ObjectivesOur study aimed to inform insurance decision-making in China by investigating patients’ preferences for insurance coverage of new technologies for treating chronic diseases.DesignWe identified six attributes of new medical technologies for treating chronic diseases and used Bayesian-efficient design to generate choice sets for a discrete choice experiment (DCE). After conducting the DCE, we analysed the data by mixed logit regression to examine patient-reported preferences for each attribute.SettingThe DCE was conducted with patients in six tertiary hospitals from four cities in Jiangsu province.ParticipantsPatients aged 18 years or older with a history of diabetes or hypertension and taking medications regularly for more than 1 year were recruited (n=408).ResultsThe technology attributes regarding expected gains in health outcomes from the treatment, high likelihood of effective treatment and low incidence of serious adverse events were significant, positive predictors of choice by the study patients (p<0.01). The out-of-pocket cost was a significant, negative attribute for the entire study sample (β = −0.258, p<0.01) and for the patients with Urban-Rural Residents Basic Medical Insurance (URRBMI) (β = −0.511, p<0.01), but not for all the patients with Urban Employees Basic Medical Insurance (UEBMI) (β = −0.071, p>0.05). The severity of target disease was valued by patients with lower EQ-5D-5L index value as well as URRBMI enrollees.ConclusionsPatients highly valued the health benefits and risks of new technologies, which were closely linked to their feelings of disease and perceptions of health-related quality of life. However, there existed heterogeneity in preferences between URRBMI and UEBMI patients. Further efforts should be made to reduce the gap between insurance schemes and make safe and cost-effective new technologies as a priority for health insurance reimbursement.
Developing attributes and levels for a discrete choice experiment on basic health insurance in Iran
