Abstract
OBJECTIVE: The aim of this study was to calculate preference weights for the Labor and Delivery Index (LADY-X) to make it suitable as a utility measure for perinatal care studies.
METHODS: In an online discrete choice experiment, 18 pairs of hypothetical scenarios were presented to respondents, from which they had to choose a preferred option. The scenarios describe the birth experience in terms of the seven LADY-X attributes. A D-efficient discrete choice experiment design with priors based on a small sample (N = 110) was applied. Two samples were gathered, women who had recently given birth and subjects from the general population. Both samples were analyzed separately using a panel mixed logit (MMNL) model. Using the panel mixed multinomial logit (MMNL) model results and accounting for preference heterogeneity, we calculated the average preference weights for LADY-X attribute levels. These were transformed to represent a utility score between 0 and 1, with 0 representing the worst and 1 representing the best birth experience.
RESULTS: In total, 1097 women who had recently given birth and 367 subjects from the general population participated. Greater value was placed on differences between bottom and middle attribute levels than on differences between middle and top levels. The attributes that resulted in larger utility increases than the other attributes were "feeling of safety" in the sample of women who had recently given birth and "feeling of safety" and "availability of professionals" in the general population sample.
CONCLUSIONS: By using the derived preference weights, LADY-X has the potential to be used as a utility measure for perinatal (cost-) effectiveness studies.
Original language | English |
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Pages (from-to) | 856-864 |
Number of pages | 9 |
Journal | Value in Health |
Volume | 18 |
Issue number | 6 |
DOIs | |
Publication status | Published - Sept 2015 |
Keywords
- Adult
- Algorithms
- Choice Behavior
- Female
- Health Knowledge, Attitudes, Practice
- Health Services Research
- Humans
- Life Change Events
- Logistic Models
- Male
- Middle Aged
- Mothers
- Parturition
- Patient Preference
- Patient Safety
- Perinatal Care
- Physician-Patient Relations
- Pregnancy
- Psychometrics
- Quality Indicators, Health Care
- Surveys and Questionnaires