Evidence map›Paper›PMID 22788261›Full record

ArticlePharmacoEconomics2012

Developing adolescent-specific health state values for economic evaluation: an application of profile case best-worst scaling to the Child Health Utility 9D.

Julie Ratcliffe, Terry Flynn, Frances Terlich, Katherine Stevens, John Brazier, Michael Sawyer

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Article in PharmacoEconomics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
73citing papers in PubMed, 7 pooled it
–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

73 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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  4. Discrete choice experiments to generate utility values for multi-attribute utility instruments: a systematic review of methods.The European journal of health economics : HEPAC : health economics in prevention and care · 2020
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  9. Mapping PedsQL™ scores to CHU9D utility weights for children with chronic conditions in a multi-ethnic and deprived metropolitan population.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2023
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  17. BMJ open · 2026
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13 more citing papers are in PubMed but not listed here.

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

6 authors.

Julie RatcliffeFlinders Clinical Effectiveness, Flinders University, Adelaide, SA, Australia. julie.ratcliffe@flinders.edu.au
Terry Flynn
Frances Terlich
Katherine Stevens
John Brazier
Michael Sawyer

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe way that health is measured and valued is fundamental to economic evaluation. To date, adult health state values have been routinely used in the calculation of QALYs for the economic evaluation of healthcare treatment and preventive programmes, including those targeted at adolescents.

objectivesThe main objective of this study was to apply profile case best-worst scaling (BWS) discrete-choice experiment (DCE) methods to obtain adolescent-specific values for the Child Health Utility 9D (CHU9D), a new generic preference-based measure of health-related quality of life developed specifically for application in cost-effectiveness analyses of treatments and interventions targeted at young people. A secondary aim was to assess the feasibility of a web-based method of data collection for the valuation of health states defined by the CHU9D.

methodsA web-based survey was developed including the CHU9D instrument and a series of BWS DCE questions. Specifically, respondents were asked to indicate the best and worst attribute levels from a series of ten health states defined by the CHU9D, presented one at a time. The survey was administered to a community-based sample of consenting adolescents (n = 590) aged 11-17 years. A conditional logistic regression model was applied to estimate values (part-worth utilities) for each level of the nine attributes relating to the CHU9D. A marginal utility matrix was then estimated to generate an adolescent-specific scoring algorithm on the full health = 1 and dead = 0 scale required for the calculation of QALYs.

resultsThe results indicate that participants were able to readily choose 'best' and 'worst' attribute levels for the CHU9D health states. Large differences in value were found between the first and fifth levels (indicating 'no problems' and 'severe problems', respectively) for all nine attributes relating to the CHU9D. In general, there was little differentiation between the middle levels of all attributes indicating only limited additional value for adolescents of moving between these levels. Comparison of the adolescent-specific algorithm and the existing adult scoring algorithm for the CHU9D revealed some significant differences in values for identical health states, which may have important implications for the application of the CHU9D to value adolescent treatment and service programmes particularly for mental health. In general, adolescents appeared to place more weight upon the CHU9D attributes relating to mental health (worried, sad and annoyed) than would be implied by application of the existing algorithm based upon adult values.

conclusionThis study provides preliminary indications that there may be potentially important and systematic differences in the valuations attached to identical health states by adolescents in comparison with adult population groups. The study findings lend support to the potential future application of profile case BWS DCE methods to undertake large-scale health state valuation studies directly with young adolescent population samples and provide support for the feasibility and acceptability of a web-based mode of administration for this purpose.

Indexed as

Health StatusQuality-Adjusted Life YearsQuality of LifeAdolescentAdultAge FactorsAlgorithmsChildCost-Benefit AnalysisData CollectionFeasibility StudiesFemaleHumansInternetLogistic ModelsMale

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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.