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Eliciting preferences for priority setting in genetic testing: a pilot study comparing best-worst scaling and
Franziska Severin1, Jörg Schmidtke, Axel Mühlbacher
1Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Health Economics and Health Care Management (IGM), Neuherberg, Germany.
European Journal of Human Genetics : EJHG
|March 15, 2013
Summary
Healthcare resource allocation for genetic tests can be guided by discrete-choice experiments (DCEs) and best-worst scaling (BWS). These methods reveal that clinical utility and prevalence are key factors in prioritizing genetic testing.
Area of Science:
- Health Economics
- Genetics
- Decision Science
Background:
- Limited healthcare resources necessitate prioritization of genetic tests.
- Various criteria exist for priority setting, but their relative importance is unclear.
Purpose of the Study:
- To evaluate discrete-choice experiments (DCEs) and best-worst scaling (BWS) for prioritizing genetic tests.
- To compare the findings from DCE and BWS methods in assessing criteria for genetic test prioritization.
Main Methods:
- Pilot DCE and BWS questionnaires were administered to genetics professionals.
- Criteria assessed included prevalence, severity, clinical utility, availability of alternatives, infrastructure, and urgency.
- Weights for attributes were estimated using conditional logit models.
Main Results:
- Both DCE and BWS approaches yielded similar patterns of valuation.
- Respondents prioritized genetic tests with high clinical utility (treatment availability) and for highly prevalent conditions.
- Detailed results from DCE and BWS experiments showed some differences but consistent overall trends.
Conclusions:
- Quantitative preference elicitation methods like DCE and BWS can measure aggregated preferences for prioritizing clinical interventions.
- Clinical utility and disease prevalence emerged as the most significant factors in prioritizing genetic tests.
- Further research is needed to validate these findings in broader populations.
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