关键词: GBD数据库, 药物滥用障碍, 联结点回归模型, 贝叶斯年龄时期队列模型

Abstract: Background　Drug use disorders have become a global public health challenge, endangering the health of the population, increasing the burden of disease, and restricting economic development and social progress. The burden of disease associated with different types of drug use disorders varies, and clarifying the priorities for prevention and control has become a focal point of interest across various sectors. Objective　We aims to analyze the trends in the disease burden of drug use disorders in China over the past 32 years (1990-2021), It also investigates the similarities and differences in the standardized incidence rates and disability-adjusted life years (DALY) among drug use disorders and their five subtypes. Furthermore, the study predicts the changes in incidence rates and DALY of drug use disorders in China over the next 25 years. The findings are intended to provide a scientific basis for policy-making and the implementation of intervention measures, with the goal of reducing the disease burden and improving population health. Methods　We utilized data from the Global Burden of Diseases 2021 (GBD 2021) database. Join point regression (JPR) models were used to analyze the age-standardized incidence, prevalence, mortality, and DALY rates annual percent change (APC) and average annual percent change (AAPC). Bayesian age-period-cohort models (BAPC) were used to predict the trend of incidence and DALY rates in the next 25 years from 2022-2046. Results　The Joinpoint regression analysis revealed that from 1990 to 2021, the age-standardized incidence and DALY rates of drug use disorders among the entire population aged 0-95+ years in China exhibited an overall downward trend, with AAPC values of -0.76% and -2.75%, respectively, both of which were statistically significant. Both males and females experienced a decline in age-standardized incidence and DALY rates of drug use disorders, with AAPC values for males at -0.69% and -2.50%, and for females at -0.85% and -3.09%, respectively, all of which were statistically significant. The age-standardized incidence AAPC values for different types of drug use disorders were as follows: cannabis, 0.66%; opioids, -1.97%; amphetamines, -1.50%; cocaine, -0.66%; and other drugs, -0.64%, all of which were statistically significant. The age-standardized DALY rate AAPC values were cannabis, 0.71%; opioids, -3.41%; amphetamines, -1.66%; cocaine, -2.12%; and other drugs, -3.83%, all of which were statistically significant. The BAPC model predicted that from 2022 to 2046, the incidence and DALY rates of drug use disorders in both male and female populations in China would increase. The projected increase in incidence was approximately 50.80% for males and 24.27% for females, with a higher increase in males than females. The projected increase in DALY rates was approximately 48.34% for males and 41.46% for females, with a higher increase in males than females. Conclusion　Over the past 30 years, the disease burden of drug use disorders in China has shown a downward trend, with a higher burden observed in males compared to females. Except for cannabis, the disease burden of all types of drug use disorders nationwide has generally declined, with opioid use disorders contributing the most severe burden. However, projections indicate that the incidence and disability-adjusted life year (DALY) rates of drug use disorders will rise over the next 25 years. In summary, it is essential to continuously monitor the epidemiological trends of drug use disorders, taking into account the evolving gender differences, the emergence of new types of drug use, and the complex social context. Targeted prevention and intervention measures should be developed to consolidate the achievements in drug control and address emerging challenges.

Key words: GBD database, Drug use disorders, Join point Regression, Bayesian age-period-cohort models