Evidence map›Paper›PMID 26155199›Full record

ArticlePopulation health metrics2015

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.

Giorgi Kvizhinadze, Nick Wilson, Nisha Nair, Melissa McLeod, Tony Blakely

Abstract read
In one paragraph

Article in Population health metrics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Giorgi KvizhinadzeDepartment of Public Health, University of Otago, Wellington, PO Box 7343, Wellington New Zealand.
Nick WilsonDepartment of Public Health, University of Otago, Wellington, PO Box 7343, Wellington New Zealand.
Nisha NairDepartment of Public Health, University of Otago, Wellington, PO Box 7343, Wellington New Zealand.
Melissa McLeodDepartment of Public Health, University of Otago, Wellington, PO Box 7343, Wellington New Zealand.
Tony BlakelyDepartment of Public Health, University of Otago, Wellington, PO Box 7343, Wellington New Zealand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveWe 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.

methodsNew 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.

findingsThe 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.

conclusionsWe 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.

Indexed as

AgeCost-effectiveCost-effectiveness thresholdHealth-adjusted life expectancy (HALE)Health-adjusted life-years (HALYs)Health system costsMorbidityQuality-adjusted life-years (QALYs)

Identifiers

PMID26155199
PMCPMC4493819

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.