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QALY league table of Iran: a practical method for better resource allocation
Reza Hashempour1, Behzad Raei1, Majid Safaei Lari1
1Department of Health Management and Economics, School of Public Health, Tehran University of Medical Sciences, 0000-0002-2043-8451, Tehran, Iran.
This study summarized 51 cost-utility analyses in Iran, finding preventive programs to be cost-effective. The research highlights the need for efficient resource allocation in healthcare decision-making.
Area of Science:
- Health Economics
- Public Health Policy
- Intervention Analysis
Background:
- Limited healthcare resources necessitate careful selection of interventions.
- Decision-makers require evidence to prioritize feasible health programs.
- This study addresses the need for a comprehensive overview of cost-utility analyses in Iran.
Purpose of the Study:
- To systematically review and summarize all cost-utility analysis (CUA) studies conducted in Iran.
- To develop a Quality Adjusted Life Year (QALY) league table for health interventions.
- To inform resource allocation and policy decisions in the Iranian healthcare system.
Main Methods:
- A systematic mapping review was performed, searching multiple databases (PubMed, Embase, Cochrane, Web of Science, Iranian databases) up to January 2020.
- Data extracted included Cost per QALY and Incremental Cost Utility Ratio (ICUR).
- The Joanna Briggs checklist was used for quality appraisal of the included studies.
Main Results:
- 51 CUAs were analyzed, with a focus on cancer (14 studies) and coronary heart diseases (6 studies).
- Markov models were prevalent (45% of studies), with discount rates varying from 0% to 7.2%.
- Cost per QALY varied widely, from $0.144 to over $4.5 million, indicating significant heterogeneity and precluding direct ranking.
Conclusions:
- Summarizing economic evaluation studies provides valuable insights for efficient resource allocation.
- Preventive programs were generally found to be cost-effective across various perspectives in Iran.
- The findings support evidence-based decision-making for optimizing healthcare interventions.
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