• Yingying Peng, Mingzhu Jiang, Xiao Shen, Xianglin Li, Erping Jia, and Juyang Xiong.
• School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, People's Republic of China.
• Patient Prefer Adher. 2020 Jan 1; 14: 1625-1637.

PurposeThe aim of this study was to quantify the relative importance of the preference heterogeneity of Chinese older adults with chronic disease for primary healthcare service (PHCS) in the hypothetical minor chronic disease scenario.Patients And MethodsA discrete choice experiment (DCE) was administered to the patients aged 60 and above with at least one chronic disease in China. Five DCE attributes were considered, including types of service, treatment options, out-of-pocket (OOP) cost per visit, distance to practice, and the seniority of medical practitioners. DCE data were analysed taking into account of potential preference heterogeneity using both a mixed logit model (MLM) and a latent class logit model (LCLM).ResultsA total of 432 respondents consented to complete the questionnaires and 372 valid respondents were included in analysis. All attributes were significantly influencing respondents' PHCS choice except for the types of service. Significant preference heterogeneity was observed among respondents. Based on the preferred LCLM estimates, four latent classes were identified. The first class (28.8%) valued modern medicine service the most, the second class (17.8%) was dominated by distance to practice, the third class (29%) preferred all the attributes except the types of services and valued TCM service most, the fourth class (24.4%) paid more attention to the types of service. Education, gender, age, income, regions of residence, and status of the chronic condition were found to be associated with latent class memberships.ConclusionA better understanding of the relative importance of PHCS characteristics is a crucial step for the future policy implementations. The significant preference heterogeneity identified in this study highlights that effective policy interventions should be tailored to different patients' characteristics.© 2020 Peng et al.

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