How much might a society spend on life-saving interventions at different ages while remaining cost-effective? A case study in a country with detailed data
Web of Science
AuthorKvizhinadze, G; Wilson, N; Nair, N; McLeod, M; Blakely, T
Source TitlePopulation Health Metrics
University of Melbourne Author/sBlakely, Antony
AffiliationMelbourne School of Population and Global Health
Document TypeJournal Article
CitationsKvizhinadze, G., Wilson, N., Nair, N., McLeod, M. & Blakely, T. (2015). How much might a society spend on life-saving interventions at different ages while remaining cost-effective? A case study in a country with detailed data. POPULATION HEALTH METRICS, 13 (1), https://doi.org/10.1186/s12963-015-0052-2.
Access StatusOpen Access
OBJECTIVE: We aimed to estimate the maximum intervention cost (EMIC) a society could invest in a life-saving intervention at different ages while remaining cost-effective according to a user-specified cost-effectiveness threshold. METHODS: New Zealand (NZ) was used as a case study, and a health system perspective was taken. Data from NZ life tables and morbidity data from a burden of disease study were used to estimate health-adjusted life-years (HALYs) gained by a life-saving intervention. Health system costs were estimated from a national database of all publicly funded health events (hospitalizations, outpatient events, pharmaceuticals, etc.). For illustrative purposes we followed the WHO-CHOICE approach and used a cost-effectiveness threshold of the gross domestic product (GDP) per capita (NZ$45,000 or US$30,000 per HALY). We then calculated EMICs for an "ideal" life-saving intervention that fully returned survivors to the same average morbidity, mortality, and cost trajectories as the rest of their cohort. FINDINGS: The EMIC of the "ideal" life-saving intervention varied markedly by age: NZ$1.3 million (US$880,000) for an intervention to save the life of a child, NZ$0.8 million (US$540,000) for a 50-year-old, and NZ$0.235 million (US$158,000) for an 80-year-old. These results were predictably very sensitive to the choice of discount rate and to the selected cost-effectiveness threshold. Using WHO data, we produced an online calculator to allow the performance of similar calculations for all other countries. CONCLUSIONS: We present an approach to estimating maximal cost-effective investment in life-saving health interventions, under various assumptions. Our online calculator allows this approach to be applied in other countries. Policymakers could use these estimates as a rapid screening tool to determine if more detailed cost-effectiveness analyses of potential life-saving interventions might be worthwhile or which proposed life-saving interventions are very unlikely to benefit from such additional research.
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