аIJʹÙÍø

This research program seeks to improve reproductive care and long-term health outcomes for women living with chronic health conditions, such as kidney disease, diabetes, and cardiovascular diseases, and their children. These conditions can impact fertility, increase pregnancy risks, and impact child health and development. Leveraging linked Australian health data assets—including the Australian and New Zealand Assisted Reproduction Database (ANZARD), Medicare Benefits Schedule (MBS), Pharmaceutical Benefits Scheme (PBS), hospital admissions, perinatal records, and the Australian Early Development Census (AEDC)— the program addresses key evidence gaps to support safer, evidence-based reproductive care.

PhD projects are available across the following themes:

• Fertility and medically assisted reproduction (MAR)
  Analyse trends and outcomes of fertility treatments, including IVF and ovulation induction, among women with chronic conditions to inform patient counselling and decision-making.
• Pregnancy and maternal outcomes
  Investigate pregnancy complications, birth outcomes, and maternal health in women with chronic conditions, using perinatal and hospital datasets.
• Child health and healthcare utilisation
  Examine hospitalisation rates and health service use among children born to women with chronic illness to identify long-term risks.
• Child development and school readiness
  Use developmental datasets, such as the Australian Early Development Census (AEDC), to study the potential impacts of maternal health and fertility treatment on early learning and developmental vulnerability.
• Medication use and reproductive outcomes
  Explore how medications (e.g., immunosuppressants) may affect fertility, pregnancy, and child health using PBS and clinical records.

Students may focus on several sub-projects using cohort or cross-sectional designs, multivariable regression, survival analysis, and/or evidence synthesis. Findings will inform clinical care, reproductive counselling, and health policy for women with chronic conditions and their children.

Candidate requirements

Ideal candidates will have a background in public health, medicine, epidemiology, health data science, or a related field. Strong analytical skills (e.g. proficiency in Stata, R, or Python) and an interest in reproductive, maternal, or child health are highly valued.