Global burden on drug use disorders from 1990 to 2021 and projections to 2046

Global burden on drug use disorders from 1990 to 2021 and projections to 2046 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Global burden on drug use disorders from 1990 to 2021 and projections to 2046 Dongying Chen, Yanyan Sun, Xiaowu Li, Zongyi Yin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4859842/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Despite extensive research, there remains a paucity of comprehensive reports on the spatiotemporal distribution, driving factors, and future trends of drug use disorders (DUDs). We used data from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to address this gap. In 2021, the global prevalence of DUDs reached 53,115,936 (95% UI: 46,999,805 – 60,949,054), marking a 35.50% increase since 1990 and is projected to continue rising over the next 25 years. The increment in incidence, deaths, and DALYs was 35.50%, 122.22%, and 74.65%, respectively. Despite the declining trends in global rates of incidence, prevalence, and DALYs, mortality still shows an upward trend, increasing from 1.26 to 1.65 per 100,000. Opioid and cocaine use disorders were the primary contributors to the overall increase in DUDs DALYs. Population growth was the primary driver of the increase in DUDs burden (35.31%). Health inequality regarding DUDs remain prominent issues. Biological sciences/Psychology/Human behaviour Health sciences/Neurology/Neurological disorders Health sciences/Risk factors Global Burden of Disease Incidence Prevalence Death Disability-adjusted life years Drug use disorders Substance use disorders Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Drug use disorders (DUDs) constitute a significant global health challenge, imposing a substantial burden on individuals, families, and societies worldwide.( 1 – 3 ) According to the World Drug Report 2023, substance use affected more than 296 million individuals globally in 2021.( 4 ) The report also highlighted a significant upward trend in DUDs, with a 45% surge observed over the recent decade, and only 20% of those grappling with DUDs receiving pharmacological interventions. Moreover, harmful use of drugs is responsible for 494,000 deaths annually and the loss of 30.9 million healthy years of life due to premature death and disability. ( 5 ) Furthermore, the disparity in treatment availability across different geographical areas has continued to expand, exacerbating the challenges faced by affected populations in certain regions.( 5 ) Despite extensive research, several challenges persist in understanding the full scope of DUDs: ( 1 ) The importance of DUDs as a public health issue among young people is not reflected in, and remains unaddressed by, the allocated resources for an extended period.( 5 ) ( 2 ) DUDs generally receive limited research investment and political support compared to other non-communicable diseases.( 4 ) ( 3 ) The spatiotemporal distribution of DUDs presents another challenge, as patterns of drug use vary significantly across regions and change over time.( 1 , 3 ) ( 4 ) The complex relationship between DUDs and socioeconomic development remains incompletely understood, with some studies suggesting a paradoxical increase in drug use with economic growth in certain contexts.( 4 ) These knowledge gaps underscore the need for comprehensive, global studies that can capture the nuanced trends of DUDs across different countries and regions. This study aims to address these challenges by analyzing data from the Global Burden of Disease (GBD) study 2021.( 6 – 8 ) The GBD study allows for a comparison of cause-specific disease burden over time and by country through the standardization of data management and methods. By examining trends in incidence, prevalence, deaths, and disability-adjusted life years (DALYs) for various drug use disorders from 1990 to 2021, we seek to provide a comprehensive overview of the changing landscape of DUDs over the past three decades. This analysis will contribute valuable insights into the global, regional, and national patterns for informing evidence-based policies and interventions to address this global health issue effectively. Results Global burden overview of DUDs The global landscape of DUDs has undergone significant changes over the past three decades. In 2021, the global incidence of DUDs reached 13,609,362.38 (95% UI: 11,625,287.78 – 15,667,184.2), marking a 35.50% increase since 1990. Despite this absolute increase, the age-standardized incidence rate (ASIR) showed a slight decline from 184.31 (95% UI: 156.91 – 211.67) per 100,000 in 1990 to 169.39 (95% UI: 145.14 – 195.01) in 2021, with an EAPC of -0.28 (95% UI: -0.27 to -0.27). Among specific substances, cannabis and opioids dominated the ASIR, with 46.77 (95% UI: 35.25 – 61.17) and 24.54 (95% UI: 20.74 – 29.48) per 100,000, respectively. The prevalence of DUDs also increased by 34.06% from 1990 to 2021, reaching 53,115,936.38 (95% UI: 46,999,805.19 – 60,949,054.28) cases in 2021. However, the ASPR decreased from 709.15 (95% UI: 618.81 – 824.54) to 663.8 (95% UI: 584.52 – 766.14) per 100,000 over this period. The disease burden, as measured by DALYs, increased substantially by 74.65% from 1990 to 2021, reaching 162,061.67 (95% UI: 110,807.96 – 213,561.19) DALYs in 2021. The age-standardized DALY rate (ASDR) rose from 166.44 (95% UI: 132.55 – 198.4) to 190.97 (95% UI: 156.11 – 222.79) per 100,000. Deaths attributed to DUDs also saw a dramatic increase of 122.22% from 1990 to 2021, with 137,277.92 (95% UI: 129,268.62 – 146,181.36) deaths in 2021. The age-standardized mortality rate (ASMR) increased from 1.26 (95% UI: 1.17 – 1.37) to 1.65 (95% UI: 1.55 – 1.75) per 100,000. (Table 1, sTable 1-3) Table 1 Number, crude rate, ASPR for overall DUDs in 2021 and percentage change from 1990 All specific drug categories exhibited positive EAPCs in death rates over this period. Notably, opioid use disorders were the only category to show a positive estimated annual percentage change (EAPC) in ASPR [0.81 (95% UI: 0.73 – 0.91)]. Cannabis and opioid use disorders demonstrated increasing trends in ASDR, with EAPCs of 0.45 (95% UI: 0.29 – 0.49) and 0.23 (95% UI: 0.18 – 0.26), respectively. Age and gender disparities were evident in the incidence of DUDs. The highest ASIR were observed in young adults aged 15-39 years, peaking at approximately 88 per 100,000 in the 20-24 age group. A gender differential was noted, with males showing higher ASIR before age 40, while females exhibited higher rates after age 40. (Fig. 1, sFig. 1 and 2) The burden of DUDs in 2021 and temporal trends In 2021, the highest burden of DUDs was concentrated in High-income North America, Australasia, and Western Europe. At the national level, USA, Australia, Canada, and Estonia generally exhibited higher ASPR of DUDs compared to other countries or territories. The USA demonstrated the highest prevalence with 12,146,953.91 cases (95% UI: 11,024,582.17 – 13,461,043.9), followed by China with 7,680,058.66 cases (95% UI: 6,602,083.42 – 9,057,281.31), and India with 6,366,009.45 cases (95% UI: 5,297,783.08 – 7,997,066.8). In terms of incidence, China led with 2,451,314 new cases (95% UI: 2,046,472.04 – 2,907,370.53), followed by India (2,047,672.59; 95% UI: 1,706,130.84 – 2,396,268.76), USA (1,583,449.64; 95% UI: 1,384,480.18 – 1,793,912.27), and Brazil (411,752.6; 95% UI: 350,243.05 – 474,141.24). High-income countries, particularly USA, Australia, and Canada, tended to have higher ASIR, ASMR, ASDR. (Fig. 2, sFig. 3 and 4) Over the 32-year study period, Estonia, USA, and Lithuania experienced the most significant increases in ASIR. Conversely, China, Switzerland, and Italy showed notable declines. Regarding changes in absolute incidence cases, Qatar, the United Arab Emirates, Equatorial Guinea, and Jordan demonstrated more than a threefold increase over the past three decades. Interestingly, a gender disparity was observed across different SDI levels. In high SDI countries, ASIR were higher among males, while low and middle SDI countries showed an opposite trend with higher rates among females. (sFig. 5 and 6) Drivers factors of DUDs burden To elucidate the factors shaping the epidemiology of DUDs over the past three decades, we conducted a decomposition analysis of incidence, prevalence, deaths, and DALYs. This analysis considered three primary drivers: population growth, aging, and epidemiologic changes, the latter represented by age- and population-standardized morbidity and mortality rates. Globally, there was a significant increase in DUDs DALYs, with the most pronounced increases observed in High-income North America and South Asia. Conversely, East Asia exhibited a notable decline. Our analysis revealed that population growth was the primary contributor to the increased burden of DUDs DALYs between 1990 and 2021, accounting for 35.31% of the increase, followed by epidemiologic changes at 9.48%. The impact of population growth on overall DALYs was most evident in Sub-Saharan Africa (165.30%), North Africa and Middle East (85.77%), and South Asia (80.23%). Aging contributed most significantly to overall DALYs in North Africa and Middle East (14.10%), South Asia (10.04%), and Andean Latin America (11.24%). In low and middle SDI-quintiles, population growth was the primary driver of increased DUDs DALYs. Epidemiologic changes, reflecting underlying shifts in age- and population-adjusted DUDs burden over the 32-year period, showed a global increase. This increase was particularly pronounced in High-income North America. Notably, East Asia and Southern Sub-Saharan Africa were the only regions where epidemiologic changes led to a decrease in DUDs DALYs. Regarding mortality, the most significant increases in DUDs deaths were observed in High-income Asia Pacific (96.32%), Eastern Europe (150.85%), and Western Europe (181.56%). (Fig. 3 and sFig. 7-9) Country-level decomposition analysis revealed substantial heterogeneity in demographic and epidemiologic trends. In most high-income countries, epidemiologic changes and population growth were the major drivers of changes in DUDs DALYs. In contrast, aging and population growth were the primary drivers in most developing countries. Decomposition analysis by causes of DUDs To elucidate the differential contributions of specific DUDs to the overall burden, we conducted decomposition analyses for five major categories: opioid use disorders, cocaine use disorders, amphetamine use disorders, cannabis use disorders, and other drug use disorders. Globally, opioid and cocaine use disorders were the primary contributors to the overall increase in DUDs DALYs, accounting for 82.07% and 59.57% of the increase, respectively. The impact of opioid use disorders on the change in overall DALYs was particularly pronounced in Southeast Asia (59.49%), Southern Latin America (38.34%), and Eastern Sub-Saharan Africa (183.50%). Amphetamine use disorders emerged as a significant driver of change in the overall DUDs burden in specific regions, contributing 54.46% and 56.95% of the change in DUDs DALYs in Australasia and Central Asia, respectively. Notably, the burden of DUDs in High-income Asia Pacific, Central Europe, East Asia, and Eastern Europe was comparatively lower than in other regions. Cannabis use disorders were identified as the leading driver of change in DUDs DALYs, although its relative contribution varied substantially across geographical regions. Its impact was particularly high, exceeding 50% in Southern Latin America, East Asia, and Oceania. From 1990 to 2021, Cannabis use disorders, followed by opioid use disorders and cocaine use disorders, were the primary drivers of increased DALYs globally and across all SDI-quintiles. (Fig. 4) The burden of DUDs and sociodemographic development To elucidate the potential for improvement in DUDs DALY rates relative to a country’s development status, we conducted a frontier analysis. This analysis examined the relationship between age-standardized DALY rates and the SDI using data from 1990 to 2021. The effective difference from the frontier for each country or territory was calculated using the 2021 DALYs and SDI values. We found that: variability in ASDR was observed across all SDI levels from 1990 to 2021, with this variability appearing to increase at higher SDI values; High-income countries (e.g., USA, Canada, UK, Australia) exhibited higher ASDR despite their high SDI, indicating significant challenges with DUDs in developed countries; Countries with low SDI (e.g., Niger, Somalia, Chad) demonstrated lower DALY rates, potentially due to factors such as reduced drug availability, under-reporting, or cultural differences in attitudes towards drug use; The United States stood out with an exceptionally high DALY rate despite its high SDI, suggesting a particularly severe drug use problem. (Fig. 5A and B) To further investigate the distribution of the DUDs burden in relation to countries’ health system performance, we examined the relationship between burden measures and the HAQ index. This analysis revealed: A positive relationship between HAQ and ASDR, with countries exhibiting high HAQ scores also showing relatively higher ASDR; After accounting for regional confounds and controlling for SDI, a near-linear positive relationship between ASDR and HAQ was also observed. (Fig. 5C) The burden of DUDs and health inequality To identifying health inequalities and their drivers in achieving health equity, we conducted an in-depth analysis of relative and absolute health inequalities in the burden of DUDs. Our findings reveal that global health inequalities in the burden of DUDs have significantly worsened over the past three decades, the concentration index was 0.22(95%CI 0.18, 0.27) in 1990 and 0.48(95%CI 0.35, 0.62) in 2021 (p<0.01). The burden of DUDs is disproportionately concentrated in countries with higher socioeconomic development. The USA emerges as a striking outlier, exhibiting exceptionally high DALY rates at both time points examined. Moreover, the country demonstrated a marked increase in its ASDR in 2021 compared to 1990. China and Brazil, despite their large populations, display relatively low ASDR. These countries have experienced increases in ASDR from 1990 to 2021, signaling a growing health inequality in these populous countries. In contrast, India and Uganda, representative of low/middle SDI countries, exhibits relatively low ASDR with minimal change observed between 1990 and 2021, indicating the health inequalities situation in this country has not significantly changed. (Fig. 6) Prediction of DUDs burden in the next 25 years Forecasting the future burden of DUDs can provides essential insights for policymakers and healthcare administrators to effectively plan and allocate resources. Our predictive analysis for the next two decades reveals several key trends: The overall number of DUDs prevalence is projected to continue its upward trajectory over the next 25 years, albeit at a decelerated rate; A notable shift is anticipated in the landscape of specific DUDs, with opioid use disorders predicted to surpass cannabis use disorders in ASPR by approximately 2030; DUDs-related incidence cases, prevalence cases and DALYs would increase to 10176246, 45105497, and 18822146, respectively; These increased cases in some countries represent a substantial multiplication of the corresponding number observed in 2021; In contrast to the absolute number increases, the ASIR, ASPR, ASDR are projected to decline to approximately half of their 2021 levels; A divergent trend is anticipated in High-income North America, particularly in USA, where both absolute numbers and age-standardized rates are expected to increase, contrary to the global trend. (Fig. 7 and sFig. 10 and 11) Discussion This comprehensive analysis of GBD 2021 reveals significant trends and patterns in the global burden of DUDs. It revealed that the absolute number of DUDs exceeded 53 million people in 2021, and is projected to continue rising over the next 25 years. Despite the declining trends in global ASPR, ASIR, and ASDR, the ASMR still shows an upward trend, even without including mortality data for cannabis use disorders. Higher burden was observed in males, 15–39 years old populations. Population growth was the primary contributor to the increased burden of DUDs DALYs, accounting for 35.31%. Health inequality and insufficient healthcare performance regarding the burden of DUDs remains a prominent issue, both in high SDI and low SDI regions. The global incidence and prevalence of DUDs have shown substantial increases in absolute numbers over the past three decades, although age-standardized rates have declined. This paradoxical trend can be largely attributed to population growth and changes in age structure, particularly in low and middle SDI countries.( 4 ) Besides, many low and middle SDI countries have strengthened drug prevention education over the past decade. For instance, according to the United Nations Office on Drugs and Crime (UNODC), countries like China, Kenya, and Nigeria have introduced drug prevention education into school curricula.( 5 ) However, the significant rise in ASMR associated with DUDs is particularly concerning. The reason may be that, despite potential decreases in overall incidence rates, many regions still lack adequate treatment resources.( 5 , 20 ) Prevention efforts may have helped reduce new cases in some regions, but the increased potency of drugs and the rise of polydrug use have made existing cases more severe. The age and gender disparities observed in DUDs incidence highlight the need for targeted interventions. Our findings indicated that DUDs were still very serious among young adults. This age group is particularly vulnerable to DUDs due to a combination of neurobiological, psychological, and social factors.( 21 – 24 ) The earlier the use of psychoactive drugs, the greater the lifelong risk of DUDs.( 23 ) This age group may suffer deprivation, poverty, homelessness, famine, gender-based discrimination and frequent displacement.( 20 , 23 ) As a result, they can develop various mental and physical health issues. Thus, reducing contact with drugs and better treatment services for DUDs should be provided promptly to accurately identify and meet the needs of such people.( 21 , 22 ) The gender differential, with males showing higher rates before age 40 and females after, suggests the need for gender-specific approaches in both prevention and treatment programs.( 22 ) Males were more likely to receive higher doses of psychotropic drugs and suffer from DUDs and drug dependence before age 40.( 21 – 23 ) The 2021 World Drug Report indicates that men are about twice as likely as women to use cannabis, cocaine, or amphetamines.( 4 ) After age 40, females were more prone to mental disorders and dependent on psychotropic drugs compared to males.( 5 , 22 ) Additionally, there are more obstacles for females in accessing medication, leading to insufficient medication treatment.( 4 , 5 ) They may endure more social stigmatization, fear legal sanctions, and possibly even lose custody of their children. Therefore, more practical and effective strategies for women should also be developed and implemented to alleviate or even relieve these gender-specific burdens. Geographical variations in DUDs burden reveal significant disparities between high-income and low/middle-income countries. The concentration of high prevalence rates in North America, Australasia, and Western Europe may reflect differences in drug availability, societal attitudes, and reporting practices.( 4 , 19 ) However, the rapid increases observed in some developing countries, particularly in the Middle East and Africa, signal an urgent need for proactive measures in these regions. The higher prevalence of DUDs in high SDI countries can be attributed to several factors: ( 1 ) Greater economic resources: Higher disposable incomes may increase access to drugs. For instance, the USA, with its high SDI, has seen a significant, partly fueled by the widespread prescription of opioid painkillers.( 19 , 20 ) ( 2 ) Advanced drug trafficking networks: Developed countries often have more sophisticated drug distribution systems. The European Monitoring Centre for Drugs and Drug Addiction reports that online drug markets on the dark web have grown significantly, with annual revenues estimated to be in the hundreds of millions of euros.( 25 ) ( 3 ) Cultural factors: Some high SDI countries have more permissive attitudes towards recreational drug use. For example, the Netherlands' policy of tolerance towards cannabis has led to higher reported use rates compared to many other European countries.( 25 , 26 ) Conversely, the rapid increase in DUDs in some developing countries, particularly in the Middle East and Africa, can be explained by: ( 1 ) Demographic dividend: Many developing countries have a large youth population, who are more susceptible to drug use.( 27 ) ( 2 ) Weak regulatory frameworks: Many developing countries lack robust systems to control prescription drugs, leading to their misuse. For instance, tramadol abuse has become a significant problem in parts of Africa and the Middle East, with the UNODC reporting seizures increasing from 10 tons in 2010 to over 125 tons in 2017.( 4 , 5 ) The decomposition analysis provides crucial insights into the drivers of DUDs epidemiology. While population growth emerges as the primary contributor to increased DUDs burden globally, the significant role of epidemiologic changes in certain regions, particularly High-income North America, suggests that factors beyond demographics are at play.( 4 , 19 ) These may include changes in drug potency, shifts in drug use patterns, and variations in healthcare and policy responses. The dominance of cannabis and opioids in incidence rates reflects global patterns of drug availability and use. The increasing trend in opioid use disorder prevalence is especially alarming, given the high mortality risk associated with opioid use.( 4 , 5 , 20 ) This trend aligns with the ongoing opioid crisis in several countries, particularly in North America. The differential impact of specific drug categories across regions highlights the need for tailored approaches to drug policy and intervention. The dominant role of opioid and cocaine use disorders in driving the global increase in DUDs DALYs calls for intensified efforts in prevention, treatment, and harm reduction for these substances. The relationship between DUDs burden and socio-economic development exhibits a complex pattern, defying simple correlations. While high SDI countries generally show higher prevalence rates of DUDs, the rapid increases observed in some lower SDI countries indicate that economic development alone may not lead to reduced drug use problems. Economic development can have contradictory effects on DUDs. While it may improve healthcare systems and prevention efforts, it can also increase disposable income and drug availability.( 5 , 20 ) A study found that for every 10% increase in GDP per capita across 181 countries, there was an associated 4.3% increase in the prevalence of drug use.( 28 ) Implementing preemptive strategies may result in a relatively low official drug use prevalence.( 29 , 30 ) Besides, decriminalizing personal drug use and investing heavily in treatment and harm reduction may be another successful policy.( 30 ) We found that health inequality and insufficient healthcare performance regarding DUDs remains a prominent issue, both in high SDI and low SDI regions. In high SDI regions, despite abundant overall medical resources, DUDs patients may face social stigma and discrimination, leading to reluctance in seeking help or inability to access appropriate treatment.( 28 ) Simultaneously, healthcare systems might lack comprehensive intervention programs specifically tailored for DUDs or suffer from inadequate policy implementation. In contrast, low SDI regions may confront more fundamental challenges, such as a shortage of specialized medical professionals, limited financial resources, and underdeveloped healthcare infrastructure, all of which directly impact the accessibility and quality of DUDs-related services.( 4 , 5 , 28 ) To ameliorate this situation, multi-faceted strategies are necessary. For instance, performing comprehensive reforms to integrate DUDs prevention, treatment, and rehabilitation services into routine medical care, while enhancing the capacity of primary healthcare to manage DUDs.( 31 , 32 ) While acknowledging previous discussions on GBD limitations, it remains crucial to elucidate the specific constraints of this study.( 6 , 7 ) Firstly, the GBD 2021 study defines DUDs based on DSM-IV-TR or ICD-10 criteria. The adoption of DSM-5 criteria could potentially alter DUDs estimates, as it introduces changes in diagnostic thresholds and criteria.( 33 ) Secondly, despite improvements in GBD 2021's modeling approach, the limited granularity of data from developing countries and regions may lead to underestimation of DUDs burden in these areas.( 34 ) Lastly, our study's predictions, based on GBD 2021 data, may lack precision due to the inherent lag in data reporting and collection. The rapidly evolving nature of drug use patterns, exemplified by the opioid crisis in North America or the rise of new psychoactive substances globally, means that even recent data may not fully capture current trends. Methods Data sources For the study, we extracted data pertaining to DUDs burden and population statistics from the Global Health Data Exchange (GHDx) query tool ( https://vizhub.healthdata.org/gbd-results/ ). This resource provided us with detailed information on DUDs-related burden, encompassing incidence, prevalence, mortality, and DALYs. The data were disaggregated by various demographic factors, including, sex, and geographical location for the period 1990–2021. Socio-Demographic Index (SDI) was collected from ( https://ghdx.healthdata.org/record/global-burden-disease-study-2021-gbd-2021-socio-demographic-index-sdi-1950%E2%80%932021 ). Healthcare Access and Quality (HAQ) index was collected from. Cause definition and classification In GBD 2021, the DUDs were defined based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) or the International Classification of Diseases (ICD-10) diagnostic criteria, including opioid use disorders, cocaine use disorders, cannabis use disorders, amphetamine use disorders, and other DUDs.( 6 – 8 ) Other DUDs included hallucinogen dependence, inhalant or solvent dependence, sedative dependence, tranquilizer dependence, and other medicines, drugs, substance dependence.( 6 – 8 ) Measures of burden The key metrics used to assess DUDs burden included prevalence, incidence, mortality, and DALYs. The estimation process for these metrics incorporated sophisticated statistical modeling techniques, tailored to capture the complex nature of DUDs across various demographic and geographic dimensions.( 6 – 8 ) For the estimation of prevalence, incidence, and years lived with disability (YLDs), the study leveraged the Bayesian meta-regression tool DisMod-MR 2.1.( 6 ) The study reported these burden measures in two formats: absolute numbers and age-standardized rates per 100,000 population. For age standardization, the World Health Organization's world population standard age structure was employed as the reference population.( 9 ) Spatial-temporal trend analysis To elucidate the temporal trends in the burden of DUDs, we employed several sophisticated statistical approaches: ( 1 ) EAPC: We calculated the EAPC for age-standardized rates and absolute numbers of incidence, mortality, prevalence, and DALYs associated with DUDs over the period 1990–2021.( 10 ) The EAPC was subsequently computed as: EAPC = 100 × (exp(β) − 1). ( 2 ) Age-period-cohort (APC) analysis: We implemented an APC analysis to disentangle the effects of age, period, and birth cohort on DUDs trends.( 11 ) Decomposition analysis To elucidate the complex dynamics underlying the temporal and population-specific variations in the burden of DUDs, we implemented a rigorous decomposition analysis. This analytical approach allows us to quantify the relative contributions of three primary factors driving changes in the DUDs burden: Population growth; Population aging; Epidemiologic changes.( 12 , 13 ) Health care access and quality To address the potential non-linear relationship between the HAQ Index and DALYs, we employed a sophisticated statistical approach. The HAQ Index was modeled as a restricted cubic spline function, while simultaneously controlling for SDI.( 13 , 14 ) Knots for the cubic function were strategically placed at each quartile to capture the nuanced relationships between these variables. We examined the relationship between age-standardized DALY rates for DUDs in 2021 and the corresponding HAQ Index values from 2019.( 13 ) Health inequality analysis We utilized the slope index of inequality (to assess absolute inequality) and the concentration index (to assess relative inequality) to summarize health inequality. A key strength of both the sophisticated metrics lies in their population-weighted approach to calculation. This methodology ensures that the resulting single numerical value encapsulates inequality across all subgroups while accounting for variations in population size.( 15 , 16 ) Frontier analysis To evaluate the relationship between the burden of DUDs and socio-demographic development, we employed a frontier analysis as a quantitative methodology.( 13 ) The DALYs frontier delineates the minimum DALYs that could theoretically be attained for each country or territory given its SDI value. To account for uncertainty in our estimates, we utilized 100 bootstrapped samples of the data, randomly sampling with replacement from all countries and territories across all years. We computed the mean DUDs DALYs at each SDI value from these bootstrapped samples. Subsequently, we developed a locally weighted regression model with a local polynomial degree of 1 and a span of 0.3 to generate a smoothed frontier. Forecasting analysis To evaluate the trends of DUDs over the next 25 years, we employed two sophisticated models: the Nordpred model and the Bayesian age-period-cohort (BAPC) model.( 17 , 18 ) These models account for three types of time-varying phenomena: age effects, period effects, and cohort effects. To validate the stability of the prediction results, we further applied the BAPC model integrated with nested laplace approximations to perform a sensitivity analysis.( 17 , 19 ) Statistical analysis Uncertainty intervals (UIs) were used to describe the point estimates of uncertainty from model specification, stochastic variation, and measurement bias. The point estimate is defined by the mean of the draws, while the 95% UIs are represented by the 2.5th and 97.5th percentiles of ranked estimates from the draws. All statistical analyses and visualization of results were conducted using the R software (Version 4.3.3; https://www.R-project.org/ ), and the two-tailed P value < 0.05 was considered statistically significant. Abbreviations DUDs - Drug use disorders GBD - Global burden of diseases, injuries, and risk factors DALYs - Disability-adjusted life years UI - Uncertainty intervals SDI - Socio-demographic index HAQ - Healthcare access and quality DSM-IV-TR - Diagnostic and statistical manual of mental disorders (4th edition, text revision) ICD-10 - International classification of diseases (10th revision) YLDs - Years lived with disability EAPC - Estimated annual percentage change APC - Age-period-cohort BAPC - Bayesian age-period-cohort ASPR - Age-standardized prevalence rate ASIR - Age-standardized incidence rate ASDR - Age-standardized DALYs rate ASMR - Age-standardized mortality rate Declarations Availability of Data and Materials The datasets generated and/or analysed during the current study are available in the Global Health Data Exchange repository, link: https://vizhub.healthdata.org/gbd-results/. Acknowledgments We thank the editors and reviewers of the paper for their warm work earnestly. We also thank the National Natural Science Foundation of China [grant numbers 81430063, 8210140740, 82104597], Guangdong Provincial Science and Technology Program [grant numbers 2019B030301009], Natural Science Foundation of Guangdong Province of China [grant numbers 2021A1515012161]，Guangdong Province Regional Joint Fund-Key Projects [grant numbers 2020B1515120096]，Guangdong Basic and Applied Basic Research Foundation, Sanming Project of Medicine in Shenzhen [grant numbers SZSM202003009], Shenzhen Key Laboratory Foundation [grant numbers ZDSYS20200811143757022] and Shenzhen International Cooperative Research Project [grant numbers GJHZ20200731095210030]. Ethics approval and consent to participate The data utilized in this study were obtained from the publicly available GBD database and did not require institutional ethics approval/review. Consent for publication All authors have agreed on the contents and publication of the manuscript. Competing interests The authors declared that they have no conflicts of interest to this work. Funding This study was supported by grants from National Natural Science Foundation of China [grant numbers 81430063, 8210140740, 82104597], Guangdong Provincial Science and Technology Program [grant numbers 2019B030301009], Natural Science Foundation of Guangdong Province of China [grant numbers 2021A1515012161]，Guangdong Province Regional Joint Fund-Key Projects [grant numbers 2020B1515120096]，Guangdong Basic and Applied Basic Research Foundation, Sanming Project of Medicine in Shenzhen [grant numbers SZSM202003009], Shenzhen Key Laboratory Foundation [grant numbers ZDSYS20200811143757022] and Shenzhen International Cooperative Research Project [grant numbers GJHZ20200731095210030]. Authors' contributions All authors contributed to data analysis, drafting or revising the article, have agreed on the journal to which the article will be submitted, gave final approval of the version to be published, and agree to be accountable for all aspects of the work. References Shen J, Hua G, Li C, Liu S, Liu L, Jiao J. Prevalence, incidence, deaths, and disability-adjusted life-years of drug use disorders for 204 countries and territories during the past 30 years. Asian J Psychiatr. 2023;86:103677. Pan Z, Zhang J, Cheng H, Bu Q, Li N, Deng Y, et al. Trends of the incidence of drug use disorders from 1990 to 2017: an analysis based on the Global Burden of Disease 2017 data. Epidemiol Psychiatr Sci. 2020;29:e148. Lu W, Xu L, Goodwin RD, Munoz-Laboy M, Sohler N. Widening Gaps and Disparities in the Treatment of Adolescent Alcohol and Drug Use Disorders. Am J Prev Med. 2023;64(5):704-15. UNOoDaCWdr. 2023 [Available from: https://www.unodc.org/unodc/en/data-and-analysis/world-drug-report-2023.html. Geneva. International standards for the treatment of drug use disorders: revised edition incorporating results of field-testing. World Health Organization and United Nations Office on Drugs and Crime; 2020. Collaborators GCoD. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life- years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440). Collaborators GCoD. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440). Collaborators GRF. Global burden and strength of evi- dence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440). Collaborators GD. Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID- 19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440). Kim HJ FM, Feuer EJ, Midthune DN. Permutation tests for joinpoint regression with applications to cancer rates. Stat Med 2000;19. Rosenberg PS, Check DP, Anderson WF. A web tool for age-period-cohort analysis of cancer incidence and mortality rates. Cancer Epidemiol Biomarkers Prev. 2014;23(11):2296-302. Cheng X, Yang Y, Schwebel DC, Liu Z, Li L, Cheng P, et al. Population ageing and mortality during 1990-2017: A global decomposition analysis. PLoS Med. 2020;17(6):e1003138. Xie Y, Bowe B, Mokdad AH, Xian H, Yan Y, Li T, et al. Analysis of the Global Burden of Disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016. Kidney Int. 2018;94(3):567-81. Network GBoDC. Global Burden of Disease Study 2019 (GBD 2019) Healthcare Access and Quality Index 1990-2019. 2022. Organization WH. Handbook on Health Inequality Monitoring2017. The Surveillance E, and End Results (SEER) Program. Measures of Disparity 2024 [Available from: https://seer.cancer.gov/help/hdcalc/inference-methods/individual-level-survey-sample-1. Volker J. Schmid LH. BAMP – Bayesian age-period-cohort modeling and prediction. Journal of Statistical Software. 2007;21. Møller B. FH, Hakulinen T., Sigvaldason H, Storm H. H., Talbäck M. and Haldorsen T. Prediction of cancer incidence in the Nordic countries: Empirical comparison of different approaches. Statistics in medicine. 2003;22. Zhang T, Sun L, Yin X, Chen H, Yang L, Yang X. Burden of drug use disorders in the United States from 1990 to 2021 and its projection until 2035: results from the GBD study. BMC Public Health. 2024;24(1):1639. Statistics NCfDA. Drug abuse statistics 2024 [Available from: https://drugabusestatistics.org/. Silveri MM, Dager AD, Cohen-Gilbert JE, Sneider JT. Neurobiological signatures associated with alcohol and drug use in the human adolescent brain. Neurosci Biobehav Rev. 2016;70:244-59. Marinelli S, Basile G, Manfredini R, Zaami S. Sex- and Gender-Specific Drug Abuse Dynamics: The Need for Tailored Therapeutic Approaches. J Pers Med. 2023;13(6). Grant BF, Saha TD, Ruan WJ, Goldstein RB, Chou SP, Jung J, et al. Epidemiology of DSM-5 Drug Use Disorder: Results From the National Epidemiologic Survey on Alcohol and Related Conditions-III. JAMA Psychiatry. 2016;73(1):39-47. Conrod PJ, Nikolaou K. Annual Research Review: On the developmental neuropsychology of substance use disorders. J Child Psychol Psychiatry. 2016;57(3):371-94. Addiction EMCfDaD. European Drug Report 2024: Trends and Developments 2024 [Available from: https://www.euda.europa.eu/index_en. Netherlands Go. Toleration policy regarding soft drugs [Available from: https://www.government.nl/topics/drugs/toleration-policy-regarding-soft-drugs-and-coffee-shops. United Nations Department of Economic and Social Affairs PD. World Population Prospects 2024: Summary of Results (UN DESA/POP/2024/TR/NO. 9) 2024 [ Crime UNOoDa. World Drug Report 2020 (United Nations publication, Sales No. E.20.XI.6). 2020. Wu Z, Detels R, Zhang J, Li V, Li J. Community-based trial to prevent drug use among youths in Yunnan, China. Am J Public Health. 2002;92(12):1952-7. America EotPsRoCitUSo. Introduction to China’s Successful Efforts in Drug Control 2023 [Available from: http://us.china-embassy.gov.cn/eng/zggs/202307/t20230706_11108971.htm. Del Pozo B, Park JN, Taylor BG, Wakeman SE, Ducharme L, Pollack HA, et al. Knowledge, Attitudes, and Beliefs About Opioid Use Disorder Treatment in Primary Care. JAMA Netw Open. 2024;7(6):e2419094. Campopiano von Klimo M, Nolan L, Corbin M, Farinelli L, Pytell JD, Simon C, et al. Physician Reluctance to Intervene in Addiction: A Systematic Review. JAMA Netw Open. 2024;7(7):e2420837. First MB, Yousif LH, Clarke DE, Wang PS, Gogtay N, Appelbaum PS. DSM-5-TR: overview of what's new and what's changed. World Psychiatry. 2022;21(2):218-9. Abuse TWDoMHaS. ATLAS on Resources for the Prevention and Treatment of Substance Use Disorders. 2010. Table Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files stable1DALYs.pdf stable2incidence.pdf stable3Deaths.pdf Table1Number.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4859842","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":351529315,"identity":"e95d10b9-d8e1-4fe9-858e-1183e098bff6","order_by":0,"name":"Dongying Chen","email":"","orcid":"","institution":"Shenzhen University General Hospital, Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Dongying","middleName":"","lastName":"Chen","suffix":""},{"id":351529316,"identity":"467fcd49-3156-423e-b9a6-3337341a8504","order_by":1,"name":"Yanyan Sun","email":"","orcid":"","institution":"Shenzhen University General Hospital, Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Yanyan","middleName":"","lastName":"Sun","suffix":""},{"id":351529317,"identity":"262d73f7-6a55-436f-bb17-6d75080a4d02","order_by":2,"name":"Xiaowu Li","email":"","orcid":"","institution":"Shenzhen University General Hospital, Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Xiaowu","middleName":"","lastName":"Li","suffix":""},{"id":351529319,"identity":"e5f92813-5762-4da3-9446-bebc4ae162e3","order_by":3,"name":"Zongyi Yin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYDACZhBRAWFLkKDlDElaQICxjRQtBsd5D3/mnVdnb3CA+eBtHga7PMJaDvMlGPNuO8xscIAt2ZqHIbmYCC08Bsm82w6wGRzgMZPmYTiQ2ECMlsO8c+p4DA7wfyNai2EzbwOzBNAWNuK0SB7mMWacc+ywgeRhNmPLOQbJhLXwnT9j/OFNTZ093/HmhzfeVNgR1qJwgIGBiQfEAsepASH1QCAPNJTxBxEKR8EoGAWjYAQDADCjNT/Zn5UhAAAAAElFTkSuQmCC","orcid":"","institution":"Shenzhen University General Hospital, Shenzhen University","correspondingAuthor":true,"prefix":"","firstName":"Zongyi","middleName":"","lastName":"Yin","suffix":""}],"badges":[],"createdAt":"2024-08-05 07:00:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4859842/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4859842/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64220799,"identity":"0b3b35b0-b6b8-46e2-a07d-254169519cd3","added_by":"auto","created_at":"2024-09-10 11:32:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":23475,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eASIR of DUDs by sex in 2021.\u003c/strong\u003e Age and gender disparities were evident in the incidence of DUDs. The highest incidence rates were observed in young adults aged 15-39 years, peaking at approximately 88 per 100,000 in the 20-24 age group. A gender differential was noted, with males showing higher incidence rates before age 40, while females exhibited higher rates after age 40.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4859842/v1/13094b16d48af9afcda78e6f.png"},{"id":64220809,"identity":"23506ea8-6e1a-4fb9-8614-3390b8ef1c49","added_by":"auto","created_at":"2024-09-10 11:32:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":215104,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAge-standardized rates of DUDs-related burden in 2021 by countries or territories\u003c/strong\u003e. A. ASIR of drug use disorders. B. ASPR of drug use disorders. C. ASPR of drug use disorders. D. ASDR of drug use disorders. In 2021, the highest burden of DUDs was concentrated in High-income North America, Australasia, and Europe. Countries near the equator have a relatively lower burden of DUDs.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4859842/v1/fce261a980e03930b293bb32.png"},{"id":64221099,"identity":"ca7a3a2f-3518-4ed2-a061-078441ac1377","added_by":"auto","created_at":"2024-09-10 11:40:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":55070,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDrivers factors of DUDs DALYs from 1990 to 2021.\u003c/strong\u003e A. The number of changes contributed by all 3 factors. B. The percent of change contributed by all 3 factors. Globally, there was a significant increase in DUDs DALYs, with the most pronounced increases drove by population growth. The exception is High-income North America, where epidemiological change is the main driving factor. East Asia had a significant decrease in DUDs DALYs contributed by epidemiological change. The black dot represents the overall value of change contributed by all 3 components.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4859842/v1/4f82393cfd86914a4cd4cfea.png"},{"id":64220807,"identity":"382c71a3-aab2-4a86-9a36-ed99d011ec4e","added_by":"auto","created_at":"2024-09-10 11:32:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":55844,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChanges in DUDs burden according to the 4 causes from 1990 to 2021.\u003c/strong\u003e A. Changes in DUDs incidence cases. B. Changes in DUDs prevalence cases. C. Changes in DUDs DALYs. Opioid and cocaine use disorders were the primary contributors to the overall increase in DUDs burden. There were significant regional differences in the overall increase in DUDs burden contributed by these five types of drugs.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4859842/v1/77d5c63e03021bf5fe0967d6.png"},{"id":64220803,"identity":"0f619ca4-ee0f-4b7d-a78c-67cc1182b21a","added_by":"auto","created_at":"2024-09-10 11:32:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":73727,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrontier analysis based on SDI and ASDR from 1990 to 2021. \u0026nbsp;A\u003c/strong\u003e. The effective difference from the frontier for each country orterritory by single year (2021 v.s 1990). B. The effective difference from the frontier for each country or territory by all years (from 1990 to 2021). C. The relationship between healthcare access and quality index and ASDR. The frontier line delineatedthe countries or territories with lowest ASDR (optimal performers) given their SDI. Higher SDI countries had larger gap between theircountry’s observed and potentially achievable DALYs; this gap could be potentially reduced or eliminated based on the country or territory’s sociodemographic resources. High HAQ scores also showing relatively higher ASDR.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4859842/v1/ece3b740eb948fe32256b6ad.png"},{"id":64220806,"identity":"0e4cea7d-f0e9-4f84-9e89-538f77739987","added_by":"auto","created_at":"2024-09-10 11:32:26","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":45073,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHealth inequality analysis based on SDI and ASDR\u003c/strong\u003e \u003cstrong\u003efrom 1990 to 2021.\u003c/strong\u003e A. Relative inequality analysis. B. Absolute inequality analysis. Global health inequalities in the burden of DUDs have significantly worsened over the past three decades. The burden of DUDs is disproportionately concentrated in countries with higher socioeconomic development.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4859842/v1/609df3211fcc98c892a52412.png"},{"id":64220802,"identity":"e064843c-9dcb-4d25-b02e-3cc68a37600b","added_by":"auto","created_at":"2024-09-10 11:32:26","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":41331,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrediction of DUDs burden in the next 20 years\u003c/strong\u003e. A. Prediction for the overall prevalence cases and ASPR of DUDs at global level. B. Prediction for ASPR of DUDs subtypes.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4859842/v1/98efe83e501c3a19ef040254.png"},{"id":71566312,"identity":"5b3d3907-6985-4415-992d-4b3ceba577f3","added_by":"auto","created_at":"2024-12-16 17:38:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":980818,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4859842/v1/ab8e81e4-5bb3-4645-8279-b1a69468ab7f.pdf"},{"id":64221102,"identity":"ff95e336-17cd-460d-b55e-d5856b628d10","added_by":"auto","created_at":"2024-09-10 11:40:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":152769,"visible":true,"origin":"","legend":"","description":"","filename":"stable1DALYs.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4859842/v1/482cdca31f2a81906b2016cb.pdf"},{"id":64220800,"identity":"b3959f6b-e0c7-465d-b1bf-f3dbdad4490a","added_by":"auto","created_at":"2024-09-10 11:32:26","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":144636,"visible":true,"origin":"","legend":"","description":"","filename":"stable2incidence.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4859842/v1/3e01ad21bbfd615532222bc3.pdf"},{"id":64220804,"identity":"d657d03c-0df7-42a2-9354-557c0e7a9b8a","added_by":"auto","created_at":"2024-09-10 11:32:26","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":131986,"visible":true,"origin":"","legend":"","description":"","filename":"stable3Deaths.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4859842/v1/964cf5304b87c67feb79f80a.pdf"},{"id":64221098,"identity":"9fc8bd13-a76b-4680-82e1-8d874f33035c","added_by":"auto","created_at":"2024-09-10 11:40:26","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":26675,"visible":true,"origin":"","legend":"","description":"","filename":"Table1Number.docx","url":"https://assets-eu.researchsquare.com/files/rs-4859842/v1/8939e425f59afc946570c1c5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global burden on drug use disorders from 1990 to 2021 and projections to 2046","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDrug use disorders (DUDs) constitute a significant global health challenge, imposing a substantial burden on individuals, families, and societies worldwide.(\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) According to the World Drug Report 2023, substance use affected more than 296\u0026nbsp;million individuals globally in 2021.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) The report also highlighted a significant upward trend in DUDs, with a 45% surge observed over the recent decade, and only 20% of those grappling with DUDs receiving pharmacological interventions. Moreover, harmful use of drugs is responsible for 494,000 deaths annually and the loss of 30.9\u0026nbsp;million healthy years of life due to premature death and disability. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Furthermore, the disparity in treatment availability across different geographical areas has continued to expand, exacerbating the challenges faced by affected populations in certain regions.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eDespite extensive research, several challenges persist in understanding the full scope of DUDs: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) The importance of DUDs as a public health issue among young people is not reflected in, and remains unaddressed by, the allocated resources for an extended period.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) DUDs generally receive limited research investment and political support compared to other non-communicable diseases.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) The spatiotemporal distribution of DUDs presents another challenge, as patterns of drug use vary significantly across regions and change over time.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) The complex relationship between DUDs and socioeconomic development remains incompletely understood, with some studies suggesting a paradoxical increase in drug use with economic growth in certain contexts.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) These knowledge gaps underscore the need for comprehensive, global studies that can capture the nuanced trends of DUDs across different countries and regions.\u003c/p\u003e \u003cp\u003eThis study aims to address these challenges by analyzing data from the Global Burden of Disease (GBD) study 2021.(\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) The GBD study allows for a comparison of cause-specific disease burden over time and by country through the standardization of data management and methods. By examining trends in incidence, prevalence, deaths, and disability-adjusted life years (DALYs) for various drug use disorders from 1990 to 2021, we seek to provide a comprehensive overview of the changing landscape of DUDs over the past three decades. This analysis will contribute valuable insights into the global, regional, and national patterns for informing evidence-based policies and interventions to address this global health issue effectively.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eGlobal burden overview of DUDs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe global landscape of DUDs has undergone significant changes over the past three decades. In 2021, the global incidence of DUDs reached 13,609,362.38 (95% UI: 11,625,287.78 \u0026ndash; 15,667,184.2), marking a 35.50% increase since 1990. Despite this absolute increase, the age-standardized incidence rate (ASIR) showed a slight decline from 184.31 (95% UI: 156.91 \u0026ndash; 211.67) per 100,000 in 1990 to 169.39 (95% UI: 145.14 \u0026ndash; 195.01) in 2021, with an EAPC of -0.28 (95% UI: -0.27 to -0.27). Among specific substances, cannabis and opioids dominated the ASIR, with 46.77 (95% UI: 35.25 \u0026ndash; 61.17) and 24.54 (95% UI: 20.74 \u0026ndash; 29.48) per 100,000, respectively. The prevalence of DUDs also increased by 34.06% from 1990 to 2021, reaching 53,115,936.38 (95% UI: 46,999,805.19 \u0026ndash; 60,949,054.28) cases in 2021. However, the ASPR decreased from 709.15 (95% UI: 618.81 \u0026ndash; 824.54) to 663.8 (95% UI: 584.52 \u0026ndash; 766.14) per 100,000 over this period. The disease burden, as measured by DALYs, increased substantially by 74.65% from 1990 to 2021, reaching 162,061.67 (95% UI: 110,807.96 \u0026ndash; 213,561.19) DALYs in 2021. The age-standardized DALY rate (ASDR) rose from 166.44 (95% UI: 132.55 \u0026ndash; 198.4) to 190.97 (95% UI: 156.11 \u0026ndash; 222.79) per 100,000. Deaths attributed to DUDs also saw a dramatic increase of 122.22% from 1990 to 2021, with 137,277.92 (95% UI: 129,268.62 \u0026ndash; 146,181.36) deaths in 2021. The age-standardized mortality rate (ASMR) increased from 1.26 (95% UI: 1.17 \u0026ndash; 1.37) to 1.65 (95% UI: 1.55 \u0026ndash; 1.75) per 100,000. (Table 1, sTable 1-3)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 Number, crude rate, ASPR for overall DUDs in 2021 and percentage change from 1990\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll specific drug categories exhibited positive EAPCs in death rates over this period. Notably, opioid use disorders were the only category to show a positive estimated annual percentage change (EAPC) in ASPR [0.81 (95% UI: 0.73 \u0026ndash; 0.91)]. Cannabis and opioid use disorders demonstrated increasing trends in ASDR, with EAPCs of 0.45 (95% UI: 0.29 \u0026ndash; 0.49) and 0.23 (95% UI: 0.18 \u0026ndash; 0.26), respectively. Age and gender disparities were evident in the incidence of DUDs. The highest ASIR were observed in young adults aged 15-39 years, peaking at approximately 88 per 100,000 in the 20-24 age group. A gender differential was noted, with males showing higher ASIR before age 40, while females exhibited higher rates after age 40. (Fig. 1, sFig. 1 and 2)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe burden of DUDs in 2021 and temporal trends\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn 2021, the highest burden of DUDs was concentrated in High-income North America, Australasia, and Western Europe. At the national level, USA, Australia, Canada, and Estonia generally exhibited higher ASPR of DUDs compared to other countries or territories. The USA demonstrated the highest prevalence with 12,146,953.91 cases (95% UI: 11,024,582.17 \u0026ndash; 13,461,043.9), followed by China with 7,680,058.66 cases (95% UI: 6,602,083.42 \u0026ndash; 9,057,281.31), and India with 6,366,009.45 cases (95% UI: 5,297,783.08 \u0026ndash; 7,997,066.8). In terms of incidence, China led with 2,451,314 new cases (95% UI: 2,046,472.04 \u0026ndash; 2,907,370.53), followed by India (2,047,672.59; 95% UI: 1,706,130.84 \u0026ndash; 2,396,268.76), USA (1,583,449.64; 95% UI: 1,384,480.18 \u0026ndash; 1,793,912.27), and Brazil (411,752.6; 95% UI: 350,243.05 \u0026ndash; 474,141.24). High-income countries, particularly USA, Australia, and Canada, tended to have higher ASIR, ASMR, ASDR. (Fig. 2, sFig. 3 and 4)\u003c/p\u003e\n\u003cp\u003eOver the 32-year study period, Estonia, USA, and Lithuania experienced the most significant increases in ASIR. Conversely, China, Switzerland, and Italy showed notable declines. Regarding changes in absolute incidence cases, Qatar, the United Arab Emirates, Equatorial Guinea, and Jordan demonstrated more than a threefold increase over the past three decades. Interestingly, a gender disparity was observed across different SDI levels. In high SDI countries, ASIR were higher among males, while low and middle SDI countries showed an opposite trend with higher rates among females. (sFig. 5 and 6)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrivers factors of DUDs burden\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate the factors shaping the epidemiology of DUDs over the past three decades, we conducted a decomposition analysis of incidence, prevalence, deaths, and DALYs. This analysis considered three primary drivers: population growth, aging, and epidemiologic changes, the latter represented by age- and population-standardized morbidity and mortality rates.\u003c/p\u003e\n\u003cp\u003eGlobally, there was a significant increase in DUDs DALYs, with the most pronounced increases observed in High-income North America and South Asia. Conversely, East Asia exhibited a notable decline. Our analysis revealed that population growth was the primary contributor to the increased burden of DUDs DALYs between 1990 and 2021, accounting for 35.31% of the increase, followed by epidemiologic changes at 9.48%. The impact of population growth on overall DALYs was most evident in Sub-Saharan Africa (165.30%), North Africa and Middle East (85.77%), and South Asia (80.23%). Aging contributed most significantly to overall DALYs in North Africa and Middle East (14.10%), South Asia (10.04%), and Andean Latin America (11.24%). In low and middle SDI-quintiles, population growth was the primary driver of increased DUDs DALYs. Epidemiologic changes, reflecting underlying shifts in age- and population-adjusted DUDs burden over the 32-year period, showed a global increase. This increase was particularly pronounced in High-income North America. Notably, East Asia and Southern Sub-Saharan Africa were the only regions where epidemiologic changes led to a decrease in DUDs DALYs. Regarding mortality, the most significant increases in DUDs deaths were observed in High-income Asia Pacific (96.32%), Eastern Europe (150.85%), and Western Europe (181.56%). (Fig. 3 and sFig. 7-9)\u003c/p\u003e\n\u003cp\u003eCountry-level decomposition analysis revealed substantial heterogeneity in demographic and epidemiologic trends. In most high-income countries, epidemiologic changes and population growth were the major drivers of changes in DUDs DALYs. In contrast, aging and population growth were the primary drivers in most developing countries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDecomposition analysis by causes of DUDs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate the differential contributions of specific DUDs to the overall burden, we conducted decomposition analyses for five major categories: opioid use disorders, cocaine use disorders, amphetamine use disorders, cannabis use disorders, and other drug use disorders. Globally, opioid and cocaine use disorders were the primary contributors to the overall increase in DUDs DALYs, accounting for 82.07% and 59.57% of the increase, respectively. The impact of opioid use disorders on the change in overall DALYs was particularly pronounced in Southeast Asia (59.49%), Southern Latin America (38.34%), and Eastern Sub-Saharan Africa (183.50%). Amphetamine use disorders emerged as a significant driver of change in the overall DUDs burden in specific regions, contributing 54.46% and 56.95% of the change in DUDs DALYs in Australasia and Central Asia, respectively. Notably, the burden of DUDs in High-income Asia Pacific, Central Europe, East Asia, and Eastern Europe was comparatively lower than in other regions. Cannabis use disorders were identified as the leading driver of change in DUDs DALYs, although its relative contribution varied substantially across geographical regions. Its impact was particularly high, exceeding 50% in Southern Latin America, East Asia, and Oceania. From 1990 to 2021, Cannabis use disorders, followed by opioid use disorders and cocaine use disorders, were the primary drivers of increased DALYs globally and across all SDI-quintiles. (Fig. 4)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe burden of DUDs and sociodemographic development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate the potential for improvement in DUDs DALY rates relative to a country\u0026rsquo;s development status, we conducted a frontier analysis. This analysis examined the relationship between age-standardized DALY rates and the SDI using data from 1990 to 2021. The effective difference from the frontier for each country or territory was calculated using the 2021 DALYs and SDI values. We found that: variability in ASDR was observed across all SDI levels from 1990 to 2021, with this variability appearing to increase at higher SDI values; High-income countries (e.g., USA, Canada, UK, Australia) exhibited higher ASDR despite their high SDI, indicating significant challenges with DUDs in developed countries; Countries with low SDI (e.g., Niger, Somalia, Chad) demonstrated lower DALY rates, potentially due to factors such as reduced drug availability, under-reporting, or cultural differences in attitudes towards drug use; The United States stood out with an exceptionally high DALY rate despite its high SDI, suggesting a particularly severe drug use problem. (Fig. 5A and B)\u003c/p\u003e\n\u003cp\u003eTo further investigate the distribution of the DUDs burden in relation to countries\u0026rsquo; health system performance, we examined the relationship between burden measures and the HAQ index. This analysis revealed: A positive relationship between HAQ and ASDR, with countries exhibiting high HAQ scores also showing relatively higher ASDR; After accounting for regional confounds and controlling for SDI, a near-linear positive relationship between ASDR and HAQ was also observed. (Fig. 5C)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe burden of DUDs and health inequality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identifying health inequalities and their drivers in achieving health equity, we conducted an in-depth analysis of relative and absolute health inequalities in the burden of DUDs. Our findings reveal that global health inequalities in the burden of DUDs have significantly worsened over the past three decades, the concentration index was 0.22(95%CI 0.18, 0.27) in 1990 and 0.48(95%CI 0.35, 0.62) in 2021 (p\u0026lt;0.01). The burden of DUDs is disproportionately concentrated in countries with higher socioeconomic development. The USA emerges as a striking outlier, exhibiting exceptionally high DALY rates at both time points examined. Moreover, the country demonstrated a marked increase in its ASDR in 2021 compared to 1990. China and Brazil, despite their large populations, display relatively low ASDR. These countries have experienced increases in ASDR from 1990 to 2021, signaling a growing health inequality in these populous countries. In contrast, India and Uganda, representative of low/middle SDI countries, exhibits relatively low ASDR with minimal change observed between 1990 and 2021, indicating the health inequalities situation in this country has not significantly changed. (Fig. 6)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction of DUDs burden in the next 25 years\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eForecasting the future burden of DUDs can provides essential insights for policymakers and healthcare administrators to effectively plan and allocate resources. Our predictive analysis for the next two decades reveals several key trends: The overall number of DUDs prevalence is projected to continue its upward trajectory over the next 25 years, albeit at a decelerated rate; A notable shift is anticipated in the landscape of specific DUDs, with opioid use disorders predicted to surpass cannabis use disorders in ASPR by approximately 2030; DUDs-related incidence cases, prevalence cases and DALYs would increase to 10176246, 45105497, and 18822146, respectively; These increased cases in some countries represent a substantial multiplication of the corresponding number observed in 2021; In contrast to the absolute number increases, the ASIR, ASPR, ASDR are projected to decline to approximately half of their 2021 levels; A divergent trend is anticipated in High-income North America, particularly in USA, where both absolute numbers and age-standardized rates are expected to increase, contrary to the global trend. (Fig. 7 and sFig. 10 and 11)\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis comprehensive analysis of GBD 2021 reveals significant trends and patterns in the global burden of DUDs. It revealed that the absolute number of DUDs exceeded 53\u0026nbsp;million people in 2021, and is projected to continue rising over the next 25 years. Despite the declining trends in global ASPR, ASIR, and ASDR, the ASMR still shows an upward trend, even without including mortality data for cannabis use disorders. Higher burden was observed in males, 15–39 years old populations. Population growth was the primary contributor to the increased burden of DUDs DALYs, accounting for 35.31%. Health inequality and insufficient healthcare performance regarding the burden of DUDs remains a prominent issue, both in high SDI and low SDI regions.\u003c/p\u003e \u003cp\u003eThe global incidence and prevalence of DUDs have shown substantial increases in absolute numbers over the past three decades, although age-standardized rates have declined. This paradoxical trend can be largely attributed to population growth and changes in age structure, particularly in low and middle SDI countries.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Besides, many low and middle SDI countries have strengthened drug prevention education over the past decade. For instance, according to the United Nations Office on Drugs and Crime (UNODC), countries like China, Kenya, and Nigeria have introduced drug prevention education into school curricula.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) However, the significant rise in ASMR associated with DUDs is particularly concerning. The reason may be that, despite potential decreases in overall incidence rates, many regions still lack adequate treatment resources.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) Prevention efforts may have helped reduce new cases in some regions, but the increased potency of drugs and the rise of polydrug use have made existing cases more severe.\u003c/p\u003e \u003cp\u003eThe age and gender disparities observed in DUDs incidence highlight the need for targeted interventions. Our findings indicated that DUDs were still very serious among young adults. This age group is particularly vulnerable to DUDs due to a combination of neurobiological, psychological, and social factors.(\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e–\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) The earlier the use of psychoactive drugs, the greater the lifelong risk of DUDs.(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) This age group may suffer deprivation, poverty, homelessness, famine, gender-based discrimination and frequent displacement.(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) As a result, they can develop various mental and physical health issues. Thus, reducing contact with drugs and better treatment services for DUDs should be provided promptly to accurately identify and meet the needs of such people.(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) The gender differential, with males showing higher rates before age 40 and females after, suggests the need for gender-specific approaches in both prevention and treatment programs.(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) Males were more likely to receive higher doses of psychotropic drugs and suffer from DUDs and drug dependence before age 40.(\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e–\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) The 2021 World Drug Report indicates that men are about twice as likely as women to use cannabis, cocaine, or amphetamines.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) After age 40, females were more prone to mental disorders and dependent on psychotropic drugs compared to males.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) Additionally, there are more obstacles for females in accessing medication, leading to insufficient medication treatment.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) They may endure more social stigmatization, fear legal sanctions, and possibly even lose custody of their children. Therefore, more practical and effective strategies for women should also be developed and implemented to alleviate or even relieve these gender-specific burdens.\u003c/p\u003e \u003cp\u003eGeographical variations in DUDs burden reveal significant disparities between high-income and low/middle-income countries. The concentration of high prevalence rates in North America, Australasia, and Western Europe may reflect differences in drug availability, societal attitudes, and reporting practices.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) However, the rapid increases observed in some developing countries, particularly in the Middle East and Africa, signal an urgent need for proactive measures in these regions. The higher prevalence of DUDs in high SDI countries can be attributed to several factors: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Greater economic resources: Higher disposable incomes may increase access to drugs. For instance, the USA, with its high SDI, has seen a significant, partly fueled by the widespread prescription of opioid painkillers.(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Advanced drug trafficking networks: Developed countries often have more sophisticated drug distribution systems. The European Monitoring Centre for Drugs and Drug Addiction reports that online drug markets on the dark web have grown significantly, with annual revenues estimated to be in the hundreds of millions of euros.(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Cultural factors: Some high SDI countries have more permissive attitudes towards recreational drug use. For example, the Netherlands' policy of tolerance towards cannabis has led to higher reported use rates compared to many other European countries.(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) Conversely, the rapid increase in DUDs in some developing countries, particularly in the Middle East and Africa, can be explained by: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Demographic dividend: Many developing countries have a large youth population, who are more susceptible to drug use.(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Weak regulatory frameworks: Many developing countries lack robust systems to control prescription drugs, leading to their misuse. For instance, tramadol abuse has become a significant problem in parts of Africa and the Middle East, with the UNODC reporting seizures increasing from 10 tons in 2010 to over 125 tons in 2017.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe decomposition analysis provides crucial insights into the drivers of DUDs epidemiology. While population growth emerges as the primary contributor to increased DUDs burden globally, the significant role of epidemiologic changes in certain regions, particularly High-income North America, suggests that factors beyond demographics are at play.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) These may include changes in drug potency, shifts in drug use patterns, and variations in healthcare and policy responses. The dominance of cannabis and opioids in incidence rates reflects global patterns of drug availability and use. The increasing trend in opioid use disorder prevalence is especially alarming, given the high mortality risk associated with opioid use.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) This trend aligns with the ongoing opioid crisis in several countries, particularly in North America. The differential impact of specific drug categories across regions highlights the need for tailored approaches to drug policy and intervention. The dominant role of opioid and cocaine use disorders in driving the global increase in DUDs DALYs calls for intensified efforts in prevention, treatment, and harm reduction for these substances.\u003c/p\u003e \u003cp\u003eThe relationship between DUDs burden and socio-economic development exhibits a complex pattern, defying simple correlations. While high SDI countries generally show higher prevalence rates of DUDs, the rapid increases observed in some lower SDI countries indicate that economic development alone may not lead to reduced drug use problems. Economic development can have contradictory effects on DUDs. While it may improve healthcare systems and prevention efforts, it can also increase disposable income and drug availability.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) A study found that for every 10% increase in GDP per capita across 181 countries, there was an associated 4.3% increase in the prevalence of drug use.(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) Implementing preemptive strategies may result in a relatively low official drug use prevalence.(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) Besides, decriminalizing personal drug use and investing heavily in treatment and harm reduction may be another successful policy.(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eWe found that health inequality and insufficient healthcare performance regarding DUDs remains a prominent issue, both in high SDI and low SDI regions. In high SDI regions, despite abundant overall medical resources, DUDs patients may face social stigma and discrimination, leading to reluctance in seeking help or inability to access appropriate treatment.(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) Simultaneously, healthcare systems might lack comprehensive intervention programs specifically tailored for DUDs or suffer from inadequate policy implementation. In contrast, low SDI regions may confront more fundamental challenges, such as a shortage of specialized medical professionals, limited financial resources, and underdeveloped healthcare infrastructure, all of which directly impact the accessibility and quality of DUDs-related services.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) To ameliorate this situation, multi-faceted strategies are necessary. For instance, performing comprehensive reforms to integrate DUDs prevention, treatment, and rehabilitation services into routine medical care, while enhancing the capacity of primary healthcare to manage DUDs.(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eWhile acknowledging previous discussions on GBD limitations, it remains crucial to elucidate the specific constraints of this study.(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) Firstly, the GBD 2021 study defines DUDs based on DSM-IV-TR or ICD-10 criteria. The adoption of DSM-5 criteria could potentially alter DUDs estimates, as it introduces changes in diagnostic thresholds and criteria.(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) Secondly, despite improvements in GBD 2021's modeling approach, the limited granularity of data from developing countries and regions may lead to underestimation of DUDs burden in these areas.(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) Lastly, our study's predictions, based on GBD 2021 data, may lack precision due to the inherent lag in data reporting and collection. The rapidly evolving nature of drug use patterns, exemplified by the opioid crisis in North America or the rise of new psychoactive substances globally, means that even recent data may not fully capture current trends.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Methods","content":"\u003ch2\u003eData sources\u003c/h2\u003e\u003cp\u003eFor the study, we extracted data pertaining to DUDs burden and population statistics from the Global Health Data Exchange (GHDx) query tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vizhub.healthdata.org/gbd-results/\u003c/span\u003e\u003cspan address=\"https://vizhub.healthdata.org/gbd-results/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This resource provided us with detailed information on DUDs-related burden, encompassing incidence, prevalence, mortality, and DALYs. The data were disaggregated by various demographic factors, including, sex, and geographical location for the period 1990–2021. Socio-Demographic Index (SDI) was collected from (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ghdx.healthdata.org/record/global-burden-disease-study-2021-gbd-2021-socio-demographic-index-sdi-1950%E2%80%932021\u003c/span\u003e\u003cspan address=\"https://ghdx.healthdata.org/record/global-burden-disease-study-2021-gbd-2021-socio-demographic-index-sdi-1950%E2%80%932021\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Healthcare Access and Quality (HAQ) index was collected from.\u003c/p\u003e\u003ch2\u003eCause definition and classification\u003c/h2\u003e\u003cp\u003eIn GBD 2021, the DUDs were defined based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) or the International Classification of Diseases (ICD-10) diagnostic criteria, including opioid use disorders, cocaine use disorders, cannabis use disorders, amphetamine use disorders, and other DUDs.(\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e–\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) Other DUDs included hallucinogen dependence, inhalant or solvent dependence, sedative dependence, tranquilizer dependence, and other medicines, drugs, substance dependence.(\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e–\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e\u003ch2\u003eMeasures of burden\u003c/h2\u003e\u003cp\u003eThe key metrics used to assess DUDs burden included prevalence, incidence, mortality, and DALYs. The estimation process for these metrics incorporated sophisticated statistical modeling techniques, tailored to capture the complex nature of DUDs across various demographic and geographic dimensions.(\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e–\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) For the estimation of prevalence, incidence, and years lived with disability (YLDs), the study leveraged the Bayesian meta-regression tool DisMod-MR 2.1.(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) The study reported these burden measures in two formats: absolute numbers and age-standardized rates per 100,000 population. For age standardization, the World Health Organization's world population standard age structure was employed as the reference population.(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e\u003ch2\u003eSpatial-temporal trend analysis\u003c/h2\u003e\u003cp\u003eTo elucidate the temporal trends in the burden of DUDs, we employed several sophisticated statistical approaches: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) EAPC: We calculated the EAPC for age-standardized rates and absolute numbers of incidence, mortality, prevalence, and DALYs associated with DUDs over the period 1990–2021.(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) The EAPC was subsequently computed as: EAPC = 100 × (exp(β) − 1). (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Age-period-cohort (APC) analysis: We implemented an APC analysis to disentangle the effects of age, period, and birth cohort on DUDs trends.(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e\u003ch2\u003eDecomposition analysis\u003c/h2\u003e\u003cp\u003eTo elucidate the complex dynamics underlying the temporal and population-specific variations in the burden of DUDs, we implemented a rigorous decomposition analysis. This analytical approach allows us to quantify the relative contributions of three primary factors driving changes in the DUDs burden: Population growth; Population aging; Epidemiologic changes.(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e\u003ch2\u003eHealth care access and quality\u003c/h2\u003e\u003cp\u003eTo address the potential non-linear relationship between the HAQ Index and DALYs, we employed a sophisticated statistical approach. The HAQ Index was modeled as a restricted cubic spline function, while simultaneously controlling for SDI.(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) Knots for the cubic function were strategically placed at each quartile to capture the nuanced relationships between these variables. We examined the relationship between age-standardized DALY rates for DUDs in 2021 and the corresponding HAQ Index values from 2019.(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e\u003ch2\u003eHealth inequality analysis\u003c/h2\u003e\u003cp\u003eWe utilized the slope index of inequality (to assess absolute inequality) and the concentration index (to assess relative inequality) to summarize health inequality. A key strength of both the sophisticated metrics lies in their population-weighted approach to calculation. This methodology ensures that the resulting single numerical value encapsulates inequality across all subgroups while accounting for variations in population size.(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e\u003ch2\u003eFrontier analysis\u003c/h2\u003e\u003cp\u003eTo evaluate the relationship between the burden of DUDs and socio-demographic development, we employed a frontier analysis as a quantitative methodology.(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) The DALYs frontier delineates the minimum DALYs that could theoretically be attained for each country or territory given its SDI value. To account for uncertainty in our estimates, we utilized 100 bootstrapped samples of the data, randomly sampling with replacement from all countries and territories across all years. We computed the mean DUDs DALYs at each SDI value from these bootstrapped samples. Subsequently, we developed a locally weighted regression model with a local polynomial degree of 1 and a span of 0.3 to generate a smoothed frontier.\u003c/p\u003e\u003ch2\u003eForecasting analysis\u003c/h2\u003e\u003cp\u003eTo evaluate the trends of DUDs over the next 25 years, we employed two sophisticated models: the Nordpred model and the Bayesian age-period-cohort (BAPC) model.(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) These models account for three types of time-varying phenomena: age effects, period effects, and cohort effects. To validate the stability of the prediction results, we further applied the BAPC model integrated with nested laplace approximations to perform a sensitivity analysis.(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eUncertainty intervals (UIs) were used to describe the point estimates of uncertainty from model specification, stochastic variation, and measurement bias. The point estimate is defined by the mean of the draws, while the 95% UIs are represented by the 2.5th and 97.5th percentiles of ranked estimates from the draws. All statistical analyses and visualization of results were conducted using the R software (Version 4.3.3; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the two-tailed P value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDUDs - Drug use disorders\u003c/p\u003e\n\u003cp\u003eGBD - Global burden of diseases, injuries, and risk factors\u003c/p\u003e\n\u003cp\u003eDALYs - Disability-adjusted life years\u003c/p\u003e\n\u003cp\u003eUI - Uncertainty intervals\u003c/p\u003e\n\u003cp\u003eSDI - Socio-demographic index\u003c/p\u003e\n\u003cp\u003eHAQ - Healthcare access and quality\u003c/p\u003e\n\u003cp\u003eDSM-IV-TR - Diagnostic and statistical manual of mental disorders (4th edition, text revision)\u003c/p\u003e\n\u003cp\u003eICD-10 - International classification of diseases (10th revision)\u003c/p\u003e\n\u003cp\u003eYLDs - Years lived with disability\u003c/p\u003e\n\u003cp\u003eEAPC - Estimated annual percentage change\u003c/p\u003e\n\u003cp\u003eAPC - Age-period-cohort\u003c/p\u003e\n\u003cp\u003eBAPC - Bayesian age-period-cohort\u003c/p\u003e\n\u003cp\u003eASPR - Age-standardized prevalence rate\u003c/p\u003e\n\u003cp\u003eASIR - Age-standardized incidence rate\u003c/p\u003e\n\u003cp\u003eASDR - Age-standardized DALYs rate\u003c/p\u003e\n\u003cp\u003eASMR - Age-standardized mortality rate\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the Global Health Data Exchange repository, link: https://vizhub.healthdata.org/gbd-results/.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eWe thank the editors and reviewers of the paper for their warm work earnestly. We also thank the National Natural Science Foundation of China [grant numbers 81430063, 8210140740, 82104597], Guangdong Provincial Science and Technology Program [grant numbers 2019B030301009], Natural Science Foundation of Guangdong Province of China [grant numbers 2021A1515012161]，Guangdong Province Regional Joint Fund-Key Projects [grant numbers 2020B1515120096]，Guangdong Basic and Applied Basic Research Foundation, Sanming Project of Medicine in Shenzhen [grant numbers SZSM202003009], Shenzhen Key Laboratory Foundation [grant numbers ZDSYS20200811143757022] and Shenzhen International Cooperative Research Project [grant numbers GJHZ20200731095210030].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data utilized in this study were obtained from the publicly available GBD database and did not require institutional ethics approval/review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have agreed on the contents and publication of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared that they have no conflicts of interest to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from National Natural Science Foundation of China [grant numbers 81430063, 8210140740, 82104597], Guangdong Provincial Science and Technology Program [grant numbers 2019B030301009], Natural Science Foundation of Guangdong Province of China [grant numbers 2021A1515012161]，Guangdong Province Regional Joint Fund-Key Projects [grant numbers 2020B1515120096]，Guangdong Basic and Applied Basic Research Foundation, Sanming Project of Medicine in Shenzhen [grant numbers SZSM202003009], Shenzhen Key Laboratory Foundation [grant numbers ZDSYS20200811143757022] and Shenzhen International Cooperative Research Project [grant numbers GJHZ20200731095210030].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to data analysis, drafting or revising the article, have agreed on the journal to which the article will be submitted, gave final approval of the version to be published, and agree to be accountable for all aspects of the work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eShen J, Hua G, Li C, Liu S, Liu L, Jiao J. Prevalence, incidence, deaths, and disability-adjusted life-years of drug use disorders for 204 countries and territories during the past 30 years. Asian J Psychiatr. 2023;86:103677.\u003c/li\u003e\n\u003cli\u003ePan Z, Zhang J, Cheng H, Bu Q, Li N, Deng Y, et al. Trends of the incidence of drug use disorders from 1990 to 2017: an analysis based on the Global Burden of Disease 2017 data. Epidemiol Psychiatr Sci. 2020;29:e148.\u003c/li\u003e\n\u003cli\u003eLu W, Xu L, Goodwin RD, Munoz-Laboy M, Sohler N. Widening Gaps and Disparities in the Treatment of Adolescent Alcohol and Drug Use Disorders. Am J Prev Med. 2023;64(5):704-15.\u003c/li\u003e\n\u003cli\u003eUNOoDaCWdr. 2023 [Available from: https://www.unodc.org/unodc/en/data-and-analysis/world-drug-report-2023.html.\u003c/li\u003e\n\u003cli\u003eGeneva. International standards for the treatment of drug use disorders: revised edition incorporating results of field-testing. 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Global burden and strength of evi- dence for 88 risk factors in 204 countries and 811 subnational locations, 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440).\u003c/li\u003e\n\u003cli\u003eCollaborators GD. Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950\u0026ndash;2021, and the impact of the COVID- 19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440).\u003c/li\u003e\n\u003cli\u003eKim HJ FM, Feuer EJ, Midthune DN. Permutation tests for joinpoint regression with applications to cancer rates. Stat Med 2000;19.\u003c/li\u003e\n\u003cli\u003eRosenberg PS, Check DP, Anderson WF. A web tool for age-period-cohort analysis of cancer incidence and mortality rates. Cancer Epidemiol Biomarkers Prev. 2014;23(11):2296-302.\u003c/li\u003e\n\u003cli\u003eCheng X, Yang Y, Schwebel DC, Liu Z, Li L, Cheng P, et al. Population ageing and mortality during 1990-2017: A global decomposition analysis. PLoS Med. 2020;17(6):e1003138.\u003c/li\u003e\n\u003cli\u003eXie Y, Bowe B, Mokdad AH, Xian H, Yan Y, Li T, et al. Analysis of the Global Burden of Disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016. Kidney Int. 2018;94(3):567-81.\u003c/li\u003e\n\u003cli\u003eNetwork GBoDC. Global Burden of Disease Study 2019 (GBD 2019) Healthcare Access and Quality Index 1990-2019. 2022.\u003c/li\u003e\n\u003cli\u003eOrganization WH. Handbook on Health Inequality Monitoring2017.\u003c/li\u003e\n\u003cli\u003eThe Surveillance E, and End Results (SEER) Program. Measures of Disparity 2024 [Available from: https://seer.cancer.gov/help/hdcalc/inference-methods/individual-level-survey-sample-1.\u003c/li\u003e\n\u003cli\u003eVolker J. Schmid LH. BAMP \u0026ndash; Bayesian age-period-cohort modeling and prediction. Journal of Statistical Software. 2007;21.\u003c/li\u003e\n\u003cli\u003eM\u0026oslash;ller B. FH, Hakulinen T., Sigvaldason H, Storm H. H., Talb\u0026auml;ck M. and Haldorsen T. Prediction of cancer incidence in the Nordic countries: Empirical comparison of different approaches. Statistics in medicine. 2003;22.\u003c/li\u003e\n\u003cli\u003eZhang T, Sun L, Yin X, Chen H, Yang L, Yang X. Burden of drug use disorders in the United States from 1990 to 2021 and its projection until 2035: results from the GBD study. BMC Public Health. 2024;24(1):1639.\u003c/li\u003e\n\u003cli\u003eStatistics NCfDA. Drug abuse statistics 2024 [Available from: https://drugabusestatistics.org/.\u003c/li\u003e\n\u003cli\u003eSilveri MM, Dager AD, Cohen-Gilbert JE, Sneider JT. Neurobiological signatures associated with alcohol and drug use in the human adolescent brain. Neurosci Biobehav Rev. 2016;70:244-59.\u003c/li\u003e\n\u003cli\u003eMarinelli S, Basile G, Manfredini R, Zaami S. Sex- and Gender-Specific Drug Abuse Dynamics: The Need for Tailored Therapeutic Approaches. J Pers Med. 2023;13(6).\u003c/li\u003e\n\u003cli\u003eGrant BF, Saha TD, Ruan WJ, Goldstein RB, Chou SP, Jung J, et al. Epidemiology of DSM-5 Drug Use Disorder: Results From the National Epidemiologic Survey on Alcohol and Related Conditions-III. JAMA Psychiatry. 2016;73(1):39-47.\u003c/li\u003e\n\u003cli\u003eConrod PJ, Nikolaou K. Annual Research Review: On the developmental neuropsychology of substance use disorders. J Child Psychol Psychiatry. 2016;57(3):371-94.\u003c/li\u003e\n\u003cli\u003eAddiction EMCfDaD. European Drug Report 2024: Trends and Developments 2024 [Available from: https://www.euda.europa.eu/index_en.\u003c/li\u003e\n\u003cli\u003eNetherlands Go. Toleration policy regarding soft drugs [Available from: https://www.government.nl/topics/drugs/toleration-policy-regarding-soft-drugs-and-coffee-shops.\u003c/li\u003e\n\u003cli\u003eUnited Nations Department of Economic and Social Affairs PD. World Population Prospects 2024: Summary of Results (UN DESA/POP/2024/TR/NO. 9) 2024 [\u003c/li\u003e\n\u003cli\u003eCrime UNOoDa. World Drug Report 2020 (United Nations publication, Sales No. E.20.XI.6). 2020.\u003c/li\u003e\n\u003cli\u003eWu Z, Detels R, Zhang J, Li V, Li J. Community-based trial to prevent drug use among youths in Yunnan, China. Am J Public Health. 2002;92(12):1952-7.\u003c/li\u003e\n\u003cli\u003eAmerica EotPsRoCitUSo. Introduction to China\u0026rsquo;s Successful Efforts in Drug Control 2023 [Available from: http://us.china-embassy.gov.cn/eng/zggs/202307/t20230706_11108971.htm.\u003c/li\u003e\n\u003cli\u003eDel Pozo B, Park JN, Taylor BG, Wakeman SE, Ducharme L, Pollack HA, et al. Knowledge, Attitudes, and Beliefs About Opioid Use Disorder Treatment in Primary Care. JAMA Netw Open. 2024;7(6):e2419094.\u003c/li\u003e\n\u003cli\u003eCampopiano von Klimo M, Nolan L, Corbin M, Farinelli L, Pytell JD, Simon C, et al. Physician Reluctance to Intervene in Addiction: A Systematic Review. JAMA Netw Open. 2024;7(7):e2420837.\u003c/li\u003e\n\u003cli\u003eFirst MB, Yousif LH, Clarke DE, Wang PS, Gogtay N, Appelbaum PS. DSM-5-TR: overview of what\u0026apos;s new and what\u0026apos;s changed. World Psychiatry. 2022;21(2):218-9.\u003c/li\u003e\n\u003cli\u003eAbuse TWDoMHaS. ATLAS on Resources for the Prevention and Treatment of Substance Use Disorders. 2010.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Global Burden of Disease, Incidence, Prevalence, Death, Disability-adjusted life years, Drug use disorders, Substance use disorders","lastPublishedDoi":"10.21203/rs.3.rs-4859842/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4859842/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite extensive research, there remains a paucity of comprehensive reports on the spatiotemporal distribution, driving factors, and future trends of drug use disorders (DUDs). We used data from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to address this gap. In 2021, the global prevalence of DUDs reached 53,115,936 (95% UI: 46,999,805 – 60,949,054), marking a 35.50% increase since 1990 and is projected to continue rising over the next 25 years. The increment in incidence, deaths, and DALYs was 35.50%, 122.22%, and 74.65%, respectively. Despite the declining trends in global rates of incidence, prevalence, and DALYs, mortality still shows an upward trend, increasing from 1.26 to 1.65 per 100,000. Opioid and cocaine use disorders were the primary contributors to the overall increase in DUDs DALYs. Population growth was the primary driver of the increase in DUDs burden (35.31%). Health inequality regarding DUDs remain prominent issues.\u003c/p\u003e","manuscriptTitle":"Global burden on drug use disorders from 1990 to 2021 and projections to 2046","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-10 11:32:21","doi":"10.21203/rs.3.rs-4859842/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"05d953b0-c146-40f6-9e8b-546de3bc3653","owner":[],"postedDate":"September 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":37325263,"name":"Biological sciences/Psychology/Human behaviour"},{"id":37325264,"name":"Health sciences/Neurology/Neurological disorders"},{"id":37325265,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-12-16T17:38:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-10 11:32:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4859842","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4859842","identity":"rs-4859842","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}