Fuming Li,1,2 Shimeng Liu,1,2 Yuanyuan Gu,3 Shunping Li,4 Ying Tao,1,2 Yan Wei,1,2 Yingyao Chen1,2 

1School of Public Health, Fudan University, Shanghai, People’s Republic of China; 2National Health Commission Key Laboratory of Health Technology Assessment (Fudan University), Shanghai, People’s Republic of China; 3Macquarie University Centre for the Health Economy, Macquarie Business School & Australian Institute of Health Innovation, Macquarie University, Macquarie Park, Macquarie Park, New South Wales, Australia; 4Centre for Health Management and Policy Research, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, People’s Republic of China

Correspondence: Shimeng Liu; Yingyao Chen, School of Public Health, Fudan University, 130 Dongan Road, Xuhui, Shanghai, 200032, People’s Republic of China, Tel +86 198 2182 4471 ; +86 135 6450 8981, Email smliu19@fudan.edu.cn; yychen@shmu.edu.cn

Purpose: Discrete choice experiment (DCE) and profile case (case 2) best-worst scaling (BWS) present uncertainties regarding the acceptability of quantifying individual healthcare preferences, which may adversely affect the validity of responses and impede the reflection of true healthcare preferences. This study aimed to assess the acceptability of these two methods from the perspective of patients with type 2 diabetes mellitus (T2DM) and examine their association with specific characteristics of the target population.
Patients and Methods: This cross-sectional study was based on a nationally representative survey; data were collected using a multistage stratified cluster-sampling procedure between September 2021 and January 2022. Eligible adults with confirmed T2DM voluntarily participated in this study. Participants completed both the DCE and case 2 BWS (BWS-2) choice tasks in random order and provided self-reported assessments of acceptability, including task completion difficulty, comprehension of task complexity, and response preference. Logistic regression and random forest models were used to identify variables associated with acceptability.
Results: In total, 3286 patients with T2DM were included in the study. Respondents indicated there was no statistically significant difference in completion difficulty between the DCE and BWS-2, although the DCE scores were slightly higher (3.07 ± 0.68 vs 3.03 ± 0.67, P = 0.06). However, 1979 (60.2%) respondents found the DCE easier to comprehend. No significant preferences were observed between the two methods (1638 (49.8%) vs 1648 (50.2%)). Sociodemographic factors, such as residence, monthly out-of-pocket costs, and illness duration were significantly associated with comprehension complexity and response preference.
Conclusion: This study yielded contrasting results to most of previous studies, suggesting that DCE may be less cognitively demanding and more suitable for patients with T2DM from the perspective of self-reported acceptability of DCE and BWS. This study promotes a focus on patient acceptability in quantifying individual healthcare preferences to inform tailored optimal stated-preference method for a target population.
Plain Language Summary: Stated preference methodologies such as the discrete choice experiment (DCE) and case 2 best-worst scaling (BWS-2) are gaining popularity as methods for quantifying individual preferences in healthcare. However, the acceptability of the two methods to participants must be considered in practice to reduce cognitive burden and ensure the validity of preference elicitation.DCE was perceived to be less cognitively burdensome than BWS-2. In contrast to patients who thought that DCE was more acceptable, BWS-2 was more accepted by rural patients, patients who lived with the disease for a longer period, and those who had lower monthly out-of-pocket costs.These findings demonstrate potential differences in the acceptability of DCE and BWS-2 for patients with type 2 diabetes mellitus. To improve efficiency, it would be useful for researchers to consider the optimal stated preference method for identifying target populations according to sociodemographic and disease-related characteristics.

Keywords: patient-reported outcomes, acceptability, discrete choice experiment, best-worst scaling, preference, type 2 diabetes