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.

Risk perception is a key influencing factor on the adoption of preventative health behaviours. This study aimed to understand the role of health communication on how people perceived the risk of COVID-19 and influenced relevant health behaviours to minimise disease susceptibility during the COVID-19 pandemic among people with a chronic disease.

This qualitative study involved a semi-structured interview of participants diagnosed with a chronic disease. In analysing interview responses, the Health Belief Model was utilised as a sensitising framework to facilitate analysis and explore themes within the domains of the model.

Interviews were completed between August and December 2020 through online platforms with individual participants.

Participants were Australian residents aged ≥18 years with self-reported chronic disease(s). Ninety interviews were completed, and a sample of 33 participants were enrolled for analysis.

Two main themes were identified: cues to action and perception of the threat of infection. Many participants had implemented external cues to preventative behaviours, including, but not limited to, social distancing, hand hygiene and, in some cases, mask use, mirroring enforced government restrictions. Individuals also had several social motivators from family, particularly those working in the health field, and the wider community to employ the enforced preventative behaviours. However, despite having a chronic disease, many participants did not recognise themselves as being susceptible to COVID-19. Rather, they were more concerned for others that they characterised as being at high risk, including the elderly. Geographical location also played a role in risk prevention behaviour; owing to low case numbers in rural and remote areas, the risk of susceptibility was not perceived to be high.

These findings demonstrate the need to clearly communicate the risk of infection to allow individuals to make informed decisions on preventative behaviours. This has ongoing relevance to future emergencies, including future pandemics/epidemics, and highlights the greater challenge if similar public health measures are contemplated again.