Dataset Information

Time trade-off health state utility values for depression: a systematic review and meta-analysis.

ABSTRACT:

Purpose

This study aims to systematically review the literature on health utility in depression generated by time trade-off (TTO) method and to compare health state vignettes.

Methods

Systematic literature search was conducted following PRISMA guideline in 2020 November (updated in 2022 March) in Pubmed, Web of Science, PsycInfo, and Cochrane Database of Systematic Reviews. Random effect meta-analysis was conducted to pool vignette-based utility values of mild, moderate, and severe depression and to compare the preferences of depressed and nondepressed population.

Results

Overall, 264 records were found, 143 screened by title and abstract after removing duplicates, 18 assessed full text, and 14 original publications included. Majority of the studies (n = 9) used conventional TTO method, and most of the studies (n = 8) applied 10-year timeframe. Eight studies evaluated self-experienced health (own-current depression). Six studies assessed vignette-based health states of remitted, mild, moderate, and severe depression, half of them applied McSad measure based health description. Altogether, 61 different utility values have been cataloged, mean utility of self-experienced depression states (n = 33) ranged between 0.89 (current-own depression) and 0.24 (worst experienced depression). Pooled utility estimates for vignette-based mild, moderate, and severe depression was 0.75, 0.66 and 0.50, respectively. Meta-regression showed that severe depression (β = -0.16) and depressed sample populations (β = -0.13) significantly decrease vignette-based utility scores.

Conclusion

Our review revealed extent heterogeneity both in TTO methodology and health state vignette development. Patient's perception of depression health states was worse than healthy respondents.

SUBMITTER: Balazs PG

PROVIDER: S-EPMC10063515 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Publications

Time trade-off health state utility values for depression: a systematic review and meta-analysis.

Balázs Péter György PG Erdősi Dalma D Zemplényi Antal A Brodszky Valentin V

Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation 20220930 4

<h4>Purpose</h4>This study aims to systematically review the literature on health utility in depression generated by time trade-off (TTO) method and to compare health state vignettes.<h4>Methods</h4>Systematic literature search was conducted following PRISMA guideline in 2020 November (updated in 2022 March) in Pubmed, Web of Science, PsycInfo, and Cochrane Database of Systematic Reviews. Random effect meta-analysis was conducted to pool vignette-based utility values of mild, moderate, and sever ...[more]

PMID: 36178658

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Project description:BackgroundUsing appropriate health state utility values (HSUVs) is critical for economic evaluation of new lung cancer interventions, such as low-dose computed tomography screening and immunotherapy. Therefore, we provide a systematic review and meta-analysis of community- and choice-based HSUVs for lung cancer.MethodsOn 6 March 2017, we conducted a systematic search of the following databases: Embase, Ovid MEDLINE, Web of Science, Cochrane CENTRAL, Google Scholar, and the School of Health and Related Research Health Utility Database. The search was updated on 17 April 2019. Studies reporting mean or median lung cancer-specific HSUVs including a measure of variance were included and assessed for relevance and validity. Studies with high relevance (i.e. community- and choice-based) were further analysed. Mean HSUVs were pooled using random-effects models for all stages, stages I-II, and stages III-IV. For studies with a control group, we calculated the disutility due to lung cancer. A sensitivity analysis included only the methodologically most comparable studies (i.e. using the EQ-5D instrument and matching tariff). Subgroup analyses were conducted by time to death, histology, sex, age, treatment modality, treatment line, and progression status.ResultsWe identified and analysed 27 studies of high relevance. The pooled HSUV was 0.68 (95% confidence interval [CI] 0.61-0.75) for all stages, 0.78 (95% CI 0.70-0.86) for stages I-II, and 0.69 (95% CI 0.65-0.73) for stages III-IV (p = 0.02 vs. stage I-II). Heterogeneity was present in each pooled analysis (p < 0.01; I2 = 92-99%). Disutility due to lung cancer ranged from 0.11 (95% CI 0.05-0.17) to 0.27 (95% CI 0.18-0.36). In the sensitivity analysis with the methodologically most comparable studies, stage-specific HSUVs varied by country. Such studies were only identified for Canada, China, Spain, the UK, the USA, Denmark, Germany, and Thailand. In the subgroup analysis by time to death, HSUVs for metastatic non-small-cell lung cancer ranged from 0.83 (95% CI 0.82-0.85) at ≥ 360 days from death to 0.56 (95% CI 0.46-0.66) at < 30 days from death. Among patients with metastatic non-small-cell lung cancer, HSUVs were lower for those receiving third- or fourth-line treatment and for those with progressed disease. Results of subgroup analyses by histology, sex, age, and treatment modality were ambiguous.ConclusionsThe presented evidence supports the use of stage- and country-specific HSUVs. However, such HSUVs are unavailable for most countries. Therefore, our pooled HSUVs may provide the best available stage-specific HSUVs for most countries. For metastatic non-small-cell lung cancer, adjusting for the decreased HSUVs in the last year of life may be considered, as may further stratification of HSUVs by treatment line or progression status. If required, HSUVs for other health states may be identified using our comprehensive breakdown of study characteristics.

Project description:INTRODUCTION:Increasingly, assessment of healthcare technologies and interventions requires the assessment of both costs and utilities. Health state utility values (HSUVs) are measured using a range of generic and condition-specific measures. While reviews have identified that generic measures of HSUVs may lack validity in adults with conditions that result in physical disability, there is little information available on the methods used to obtain HSUVs in children and adolescents with disabilities. The objectives of this systematic review are to describe the methods used to obtain HSUVs, including mode of administration and psychometric properties, and provide summary statistics for HSUVs among children and adolescents with disabilities. METHODS AND ANALYSIS:The following databases will be searched from inception for English-language studies of any design: PubMed, PsychInfo, Medline, Scopus, CINAHL Plus, Econlit and EMBASE databases. Two reviewers will independently screen titles, abstracts and full text articles for studies reporting HSUVs and/or data on the psychometric properties of preference-based measures for children and adolescents with disabilities aged up to 19 years. Two reviewers will independently extract data items including descriptors of the study methods and sample, instruments used to capture HSUVs, summary statistics for HSUVs and items relating to the quality of reporting. A descriptive summary of results from included studies and summary statistics for HSUVs will be presented. If sufficient data is identified, we will pool summary statistics for HSUVs according to the method used to obtain the HSUV using a random effects model. In addition, we will explore the determinants of the HSUVs using a meta-regression. ETHICS AND DISSEMINATION:Ethical approval will not be required as no original data will be collected as part of this review. The completed review will be submitted for publication in a peer-reviewed journal and presentation at conferences. PROSPERO REGISTRATION NUMBER:CRD42018086574.

Dataset Information

Time trade-off health state utility values for depression: a systematic review and meta-analysis.

Purpose

Methods

Results

Conclusion

Publications

Time trade-off health state utility values for depression: a systematic review and meta-analysis.

Similar Datasets

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