Poster Presentation Clinical Oncology Society of Australia Annual Scientific Meeting 2026

Developing a large-scale population microsimulation model to evaluate the benefits, harms, and economic effects of risk-stratified melanoma screening in Australia (146099)

Aminath Shausan 1 , Dan Lindsay 1 2 3 , Louisa Collins 1 4 5
  1. Viertel Cancer Research Centre, Cancer Council Queensland, Brisbane, Queensland, Australia
  2. Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
  3. Faculty of Health, Medicine and Behavioural Sciences, The University of Queensland, Brisbane, Queensland, Australia
  4. School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, Queensland, Australia
  5. Cancer and Palliative Care Outcomes Centre, Queensland University of Technology, Brisbane, Queensland, Australia

Aims

To develop an individual-level microsimulation model to evaluate the benefits, harms and economic effects of risk-stratified melanoma screening in Australia as part of the Roadmap for a National Targeted Melanoma Screening Program.

Methods

A hybrid individual-level microsimulation model was developed in Python using a discrete-event simulation framework and condition-event architecture to represent melanoma risk assessment, screening, diagnosis, treatment and follow-up. Individuals can be screened through either a targeted screening pathway based on melanoma risk assessment or through standard clinical care. Following whole-body skin examinations with dermascope (and possible imaging modalities), suspicious lesions may undergo biopsy and/or excision and histopathological assessment and are classified  as benign, keratinocyte cancer (KC), or melanoma. Confirmed melanoma cases are assigned to stage 0, I, II, III or IV and managed according to contemporary Australian practice. The model incorporates screening participation, repeat risk assessment, false-positive and false-negative diagnoses, treatment-related adverse events, , subsequent primary melanoma, melanoma-specific mortality and background mortality. Costs will be evaluated from the health system (including patient) perspective, and outcomes will be measured in quality-adjusted life years (QALYs).

Results

The model structure and parameterisation have been completed using Australian epidemiological, clinical and economic data, supplemented by published literature and expert clinical input.The model captures heterogeneity in age, melanoma history, risk status and screening participation, enabling evaluation of alternative screening ages, intervals, participation rates and risk thresholds. Planned outputs include melanoma detections, stage at diagnosis, KC detections, melanoma deaths, healthcare costs, QALYs and incremental cost-effectiveness ratios.

Conclusions

This hybrid microsimulation model provides a flexible framework for evaluating risk-stratified melanoma screening strategies in Australia. Developed in collaboration with key Roadmap stakeholders, it complements existing melanoma natural history models by focusing on risk assessment, screening, diagnosis, and management pathways, and supports evidence-based implementation of a targeted melanoma screening program.