| Title |
A structured review of health utility measures and elicitation in advanced/metastatic breast cancer
|
|---|---|
| Published in |
ClinicoEconomics and Outcomes Research: CEOR, June 2016
|
| DOI | 10.2147/ceor.s100448 |
| Pubmed ID | |
| Authors |
Yanni Hao, Verena Wolfram, Jennifer Cook |
| Abstract |
Health utilities are increasingly incorporated in health economic evaluations. Different elicitation methods, direct and indirect, have been established in the past. This study examined the evidence on health utility elicitation previously reported in advanced/metastatic breast cancer and aimed to link these results to requirements of reimbursement bodies. Searches were conducted using a detailed search strategy across several electronic databases (MEDLINE, EMBASE, Cochrane Library, and EconLit databases), online sources (Cost-effectiveness Analysis Registry and the Health Economics Research Center), and web sites of health technology assessment (HTA) bodies. Publications were selected based on the search strategy and the overall study objectives. A total of 768 publications were identified in the searches, and 26 publications, comprising 18 journal articles and eight submissions to HTA bodies, were included in the evidence review. Most journal articles derived utilities from the European Quality of Life Five-Dimensions questionnaire (EQ-5D). Other utility measures, such as the direct methods standard gamble (SG), time trade-off (TTO), and visual analog scale (VAS), were less frequently used. Several studies described mapping algorithms to generate utilities from disease-specific health-related quality of life (HRQOL) instruments such as European Organization for Research and Treatment of Cancer Quality of Life Questionnaire - Core 30 (EORTC QLQ-C30), European Organization for Research and Treatment of Cancer Quality of Life Questionnaire - Breast Cancer 23 (EORTC QLQ-BR23), Functional Assessment of Cancer Therapy - General questionnaire (FACT-G), and Utility-Based Questionnaire-Cancer (UBQ-C); most used EQ-5D as the reference. Sociodemographic factors that affect health utilities, such as age, sex, income, and education, as well as disease progression, choice of utility elicitation method, and country settings, were identified within the journal articles. Most submissions to HTA bodies obtained utility values from the literature rather than exploring the HRQOL data obtained during clinical development. This was critiqued by the National Institute for Health and Clinical Excellence (NICE). Furthermore, the impact of age on utilities was highlighted by NICE and it was suggested that an age match of the study population should be attempted. Health utilities are recorded across the globe to varying extents and using differing elicitation methods. Manufacturers seeking reimbursement need to be aware of the country-specific requirements for elicitation of health utilities. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 25% |
| Unknown | 3 | 75% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Practitioners (doctors, other healthcare professionals) | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 85 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 1% |
| Unknown | 84 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 13 | 15% |
| Researcher | 12 | 14% |
| Student > Bachelor | 11 | 13% |
| Student > Ph. D. Student | 7 | 8% |
| Student > Postgraduate | 5 | 6% |
| Other | 12 | 14% |
| Unknown | 25 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 18 | 21% |
| Nursing and Health Professions | 8 | 9% |
| Psychology | 8 | 9% |
| Pharmacology, Toxicology and Pharmaceutical Science | 5 | 6% |
| Economics, Econometrics and Finance | 4 | 5% |
| Other | 12 | 14% |
| Unknown | 30 | 35% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 July 2016.
All research outputs
#15,309,599
of 25,593,129 outputs
Outputs from ClinicoEconomics and Outcomes Research: CEOR
#275
of 524 outputs
Outputs of similar age
#190,286
of 354,199 outputs
Outputs of similar age from ClinicoEconomics and Outcomes Research: CEOR
#15
of 24 outputs
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