The Disease Burden of Drug Use Disorders in the Chinese Population from 1990 to 2021 and Trend Predictions from 2022 to 2046

Zhang Ziyu¹, Han Shukui¹, Ma Xin¹², Song Panpan¹³, Ma Jinxiang¹, Ren Yitao¹, Chen Hongru¹*

¹Department of Public Health, Qinghai University Medical College, Xining 810008, China
²Department of Health Technology, Qinghai Institute of Health Sciences, Xining 810016, China
³Department of Clinical Medicine, Qinghai Institute of Health Sciences, Xining 810016, China

Corresponding author: Chen Hongru, Associate Professor; E-mail: chenhongru@qhu.edu.cn

Abstract

Background: Drug use disorders have become a major global public health challenge, threatening lives and health, increasing disease burden, and hindering economic development and social progress. As the burden of disease varies among different substances, identifying prevention and control priorities has become a focus of attention across society.

Objective: To analyze trends in the disease burden of drug use disorders in China from 1990 to 2021, including age-standardized incidence and disability-adjusted life year (DALY) rates for drug use disorders and five subcategories, and to predict trends in incidence and DALY rates from 2022 to 2046, providing a scientific basis for policy formulation and intervention implementation.

Methods: Using data from the Global Burden of Diseases 2021 (GBD 2021) database, we extracted age-standardized incidence and DALY rates for drug use disorders and five subcategories (opioid use disorders, cannabis use disorders, cocaine use disorders, amphetamine use disorders, and other drug use disorders) in China from 1990 to 2021. Joinpoint regression models were used to analyze annual percent change (APC) and average annual percent change (AAPC). Bayesian age-period-cohort prediction models were used to forecast trends in age-standardized incidence and DALY rates from 2022 to 2046.

Results: Joinpoint regression analysis revealed that from 1990 to 2021, both age-standardized incidence and DALY rates for drug use disorders in China showed overall decreasing trends (AAPC values of -0.76% and -2.75%, respectively; both P<0.05). Both male and female populations exhibited decreasing trends in age-standardized incidence and DALY rates (AAPC values for males: -0.69% and -2.50%; for females: -0.85% and -3.09%; all P<0.05). Among the five subcategories, cannabis use disorders showed increasing trends in age-standardized incidence and DALY rates (AAPC=0.66% and 0.71%, respectively; both P<0.05), while the remaining four subcategories showed decreasing trends (opioids: AAPC=-1.97% and -3.41%; amphetamines: AAPC=-1.50% and -1.66%; cocaine: AAPC=-0.66% and -2.12%; other drugs: AAPC=-0.64% and -3.83%; all P<0.05). Bayesian age-period-cohort model predictions indicated that from 2022 to 2046, age-standardized incidence and DALY rates for drug use disorders would increase for both males and females. The projected increase in incidence was approximately 50.80% for males and 24.27% for females, with a higher increase among males. The projected increase in DALY rates was approximately 48.34% for males and 41.46% for females, also higher among males.

Conclusion: From 1990 to 2021, the disease burden of drug use disorders in China decreased, with males bearing a higher burden than females. Except for cannabis use disorders, the remaining four subcategories showed overall decreasing trends, with opioid use disorders causing the most severe burden. However, predictions for 2022–2046 indicate increasing trends in age-standardized incidence and DALY rates.

Keywords: Drug use disorders; GBD database; Joinpoint regression model; Bayesian age-period-cohort model

Introduction

Drug use disorders impose a substantial global disease burden. Against complex social backdrops of population growth, globalization, and demographic aging, the disease burden of drug use disorders has undergone significant changes, with severity showing regional correlation—areas with higher sociodemographic indices experience more severe burdens. Long-term drug abuse damages the nervous system, interferes with cognitive processes, causes mood disorders and developmental delays, and increases risks of accidental injury or death. Research shows drug overdose is a leading cause of injury-related mortality. In the United States, overdose deaths have risen over recent decades, often involving synthetic opioids (e.g., fentanyl) and stimulants (e.g., cocaine, methamphetamine) \cite{1,2,3}. Additionally, non-sterile injection practices, such as needle sharing, expose individuals to infectious diseases including HIV \cite{4}, viral hepatitis \cite{5,6}, and syphilis \cite{7}. Drug abuse is also associated with various non-communicable diseases, including cancers (breast \cite{9}, prostate \cite{10}, cervical \cite{11}) and cardiovascular diseases (pulmonary hypertension, arrhythmias, cardiomyopathy) \cite{12,13}. Beyond individual health impacts, drug abuse significantly affects society by increasing crime rates \cite{14}, including drugged driving \cite{15} and violent crimes such as robbery, kidnapping, and homicide \cite{16}.

Illegal drugs refer to substances prohibited by international drug control conventions for non-medical use (commonly for recreational purposes) \cite{18} that carry addiction risks, or medical substances that may cause physical or psychological harm when overused. These include but are not limited to opioids (morphine, opium, heroin, and other synthetic or semi-synthetic opioids), amphetamines, cocaine, and cannabis \cite{19}. This study evaluates the disease burden of drug use disorders and five subcategories in China from 1990–2021 using GBD 2021 data. We employed Joinpoint regression models to analyze APC and AAPC trends across different periods and used Bayesian age-period-cohort (BAPC) models to forecast trends for the next 25 years (2022–2046), aiming to provide additional data references for developing intervention measures and achieving population-wide health goals.

Methods

Data Sources

Data were obtained from the Global Burden of Diseases 2021 (GBD 2021) database \cite{20}, which includes comprehensive health data from 1990 to 2021. This study focuses on drug use disorders and five subcategories: drug use disorders, opioid use disorders, cannabis use disorders, cocaine use disorders, amphetamine use disorders, and other drug use disorders. We examined age-standardized incidence and disability-adjusted life year (DALY) rates in China from 1990–2021. Detailed data sources and information can be accessed through the Global Health Data Exchange (GHDx) website.

Data Selection

From the GBD 2021 public database, we selected data on the disease burden of drug use disorders in China from 1990–2021. Disease categories included "Drug use disorders," "Amphetamine use disorders," "Opioid use disorders," "Cocaine use disorders," "Cannabis use disorders," and "Other drug use disorders." Indicators selected were "Incidence" and "DALYs." Geographic coverage was "China," years "1990–2021," genders "Both," "Male," and "Female," and ages "0-95 plus" across 20 age groups, "Age-standardized," and "All ages." Age-standardized rates were used for Joinpoint analysis, while age groups "15–19," "20–24," "25–29," "30–34," "35–39," "40–44," "45–49," "50–54," "55–59," "60–64," and "65–69" were used for BAPC model predictions.

Statistical Analysis

Joinpoint Regression Model: This model segments long-term trends into statistically significant periods, identifying turning points and calculating APC and AAPC for each segment. It is primarily used for long-term trend analysis, as short-term analysis may be affected by observation counts and outliers, potentially compromising reliability \cite{21}. Using Joinpoint software version 5.2.0.0 (April 2024), we constructed log-linear models to analyze trends in age-standardized incidence and DALY rates for drug use disorders and five subcategories from 1990–2021. Grid search methodology identified optimal joinpoints by calculating mean squared errors (MSE) for all possible configurations, selecting the grid point with minimum MSE. Monte Carlo permutation tests determined the number of joinpoints, and the optimal model was used to compute APC, AAPC, and 95% confidence intervals (95%CI). APC>0 indicates increasing incidence, APC<0 indicates decreasing incidence, with P<0.05 considered statistically significant.

Bayesian Age-Period-Cohort Model: This model extends the traditional age-period-cohort framework within a Bayesian context using a random exponential model as the link function. By smoothing age, period, and cohort effects, Bayesian models enable effective estimation of sparse rates and zero counts \cite{22}, analyzing how these effects influence outcomes and forecasting future trends. The BAPC model employs Integrated Nested Laplace Approximation (INLA) for Bayesian inference, providing a computationally efficient alternative to Markov Chain Monte Carlo (MCMC) methods for calculating marginal likelihoods and posterior distributions. This yields approximate posterior distributions closely matching true distributions, enabling effective parameter estimation and prediction of age-standardized or age-specific rates. Our study used the BAPC model with populations aged 15–70 years to predict trends in age-standardized incidence and DALY rates for drug use disorders from 2022–2046.

Results

Joinpoint Regression Model Trend Analysis

Incidence Trends: From 1990–2021, the age-standardized incidence rate of drug use disorders in China showed an overall decreasing trend (AAPC=-0.76%, 95%CI=-0.83% to -0.69%). The male AAPC was slightly higher than female (-0.69% vs. -0.85%). Notably, both the total population and females showed slight increases during 2015–2021 (total: APC=0.51%, 95%CI=0.41%–0.61%; females: APC=0.52%, 95%CI=0.43%–0.61%), while males showed increases during 2016–2021 (APC=0.66%, 95%CI=0.52%–0.81%) [TABLE:1], [FIGURE:1].

Among the five subcategories, cannabis use disorders showed an increasing trend in age-standardized incidence (AAPC=0.66%, 95%CI=0.64%–0.68%), while the other four subcategories showed decreasing trends (opioids: AAPC=-1.97%, 95%CI=-2.14% to -1.80%; amphetamines: AAPC=-1.50%, 95%CI=-1.61% to -1.39%; cocaine: AAPC=-0.66%, 95%CI=-0.78% to -0.55%; other drugs: AAPC=-0.64%, 95%CI=-0.70% to -0.59%). Except for other drug use disorders, male AAPC values were higher than female across the remaining four subcategories. Additionally, amphetamine use disorders showed increased incidence during 2012–2021 (APC=0.41%, 95%CI=0.34%–0.49%); cocaine use disorders during 2010–2021 (APC=0.27%, 95%CI=0.20%–0.34%); opioid use disorders during 2017–2021 (APC=2.33%, 95%CI=1.66%–3.01%); other drug use disorders during 2018–2021 (APC=0.15%, 95%CI=-0.14%–0.45%); and cannabis use disorders showed stable trends before 2015, increased during 2015–2019 (APC=5.49%, 95%CI=5.38%–5.60%), then slightly decreased during 2019–2021 (APC=-0.44%, 95%CI=-0.65% to -0.24%) [TABLE:1], [FIGURE:2]–[FIGURE:6].

DALY Rate Trends: From 1990–2021, age-standardized DALY rates for drug use disorders in China showed decreasing trends (total: AAPC=-2.75%, 95%CI=-2.97% to -2.53%; males: AAPC=-2.50%, 95%CI=-2.73% to -2.27%; females: AAPC=-3.09%, 95%CI=-3.31% to -2.87%). However, total, male, and female populations all showed increases during 2016–2021 (total: APC=2.01%, 95%CI=1.46%–2.57%; males: APC=2.08%, 95%CI=1.47%–2.70%; females: APC=1.70%, 95%CI=1.22%–2.19%) [TABLE:1], [FIGURE:1].

Among subcategories, cannabis use disorders showed increasing DALY rates (AAPC=0.71%, 95%CI=0.69%–0.73%), while the other four showed decreasing trends (opioids: AAPC=-3.41%, 95%CI=-3.65% to -3.16%; amphetamines: AAPC=-1.66%, 95%CI=-1.81% to -1.50%; cocaine: AAPC=-2.12%, 95%CI=-2.45% to -1.80%; other drugs: AAPC=-3.83%, 95%CI=-4.31% to -3.36%). Except for opioids, male AAPC values were higher than female across the remaining subcategories. Additionally, amphetamine use disorders showed increased DALY rates during 2015–2021 (APC=1.53%, 95%CI=1.30%–1.76%); cocaine use disorders during 2012–2021 (APC=1.06%, 95%CI=0.81%–1.31%); opioid use disorders during 2017–2021 (APC=2.68%, 95%CI=1.60%–3.78%); other drug use disorders during 2016–2021 (APC=2.90%, 95%CI=1.55%–4.27%); and cannabis use disorders showed stable trends before 2015, increased during 2015–2019 (APC=5.87%, 95%CI=5.74%–6.00%), then slightly decreased during 2019–2021 (APC=-0.44%, 95%CI=-0.68% to -0.19%) [TABLE:1], [FIGURE:2]–[FIGURE:6].

BAPC Model Prediction Results

Incidence Predictions: Male age-standardized incidence of drug use disorders is projected to increase from 267.74/100,000 in 2021 (95%CI=267.25–268.23) to 403.76/100,000 in 2046 (95%CI=73.05–734.46), representing a 50.80% increase. Female incidence is projected to increase from 245.24/100,000 in 2021 (95%CI=244.76–245.72) to 304.75/100,000 in 2046 (95%CI=87.64–521.86), a 24.27% increase. Male incidence and its growth rate will remain higher than female throughout 2022–2046 [FIGURE:7].

DALY Rate Predictions: Male age-standardized DALY rates are projected to increase from 207.65/100,000 in 2021 (95%CI=207.23–208.08) to 308.02/100,000 in 2046 (95%CI=-477.09 to 1,093.14), a 48.34% increase. Female DALY rates are projected to increase from 136.72/100,000 in 2021 (95%CI=136.36–137.08) to 193.40/100,000 in 2046 (95%CI=-132.44 to 519.24), a 41.46% increase. Male DALY rates will remain higher than female throughout the next 25 years, with greater growth [FIGURE:8].

Discussion

This study reveals dynamic epidemiological evolution in China's drug use disorders. The significant decline in overall incidence and DALY rates from 1990–2021 validates the effectiveness of China's drug control governance system. Gender analysis shows males bear a higher disease burden (AAPC values for age-standardized incidence and DALY rates were 0.16% and 0.59% higher than females), consistent with sociological characteristics showing greater male exposure to risk factors \cite{23,24}. Notably, Joinpoint regression reveals concerning gender-specific shifts: other drug use disorders show female incidence surpassing males, and opioid use disorders exhibit similar trends post-2010. This structural transformation may stem from three factors: (1) accelerated normalization of substance use among young women \cite{25}; (2) cross-influence from partners' drug use behaviors \cite{25}; and (3) biological specificity in female addiction pathways—international research confirms women have shorter trajectories from initial use to dependence \cite{26,27}. Spanish survey data (1995–2009) further demonstrate converging gender differences in lifetime use rates for alcohol, tobacco, and multiple illegal drugs \cite{28}, suggesting global shifts in the gender epidemiology of substance use disorders.

By substance category, all drug use disorders except cannabis showed negative growth trends, particularly heroin abuse which decreased 26.7% over the past decade \cite{29}, marking significant achievements in China's multi-tiered drug control system. From the century-long opium epidemic in the late Qing Dynasty (1636–1912) to the "drug-free nation" miracle achieved through legislative bans and social transformation in the early People's Republic (1949–1978), and subsequently the "compulsory isolation rehabilitation—community recovery—social support" triad system addressing new psychoactive substances post-reform \cite{30,31,32,33}, China has forged a distinctive drug governance path.

However, both Joinpoint segmentation and BAPC models warn of emerging risks: inflection points appeared in 2015–2016, with projections indicating age-standardized incidence and DALY rates will rebound to 390.92/100,000 and 533.10/100,000, respectively, during 2022–2046. This trend may be driven by multiple emerging risk factors: (1) new psychoactive substances (NPS) growing at 15–20% annually, with poly-drug use exceeding 40%; (2) "elite diffusion" of substance use in high-SDI regions \cite{34}; and (3) pandemic-related increases in home-based drug abuse and darknet transactions \cite{35}.

This study has limitations. First, passive surveillance data may contain reporting bias and underreporting. Second, prediction models did not fully incorporate discontinuous variables such as AI-enabled drug manufacturing breakthroughs. Third, mechanisms of social determinants of health remain unexplored. Future research should: (1) construct socio-ecological models of substance use disorders; (2) develop dynamic prediction systems integrating policy intervention parameters; and (3) conduct gender-specific addiction neuro mechanism studies.

In summary, China's drug use disorder burden decreased from 1990–2021, highlighting governance successes, but males continue to bear higher burdens than females. Vigilance is needed regarding shifting gender patterns and future risks. Predictions for 2022–2046 show increasing age-standardized incidence and DALY rates, warranting attention from all sectors.

Author Contributions

Zhang Ziyu conceptualized the study, retrieved and analyzed GBD 2021 data, created visualizations, and drafted the manuscript. Han Shukui revised the manuscript and figures. Ma Xin and Song Panpan conducted secondary data verification. Ma Jinxiang and Ren Yitao performed quality control and final version revision. Chen Hongru supervised the entire research process. All authors approved the final manuscript.

Conflicts of Interest: None declared.

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ORCID: Zhang Ziyu https://orcid.org/0009-0008-7502-9808

Funding: Qinghai Plateau Natural Population Cohort Demonstration Study (2024-SF-125); "14th Five-Year" Health Development Plan for Huzhu County, Qinghai Province (2020-sk-1)

Citation: Zhang ZY, Han SK, Ma X, et al. The disease burden of drug use disorders in the Chinese population from 1990 to 2021 and trend predictions from 2022 to 2046. Chinese General Practice, 2025. [Epub ahead of print]

Copyright: © Editorial Office of Chinese General Practice. This is an open access article under the CC BY-NC-ND 4.0 license.

Received: 2025-04-10; Revised: 2025-07-02; Accepted: [not provided]

Editor: Mao Yamin