Health Economics of Haemochromatosis

Haemochromatosis has considerable impact on the Australian healthcare system. These impacts include: the healthcare costs of diagnosing, treating and monitoring haemochromatosis; the costs (including productivity losses) and disease burden (including mortality) of haemochromatosis and its severe complications.

The aims of this project are to:

  1. Review the existing national and international literature on the burden of illness of haemochromatosis and cost effectiveness of interventions and screening in haemochromatosis.
  2. Assess the cost of illness of haemochromatosis in Australia in 2012;
  3. Develop a comprehensive health economics model of haemochromatosis;
  4. Using this model, identify cost-effective screening/early diagnosis and management strategies for optimal identification and treatment of haemochromatosis.

To inform the economic analysis, a targeted search of medical databases and relevant web pages to gather published national and international data relating to haemochromatosis epidemiology, treatment and costs will be made.
Using information collected in the literature/data review, a comprehensive cost of illness analysis will be performed, with the primary aim of demonstrating the economic burden related to haemochromatosis. Given its primary purpose will be to inform government policy, this analysis would be primarily conducted from a government cost perspective. However, broader costs associated with haemochromatosis, including productivity losses due to haemochromatosis associated complications, would also be considered so that policy makers would be better informed about the broader societal costs of haemochromatosis.

An economic model would be constructed in TreeAge Decision analysis software. The model will evaluate the costs and health outcomes of the proposed interventions/early screening compared to current practice. From these outputs, we could undertake a cost-effectiveness analysis or cost-utility analysis to estimate the incremental cost per quality-adjusted life year gained. To account for uncertainty, one-way sensitivity analysis on key parameters driving the results will be performed. To account for uncertainty in multiple parameters, 2nd order Monte Carlo (probabilistic sensitivity) analysis will be performed, involving sampling from distributions of key input parameters.

Research Groups

Related Diseases

Staff

Doctoral Students

  • Barbara de Graaff