and machine learning algorithms will explore the best-performing and easily applicable prediction models. We will consider bootstrapping or K-fold cross-validation for internal model validation. If computationally difficult due to the expected large sample size, we will consider developing the model using 80% of available data and evaluating using a 20% random subset. We will consider external or temporal validation of the prediction model based on the availability of data. The prediction model’s performance will be assessed by using discrimination (area under the receiver operating characteristic curve, calibration (calibration slope, calibration intercept, calibration-in-the-large and observed-to-expected ratio), model overall fit (Brier score and Cox-Snell R2) and net benefit (decision curve analysis). To examine algorithm equity, the model’s predictive performance across ethnic groups and parity will be analysed. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis-Artificial Intelligence (TRIPOD+AI) statements will be followed.

Ethics approvals have been received from Deakin University Human Research Ethics Committee (2021–179); Monash Health Human Research Ethics Committee (RES-22-0000-048A); the Australian Institute of Health and Welfare (EO2022/5/1369); the Aboriginal Health Research Ethics Committee of South Australia (SA) (04-23-1056); in addition to a Site-Specific Assessment to cover the involvement of the Preventative Health SA (formerly Wellbeing SA) (2023/SSA00065). Project findings will be disseminated in peer-reviewed journals and at scientific conferences and provided to relevant stakeholders to enable the translation of research findings into population health programmes and health policy.

The goal of the study was to determine the magnitude and contributing factors of low back pain among primary school teachers in Borama Town, Somaliland.

An institution-based descriptive cross-sectional study design was employed. Simple random sampling was used to select the study units from each school.

The study was conducted in Borama, Somaliland.

A total of 268 primary school teachers participated in the study.

The primary outcome of the study was the prevalence of low back pain.

The study found that 51.5% of school teachers had low back pain. There was a strong link between low back pain and having a higher Body Mass Index (adjusted OR (AOR)=2.63) and stress at work (AOR=3.34). Sleep disturbance (AOR=1.73), lifting heavy materials (AOR=1.67) and a history of low back injury (AOR=2.12) were also significant predictors of low back pain.

More than half of primary school teachers had low back pain over the past 12 months. Higher Body Mass Index, history of low back injury, stress at work, lifting heavy material and sleep disturbance were significant and independent predictors of low back pain among primary school teachers.

Cystic fibrosis (CF) is a genetic condition of impaired membrane electrolyte transport and is characterised by defects in the production and function of the cystic fibrosis transmembrane conductance regulator (CFTR) protein. Ground-breaking CFTR modulator therapy has resulted in a notable shift in the clinical presentation and progressive nature of CF, across both pulmonary and extrapulmonary systems. Access to CFTR modulator therapies in people with CF is occurring in a staged, descending age process, with clinical trials focusing primarily on safety and efficacy. There is a lack of robust, real-world longitudinal data on CFTR modulator therapy in infants and young children where extrapulmonary outcomes such as growth, micronutrient status and pancreatic function are the key focus.

Pancreatic, nutritional and clinical outcomes in children 0–5 years with CF during the first 2 years of CFTR modulator therapy (PaNC) is a prospective cohort study involving all eight tertiary paediatric CF centres in Australia. Infants and children 4 months to 5 years of age who are eligible for elexacaftor/tezacaftor/ivacaftor (ETI) or ivacaftor (IVA) meet the inclusion criteria for PaNC, with a total eligible cohort of 303 children at the commencement of recruitment. The primary outcomes are change in weight-for-length/body mass index z score and change in serum micronutrient status, at 6–12 monthly intervals, during the first 2 years of treatment with ETI or IVA. Secondary outcomes include change in exocrine pancreatic function, measured by faecal elastase-1, change in the use and dose of pancreatic enzyme replacement therapy, nutritional and gastrointestinal therapies and change in sweat chloride levels. Linear mixed modelling will be used to analyse primary and secondary endpoints. This protocol is reported in accordance with ‘The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement’ reporting guidelines.

Overarching governance and ethics approval has been granted by Monash Health Human Research Ethics Committee, in addition to all eight sites receiving site-specific authorisation approvals prior to the commencement of recruitment. Opportunities for CF consumers to be involved in targeted dissemination plans will be initiated via CF Australia at the completion of the study period. Additionally, a summary of non-identifiable results will be provided to CF consumers and CF healthcare providers via scientific and lay conferences and via peer-reviewed journals.

ACTRN12624001185550; Pre-results.

Adolescents’ experiences (10–19 years-old) with tuberculosis (TB) remain poorly understood. Descriptions of adolescent TB experiences, particularly how they interact with the health system, are scarce. We aimed to understand adolescents’ experiences of TB health services in the Western Cape, South Africa. We focused on how TB services were aided or hindered through interactions with healthcare providers and health system processes.

Teen TB, an observational study in Cape Town, enrolled 50 newly diagnosed adolescents with multidrug-resistant and drug-susceptible TB. A subset of 20 was selected for serial qualitative data collection, with 19 completing all tasks between December 2020 and September 2021. 52 interviews were conducted and thematically analysed using a case descriptive process for experiences across the TB care cascade.

Adolescents criticised the difficulties and delays they encountered in obtaining an accurate TB diagnosis. Initial misdiagnoses and delayed TB diagnoses were reported, despite seeking help from multiple healthcare providers at different facilities. Adolescents questioned whether the financial, social and emotional costs of TB care outweighed the costs of delaying treatment initiation and adherence. Adolescents reported that the treatment regimen, adherence support processes and interactions with the health system posed significant challenges to maintaining adherence. Encouragingly, however, most adolescents reported being well treated and cared for by health workers.

Our study shows that adolescents experience challenges throughout their TB treatment journeys. More adolescent-focused research is needed to tailor treatment and healthcare processes to their needs.

Selection of antiseizure medications (ASMs) for newly diagnosed epilepsy remains largely a trial-and-error process. We have developed a machine learning (ML) model using retrospective data collected from five international cohorts that predicts response to different ASMs as the initial treatment for individual adults with new-onset epilepsy. This study aims to prospectively evaluate this model in Australia using a randomised controlled trial design.

At least 234 adult patients with newly diagnosed epilepsy will be recruited from 14 centres in Australia. Patients will be randomised 1:1 to the ML group or usual care group. The ML group will receive the ASM recommended by the model unless it is considered contraindicated by the neurologist. The usual care group will receive the ASM selected by the neurologist alone. Both the patient and neurologists conducting the follow-up will be blinded to the group assignment. Both groups will be followed up for 52 weeks to assess treatment outcomes. Additional information on adverse events, quality of life, mood and use of healthcare services and productivity will be collected using validated questionnaires. Acceptability of the model will also be assessed.

The primary outcome will be the proportion of participants who achieve seizure-freedom (defined as no seizures during the 12-month follow-up period) while taking the initially prescribed ASM. Secondary outcomes include time to treatment failure, time to first seizure after randomisation, changes in mood assessment score and quality of life score, direct healthcare costs, and loss of productivity during the treatment period.

This trial will provide class I evidence for the effectiveness of a ML model as a decision support tool for neurologists to select the first ASM for adults with newly diagnosed epilepsy.

This study is approved by the Alfred Health Human Research Ethics Committee (Project 130/23). Findings will be presented in academic conferences and submitted to peer-reviewed journals for publication.

ACTRN12623000209695.