Abstract
BackgroundStated preference elicitation methods such as discrete choice experiments (DCEs) are now widely used in the health domain. However, the “quality” of health-related DCEs has come under criticism due to the lack of rigour in conducting and reporting some aspects of the design process such as attribute and level development. Superficially selecting attributes and levels and vaguely reporting the process might result in misspecification of attributes which may, in turn, bias the study and misinform policy. To address these concerns, we meticulously conducted and report our systematic attribute development and level selection process for a DCE to elicit the preferences of health care providers for the attributes of a capitation payment mechanism in Kenya.MethodologyWe used a four-stage process proposed by Helter and Boehler to conduct and report the attribute development and level selection process. The process entailed raw data collection, data reduction, removing inappropriate attributes, and wording of attributes. Raw data was collected through a literature review and a qualitative study. Data was reduced to a long list of attributes which were then screened for appropriateness by a panel of experts. The resulting attributes and levels were worded and pretested in a pilot study. Revisions were made and a final list of attributes and levels decided.ResultsThe literature review unearthed seven attributes of provider payment mechanisms while the qualitative study uncovered 10 capitation attributes. Then, inappropriate attributes were removed using criteria such as salience, correlation, plausibility, and capability of being traded. The resulting five attributes were worded appropriately and pretested in a pilot study with 31 respondents. The pilot study results were used to make revisions. Finally, four attributes were established for the DCE, namely, payment schedule, timeliness of payments, capitation rate per individual per year, and services to be paid by the capitation rate.ConclusionBy rigorously conducting and reporting the process of attribute development and level selection of our DCE,we improved transparency and helped researchers judge the quality.
Highlights
Stated preference elicitation methods such as discrete choice experiments (DCEs) are being widely used in health preference research in areas such as priority setting, health workforce, and valuation of health outcomes among others [1,2,3,4]
Four attributes were established for the DCE, namely, payment schedule, timeliness of payments, capitation rate per individual per year, and services to be paid by the capitation rate
We address these research gaps and contribute to the limited literature on attribute development and level selection by rigorously conducting and reporting the process followed in deriving attributes and levels for a DCE to elicit the preferences of health care providers for the attributes of capitation payment mechanism in Kenya
Summary
Stated preference elicitation methods such as discrete choice experiments (DCEs) are being widely used in health preference research in areas such as priority setting, health workforce, and valuation of health outcomes among others [1,2,3,4]. From the choices made in a DCE survey, researchers can determine the relative importance respondents place on the attributes of the goods or services under consideration, and tradeoffs study participants are willing to make on one attribute over another [7]. Stated preference elicitation methods such as discrete choice experiments (DCEs) are widely used in the health domain. Selecting attributes and levels and vaguely reporting the process might result in misspecification of attributes which may, in turn, bias the study and misinform policy To address these concerns, we meticulously conducted and report our systematic attribute development and level selection process for a DCE to elicit the preferences of health care providers for the attributes of a capitation payment mechanism in Kenya
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have