Analysis on Care Service Preferences of Community Patients With Severe Mental Disorders in Beijing Based on Discrete Choice Experiment.

Objective To gain insights into the care service preferences and willingness of patients with severe mental disorders in Beijing,analyze the main factors affecting their participation in care services,and provide data support and decision-making reference for the optimal design of care services for patients with severe mental disorders and the improvement of relevant policies.Methods In July 2022,a questionnaire survey was conducted for a part of Beijing community patients with severe mental disorders selected by multi-stage stratified sampling,including the basic personal information and the preferences of discrete choice experiment.A mixed Logit model was used to perform regression analysis on the care service preferences,and the trade off between general and monetary attributes was quantified by willingness to pay(WTP).Results A total of 242 questionnaires were distributed,and 181 valid questionnaires were collected,with a response rate of 74.79%.The regression coefficients for the four attributes-service type,service content,service frequency,and service cost-all showed statistical significance(all <i>P</i><0.05).Patients' most preferred attribute level was a service frequency covering about 90% of the time per month/year( β=1.059),while the least preferred was full-time residential care( β=-1.025).Increasing the service frequency from 30% to 90% corresponded to a WTP of 492.5 yuan,while changing the service type from home-based care to full-time residential care resulted in a WTP of -476.6 yuan.Moreover,there were differences in care service preferences and WTP among patient groups with different characteristics(all <i>P</i><0.05).Conclusions Service type,service content,service frequency,and service cost all significantly affect the care service preferences of patients with severe mental disorders.There is heterogeneity in care service preferences among patient groups with different characteristics.