文献类型：期刊文献

英文题名：Analysis of medical costs and two-model prediction for patients with severe mental disorders in Gansu Province, China

作者：Miao, Peiji[1];Jiang, Xiaomei[2];Li, Jinjuan[1];Pan, Weimin[3];Xue, Aixiang[4];Cao, Juan[5];Fan, Jingchun[1,6]

第一作者：Miao, Peiji

通信作者：Fan, JC[1];Cao, J[2];Fan, JC[3]

机构：[1]Gansu Univ Chinese Med, Sch Publ Hlth, Lanzhou, Gansu, Peoples R China;[2]Lanzhou Petrochem Gen Hosp, Dept Psychosomat & Sleep Med, Lanzhou, Gansu, Peoples R China;[3]Gansu Prov Ctr Dis Control & Prevent, Dept Mental Hlth, Lanzhou, Gansu, Peoples R China;[4]Gansu Univ Chinese Med, Affiliated Hosp, Dept Comprehens Outpatient, Lanzhou, Gansu, Peoples R China;[5]Gansu Univ Chinese Med, Affiliated Hosp, Dept Publ Hlth, Lanzhou, Gansu, Peoples R China;[6]Gansu Univ Chinese Med, Ctr Evidence based Med, Lanzhou, Gansu, Peoples R China

第一机构：甘肃中医药大学公共卫生学院

通信机构：[1]corresponding author), Gansu Univ Chinese Med, Sch Publ Hlth, Lanzhou, Gansu, Peoples R China;[2]corresponding author), Gansu Univ Chinese Med, Affiliated Hosp, Dept Publ Hlth, Lanzhou, Gansu, Peoples R China;[3]corresponding author), Gansu Univ Chinese Med, Ctr Evidence based Med, Lanzhou, Gansu, Peoples R China.|[10735]甘肃中医药大学;[10735b845793de6ae2b30]甘肃中医药大学第二附属医院;[10735e9d5e7087247e71b]甘肃中医药大学公共卫生学院;

年份：2026

卷号：14

外文期刊名：FRONTIERS IN PUBLIC HEALTH

收录：;Scopus(收录号：2-s2.0-105032106749);WOS:【SSCI(收录号:WOS:001709240300001)，SCI-EXPANDED(收录号:WOS:001709240300001)】；

基金：The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the Gansu Provincial Science and Technology Program (Joint Research Fund) (Grant number 23JRRA1528), Gansu Provincial Science and Technology Program (Basic Research Program) (Grant number 24JRRA775), Gansu Higher Education Faculty Innovation Fund (Grant number 2024A-084), Gansu Provincial Key Research and Development Program (Grant number 25YFFA044), Gansu Provincial Department of Education Young Faculty Doctoral Program (Grant number 20250B-064) and Key Talent Project of Health and Wellness in Gansu Province. This research received no specific grant from any funding agency, commercial or not-for-profit sectors.

语种：英文

外文关键词：Bayesian ridge regression; inpatient costs; medical costs; outpatient costs; random forest regression; severe mental disorders

摘要：Background: The economic burden of severe psychiatric disorders presents a major global public health challenge, particularly in regions with underdeveloped healthcare systems. Analysing medical costs is essential for optimizing resource allocation and improving patient outcomes. Aims: This study provides the first comprehensive analysis of medical expenditures for severe mental disorders in Gansu Province, China, and compares the predictive performance of the Bayesian Regression Model based on Gaussian Processes with Random Forest regression for outpatient and inpatient costs. Methods: This retrospective analysis utilized data from the Gansu Provincial Healthcare Security Administration, covering 284,447 outpatient and 8,962 inpatient cases diagnosed between 2021 and 2023. Data distribution was assessed using the Kolmogorov-Smirnov test, and group comparisons were conducted using chi-square and Mann-Whitney U tests. Medical costs were predicted using the Bayesian Regression Model based on Gaussian Processes and Random Forest regression models. Results: Between 2021 and 2023, the average costs per outpatient visit and inpatient admission were US$77.29 and US$922.86, respectively. The median outpatient cost declined annually from US$65.98 in 2021 to US$46.84 in 2023, whereas the median inpatient cost in 2023 exceeded that of 2021 and 2022 (p < 0.001). In the prediction of outpatient costs, the Bayesian regression model based on Gaussian processes performed slightly better than the Random Forest model; however, the predictive ability of both models was quite limited, with a very low proportion of cost variance explained (Bayesian regression: R-2 = 0.3977, 95% CI: 0.03918-0.4022; Random Forest: R-2 = 0.0620, 95% CI: 0.0586-0.0653). Random Forest demonstrated markedly superior performance in predicting inpatient costs (R-2 = 0.7741, 95% CI: 0.7013-0.7982), significantly outperforming Bayesian regression (R-2 = 0.3405, 95% CI 0.3802-0.4098). Conclusion: Outpatient costs continued to decline, while inpatient costs increased significantly. In predicting outpatient costs, the Bayesian regression model based on Gaussian processes performed relatively well but its overall predictive capability remained limited; the Random Forest model demonstrated superior performance in predicting inpatient costs. The study suggests that in underdeveloped regions, data-driven cost analysis should be prioritized to optimize the allocation of mental health resources and alleviate the economic burden.