Societal preferences for fertility treatment in Australia: a stated preference discrete choice experiment. (2nd January 2019)

• Record Type: Journal Article
• Authors: Botha, Willings; Donnolley, Natasha; Shanahan, Marian; Norman, Robert J.; Chambers, Georgina M.
• Is Part Of: Journal of medical economics. Volume 22:Number 1(2019)
• Page Start: 95
• Page End: 107
• Publication Date: 2019-01-02
• Subjects: Stated preference discrete choice experiment -- socially-inclusive willingness-to-pay -- generalized multinomial logit model -- Bayesian fractional experimental design; J13 -- C35; Medical care -- Cost control -- Periodicals; Medical economics -- Periodicals; 362.10941
• DOI: 10.1080/13696998.2018.1549055
• Languages: English
• ISSNs: 1369-6998

Abstract

Abstract: Objective: To investigate preferences for fertility treatment from the Australian general population with the aims of calculating the willingness to pay in tax contribution for attributes (characteristics) that make up treatment and for an "ideal" fertility treatment program. We also assessed whether willingness-to-pay varies by the relationship status or sexual orientation of the patient. Methods: A stated preference discrete choice experiment was administered to a panel of 801 individuals representative of the Australian general population. Seven attributes of fertility treatment under three broad categories were included: outcome, process, and cost. Attributes were identified through published literature, focus group discussions, expert knowledge, and a pilot study. A Bayesian fractional experimental design was used, and data analysis was performed using a generalized multinomial logit model. Further analyses included interaction terms and latent class modeling. Results: Six of the seven attributes influenced the choice of a treatment program. Under process attributes, individuals preferred: continuity of care of clinic staff, where patients are seen by the same doctor but different nurses at each visit; "alternative" treatments being offered to all patients; and onsite clinic counseling and peer-support groups. Personalization and tailoring of the treatment journey were not important. Among outcome attributes, the improved success rate of having a baby per cycle and significant side-effects were considered important. Cost of treatment also influenced the choice of treatment program. Individual preferences for fertility treatment were not associated with patients' relationship status or sexual orientation. Latent class modeling revealed sub-groups with distinct fertility treatment preferences. Conclusion: This study provides important insights into the attributes that influence the preferences of fertility treatment in Australia. It also estimates socially-inclusive willingness-to-pay values in tax contributions for an "ideal" package of treatment. The results can inform economic evaluations of fertility treatment programs.