Global and regional burden of four drug use disorders in the elderly, 1990 to 2021: an analysis of the Global Burden of Disease Study

Global and regional burden of four drug use disorders in the elderly, 1990 to 2021: an analysis of the Global Burden of Disease Study | 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 Research Article Global and regional burden of four drug use disorders in the elderly, 1990 to 2021: an analysis of the Global Burden of Disease Study Bochao Jia, Rui Wei, Zhiqi Li, Meiyu Feng, Mengxue Wang, Yi Wei, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5977182/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 Background As the global population ages, the burden of drug use disorders (DUDs) among the elderly is rising. It is imperative to conduct a quantitative analysis of the disease burden affecting this vulnerable population. Methods Utilize the Global Burden of Disease Study 2021 database to obtain incidence rates and disability-adjusted life years (DALYs) for opioids, cocaine, amphetamines, and cannabis among the elderly (aged 60–89) across 204 countries and 5 SDI regions from 1990 to 2021. Employ Joinpoint regression analysis to calculate the average annual percentage change (AAPC) of age-standardized incidence rates (ASIR) and age-standardized DALYs rates (ASDR). Use the Das Gupta method to decompose and analyze the impacts of changes in age structure, population growth, and epidemiology on DALYs during this period. Finally, apply the Bayesian Age-Period-Cohort (BAPC) model to predict ASIR and DALYs for global and high-burden regions from 2022 to 2035. Results Of the four DUDs, opioids have the highest disease burden. Joinpoint analysis indicates that from 1990 to 2021, the ASIR for opioid use disorder decreased with an AAPC of -0.73 (95% CI: -0.79 to 0.67), while the ASDR remained stable. Cocaine use disorder ASIR remained stable, but ASDR increased with an AAPC of 0.94 (95% CI: 0.77–1.11). The burden of amphetamine and cannabis use disorders generally stabilized. Geographic heterogeneity was evident at regional and national levels, with ASDR for all four DUDs increasing in high-SDI areas while remaining stable or declining in other SDI areas. High-income North America, represented by the United States, shows a higher burden of disease. Decomposition analysis shows that population growth is the main factor affecting the change in the burden of DUDs in most regions, and high-income North America is mainly affected by epidemiological changes. According to the Predictive models, the DALYs of DUDs in the global elderly population is still on the rise, especially in the male group in North America. Conclusion The burden of DUDs among the elderly varies across countries, regions, SDI levels, and genders, underscoring the need for targeted public health policy adjustments and strategic allocation of medical resources to mitigate this burden. Drug use disorders Elderly Age-standardized rate Global burden of disease Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Drug use disorder (DUD) refers to a maladaptive pattern of drug abuse, leading to clinically significant impairment or distress that includes symptoms of dependence, such as withdrawal symptoms or progressive tolerance (Hasin et al., 2013 ). DUD encompasses a variety of substances, including opioids, cocaine, amphetamines, and cannabis (Shen et al., 2023 ). According to the Global Burden of Disease Study (GBD) 2021, the burden of DUD worldwide has been steadily increasing from 2010 to 2021, with the increasing DUD burden among the elderly population not to be ignored (GBD 2021 Diseases and Injuries Collaborators, 2024 ; Lin et al., 2023 ). This trend is attributed to several factors, including the aging of the “baby boomer generation,” who have higher drug use rates throughout their lives, and the increasing availability and prescription of drugs for chronic pain management (S. Yarnell et al., 2020a ; S. Yarnell et al., 2020b ). Due to age-related physiological changes, comorbidities, and the possibility of multi drug therapy, the elderly population is particularly vulnerable, which may exacerbate the adverse effects of drug use (Wu et al., 2014; S. Yarnell et al., 2020b ). Cultural attitudes, socio-economic factors, psychological and physiological factors of both genders, accessibility of medical services, and socio-economic factors can all affect the burden patterns and effectiveness of treatment plans for DUD. At present, there is a significant gap in understanding the epidemiology and impact of DUD in older adults. Although several studies have investigated the burden of DUD, they have focused on a country or specific region and have not conducted detailed analysis on the burden trends of specific drugs (Amirkafi et al., 2024 ; Castaldelli-Maia et al., 2023 ; Zhang et al., 2024 ), mainly focused on young populations (Kenya Adolescent Mental Health Group, 2024 ), and used relatively older data (Charlson et al., 2016 ). In response to the call for strengthening the prevention and treatment of drug abuse in the United Nations’ 2030 Agenda for Sustainable Development, it is necessary to further understand the regional differences in the burden of DUD among the elderly population, in order to strengthen monitoring, tailor interventions and policy measures, ultimately reducing the impact of DUD and improving the health and well-being of older people worldwide. As the first epidemiological study on the disease burden of drug use disorders among the elderly, we extracted the data of GBD 2021, concretized the analysis of the disease burden of the four drug use disorders of opioid, cocaine, amphetamines, and cannabis, and explored the epidemiological trend of the four DUDs incidence rates and DALYS rate of the 60–89 year old elderly at global, regional and national levels from 1990 to 2021 by using the methods of joinpoint model, decomposition analysis, correlation analysis, and the Nordpred prediction model. 2. Methods 2.1. Data collection The GBD 2021 study utilized the latest epidemiological data to comprehensively evaluate the health impacts caused by 371 diseases, injuries, and 88 risk factors in 204 countries and regions worldwide (GBD 2021 Diseases and Injuries Collaborators, 2024 ). This study employed GBD research tools ( http://ghdx.healthdata.org/gbd-results-tool ) to download data on the incidence and disability adjusted life years (DALYs) of four types of medication use disorders in elderly people aged 60–89, and compare and analyze them based on the specific types of medication use disorders. DALYs are standard indicators for quantifying disease burden, encompassing years of life lost due to premature death and years of healthy life lost due to disability caused by disease, thereby reflecting the overall impact of disease on population health (Wei et al., 2023 ). Other parameter settings include regions (global, 21 regions with similar geographic and epidemiological characteristics, 5 SDI regions, 204 countries) and calendar years (1990–2021). 2.2. Case definition The case definition of drug use disorders in GBD 2021 is mainly based on (International Classification of Diseases, Tenth Revision) ICD-10 and (Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision) DSM-IV-TR. Opioid use disorders (ICD-10 F11.20) include adult opioid overdose deaths and neonatal deaths caused by maternal opioid use; The use disorders of cocaine (DSM-IV 304.20, ICD-10 F14.20), amphetamines (ICD-10 F15.20, DSM-IV 304.40), and cannabis (DSM-IV 304.30, ICD-10 F12.20) involve a dysfunctional pattern of drug use. The precise diagnostic criteria are detailed in Table S1 . 2.3. Sociodemographic index(SDI) The SDI is utilized to examine the relationship between social and demographic development levels and disease burden across various countries and regions. The SDI is a composite measure derived from three factors: the fertility rate of individuals under 25 years old, the average educational attainment of individuals aged 15 and above, and the per capita lagged distributed income. In the GBD 2021, SDI scores range from 0 to 100. Based on these scores, countries and regions are categorized into five groups: low SDI, low-middle SDI, middle SDI, high-middle SDI, and high SDI. To explore the impact of SDI on disease burden, Spearman correlation analysis was used to calculate the correlation coefficients ρ and p-values between age-standardized rates (ASR) and SDI in 21 regions and 204 countries worldwide in 2021. 2.4. Data analysis This study employed a direct standardization method, using the GBD 2021 world standard population, to calculate estimated ASR per 100,000 people for 60–89 age groups and their corresponding 95% confidence interval (CI). The specific formula used is as follows: \(\:ASR=\frac{{\sum\:}_{i=1}^{A}{a}_{i}{w}_{i}}{{\sum\:}_{i=1}^{A}{w}_{i}}\times\:100000\) , where A represents the total number of age groups, i indicates a specific age group, a i is the age-specific rate for the i -th age group, and wi represents the standard population of the corresponding age group in the GBD 2021 standard population (Sun et al., 2024 ). Joinpoint regression analysis model was employed to evaluate trends and significant inflection points in age-standardized incidence rates (ASIR) and age-standardized DALYs rate (ASDR) from 1990 to 2021 (Tuo et al., 2024 ). This model describes ASR trends over a specified period by calculating the annual percentage change (APC) along with its 95% CI. The overall time trend of these rates is assessed by calculating the average annual percentage change (AAPC) for the population. Statistically, if the APC or AAPC and their 95% CI are greater than zero, this indicates an upward trend in rates over the specified period. Conversely, if both the APC or AAPC and their 95% CI are less than zero, this indicates a decreasing trend in rates. If neither condition is met, the disease burden is considered stable. Decomposition analysis was utilized to quantify the relative impact of three driving factors—changes in age structure, population growth, and epidemiology—on the changes in DALYs from 1990 to 2021 (GBD 2019 Dementia Forecasting Collaborators, 2022 ). The Das Gupta method was used for decomposition analysis, isolating the independent contribution of each factor while holding the others constant (Das Gupta, 1978 ). The Age-Period-Cohort model is used to predict the number of DALYs and age standardized rates of drug use disorders from 2022 to 2035. The theoretical basis of this model is to explore the correlation between rates, age structure, and population size through generalized linear models. The prediction is realized through the “Nordpred” package in R software, which has empirical validity in predicting the trend of current disease incidence rate (Huang et al., 2024). All data organization, analysis, and visualization for this study were conducted using R software (version 4.3.3). 3. Results 3.1. Global trends In 2021, opioid use disorder had the heaviest disease burden among the four types of DUDs worldwide (Fig. 1). Specifically, for every 100000 people, the ASIR and ASDR for the four types of drug use disorders are opioid: 9.41 (95% CI 5.26–14.51), 67.83 (95% CI 55.06–82.57); Cocaine: 0.20 (95% CI 0.07–0.41), 7.71 (95% CI 6.17–9.80); Amphetamine: 2.49 (95% CI 1.41–3.97), 5.41 (95% CI 4.11–7.23); Cannabis: 1.77 (95% CI 0.47–3.63), 1.40 (95% CI 0.76–2.38) (Table 1). Joinpoint analysis present that the ASIR of opioid use disorders showed a downward trend from 1990 to 2021, with an AAPC of -0.73 (95% CI -0.79-0.67) (Fig. S1). ASDR gradually decreased from 1997 to 2007 (APC=-1.87) and showed an upward trend from 2007 to 2021 (APC = 0.44 for 2007–2013, 1.87 for 2013–2021). The ASIR of cocaine use disorder remained stable, while ASDR gradually increased, with an AAPC of 0.94 (95% CI 0.77–1.11), until 2019 when it began to gradually stabilize (APC=-0.05 for 2019–2021); The ASIR and ASDR of methamphetamine and cannabis use disorders tend to stabilize overall. Table 1 Global levels and time trends of ASIR and ASDR for four drug use disorders in older adults in 1990 and 2021 Groups Sex Incidence (95% uncertainty interval) DALYs (95% uncertainty interval) Age- standardised rates per 100,000 (1990) Age- standardised rates per 100,000 (2021) AAPC (1990–2021) Age- standardised rates per 100,000 (1990) Age- standardised rates per 100,000 (2021) AAPC (1990–2021) Opioid use disorders Male 9.6 (5.30-15.03) 7.55 (4.21–11.57) -0.83 (-0.88 to -0.77) 72.73 (59.14–88.53) 70.22 (58.79–82.35) -0.18 (-0.25 to -0.11) Female 13.36 (7.39–20.82) 11.01 (6.15–17.05) -0.64 (-0.65 to -0.63) 60.36 (44.17–80.48) 65.53 (50.42–83.64) 0.27 (0.22 to 0.31) Both 11.67 (6.42–18.28) 9.41 (5.26–14.51) -0.73 (-0.79 to -0.67) 65.89 (51.47–83.17) 67.83 (55.06–82.57) 0.04 (-0.09 to 0.17) Cocaine use disorders Male 0.25 (0.08–0.51) 0.26 (0.49 − 0.11) 0.17 (0.15 to 0.2) 6.57 (4.69–9.13) 10.86 (8.68–13.91) 1.65 (1.55 to 1.74) Female 0.17 (0.03–0.39) 0.15 (0.03–0.32) -0.46 (-0.48 to -0.44) 4.94 (3.40–7.04) 4.84 (3.74–6.41) -0.05 (-0.08 to -0.02) Both 0.21 (0.06–0.45) 0.20 (0.07–0.41) -0.09 (-0.15 to -0.03) 5.71 (4.11–7.84) 7.71 (6.17–9.80) 0.94 (0.77 to 1.11) Amphetamine use disorders Male 2.55 (1.37–4.13) 2.48 (1.36–3.97) -0.07 (-0.09 to -0.05) 4.39 (2.86–6.68) 7.49 (5.86–9.66) 1.72 (1.65 to 1.79) Female 2.65 (1.52–4.18) 2.50 (1.42–3.96) -0.2 (-0.21 to -0.19) 5.10 (3.72–7.07) 3.59 (2.50–5.30) -1.17 (-1.23 to -1.1) Both 2.60 (1.46–4.16) 2.49 (1.41–3.97) -0.14 (-0.17 to -0.11) 4.72 (3.40–6.79) 5.41 (4.11–7.23) 0.39 (0.21 to 0.57) Cannabis use disorders Male 2.13 (0.59–4.36) 2.10 (0.60–4.34) -0.03 (-0.05 to -0.02) 1.81 (0.98–3.08) 1.86 (1.03–3.15) 0.07 (0.05 to 0.09) Female 1.50 (0.40–3.13) 1.46 (0.36–3.07) -0.09 (-0.1 to -0.09) 0.98 (0.52–1.73) 0.98 (0.52–1.72) -0.01 (-0.02 to 0.01) Both 1.80 (0.51–3.70) 1.77 (0.47–3.63) -0.06 (-0.07 to -0.05) 1.36 (0.74–2.36) 1.40 (0.76–2.38) 0.07 (0.04 to 0.10) ASIR, age-standardized incidence rate; ASDR, age-standardized disability-adjusted life years; DALYs, disability-adjusted life years; AAPC, average annual percentage change. From a gender perspective, among the four types of DUDs, except for opioid ASIR, which is higher in females than males, the other three are higher in males, while ASDR is higher in males than females (Fig. 2). In opioid use disorders, both male and female’s ASIR showed a decreasing trend, while ASDR showed an increasing trend in females and a decreasing trend in males (Fig. S2). In cocaine use disorders, the ASIR of males shows an upward trend while that of females shows downward, and the ASDR shows an upward trend. In methamphetamine use disorder, both male and female show a decreasing trend in ASIR, while in ASDR, males show an increasing trend while females show decrease. In cannabis use disorders, the ASIR of both male and female showed a decreasing trend, while the ASDR of men showed an increasing trend while that of women remained stable. 3.2. SDI regions level In 1990, the disease burden of opioid use disorders was higher in the middle and high-middle SDI regions (Table 2; Table 3). From 1990 to 2021, whereas the disease burden in both regions decreased significantly (Table S2; Table S3). By contrast, the disease burden in high SDI regions increased the most significantly, with the AAPC of ASIR and ASDR were 0.45 (95% CI 0.37–0.54) and 4.05 (95% CI 3.94–4.17), respectively. Fig. S3 illustrates a comparison of the changes in ASIR and ASDR for the four DUDs in the five SDI regions between 1990 and 2021. Given the prominent burden of opioid use disorders, further analysis of their incidence and DALYs was conducted based on three dimensions: SDI, gender, and age (Fig. S4). In terms of the rate, the trend of male and female is basically the same. In terms the number, the incidence number is higher in middle and high SDI regions, and it is higher in females than males. The DALYs number is concentrated in high SDI areas, with males having a higher DALYs number than females in the 60–64 age group, and females gradually exceeding males in DALYs number as they age. Overall, the disease burden of opioid use disorders decreases with age. Table 2 ASIR for four drug use disorders in the elderly in 1990 and 2021 at the regional level. Regions ASIR (95% uncertainty interval) Age- standardised rates per 100,000 (1990) Age- standardised rates per 100,000 (2021) Opioid Cocaine Amphetamine Cannabis Opioid Cocaine Amphetamine Cannabis Low SDI 8.92 (5.01,13.66) 0.22 (0.12,0.36) 1.1 (0.55,1.9) 1.5 (0.33,3.14) 8.87 (5.17,13.14) 0.26 (0.18,0.37) 1.09 (0.55,1.85) 1.52 (0.34,3.25) Low-middle SDI 10.1 (5.52,15.48) 0.14 (0.06,0.26) 1.28 (0.67,2.15) 1.57 (0.36,3.28) 9.73 (5.29,14.87) 0.17 (0.1,0.29) 1.3 (0.69,2.15) 1.59 (0.37,3.34) Middle SDI 15.91 (9.03,24.42) 0.15 (0.05,0.3) 3.54 (2.07,5.48) 1.65 (0.41,3.37) 9.45 (5.31,14.43) 0.16 (0.06,0.3) 2.94 (1.68,4.64) 1.67 (0.4,3.5) High-middle SDI 14.47 (8.01,22.74) 0.17 (0.03,0.39) 3.15 (1.78,5.02) 1.77 (0.52,3.53) 10.58 (5.95,16.31) 0.16 (0.05,0.32) 3.08 (1.74,4.87) 1.72 (0.47,3.53) High SDI 6.91 (3.51,11.55) 0.35 (0.06,0.79) 2.25 (1.17,3.76) 2.21 (0.68,4.45) 7.92 (4.11,13.3) 0.33 (0.05,0.74) 2.4 (1.29,3.92) 2.2 (0.65,4.49) Central Asia 12.27 (6.34,19.67) 0.17 (0.04,0.38) 2.6 (1.3,4.33) 1.41 (0.33,2.97) 13.35 (7.58,20.11) 0.27 (0.13,0.46) 2.79 (1.42,4.64) 1.44 (0.34,3.05) Central Europe 5.93 (3.18,9.36) 0.17 (0.03,0.39) 2.45 (1.24,4.07) 2 (0.59,4) 6.17 (3.46,9.38) 0.2 (0.04,0.44) 2.54 (1.29,4.22) 2.07 (0.65,4.09) Eastern Europe 16.01 (7.83,26.55) 0.17 (0.03,0.38) 2.41 (1.2,4.03) 1.9 (0.54,3.77) 15.75 (8.56,24.75) 0.21 (0.07,0.41) 2.35 (1.15,3.93) 1.98 (0.57,3.99) Australasia 9.06 (4.84,14.21) 0.34 (0.03,0.83) 3.47 (1.81,5.77) 2.15 (0.61,4.45) 8.33 (4.12,13.27) 0.31 (0.03,0.75) 3.4 (1.72,5.74) 2.27 (0.64,4.55) High-income Asia Pacific 5.52 (2.58,9.3) 0.43 (0.07,0.98) 2.09 (1.07,3.47) 2.06 (0.55,4.3) 4.86 (2.28,8.18) 0.41 (0.06,0.91) 2.05 (1.04,3.39) 2.01 (0.5,4.26) High-income North America 7.17 (3.05,13.09) 0.41 (0.05,0.96) 1.71 (0.81,3.05) 2.45 (0.73,5.03) 8.98 (3.78,17.08) 0.39 (0.05,0.94) 1.95 (0.93,3.29) 2.56 (0.73,5.29) Southern Latin America 5.61 (2.57,9.78) 0.43 (0.06,1.01) 1.29 (0.63,2.25) 1.54 (0.47,3.03) 5.38 (2.55,9.11) 0.44 (0.05,1.11) 1.38 (0.66,2.36) 1.53 (0.42,2.94) Western Europe 6.14 (3.26,9.97) 0.35 (0.06,0.79) 2.57 (1.35,4.2) 2.23 (0.73,4.37) 7.99 (4.64,12.27) 0.37 (0.07,0.84) 2.75 (1.51,4.45) 2.15 (0.68,4.28) Andean Latin America 3.99 (1.76,6.91) 0.25 (0.03,0.57) 1.85 (0.95,3.12) 1.57 (0.34,3.36) 4.14 (1.86,6.99) 0.25 (0.04,0.58) 1.93 (1.01,3.2) 1.56 (0.31,3.32) Caribbean 4.32 (1.9,7.47) 0.24 (0.03,0.65) 1.19 (0.58,2.07) 1.84 (0.47,3.85) 4.08 (1.87,6.94) 0.23 (0.03,0.6) 1.23 (0.61,2.13) 1.83 (0.46,3.84) Central Latin America 4.23 (1.88,7.28) 0.36 (0.06,0.83) 1.6 (0.81,2.7) 1.72 (0.43,3.59) 4 (1.8,6.8) 0.35 (0.05,0.81) 1.59 (0.82,2.66) 1.64 (0.41,3.36) Tropical Latin America 3.91 (1.66,6.95) 0.44 (0.07,1) 3.51 (1.86,5.77) 2.3 (0.63,4.8) 3.7 (1.61,6.49) 0.48 (0.08,1.06) 3.65 (1.93,5.95) 2.35 (0.67,4.91) North Africa and Middle East 9.36 (4.89,15.05) 0.11 (0.02,0.25) 0.65 (0.3,1.2) 1.25 (0.3,2.59) 10 (5.36,15.5) 0.12 (0.03,0.25) 0.66 (0.3,1.19) 1.26 (0.33,2.6) South Asia 12.14 (6.57,18.51) 0.09 (0.02,0.19) 0.81 (0.37,1.45) 1.64 (0.35,3.46) 11.45 (6.18,17.42) 0.1 (0.03,0.18) 0.84 (0.4,1.49) 1.69 (0.37,3.59) Southeast Asia 6.18 (3.38,9.83) 0.03 (0.01,0.08) 2.79 (1.46,4.56) 1.57 (0.35,3.32) 6.18 (3.44,9.64) 0.02 (0,0.04) 2.82 (1.49,4.62) 1.57 (0.36,3.33) East Asia 23.4 (13.41,35.55) 0.07 (0.01,0.16) 5.01 (2.98,7.63) 1.57 (0.35,3.21) 11.19 (6.37,16.93) 0.04 (0.01,0.1) 4.06 (2.34,6.28) 1.59 (0.37,3.32) Oceania 8.37 (4.72,12.8) 0.02 (0,0.06) 2.7 (1.43,4.44) 2.03 (0.51,4.15) 8.38 (4.84,12.43) 0.01 (0,0.04) 2.72 (1.44,4.44) 2.03 (0.52,4.17) Central Sub-Saharan Africa 7.27 (4.24,10.95) 0.13 (0.04,0.27) 1.33 (0.66,2.26) 1.32 (0.23,2.74) 8.01 (4.92,11.83) 0.13 (0.05,0.24) 1.32 (0.66,2.22) 1.32 (0.23,2.75) Eastern Sub-Saharan Africa 8.15 (4.85,12.19) 0.18 (0.09,0.31) 1.32 (0.67,2.23) 1.63 (0.43,3.38) 8.91 (5.62,12.82) 0.24 (0.16,0.35) 1.31 (0.67,2.22) 1.61 (0.4,3.32) Southern Sub-Saharan Africa 14.79 (7.68,23.61) 1.29 (0.62,2.16) 4.43 (2.51,6.95) 2.13 (0.67,4.16) 13.44 (7.43,20.75) 1.52 (0.73,2.6) 4.54 (2.64,7.12) 2.09 (0.66,4.12) Western Sub-Saharan Africa 6.09 (3.3,9.5) 0.59 (0.38,0.83) 1.3 (0.65,2.21) 1.1 (0.21,2.41) 6.05 (3.31,9.32) 0.89 (0.72,1.13) 1.25 (0.62,2.13) 1.09 (0.2,2.43) SDI, sociodemographic index. Table 3 ASDR for four drug use disorders in the elderly in 1990 and 2021 at the regional level. Regions ASDR (95% uncertainty interval) Age- standardised rates per 100,000 (1990) Age- standardised rates per 100,000 (2021) Opioid Cocaine Amphetamine Cannabis Opioid Cocaine Amphetamine Cannabis Low SDI 40.7 (29.83,53.92) 3.75 (2.15,6.1) 1.17 (0.68,2.05) 1.14 (0.61,2) 39.98 (29.99,51.95) 3.02 (1.84,4.68) 1.12 (0.67,1.89) 1.14 (0.6,2) Low-middle SDI 49.67 (38.21,64.28) 3.95 (2.23,6.48) 1.58 (1,2.6) 1.3 (0.69,2.25) 50.37 (39.41,63.91) 4.7 (3.35,6.55) 1.7 (1.12,2.63) 1.24 (0.66,2.14) Middle SDI 93.42 (73.18,115.96) 4.67 (3.38,6.34) 8.67 (6.37,11.91) 1.18 (0.63,2.08) 42.12 (32.16,54.63) 4.68 (3.74,5.87) 5.51 (4,7.65) 1.23 (0.66,2.13) High-middle SDI 79.13 (61.22,100.75) 4.84 (3.71,6.52) 5.51 (3.87,8.03) 1.11 (0.58,1.91) 46.7 (35.36,60.73) 3.38 (2.38,4.75) 4.48 (3.03,6.73) 1.1 (0.58,1.91) High SDI 43.41 (32.56,57.4) 8.77 (5.76,12.77) 2.84 (1.71,4.65) 1.83 (1.02,3.12) 147.3 (119.42,176.53) 20.06 (15.85,25.95) 10.39 (8.46,13.28) 2.09 (1.17,3.5) Central Asia 49.54 (34.12,70.12) 3.04 (2.06,4.2) 3.47 (1.9,5.98) 0.74 (0.35,1.39) 61.95 (46.84,80.02) 4.87 (3.86,6.12) 5.4 (3.54,8.24) 0.77 (0.37,1.42) Central Europe 25.9 (19.53,34.15) 3.27 (2.38,4.58) 2.9 (1.54,5.15) 1.16 (0.59,2.02) 31.34 (25.02,39.54) 2.92 (1.95,4.36) 3.22 (1.74,5.52) 1.19 (0.62,2.07) Eastern Europe 114.01 (88.68,146.52) 6.29 (5.38,7.3) 2.8 (1.6,4.81) 1.14 (0.59,2.02) 93.38 (73.56,117.14) 3.15 (2.49,4.08) 3.14 (1.95,5.07) 1.2 (0.63,2.09) Australasia 32.77 (20.78,47.91) 7.72 (3.31,14.01) 4.43 (1.97,8.5) 2.19 (1.05,3.96) 43.45 (29.81,60.84) 7.78 (3.4,14.21) 5.34 (2.92,9.35) 2.09 (1.08,3.64) High-income Asia Pacific 20.22 (12.55,30.62) 6.4 (3.79,10.21) 1.85 (0.97,3.29) 1.51 (0.82,2.69) 19.21 (12.23,28.81) 6.75 (4.13,10.62) 2.05 (1.16,3.52) 1.51 (0.81,2.64) High-income North America 62.62 (46.38,82.51) 14.39 (9.53,21.16) 2.21 (1.41,3.51) 2.73 (1.53,4.57) 333.05 (268.89,399.67) 46.27 (37.04,59.87) 22.81 (18.76,29.28) 3.33 (1.87,5.46) Southern Latin America 21.67 (11.19,35.58) 15.25 (8.3,25.66) 1.2 (0.64,2.16) 0.87 (0.43,1.55) 23.76 (14.55,36.21) 17.77 (10.36,28.06) 1.77 (1.12,2.84) 1.05 (0.55,1.83) Western Europe 33.08 (24.37,44.41) 7.37 (4.56,11.16) 3.06 (1.65,5.24) 1.46 (0.79,2.51) 64.13 (51.9,78.19) 8.42 (5.49,12.49) 4.11 (2.6,6.42) 1.58 (0.86,2.65) Andean Latin America 18.03 (11.09,27.46) 11.04 (8.06,15) 2.11 (1.24,3.49) 1.11 (0.55,2.06) 20.89 (13.55,30.71) 15.41 (11.33,20.56) 3.73 (2.45,5.52) 1.11 (0.53,2.03) Caribbean 16.07 (9.42,25.07) 7.27 (4.74,10.74) 1.14 (0.64,2) 1.85 (0.92,3.34) 15.05 (8.96,23.45) 8.91 (6.26,12.37) 1.85 (1.22,2.79) 1.79 (0.91,3.25) Central Latin America 18.08 (12.23,26.14) 11.83 (8.73,15.94) 1.59 (0.91,2.69) 1.38 (0.71,2.39) 16.37 (10.74,23.92) 11.27 (8.28,15.34) 2.1 (1.37,3.19) 1.33 (0.67,2.29) Tropical Latin America 13.96 (7.99,22.02) 10.31 (6.52,15.57) 3.62 (1.78,6.44) 2.11 (1.12,3.65) 14.23 (8.73,21.86) 17.85 (13.46,23.84) 3.99 (2.09,7.12) 2.13 (1.15,3.57) North Africa and Middle East 54.59 (40.97,70.94) 8.8 (5.27,13.31) 1.4 (0.85,2.27) 0.64 (0.31,1.16) 57.06 (44.59,72.47) 6.17 (4.46,8.27) 1.94 (1.3,2.82) 0.64 (0.31,1.17) South Asia 61.4 (47.94,78.4) 3.69 (1.67,6.87) 1.2 (0.76,2.01) 1.54 (0.83,2.64) 60.17 (47.39,76) 4.09 (2.73,5.88) 1.41 (0.95,2.17) 1.46 (0.78,2.55) Southeast Asia 26.29 (18.61,36.25) 1.43 (0.88,2.34) 3.06 (1.76,5.12) 1.11 (0.57,1.97) 28.71 (20.91,38.47) 1.5 (1.12,2.14) 3.57 (2.23,5.64) 1.11 (0.58,1.97) East Asia 132.04 (102.25,164.96) 1.71 (1.21,2.53) 13.7 (10.36,18.5) 0.96 (0.5,1.72) 40.59 (29.18,54.89) 0.44 (0.29,0.66) 7.54 (5.42,10.4) 0.99 (0.52,1.76) Oceania 27.74 (16.85,41.6) 0.61 (0.3,1.2) 2.71 (1.48,4.73) 1.63 (0.81,2.91) 25.49 (15.36,38.66) 0.6 (0.32,1.12) 2.79 (1.54,4.8) 1.63 (0.8,2.94) Central sub-Saharan Africa 26.59 (15.9,39.65) 1.68 (1.02,2.68) 1.1 (0.58,1.97) 0.91 (0.43,1.69) 29.29 (18.44,42.08) 1.87 (1.12,3.08) 1.18 (0.66,2.03) 0.9 (0.43,1.65) Eastern sub-Saharan Africa 38.22 (24.21,54.79) 2.33 (1.3,3.93) 1.09 (0.58,1.95) 1.03 (0.54,1.8) 35.32 (24.3,48.15) 2.48 (1.47,4.17) 1.14 (0.62,2.03) 1.01 (0.52,1.81) Southern sub-Saharan Africa 87.57 (66.39,113.43) 17.4 (12.27,24.26) 6.18 (3.83,9.69) 1.48 (0.78,2.58) 84.33 (67.95,103.32) 21.33 (15.36,28.82) 8.28 (5.63,12.05) 1.51 (0.79,2.64) Western sub-Saharan Africa 22.85 (15.63,31.49) 2.66 (1.7,4.02) 1.01 (0.51,1.83) 0.69 (0.35,1.26) 20.77 (14.3,28.96) 3.64 (2.31,5.35) 0.98 (0.5,1.77) 0.68 (0.34,1.21) SDI, sociodemographic index. In 2021, the burden of disease for cocaine use disorders was concentrated in high SDI areas, with a significant increase in the ASDR compared with 1990 (Table 2; Table 3; Table S2; Table S3). The ASIR of amphetamine use disorder is higher in the middle SDI region and high-middle SDI region, However, the ASDR in the high SDI region increased the most significantly from 1990 to 2021. The overall ASIR of cannabis use disorder remained stable, with an increase in ASDR in high SDI areas in 2021 compared to 1990. It is worth noting that compared to 1990, the ASDR in the middle SDI region and the high- middle SDI region decreased significantly in 2021, while the ASDR in the high SDI region increased significantly. Overall, from 1990–2021, the ASDR of the four drug use disorders showed an increasing trend in high SDI regions, while it remained stable or decreased in other SDI regions, indicating a close correlation between the disease burden of drug use disorders and SDI regions. Figure 3 intuitively shows the change trend of incidence crude rate and DALYs crude rate of four DUDs in five SDI regions. 3.3. Regional trends In 2021, the ASIR of opioid use disorders in Eastern European populations was the highest among 21 regions, at 15.75 (95% CI 8.56–24.75), and the ASDR was 93.38 (95% CI 73.56-117.14), ranking second (Table 2; Table 3). In 2021, the ASIR of High incoming North America was 8.98 (95% CI 3.78–17.08), while ASDR grew rapidly, with an AAPC of 5.61 (95% CI 5.37–5.84) (Table S2; Table S3). In 2021, the highest ASIR values for cocaine and amphetamine use disorders were observed in Southern Sub-Saharan Africa, at 1.52 (95% CI 0.73–2.60) and 4.54 (95% CI 2.64–7.12), respectively. It is worth noting that from 1990 to 2021, although the ASIR values for both drug use disorders decreased or slightly increased in High incoming North America, ASDR showed a significant increase, with AAPC values of 3.86 (95% CI 3.47–4.25) and 8.08 (95% CI 7.04–9.13), respectively. The disease burden distribution of cannabis use disorder is relatively even in 21 regions, with High incoming North America having the heaviest disease burden and the fastest growth rate of ASDR. It should be pointed out that in East Asia, except for cannabis use disorder which remained stable, the disease burden of the other three DUDs decreased to varying degrees (AAPC < 0, p = 0). Fig. S3 shows a comparison of the changes in ASIR and ASDR for four DUDs in 21 regions between 1990 and 2021. In terms of SDI and disease burden in 21 regions, only the disease burden of opioid use disorders was negatively correlated with regional SDI, with a correlation coefficient of ρ=-0.22, P < 0.001 for ASIR and ρ=-0.07, P = 0.026 for ASDR (Fig. S5). The disease burden of cocaine, amphetamine, and cannabis use disorders was positively correlated with regional SDI. 3.4. National trends In 2021, the three countries with the highest ASIR for opioid use disorders were Kazakhstan, Belarus, and France (Fig. 4; Table S4). The country with the lowest ASIR was the United Kingdom, and the highest AAPC for ASIR was France's (Table S6). The two countries with the highest ASDR are the United States and Canada (Table S5). The country with the lowest ASDR is the Dominican Republic, and the maximum AAPC of ASDR is 6.00 (95% CI 5.65–6.36) in the United States (Table S7). The three countries with the highest ASIR for cocaine use disorders are all located in Africa, namely South Africa, Ghana, and Cameroon. Countries with lower ASIR are mainly located in Asia, with Sri Lanka having the lowest ASIR. The maximum AAPC value for ASIR is 3.80 (95% CI 3.46–4.13) in Kazakhstan. The country with the highest ASDR is 48.99 (95% CI 39.16-64.00) in the United States, the highest AAPC in Turkmenistan (4.89, 95% CI 4.16–5.61), and the lowest AAPC in Taiwan, China Province is -8.47 (95% CI 8.86–8.08). The two countries with the highest ASIR for drug use disorders of amphetamines are Northern Mariana Islands and South Africa, the lowest country is Türkiye, and the maximum AAPC of ASIR is 1.22 (95% CI 1.13–1.30) in France. The country with the highest ASDR is the United States, with the lowest ASDR is Palestine at 0.51 (95% CI 0.24–0.99), and the highest AAPC for ASDR is the United States at 8.34 (95% CI 7.33–9.36). The two countries with the highest ASIR for cannabis drug use disorders are the United Kingdom and the United States. The maximum AAPC value for ASIR is 0.49 (95% CI 0.44–0.55) in the United Kingdom. The two countries with the highest ASDR are Canada and the United States. The maximum AAPC value for ASDR is New Zealand at 1.07 (95% CI 0.86–1.29). In terms of SDI and disease burden in 204 countries, only the ASIR of opioid use disorder is negatively correlated with regional SDI, while the ASDR of opioid use disorder and the disease burden of cocaine, amphetamine, and cannabis are positively correlated with regional SDI (Fig. 5). Of note, the ASDR in the United States is significantly higher than the fitted curve for all four DUDs. 3.5. Decomposition analysis The decomposition analysis measured the relative contributions of population aging, population growth, and epidemiological changes to the burden of drug use disorders in different regions using DALYs as units (Fig. 6). In opioid use disorders, population growth has led to changes in disease burden in most regions, particularly in low SDI and low to medium SDI areas (Table S8). North America, Western Europe, and East Asia are greatly influenced by epidemiology, with rates of 609.03%, 118.00%, and − 125.28%, respectively. In cocaine use disorders, population growth has led to changes in disease burden in most regions, particularly in low SDI, medium low SDI, and medium SDI areas (Table S9). North America, East Asia, and Eastern Europe are significantly affected by epidemiology, with rates of 312.47%, -135.32%, and − 56.50%, respectively. Population growth has led to changes in the disease burden in most regions due to methamphetamine use disorders (Table S10). North America is greatly influenced by epidemiology, accounting for 1323.23%. Population growth is the main factor affecting changes in disease burden in cannabis use disorders (Table S11). The negative growth impact of population aging is significant in East Asia and high-income Asia Pacific, with rates of -16.72% and − 25.55%, respectively. North America and South Latin America are greatly influenced by epidemiology, accounting for 32.13% and 28.43% respectively. Overall, the negative impact of aging on drug use disorders other than cannabis is relatively small. 3.6. Predictive analysis Figure 7 shows the predicted trajectories of DALY numbers and age standardized rates for four types of drug use disorders globally and the top two high burden regions from 2022 to 2035. Analysis shows that the burden of opioid use disorders is still increasing in high-income North America, with a more pronounced growth trend among males, while the disease burden in Eastern Europe is showing a downward trend. In high-income North America, the burden of cocaine and amphetamine use disorders varies more significantly between men and women, with an increasing trend in men and a stabilizing trend in women. However, in sub-Saharan Africa, the burden on both men and women is decreasing. The burden of cannabis use disorders in high-income North America is on the rise, while the burden globally and in Australasia is expected to remain stable in the coming period. 4. Discussion With the global aging process, the overall burden of DUD disease among the elderly population is still increasing, but there is still insufficient attention to this vulnerable group (GBD 2021 Diseases and Injuries Collaborators, 2024 ). A variety of chronic and acute diseases and their complications in later life may lead to drug abuse, while drug use disorders will lead to an increased risk of diseases such as diabetes, cardiovascular diseases, neurodegenerative diseases and infectious diseases (Hser et al., 2017 ; Kaye et al., 2024 ; Winhusen et al., 2019 ). In addition, due to social factors and insufficient personal cognition, there are challenges in the diagnosis and treatment of medication use disorders in the elderly population (S. Yarnell et al., 2020b ). Therefore, it is necessary to conduct in-depth research on the DUD burden of the elderly population. This study provides the latest data on the incidence rate and DALYs of drug use disorders at the global, regional and national levels from 1990 to 2021, and reveals their distribution patterns in different SDI regions and between men and women through trend, decomposition and prediction analysis. On a global scale, opioid use disorder is the heaviest burden among the four DUDs. The adverse reactions of excessive use of opioid drugs include constipation caused by intestinal dysfunction, difficulty urinating, ventricular arrhythmia, respiratory depression, and cognitive dysfunction, all of which can increase the physical damage and risk of death in elderly people (Bateman et al., 2023 ; Farmer et al., 2018 ; Krantz et al., 2023 ; Mercadante, 2019 ). From 1990 to 2021, ASDR significantly increased in high SDI regions among the five SDI regions, with the United States being a prominent representative. Pain is a significant cause of opioid use disorders in the elderly (Luchting et al., 2019; Pergolizzi et al., 2008 ). In the medical field, in the mid to late 1990s, the American Pain Society identified pain as the fifth vital sign and emphasized the patient's right to assess and manage pain, thereby relaxing restrictions on opioid prescriptions for chronic non cancer pain, which may be a factor contributing to increased abuse (Skolnick, 2018 ). Opioid abuse, represented by OxyContin, has led to a 79% increase in overdose mortality rates since 1996 (Alpert et al., 2022 ). After the successful reduction of excessive use of OxyContin due to the release of anti-abuse patented formulas, stronger side effects of heroin and illegally manufactured fentanyl emerged, leading to a continued increase in the burden of opioid use disorders (Gardner et al., 2022 ). In addition to the evolution of new opioid drugs, adverse psychological factors such as loneliness also increase the risk of addictive psychiatric prescription drugs, including opioid drugs (Vyas et al., 2021 ). Social isolation and loneliness are significant issues faced by a considerable number of elderly Americans, with approximately 25% of community residents aged 65 and above feeling isolated and lonely, leading to an increased burden of DUD (National Academies of Sciences et al., 2020). In terms of social factors, individuals with unstable housing are more prone to drug abuse (Adams et al., 2022 ). The elderly in the United States is facing an increasingly severe housing burden problem, with nearly 11.2 million elderly households experiencing excessive housing cost burdens in 2021 (JCHS, 2023). The biopsychosocial factors mentioned above may all affect the burden of opioid use disorders in the elderly, which is consistent with the results of the decomposition analysis that North America is more affected by epidemiology. However, the higher DUD burden in the United States may also be due to the country's higher diagnosis rate, such as the corresponding diagnostic criteria being more applicable to the local population, and a greater emphasis on the diagnosis and treatment of substance use disorders at the societal and healthcare levels (Castaldelli-Maia et al., 2022). The ASIR and ASDR in middle and high-middle SDI regions have significantly decreased from 2019 to 2021, with China being a representative country. China has made a series of efforts in managing drug use disorders. In 2005, the Chinese government promulgated the “Regulations on the Administration of Anesthetic Drugs and Psychotropic Substances” to regulate the clinical rational use of opioid drugs and prevent illegal abuse (Fang et al., 2019 ). Since 2011, the “Good Pain Management Program” has been launched nationwide, effectively regulating the prescription mode of opioid drugs and controlling opioid abuse caused by pain in the elderly through professional training, release of pain management guidelines, community outreach, and other methods. However, excessive prescription restrictions can also affect the clinical rational use of opioid drugs, making it difficult to meet the treatment needs of elderly patients with chronic pain. In terms of illegal drug management, the Chinese government launched a series of anti-drug policies in the 1990s, strengthened legislation to reduce the supply and circulation of drugs, and mobilized the medical and social security systems to provide rehabilitation and employment opportunities for drug users (W. Wang, 1999 ). Since 2008, the Anti-Drug Law has stipulated mandatory treatment for drug addicts (X. Wang et al., 2023 ). These policies have achieved outstanding results in the epidemiological changes of opioid use disorders, successfully reducing the disease burden, which is consistent with the results of the decomposition analysis. In addition, it should be pointed out that in the Chinese public's perception, DUD is a behavior that violates social morality and cultural norms. The propaganda and cultural constraints of mass media make this concept more firmly held among the elderly. This public stigma helps to alleviate DUD on the one hand, but on the other hand, it can also become an obstacle for drug users to receive treatment and reintegrate into society (X. Li et al., 2012 ; Luo et al., 2024 ). Meanwhile, as an alternative therapy, acupuncture and moxibustion has been widely concerned and recognized in China in terms of pain management of elderly patients, reducing opioid dependence, improving withdrawal symptoms and reducing relapse rate, which helps to reduce the burden of disease (He et al., 2020 ; T. Li et al., 2023 ; Tedesco et al., 2017 ). The disease burden in low and low-middle SDI regions represented by Africa has remained at a relatively low level, which may be due to the relatively backward medical resources in Africa leading to certain difficulties in obtaining opioid drugs clinically. The prescription sets in Africa lack opioid drugs for pain management, and although African countries have taken some measures to improve drug supply, such as free morphine therapy for cancer patients, clinical supply demand still cannot be met due to supply chain, policy-making, and education barriers (Manjiani et al., 2014 ). From a gender perspective, the ASIR of elderly women with opioid use disorders is higher than that of men, and ASDR is on the rise. The interaction of biological gender specific differences, including sex hormones and their effects on endogenous opioid drug systems and systemic inflammation, as well as gender specific genes, may affect the perception and experience of pain (Knouse et al., 2021). Firstly, chronic non-cancer painful diseases are more common in elderly women than in elderly men, leading to a higher likelihood of opioid analgesic use in elderly women (Rochon et al., 2022 ). Secondly, estrogen has been shown to have a pain protective effect. Elderly women have lower levels of estrogen in their bodies, which may make chronic pain more common and severe than when they were younger, and they may use opioid drugs more frequently and at higher doses (Walter et al., 2022 ). Thirdly, women develop substance use disorders faster than men after exposure to addictive substances, and typically require higher doses to achieve the same level of analgesic effect. Fourthly, elderly women are more susceptible to domestic violence compared to men and are more likely to use opioid drugs for self-treatment to cope with negative emotions after exposure to addictive substances (UNODC, 2024). Lastly, women suffer from a stronger sense of shame and have fewer opportunities to receive treatment for medication use disorders, which may exacerbate the progression and deterioration of the condition and cause more serious physical damage. According to the predictive model analysis, the DALYs of opioid use disorders in the global elderly population still show an upward trend, especially in North America where the growth is faster. Therefore, it is necessary to take targeted measures. In the medical field, personalized guidelines can be developed for the treatment of opioid analgesics to alleviate side effects and addiction, in response to the characteristics of elderly people with multiple diseases, atypical symptoms, and multiple medications. Meanwhile, government departments need to find a balance point in the level of control over opioid drugs, to avoid drug abuse due to loose prescription restrictions or situations where medication cannot be used due to overly strict control, and dynamically adjust the prescription restriction level of opioid drugs according to the actual situation. There is significant inequality in the production and distribution of opioid drugs between different SDI regions (Jayawardana et al., 2021 ). Implementing unified scheduling and fair distribution of global opioid drugs can reduce supply costs, improve drug productivity, achieve global health strategies, and potentially alleviate the imbalance between global drug supply and demand. In addition, while strengthening local production capacity, it is necessary for low-income areas to seek international cooperation to provide assistance in improving supply chains, quality control, and pain management (Yao et al., 2023 ). Cocaine, as an excitatory and anesthetic drug that can increase alertness, happiness, reduce anxiety and social disorders, enhance self-esteem, energy, and libido. Compared to the burden of opioid diseases, the burden of cocaine diseases in the elderly is lower, but the abuse of cocaine is associated with complications of cardiovascular, respiratory, digestive, hematological, and psychiatric disorders, and further increases the burden of inflammation in the elderly (Soder et al., 2020 ; S. Yarnell et al., 2020a ). Our research findings indicate a significant upward trend in ASDR in high SDI regions, with the United States being a representative country. The problem of unstable housing in the United States may lead to negative emotions such as anxiety and depression among the elderly; Retired elderly individuals may experience increased psychological stress due to changes in socioeconomic status, worsening of chronic diseases, social isolation, and policy changes, which may be the reasons for the increased burden of cocaine use disorders (Choi et al., 2023 ; Chun et al., 2022 ; Ghantous et al., 2022 ). In addition, long term use of cocaine increases the risk of common cardiovascular and cerebrovascular diseases, psychomotor symptoms, and neurodegenerative diseases in the elderly population (Carbone et al., 2024; O'Keefe et al., 2022 ), there is still a lack of effective drugs for treating cocaine abuse, and social and psychological treatment for the elderly needs to be strengthened (Kampman, 2019 ). Amphetamines, like cocaine, have stimulant effects that can enhance cognitive ability, improve mood, suppress appetite, and more (Sassi et al., 2020 ). The abuse of amphetamines can lead to adverse consequences such as anorexia, insomnia, slowed exercise, and memory impairment (Heal et al., 2013 ). Our research results show that the ASIR of amphetamine is about twelve times that of cocaine, but the ASDR is only about 70% of that of cocaine. This may be because amphetamine type stimulants (ATS) have more clinical applications and can be used to treat common elderly diseases such as Parkinson's disease, depression in later life, and cognitive syndrome, while their addiction and dependence are not as severe as cocaine (Farrell et al., 2019 ). Up to 5% of elderly people in the community suffer from major depressive disorder, which is related to factors such as loss of work relationships, lack of social support, and economic and living security issues (Taylor, 2014 ). When other medications are ineffective in treating refractory depression in later life, ATS can serve as an alternative to alleviate symptoms. However, the desire of elderly people for enhanced effects may further lead to an increase in the use of ATS, which often becomes a part of the development of drug abuse processes (O'Donnell et al., 2019 ). When the user’s mental health and family relationships deteriorate, this tendency towards excessive use leading to addiction is further exacerbated. Although the use of ATS increases the incidence of cardiovascular disease and mortality, as well as accidental injuries and homicides, elderly drug abusers are unwilling to completely stop using it due to ongoing mental health issues. The negative impact of excessive use of ATS on the cognitive and behavioral abilities of elderly people may prompt them to seek treatment, and the sustainability of such treatment can be continuously driven by good social factors and family relationships. It is worth noting that the majority of participants are multi substance users, often taking various ATS in combination with opioids and other substances such as alcohol, which increases the risk of cardiac toxicity and violent behavior, leading to more serious health outcomes (Farrell et al., 2019 ). Therefore, it is necessary to provide comprehensive withdrawal treatment for multi drug abuse. Unfortunately, like cocaine, there is currently a lack of effective drug treatments for ATS, and the overall effectiveness of available social and psychological interventions is relatively weak. It is necessary to adopt joint care to address the physical and mental health, welfare, and social care needs of the elderly. Our research results are consistent with previous literature, indicating that male abuse of cocaine and amphetamines, two excitatory drugs, is much higher than that of females (McHugh et al., 2024 ). The increase in disease burden among elderly people worldwide is more pronounced in males than females, and predictive analysis suggests that this trend will continue, which may be related to higher alcohol consumption rates among males (S. C. Yarnell, 2015 ). Alcohol can affect drug metabolism, leading patients to require higher doses of medication, thereby increasing the risk of drug abuse and adverse reactions. Additionally, compared to women, gender inequality makes it easier for men to achieve higher career and social status before retirement, which may lead to a greater sense of psychological gap after retirement, resulting in more negative emotions and a higher risk of stimulant drug abuse (Griffin et al., 1989 ). Cannabis is the third most commonly used controlled substance globally, second only to alcohol and tobacco. Its various pharmacological active ingredients have been found to have anticonvulsant, antianxiety, antipsychotic, anti-inflammatory, and neuroprotective effects (Connor et al., 2021 ). Long term use of cannabis by older adults is associated with an increased risk of falls, bronchitis, psychiatric complications, and cardiovascular events (Lin et al., 2023 ). Cannabinoid hyperemesis syndrome (CHS) is also a common adverse reaction, and the lack of risk perception among the elderly further increases their susceptibility and harmfulness (Sorensen et al., 2017 ). Our research results indicate that the overall trend of burden of cannabis use disorders among elderly people is stable, but the ASDR of cannabis use disorders among elderly people in high SDI areas shows an upward trend. With the process of legalizing cannabis in high SDI regions such as the United States and Canada, the stigmatization of cannabis is decreasing, and the use of cannabis for recreational or medical purposes is gradually increasing (Lin et al., 2023 ). An increasing number of elderly individuals are utilizing cannabis for therapeutic purposes without a physician's prescription, and this over-the-counter drug acquisition may increase the risk of cannabis drug abuse (Baumbusch et al., 2021). Moreover, factors such as chronic pain, sleep disorders, mental health issues, and alcohol consumption among the elderly also increase the risk of cannabis abuse (Han et al., 2020). Decomposition analysis shows that population growth is the main contributing factor to the burden of cannabis use disorders, indicating that the “baby boomer generation” is currently entering a peak of aging, and it is necessary for governments around the world to adopt proactive policies to address the related issues caused by the rapid growth of the elderly population. Predictive analysis suggests that the overall burden of cannabis use disorders worldwide will remain stable in the near future, while high-income North America will experience a slight increase. Due to the decline in physical function and increase in underlying diseases among the elderly population, caution should be exercised when using cannabis for medical purposes to consider potential adverse consequences. Appropriate restrictions should be placed on the non-prescription access to cannabis for the elderly to reduce the harm caused by cannabis abuse to this vulnerable group. However, compared to opioid drugs, cannabis has fewer side effects and has a smaller impact on the quality of life of the elderly. Cannabis may play a key role in suppressing opioid abuse, which is also a future research direction(Wiese et al., 2018). Studies have shown that as the proportion of female cannabis users increases, the gap in cannabis abuse rates between men and women is narrowing, while our study found that the gender difference in disease burden among older adults has remained stable (Cooper et al., 2018). The difference in results may be due to the different purposes of medical cannabis use among different age groups. In recent years, under the influence of multiple pressures such as occupation, social interaction, and childbirth, more and more middle-aged and young women have been using cannabis to relieve anxiety, which may be the reason for the increasing proportion of female cannabis users (Morris et al., 2023). After retirement, the pressure on elderly people from the workplace and other aspects is significantly reduced, and cannabis is mainly used to alleviate physical discomfort caused by chronic diseases. The impact of clinical symptoms caused by the disease itself is relatively fixed, and the gender difference in the burden of cannabis abuse mainly comes from the gender biological differences in the pharmacological effects of cannabis, so the burden difference between genders has remained stable. Study shows that after using cannabis, men have better pain relief effects than women, but there is a higher diagnosis rate of CHS, which may be the reason why older men have a higher abuse rate of cannabis and a higher disability weight after abuse (Cooper et al., 2016; Sorensen et al., 2017 ). Our research findings provide valuable insights for policy makers. To alleviate the burden of DUD in the elderly, it is necessary to take into account their physiological and pathological characteristics, comprehensively consider drug types, gender differences, national and regional development levels, and policy differences, and pay attention to modifiable risk factors, and take targeted measures. For the elderly population with a need for painkillers, attention should be paid to the underlying diseases that cause pain, and painkillers should be used reasonably. High SDI countries with sufficient drug supply and lax control should strengthen prescription restrictions on analgesic drugs and control the quality and promotion of new drugs before they are launched, in order to avoid the recurrence of medication accidents such as the “OxyContin incident”. On the contrary, low SDI countries should improve drug productivity and supply capacity to meet reasonable clinical needs. For elderly people with a need for psychostimulatory drugs, the focus should be on starting treatment with drugs with low side effects and low addiction, while strict control should be implemented for highly addictive drugs such as cocaine. Given the high incidence of underlying diseases, diverse types of medication, and weak physical functions among the elderly population, caution should be exercised in legalizing cannabis drugs for this group, and certain restrictive measures should be taken to achieve a protective effect. At the same time, we should improve and perfect the social security system for the elderly in terms of housing, income, medical care, etc., pay attention to the psychological health problems of the elderly, and solve the burden of DUD caused by psychological factors. In addition, it is necessary to strengthen the management of multiple prescription drugs and avoid the interaction of multiple DUDs. Finally, it is necessary to pay attention to the internal physiological, psychological, and external social and cultural differences between genders, analyze the impact of these differences on DUD, and carry out targeted personalized prevention and treatment. There are also some limitations to this study: 1. Data collection methods, technologies, and tools vary among countries, which may result in differences in diagnostic rates. Countries with lower medical and economic levels may have insufficient diagnostic rates. 2. The definition of drug use disorders in GBD 2021 follows the DSM-IV-TR and ICD-10 classifications. Different countries and regions may have different understandings of these classifications under different cultural backgrounds, resulting in uneven data quality. 3. This study only included four common DUDs in the elderly population and may overlook the burden of other types of DUDs. Despite these limitations, this study has several advantages. 1. Taking elderly people aged 60–89 as the research subjects, this study presents the differences in disease burden of four types of drug use disorders in terms of time, region, gender, etc., and combines multiple perspectives of biology, psychology, and society to find and analyze the objective factors behind the differences in disease burden, which makes up for the current lack of epidemiological research on DUD burden for the vulnerable group of elderly people and provides valuable information and suggestions for policy makers. 2. Predicting the disease burden of four drug use disorders over the next 10 years can help develop personalized health policies to address future trends in disease burden. 3. Introducing the concept of SDI is beneficial for the rational allocation of limited medical resources worldwide, thereby promoting global health. 5. Conclusion There are multiple differences in the DUD burden of the elderly, both apparent and intrinsic. In terms of degree and trend, the burden of opioid use disorders is the heaviest and shows an upward trend. The high-income North American region bears a disproportionately high burden. The overall burden of DUD is heavier in males, while the burden of opioid use disorders in females deserves attention. Among the reasons for changes in DUD burden, the impact of epidemiological changes is most prominent in high-income North America, while other regions are mainly affected by the growth of the elderly population. The multiple influencing factors of DUD burden include biological differences between genders, drug types, social traditional beliefs, economic development level, policy control flexibility orientation, and uneven distribution worldwide. Global health policy makers should pay full attention to the DUD burden of the elderly population, comprehensively consider influencing factors, adjust public health policies in a targeted manner, allocate medical resources reasonably, and establish a controlled drug management system for the elderly population to reduce the DUD burden. Abbreviations AAPC: Average Annual Percentage Change APC: Annual Percentage Change ASDR: Age-Standardized Disability-Adjusted Life Years Rates ASIR: Age-Standardized Incidence Rates ASR: Age-Standardized Rates ATS: Amphetamine Type Stimulants BAPC: Bayesian Age-Period-Cohort CHS: Cannabinoid Hyperemesis Syndrome CI: Confidence Interval DALYs: Disability-Adjusted Life Years DSM-IV-TR: Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision DUDs: Drug Use Disorders GBD: Global Burden of Disease Study ICD-10: International Classification of Diseases, Tenth Revision SDI: Sociodemographic Index Declarations Ethics approval and consent to participate Since the original data of this study came from a public database, the Research Ethics Committee of Guang 'anmen Hospital, China Academy of Chinese Medical Sciences determined that no approval was required. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analysed during the current study are available in the GBD repository (https://vizhub.healthdata.org/gbd-results/). Data supporting the findings of this study are available upon reasonable request from corresponding author Yuanhui Hu. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the National Natural Science Foundation of China (82204753), Special Scientific Research for Traditional Chinese Medicine of State Administration of Traditional Chinese Medicine of China (201507004) and High Level Chinese Medical Hospital Promotion Project (HLCMHPP2023011). Authors' contributions BCJ, RW, and ZQL wrote the main manuscript text and prepared the methodology, software, and data interpretation sections. MYF curated the data, developed the visualizations, and reviewed and edited the manuscript. MXW and YW supervised the project and validated the results. ZQL and YHH provided overall supervision. All authors reviewed the drafted manuscript and approved the final version. Acknowledgements We highly appreciate the work by the GBD 2021 collaborators. Authors' information Bochao Jia: http://orcid.org/0009-0006-8997-7839 Rui Wei: http://orcid.org/0009-0005-7056-7250 Zhiqi Li: http://orcid.org/ 0000-0002-0222-4783 Zhenquan Liu: https://orcid.org/0000-0002-7156-8723 Yuanhui Hu: http://orcid.org/0000-0001-6118-8009 References Adams EA, Spencer L, Addison M, McGovern W, Alderson H, Adley M, et al. Substance Use, Health, and Adverse Life Events amongst Amphetamine-Type Stimulant Users in North East England: A Cross-Sectional Study. Int J Environ Res Public Health. 2022;19(12). Alpert A, Evans WN, Lieber EMJ, Powell D. ORIGINS OF THE OPIOID CRISIS AND ITS ENDURING IMPACTS. Q J Econ. 2022;137(2): 1139. Amirkafi A, Mohammadi F, Tehrani-Banihashemi A, Moradi-Lakeh M, Murray CJL, Naghavi M, et al. 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Medical complications associated with substance use disorders in patients with type 2 diabetes and hypertension: electronic health record findings. Addiction. 2019;114(8):1462. Wu LT, Blazer DG. Substance use disorders and psychiatric comorbidity in mid and later life: a review. International Journal of Epidemiology. 2014;43(2):304. Yao JS, Kibu OD, Asahngwa C, Ngo NV, Ngwa W, Jasmin HM, et al. A scoping review on the availability and utilization of essential opioid analgesics in Sub-Saharan Africa. Am J Surg. 2023;226(4):409. Yarnell S, Li L, MacGrory B, Trevisan L, Kirwin P. Substance Use Disorders in Later Life: A Review and Synthesis of the Literature of an Emerging Public Health Concern. Am J Geriatr Psychiatry. 2020a;28(2):226. Yarnell S, Li LM, MacGrory B, Trevisan L, Kirwin P. Substance Use Disorders in Later Life: A Review and Synthesis of the Literature of an Emerging Public Health Concern. American Journal of Geriatric Psychiatry. 2020b;28(2):226. Yarnell SC. Cocaine Abuse in Later Life: A Case Series and Review of the Literature. Prim Care Companion CNS Disord. 2015;17(2). 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. Additional Declarations No competing interests reported. Supplementary Files Highlights.docx Supplementarymaterial.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. 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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-5977182","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":412522209,"identity":"adcb1b52-a956-4074-89fc-f956ec855710","order_by":0,"name":"Bochao Jia","email":"","orcid":"","institution":"Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Bochao","middleName":"","lastName":"Jia","suffix":""},{"id":412522210,"identity":"73ca2aae-d3bb-499e-b807-a39e560b498a","order_by":1,"name":"Rui Wei","email":"","orcid":"","institution":"China Academy of Chinese Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Wei","suffix":""},{"id":412522211,"identity":"37426555-0488-4606-a05e-7688616d5559","order_by":2,"name":"Zhiqi Li","email":"","orcid":"","institution":"Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhiqi","middleName":"","lastName":"Li","suffix":""},{"id":412522212,"identity":"079dd52f-3aa9-49e8-b4b1-9951ebb1eba6","order_by":3,"name":"Meiyu Feng","email":"","orcid":"","institution":"Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Meiyu","middleName":"","lastName":"Feng","suffix":""},{"id":412522213,"identity":"b8db36de-9dc0-45eb-a2d8-1e85ade53c87","order_by":4,"name":"Mengxue Wang","email":"","orcid":"","institution":"Department of cardiovascular, Guang’anmen Hospital, China Academy of Chinese Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mengxue","middleName":"","lastName":"Wang","suffix":""},{"id":412522214,"identity":"7f6c996b-19ac-4b36-b6d7-a093871d9a38","order_by":5,"name":"Yi Wei","email":"","orcid":"","institution":"Department of cardiovascular, Guang’anmen Hospital, China Academy of Chinese Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Wei","suffix":""},{"id":412522215,"identity":"439d65f1-2dff-48f6-a8f4-bf85acbdffa2","order_by":6,"name":"Zhenquan Liu","email":"","orcid":"","institution":"Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhenquan","middleName":"","lastName":"Liu","suffix":""},{"id":412522216,"identity":"365c830c-96fe-424b-909e-56616bef5e00","order_by":7,"name":"Yuanhui Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYBACAwbGBhDisZ9/+ACIQYIWAwm2BGK1AAFIpYEEjwFxWswlktukeXccljGX7vn84eOOwwz87d0JeLVYzkgEajlzmMdyztltkjPPHGaQOHN2A36H3QBpaTvMw3AgdxszkAF0YS7RWnIefyZNi8GNHAZp4rScedhsObctnUey55iZ5Ewgg7Bfjqc/vPG2zdqen7358YePbdZy/O29+LUAAYsEMo+HkHIQYP5AjKpRMApGwSgYwQAAxftLVHwTuNEAAAAASUVORK5CYII=","orcid":"","institution":"China Academy of Chinese Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Yuanhui","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2025-02-07 02:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5977182/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5977182/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75896676,"identity":"3dc35469-386b-45ef-9150-e3d72f737ecf","added_by":"auto","created_at":"2025-02-10 10:36:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":352040,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of disease burden for four drug use disorders.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5977182/v1/16324d72e073f650a954091c.png"},{"id":75895991,"identity":"a5129561-d85d-40a5-af12-eb19c21236fd","added_by":"auto","created_at":"2025-02-10 10:28:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":499522,"visible":true,"origin":"","legend":"\u003cp\u003eSex differences in disease burden of four drug use disorders.\u003c/p\u003e\n\u003cp\u003eA. ASIR for opioid use disorder. B. ASDR for opioid use disorder. C. ASIR for cocaine use disorder. D. ASDR for cocaine use disorder. E. ASIR for amphetamine use disorder. F. ASDR for amphetamine use disorder. G. ASIR for cannabis use disorder. H. ASDR for cannabis use disorder.\u003c/p\u003e\n\u003cp\u003eASIR, age-standardized incidence rate; ASDR, age-standardized disability-adjusted life years.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5977182/v1/e39a3a8715fcbcb7b49cd610.png"},{"id":75895995,"identity":"69e90fc1-565f-4628-93e7-bf6ff76af336","added_by":"auto","created_at":"2025-02-10 10:28:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":915246,"visible":true,"origin":"","legend":"\u003cp\u003eJoinpoint regression analysis of disease burden for four drug use disordersin the elderly at the SDI level, 1990–2021.\u003c/p\u003e\n\u003cp\u003eA. Crude rate of incidence (opioid use disorder); B. Crude rate of DALYS (opioid use disorder); C. Crude rate of incidence (cocaine use disorder); D. Crude rate of DALYS (cocaine use disorder); E. Crude rate of incidence (amphetamine use disorder); F. Crude rate of DALYS (amphetamine use disorder); G. Crude rate of incidence (cannabis use disorder); H. Crude rate of DALYS (cannabis use disorder)\u003c/p\u003e\n\u003cp\u003eAPC, annual percentage change; SDI, sociodemographic index; DALYS, disability-adjusted life years.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5977182/v1/ba2790a057be218329de9aa0.png"},{"id":75895992,"identity":"5619849c-3c7a-42c3-b06e-a5b1f1f4d3a4","added_by":"auto","created_at":"2025-02-10 10:28:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":830285,"visible":true,"origin":"","legend":"\u003cp\u003eNational disease burden of four drug use disorders in the elderly in 2021.\u003c/p\u003e\n\u003cp\u003eA. ASIR for opioid use disorder. B. ASDR for opioid use disorder. C. ASIR for cocaine use disorder. D. ASDR for cocaine use disorder. E. ASIR for amphetamine use disorder. F. ASDR for amphetamine use disorder. G. ASIR for cannabis use disorder. H. ASDR for cannabis use disorder.\u003c/p\u003e\n\u003cp\u003eASIR, age-standardized incidence rate; ASDR, age-standardized disability-adjusted life years.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5977182/v1/f70ddd1585e2a88e01b625f3.png"},{"id":75895993,"identity":"41034753-2bb6-4990-be76-998d41f339bd","added_by":"auto","created_at":"2025-02-10 10:28:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":825706,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between SDI and disease burden across 204 countries.\u003c/p\u003e\n\u003cp\u003eA. ASIR for opioid use disorder. B. ASDR for opioid use disorder. C. ASIR for cocaine use disorder. D. ASDR for cocaine use disorder. E. ASIR for amphetamine use disorder. F. ASDR for amphetamine use disorder. G. ASIR for cannabis use disorder. H. ASDR for cannabis use disorder.\u003c/p\u003e\n\u003cp\u003eASIR, age-standardized incidence rate; ASDR, age-standardized disability-adjusted life years; SDI, sociodemographic index.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5977182/v1/946dc371429d05079c5a815a.png"},{"id":75896683,"identity":"2afd5ea2-0a61-4d34-b417-ac66b8993967","added_by":"auto","created_at":"2025-02-10 10:36:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":402881,"visible":true,"origin":"","legend":"\u003cp\u003eDecomposition analysis of DALYs of four drug use disorders in the elderly at the regional level.\u003c/p\u003e\n\u003cp\u003eA. Opioid use disorder. B. Cocaine use disorder. C. Amphetamine use disorder. D. Cannabis use disorder.\u003c/p\u003e\n\u003cp\u003eDALYs, disability-adjusted life years; SDI, sociodemographic index.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5977182/v1/84bd826ba88bee60b37ebfff.png"},{"id":75896006,"identity":"d92d6c84-c163-424a-866d-13d4fd88f749","added_by":"auto","created_at":"2025-02-10 10:28:57","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":438448,"visible":true,"origin":"","legend":"\u003cp\u003eThe predicted trajectories of DALY numbers and age standardized rates for four drug use disorders in the elderly from 2022 to 2035.\u003c/p\u003e\n\u003cp\u003eA. The predicted trajectories of DALY numbers and age standardized rates for opioid use disorder; B. The predicted trajectories of DALY numbers and age standardized rates for cocaine use disorder; C. The predicted trajectories of DALY numbers and age standardized rates for amphetamine use disorder. D. The predicted trajectories of DALY numbers and age standardized rates for cannabis use disorder.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5977182/v1/ab895b2731988caa674c7120.png"},{"id":76141058,"identity":"437cf758-d3ff-4574-a136-f3e7ffcfe430","added_by":"auto","created_at":"2025-02-12 17:31:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5819567,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5977182/v1/63e5642f-fa3a-4821-8118-8b6269ac862e.pdf"},{"id":75895988,"identity":"b2b374f0-f20a-4ab3-b4d1-1641e53bf4c1","added_by":"auto","created_at":"2025-02-10 10:28:56","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15860,"visible":true,"origin":"","legend":"","description":"","filename":"Highlights.docx","url":"https://assets-eu.researchsquare.com/files/rs-5977182/v1/e188cdb2fa8a95110ee8ff6d.docx"},{"id":75896684,"identity":"bed9cb6a-bb91-41bb-b691-323dde5a7db0","added_by":"auto","created_at":"2025-02-10 10:36:57","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":3674853,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5977182/v1/57fad6bf702b84f55b6557b9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global and regional burden of four drug use disorders in the elderly, 1990 to 2021: an analysis of the Global Burden of Disease Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDrug use disorder (DUD) refers to a maladaptive pattern of drug abuse, leading to clinically significant impairment or distress that includes symptoms of dependence, such as withdrawal symptoms or progressive tolerance (Hasin et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). DUD encompasses a variety of substances, including opioids, cocaine, amphetamines, and cannabis (Shen et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). According to the Global Burden of Disease Study (GBD) 2021, the burden of DUD worldwide has been steadily increasing from 2010 to 2021, with the increasing DUD burden among the elderly population not to be ignored (GBD 2021 Diseases and Injuries Collaborators, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lin et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This trend is attributed to several factors, including the aging of the \u0026ldquo;baby boomer generation,\u0026rdquo; who have higher drug use rates throughout their lives, and the increasing availability and prescription of drugs for chronic pain management (S. Yarnell et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; S. Yarnell et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). Due to age-related physiological changes, comorbidities, and the possibility of multi drug therapy, the elderly population is particularly vulnerable, which may exacerbate the adverse effects of drug use (Wu et al., 2014; S. Yarnell et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCultural attitudes, socio-economic factors, psychological and physiological factors of both genders, accessibility of medical services, and socio-economic factors can all affect the burden patterns and effectiveness of treatment plans for DUD. At present, there is a significant gap in understanding the epidemiology and impact of DUD in older adults. Although several studies have investigated the burden of DUD, they have focused on a country or specific region and have not conducted detailed analysis on the burden trends of specific drugs (Amirkafi et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Castaldelli-Maia et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), mainly focused on young populations (Kenya Adolescent Mental Health Group, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and used relatively older data (Charlson et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In response to the call for strengthening the prevention and treatment of drug abuse in the United Nations\u0026rsquo; 2030 Agenda for Sustainable Development, it is necessary to further understand the regional differences in the burden of DUD among the elderly population, in order to strengthen monitoring, tailor interventions and policy measures, ultimately reducing the impact of DUD and improving the health and well-being of older people worldwide.\u003c/p\u003e \u003cp\u003eAs the first epidemiological study on the disease burden of drug use disorders among the elderly, we extracted the data of GBD 2021, concretized the analysis of the disease burden of the four drug use disorders of opioid, cocaine, amphetamines, and cannabis, and explored the epidemiological trend of the four DUDs incidence rates and DALYS rate of the 60\u0026ndash;89 year old elderly at global, regional and national levels from 1990 to 2021 by using the methods of joinpoint model, decomposition analysis, correlation analysis, and the Nordpred prediction model.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data collection\u003c/h2\u003e \u003cp\u003eThe GBD 2021 study utilized the latest epidemiological data to comprehensively evaluate the health impacts caused by 371 diseases, injuries, and 88 risk factors in 204 countries and regions worldwide (GBD 2021 Diseases and Injuries Collaborators, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This study employed GBD research tools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ghdx.healthdata.org/gbd-results-tool\u003c/span\u003e\u003cspan address=\"http://ghdx.healthdata.org/gbd-results-tool\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to download data on the incidence and disability adjusted life years (DALYs) of four types of medication use disorders in elderly people aged 60\u0026ndash;89, and compare and analyze them based on the specific types of medication use disorders. DALYs are standard indicators for quantifying disease burden, encompassing years of life lost due to premature death and years of healthy life lost due to disability caused by disease, thereby reflecting the overall impact of disease on population health (Wei et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Other parameter settings include regions (global, 21 regions with similar geographic and epidemiological characteristics, 5 SDI regions, 204 countries) and calendar years (1990\u0026ndash;2021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Case definition\u003c/h2\u003e \u003cp\u003eThe case definition of drug use disorders in GBD 2021 is mainly based on (International Classification of Diseases, Tenth Revision) ICD-10 and (Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision) DSM-IV-TR. Opioid use disorders (ICD-10 F11.20) include adult opioid overdose deaths and neonatal deaths caused by maternal opioid use; The use disorders of cocaine (DSM-IV 304.20, ICD-10 F14.20), amphetamines (ICD-10 F15.20, DSM-IV 304.40), and cannabis (DSM-IV 304.30, ICD-10 F12.20) involve a dysfunctional pattern of drug use. The precise diagnostic criteria are detailed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Sociodemographic index(SDI)\u003c/h2\u003e \u003cp\u003eThe SDI is utilized to examine the relationship between social and demographic development levels and disease burden across various countries and regions. The SDI is a composite measure derived from three factors: the fertility rate of individuals under 25 years old, the average educational attainment of individuals aged 15 and above, and the per capita lagged distributed income. In the GBD 2021, SDI scores range from 0 to 100. Based on these scores, countries and regions are categorized into five groups: low SDI, low-middle SDI, middle SDI, high-middle SDI, and high SDI. To explore the impact of SDI on disease burden, Spearman correlation analysis was used to calculate the correlation coefficients ρ and p-values between age-standardized rates (ASR) and SDI in 21 regions and 204 countries worldwide in 2021.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Data analysis\u003c/h2\u003e \u003cp\u003eThis study employed a direct standardization method, using the GBD 2021 world standard population, to calculate estimated ASR per 100,000 people for 60\u0026ndash;89 age groups and their corresponding 95% confidence interval (CI). The specific formula used is as follows: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ASR=\\frac{{\\sum\\:}_{i=1}^{A}{a}_{i}{w}_{i}}{{\\sum\\:}_{i=1}^{A}{w}_{i}}\\times\\:100000\\)\u003c/span\u003e\u003c/span\u003e, where \u003cem\u003eA\u003c/em\u003e represents the total number of age groups, \u003cem\u003ei\u003c/em\u003e indicates a specific age group, \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the age-specific rate for the \u003cem\u003ei\u003c/em\u003e-th age group, and \u003cem\u003ewi\u003c/em\u003e represents the standard population of the corresponding age group in the GBD 2021 standard population (Sun et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eJoinpoint regression analysis model was employed to evaluate trends and significant inflection points in age-standardized incidence rates (ASIR) and age-standardized DALYs rate (ASDR) from 1990 to 2021 (Tuo et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This model describes ASR trends over a specified period by calculating the annual percentage change (APC) along with its 95% CI. The overall time trend of these rates is assessed by calculating the average annual percentage change (AAPC) for the population. Statistically, if the APC or AAPC and their 95% CI are greater than zero, this indicates an upward trend in rates over the specified period. Conversely, if both the APC or AAPC and their 95% CI are less than zero, this indicates a decreasing trend in rates. If neither condition is met, the disease burden is considered stable.\u003c/p\u003e \u003cp\u003eDecomposition analysis was utilized to quantify the relative impact of three driving factors\u0026mdash;changes in age structure, population growth, and epidemiology\u0026mdash;on the changes in DALYs from 1990 to 2021 (GBD 2019 Dementia Forecasting Collaborators, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The Das Gupta method was used for decomposition analysis, isolating the independent contribution of each factor while holding the others constant (Das Gupta, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1978\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Age-Period-Cohort model is used to predict the number of DALYs and age standardized rates of drug use disorders from 2022 to 2035. The theoretical basis of this model is to explore the correlation between rates, age structure, and population size through generalized linear models. The prediction is realized through the \u0026ldquo;Nordpred\u0026rdquo; package in R software, which has empirical validity in predicting the trend of current disease incidence rate (Huang et al., 2024).\u003c/p\u003e \u003cp\u003eAll data organization, analysis, and visualization for this study were conducted using R software (version 4.3.3).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e3.1. Global trends\u003c/h2\u003e\n \u003cp\u003eIn 2021, opioid use disorder had the heaviest disease burden among the four types of DUDs worldwide (Fig. 1). Specifically, for every 100000 people, the ASIR and ASDR for the four types of drug use disorders are opioid: 9.41 (95% CI 5.26\u0026ndash;14.51), 67.83 (95% CI 55.06\u0026ndash;82.57); Cocaine: 0.20 (95% CI 0.07\u0026ndash;0.41), 7.71 (95% CI 6.17\u0026ndash;9.80); Amphetamine: 2.49 (95% CI 1.41\u0026ndash;3.97), 5.41 (95% CI 4.11\u0026ndash;7.23); Cannabis: 1.77 (95% CI 0.47\u0026ndash;3.63), 1.40 (95% CI 0.76\u0026ndash;2.38) (Table 1). Joinpoint analysis present that the ASIR of opioid use disorders showed a downward trend from 1990 to 2021, with an AAPC of -0.73 (95% CI -0.79-0.67) (Fig. S1). ASDR gradually decreased from 1997 to 2007 (APC=-1.87) and showed an upward trend from 2007 to 2021 (APC\u0026thinsp;=\u0026thinsp;0.44 for 2007\u0026ndash;2013, 1.87 for 2013\u0026ndash;2021). The ASIR of cocaine use disorder remained stable, while ASDR gradually increased, with an AAPC of 0.94 (95% CI 0.77\u0026ndash;1.11), until 2019 when it began to gradually stabilize (APC=-0.05 for 2019\u0026ndash;2021); The ASIR and ASDR of methamphetamine and cannabis use disorders tend to stabilize overall.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eGlobal levels and time trends of ASIR and ASDR for four drug use disorders in older adults in 1990 and 2021\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eIncidence (95% uncertainty interval)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eDALYs (95% uncertainty interval)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge- standardised\u003c/p\u003e\n \u003cp\u003erates per 100,000\u003c/p\u003e\n \u003cp\u003e(1990)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge- standardised\u003c/p\u003e\n \u003cp\u003erates per 100,000\u003c/p\u003e\n \u003cp\u003e(2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAAPC\u003c/p\u003e\n \u003cp\u003e(1990\u0026ndash;2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge- standardised\u003c/p\u003e\n \u003cp\u003erates per 100,000\u003c/p\u003e\n \u003cp\u003e(1990)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge- standardised\u003c/p\u003e\n \u003cp\u003erates per 100,000\u003c/p\u003e\n \u003cp\u003e(2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAAPC\u003c/p\u003e\n \u003cp\u003e(1990\u0026ndash;2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eOpioid use disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.6\u003c/p\u003e\n \u003cp\u003e(5.30-15.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.55\u003c/p\u003e\n \u003cp\u003e(4.21\u0026ndash;11.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.83\u003c/p\u003e\n \u003cp\u003e(-0.88 to -0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.73\u003c/p\u003e\n \u003cp\u003e(59.14\u0026ndash;88.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.22\u003c/p\u003e\n \u003cp\u003e(58.79\u0026ndash;82.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003cp\u003e(-0.25 to -0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.36\u003c/p\u003e\n \u003cp\u003e(7.39\u0026ndash;20.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.01\u003c/p\u003e\n \u003cp\u003e(6.15\u0026ndash;17.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.64\u003c/p\u003e\n \u003cp\u003e(-0.65 to -0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.36\u003c/p\u003e\n \u003cp\u003e(44.17\u0026ndash;80.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.53\u003c/p\u003e\n \u003cp\u003e(50.42\u0026ndash;83.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003cp\u003e(0.22 to 0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.67\u003c/p\u003e\n \u003cp\u003e(6.42\u0026ndash;18.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.41\u003c/p\u003e\n \u003cp\u003e(5.26\u0026ndash;14.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.73\u003c/p\u003e\n \u003cp\u003e(-0.79 to -0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.89\u003c/p\u003e\n \u003cp\u003e(51.47\u0026ndash;83.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.83\u003c/p\u003e\n \u003cp\u003e(55.06\u0026ndash;82.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003cp\u003e(-0.09 to 0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eCocaine use disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003cp\u003e(0.08\u0026ndash;0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003cp\u003e(0.49\u0026thinsp;\u0026minus;\u0026thinsp;0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003cp\u003e(0.15 to 0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.57\u003c/p\u003e\n \u003cp\u003e(4.69\u0026ndash;9.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.86\u003c/p\u003e\n \u003cp\u003e(8.68\u0026ndash;13.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003cp\u003e(1.55 to 1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003cp\u003e(0.03\u0026ndash;0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003cp\u003e(0.03\u0026ndash;0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.46\u003c/p\u003e\n \u003cp\u003e(-0.48 to -0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.94\u003c/p\u003e\n \u003cp\u003e(3.40\u0026ndash;7.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.84\u003c/p\u003e\n \u003cp\u003e(3.74\u0026ndash;6.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003cp\u003e(-0.08 to -0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003cp\u003e(0.06\u0026ndash;0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003cp\u003e(0.07\u0026ndash;0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003cp\u003e(-0.15 to -0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.71\u003c/p\u003e\n \u003cp\u003e(4.11\u0026ndash;7.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.71\u003c/p\u003e\n \u003cp\u003e(6.17\u0026ndash;9.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003cp\u003e(0.77 to 1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eAmphetamine use disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.55\u003c/p\u003e\n \u003cp\u003e(1.37\u0026ndash;4.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.48\u003c/p\u003e\n \u003cp\u003e(1.36\u0026ndash;3.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003cp\u003e(-0.09 to -0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.39\u003c/p\u003e\n \u003cp\u003e(2.86\u0026ndash;6.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.49\u003c/p\u003e\n \u003cp\u003e(5.86\u0026ndash;9.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003cp\u003e(1.65 to 1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003cp\u003e(1.52\u0026ndash;4.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.50\u003c/p\u003e\n \u003cp\u003e(1.42\u0026ndash;3.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003cp\u003e(-0.21 to -0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.10\u003c/p\u003e\n \u003cp\u003e(3.72\u0026ndash;7.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.59\u003c/p\u003e\n \u003cp\u003e(2.50\u0026ndash;5.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.17\u003c/p\u003e\n \u003cp\u003e(-1.23 to -1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003cp\u003e(1.46\u0026ndash;4.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003cp\u003e(1.41\u0026ndash;3.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003cp\u003e(-0.17 to -0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.72\u003c/p\u003e\n \u003cp\u003e(3.40\u0026ndash;6.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.41\u003c/p\u003e\n \u003cp\u003e(4.11\u0026ndash;7.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003cp\u003e(0.21 to 0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eCannabis use disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003cp\u003e(0.59\u0026ndash;4.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003cp\u003e(0.60\u0026ndash;4.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003cp\u003e(-0.05 to -0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003cp\u003e(0.98\u0026ndash;3.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003cp\u003e(1.03\u0026ndash;3.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003cp\u003e(0.05 to 0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003cp\u003e(0.40\u0026ndash;3.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003cp\u003e(0.36\u0026ndash;3.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003cp\u003e(-0.1 to -0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e(0.52\u0026ndash;1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e(0.52\u0026ndash;1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003cp\u003e(-0.02 to 0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003cp\u003e(0.51\u0026ndash;3.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003cp\u003e(0.47\u0026ndash;3.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003cp\u003e(-0.07 to -0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003cp\u003e(0.74\u0026ndash;2.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003cp\u003e(0.76\u0026ndash;2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003cp\u003e(0.04 to 0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eASIR, age-standardized incidence rate; ASDR, age-standardized disability-adjusted life years; DALYs, disability-adjusted life years; AAPC,\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eaverage annual percentage change.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFrom a gender perspective, among the four types of DUDs, except for opioid ASIR, which is higher in females than males, the other three are higher in males, while ASDR is higher in males than females (Fig. 2). In opioid use disorders, both male and female\u0026rsquo;s ASIR showed a decreasing trend, while ASDR showed an increasing trend in females and a decreasing trend in males (Fig. S2). In cocaine use disorders, the ASIR of males shows an upward trend while that of females shows downward, and the ASDR shows an upward trend. In methamphetamine use disorder, both male and female show a decreasing trend in ASIR, while in ASDR, males show an increasing trend while females show decrease. In cannabis use disorders, the ASIR of both male and female showed a decreasing trend, while the ASDR of men showed an increasing trend while that of women remained stable.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e3.2. SDI regions level\u003c/h2\u003e\n \u003cp\u003eIn 1990, the disease burden of opioid use disorders was higher in the middle and high-middle SDI regions (Table 2; Table 3). From 1990 to 2021, whereas the disease burden in both regions decreased significantly (Table S2; Table S3). By contrast, the disease burden in high SDI regions increased the most significantly, with the AAPC of ASIR and ASDR were 0.45 (95% CI 0.37\u0026ndash;0.54) and 4.05 (95% CI 3.94\u0026ndash;4.17), respectively. Fig. S3 illustrates a comparison of the changes in ASIR and ASDR for the four DUDs in the five SDI regions between 1990 and 2021. Given the prominent burden of opioid use disorders, further analysis of their incidence and DALYs was conducted based on three dimensions: SDI, gender, and age (Fig. S4). In terms of the rate, the trend of male and female is basically the same. In terms the number, the incidence number is higher in middle and high SDI regions, and it is higher in females than males. The DALYs number is concentrated in high SDI areas, with males having a higher DALYs number than females in the 60\u0026ndash;64 age group, and females gradually exceeding males in DALYs number as they age. Overall, the disease burden of opioid use disorders decreases with age.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eASIR for four drug use disorders in the elderly in 1990 and 2021 at the regional level.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eRegions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eASIR (95% uncertainty interval)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eAge- standardised rates per 100,000 (1990)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eAge- standardised rates per 100,000 (2021)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOpioid\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCocaine\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAmphetamine\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCannabis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOpioid\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCocaine\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAmphetamine\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCannabis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.92\u003c/p\u003e\n \u003cp\u003e(5.01,13.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003cp\u003e(0.12,0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003cp\u003e(0.55,1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003cp\u003e(0.33,3.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.87 (5.17,13.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003cp\u003e(0.18,0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003cp\u003e(0.55,1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003cp\u003e(0.34,3.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003cp\u003e(5.52,15.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003cp\u003e(0.06,0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003cp\u003e(0.67,2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003cp\u003e(0.36,3.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.73 (5.29,14.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003cp\u003e(0.1,0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003cp\u003e(0.69,2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003cp\u003e(0.37,3.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.91\u003c/p\u003e\n \u003cp\u003e(9.03,24.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003cp\u003e(0.05,0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.54\u003c/p\u003e\n \u003cp\u003e(2.07,5.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003cp\u003e(0.41,3.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.45 (5.31,14.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003cp\u003e(0.06,0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.94\u003c/p\u003e\n \u003cp\u003e(1.68,4.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003cp\u003e(0.4,3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.47\u003c/p\u003e\n \u003cp\u003e(8.01,22.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003cp\u003e(0.03,0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.15\u003c/p\u003e\n \u003cp\u003e(1.78,5.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003cp\u003e(0.52,3.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.58 (5.95,16.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003cp\u003e(0.05,0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.08\u003c/p\u003e\n \u003cp\u003e(1.74,4.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003cp\u003e(0.47,3.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.91\u003c/p\u003e\n \u003cp\u003e(3.51,11.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003cp\u003e(0.06,0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003cp\u003e(1.17,3.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003cp\u003e(0.68,4.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.92 (4.11,13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003cp\u003e(0.05,0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003cp\u003e(1.29,3.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003cp\u003e(0.65,4.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.27\u003c/p\u003e\n \u003cp\u003e(6.34,19.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003cp\u003e(0.04,0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003cp\u003e(1.3,4.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003cp\u003e(0.33,2.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.35 (7.58,20.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003cp\u003e(0.13,0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.79\u003c/p\u003e\n \u003cp\u003e(1.42,4.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003cp\u003e(0.34,3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.93\u003c/p\u003e\n \u003cp\u003e(3.18,9.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003cp\u003e(0.03,0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003cp\u003e(1.24,4.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(0.59,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.17 (3.46,9.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003cp\u003e(0.04,0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.54\u003c/p\u003e\n \u003cp\u003e(1.29,4.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003cp\u003e(0.65,4.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEastern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.01\u003c/p\u003e\n \u003cp\u003e(7.83,26.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003cp\u003e(0.03,0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.41\u003c/p\u003e\n \u003cp\u003e(1.2,4.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003cp\u003e(0.54,3.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.75 (8.56,24.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003cp\u003e(0.07,0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.35\u003c/p\u003e\n \u003cp\u003e(1.15,3.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.98\u003c/p\u003e\n \u003cp\u003e(0.57,3.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAustralasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.06\u003c/p\u003e\n \u003cp\u003e(4.84,14.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003cp\u003e(0.03,0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.47\u003c/p\u003e\n \u003cp\u003e(1.81,5.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003cp\u003e(0.61,4.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.33 (4.12,13.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003cp\u003e(0.03,0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003cp\u003e(1.72,5.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.27\u003c/p\u003e\n \u003cp\u003e(0.64,4.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.52\u003c/p\u003e\n \u003cp\u003e(2.58,9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003cp\u003e(0.07,0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.09\u003c/p\u003e\n \u003cp\u003e(1.07,3.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003cp\u003e(0.55,4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.86 (2.28,8.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003cp\u003e(0.06,0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003cp\u003e(1.04,3.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003cp\u003e(0.5,4.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-income North America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.17\u003c/p\u003e\n \u003cp\u003e(3.05,13.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003cp\u003e(0.05,0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003cp\u003e(0.81,3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003cp\u003e(0.73,5.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.98 (3.78,17.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003cp\u003e(0.05,0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003cp\u003e(0.93,3.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.56\u003c/p\u003e\n \u003cp\u003e(0.73,5.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthern Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.61\u003c/p\u003e\n \u003cp\u003e(2.57,9.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003cp\u003e(0.06,1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003cp\u003e(0.63,2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003cp\u003e(0.47,3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.38 (2.55,9.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003cp\u003e(0.05,1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003cp\u003e(0.66,2.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003cp\u003e(0.42,2.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWestern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.14\u003c/p\u003e\n \u003cp\u003e(3.26,9.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003cp\u003e(0.06,0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003cp\u003e(1.35,4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.23\u003c/p\u003e\n \u003cp\u003e(0.73,4.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.99 (4.64,12.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003cp\u003e(0.07,0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.75\u003c/p\u003e\n \u003cp\u003e(1.51,4.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003cp\u003e(0.68,4.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAndean Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.99\u003c/p\u003e\n \u003cp\u003e(1.76,6.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003cp\u003e(0.03,0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003cp\u003e(0.95,3.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003cp\u003e(0.34,3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.14 (1.86,6.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003cp\u003e(0.04,0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.93\u003c/p\u003e\n \u003cp\u003e(1.01,3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003cp\u003e(0.31,3.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaribbean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.32\u003c/p\u003e\n \u003cp\u003e(1.9,7.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003cp\u003e(0.03,0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003cp\u003e(0.58,2.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003cp\u003e(0.47,3.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.08 (1.87,6.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003cp\u003e(0.03,0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003cp\u003e(0.61,2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003cp\u003e(0.46,3.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.23\u003c/p\u003e\n \u003cp\u003e(1.88,7.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003cp\u003e(0.06,0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003cp\u003e(0.81,2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003cp\u003e(0.43,3.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(1.8,6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003cp\u003e(0.05,0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003cp\u003e(0.82,2.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003cp\u003e(0.41,3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTropical Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.91\u003c/p\u003e\n \u003cp\u003e(1.66,6.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003cp\u003e(0.07,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.51\u003c/p\u003e\n \u003cp\u003e(1.86,5.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003cp\u003e(0.63,4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003cp\u003e(1.61,6.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003cp\u003e(0.08,1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.65\u003c/p\u003e\n \u003cp\u003e(1.93,5.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.35\u003c/p\u003e\n \u003cp\u003e(0.67,4.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.36\u003c/p\u003e\n \u003cp\u003e(4.89,15.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003cp\u003e(0.02,0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003cp\u003e(0.3,1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003cp\u003e(0.3,2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003cp\u003e(5.36,15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003cp\u003e(0.03,0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003cp\u003e(0.3,1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003cp\u003e(0.33,2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.14\u003c/p\u003e\n \u003cp\u003e(6.57,18.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003cp\u003e(0.02,0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003cp\u003e(0.37,1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003cp\u003e(0.35,3.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.45 (6.18,17.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003cp\u003e(0.03,0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003cp\u003e(0.4,1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003cp\u003e(0.37,3.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoutheast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.18\u003c/p\u003e\n \u003cp\u003e(3.38,9.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003cp\u003e(0.01,0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.79\u003c/p\u003e\n \u003cp\u003e(1.46,4.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003cp\u003e(0.35,3.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.18 (3.44,9.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003cp\u003e(0,0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.82\u003c/p\u003e\n \u003cp\u003e(1.49,4.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003cp\u003e(0.36,3.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.4\u003c/p\u003e\n \u003cp\u003e(13.41,35.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003cp\u003e(0.01,0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.01\u003c/p\u003e\n \u003cp\u003e(2.98,7.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003cp\u003e(0.35,3.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.19 (6.37,16.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003cp\u003e(0.01,0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.06\u003c/p\u003e\n \u003cp\u003e(2.34,6.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003cp\u003e(0.37,3.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.37\u003c/p\u003e\n \u003cp\u003e(4.72,12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003cp\u003e(0,0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003cp\u003e(1.43,4.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.03\u003c/p\u003e\n \u003cp\u003e(0.51,4.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.38 (4.84,12.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003cp\u003e(0,0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.72\u003c/p\u003e\n \u003cp\u003e(1.44,4.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.03\u003c/p\u003e\n \u003cp\u003e(0.52,4.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.27\u003c/p\u003e\n \u003cp\u003e(4.24,10.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003cp\u003e(0.04,0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003cp\u003e(0.66,2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003cp\u003e(0.23,2.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.01 (4.92,11.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003cp\u003e(0.05,0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003cp\u003e(0.66,2.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003cp\u003e(0.23,2.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.15\u003c/p\u003e\n \u003cp\u003e(4.85,12.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003cp\u003e(0.09,0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003cp\u003e(0.67,2.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003cp\u003e(0.43,3.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.91 (5.62,12.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003cp\u003e(0.16,0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003cp\u003e(0.67,2.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003cp\u003e(0.4,3.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.79\u003c/p\u003e\n \u003cp\u003e(7.68,23.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003cp\u003e(0.62,2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.43\u003c/p\u003e\n \u003cp\u003e(2.51,6.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003cp\u003e(0.67,4.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.44 (7.43,20.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003cp\u003e(0.73,2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.54\u003c/p\u003e\n \u003cp\u003e(2.64,7.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.09\u003c/p\u003e\n \u003cp\u003e(0.66,4.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.09\u003c/p\u003e\n \u003cp\u003e(3.3,9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003cp\u003e(0.38,0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003cp\u003e(0.65,2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003cp\u003e(0.21,2.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.05 (3.31,9.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003cp\u003e(0.72,1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003cp\u003e(0.62,2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003cp\u003e(0.2,2.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eSDI, sociodemographic index.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eASDR for four drug use disorders in the elderly in 1990 and 2021 at the regional level.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eRegions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eASDR (95% uncertainty interval)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eAge- standardised rates per 100,000 (1990)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eAge- standardised rates per 100,000 (2021)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOpioid\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCocaine\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAmphetamine\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCannabis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOpioid\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCocaine\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAmphetamine\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCannabis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.7\u003c/p\u003e\n \u003cp\u003e(29.83,53.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.75\u003c/p\u003e\n \u003cp\u003e(2.15,6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003cp\u003e(0.68,2.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003cp\u003e(0.61,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.98\u003c/p\u003e\n \u003cp\u003e(29.99,51.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.02\u003c/p\u003e\n \u003cp\u003e(1.84,4.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003cp\u003e(0.67,1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003cp\u003e(0.6,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.67\u003c/p\u003e\n \u003cp\u003e(38.21,64.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.95\u003c/p\u003e\n \u003cp\u003e(2.23,6.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003cp\u003e(1,2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003cp\u003e(0.69,2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.37\u003c/p\u003e\n \u003cp\u003e(39.41,63.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003cp\u003e(3.35,6.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003cp\u003e(1.12,2.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003cp\u003e(0.66,2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93.42\u003c/p\u003e\n \u003cp\u003e(73.18,115.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.67\u003c/p\u003e\n \u003cp\u003e(3.38,6.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.67\u003c/p\u003e\n \u003cp\u003e(6.37,11.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003cp\u003e(0.63,2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.12\u003c/p\u003e\n \u003cp\u003e(32.16,54.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.68\u003c/p\u003e\n \u003cp\u003e(3.74,5.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.51\u003c/p\u003e\n \u003cp\u003e(4,7.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003cp\u003e(0.66,2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.13\u003c/p\u003e\n \u003cp\u003e(61.22,100.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.84\u003c/p\u003e\n \u003cp\u003e(3.71,6.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.51\u003c/p\u003e\n \u003cp\u003e(3.87,8.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003cp\u003e(0.58,1.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.7\u003c/p\u003e\n \u003cp\u003e(35.36,60.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.38\u003c/p\u003e\n \u003cp\u003e(2.38,4.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.48\u003c/p\u003e\n \u003cp\u003e(3.03,6.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003cp\u003e(0.58,1.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.41\u003c/p\u003e\n \u003cp\u003e(32.56,57.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.77\u003c/p\u003e\n \u003cp\u003e(5.76,12.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.84\u003c/p\u003e\n \u003cp\u003e(1.71,4.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003cp\u003e(1.02,3.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e147.3\u003c/p\u003e\n \u003cp\u003e(119.42,176.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.06\u003c/p\u003e\n \u003cp\u003e(15.85,25.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.39\u003c/p\u003e\n \u003cp\u003e(8.46,13.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.09\u003c/p\u003e\n \u003cp\u003e(1.17,3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.54\u003c/p\u003e\n \u003cp\u003e(34.12,70.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.04\u003c/p\u003e\n \u003cp\u003e(2.06,4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.47\u003c/p\u003e\n \u003cp\u003e(1.9,5.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003cp\u003e(0.35,1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.95\u003c/p\u003e\n \u003cp\u003e(46.84,80.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.87\u003c/p\u003e\n \u003cp\u003e(3.86,6.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003cp\u003e(3.54,8.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003cp\u003e(0.37,1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.9\u003c/p\u003e\n \u003cp\u003e(19.53,34.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.27\u003c/p\u003e\n \u003cp\u003e(2.38,4.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003cp\u003e(1.54,5.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003cp\u003e(0.59,2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.34\u003c/p\u003e\n \u003cp\u003e(25.02,39.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.92\u003c/p\u003e\n \u003cp\u003e(1.95,4.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003cp\u003e(1.74,5.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003cp\u003e(0.62,2.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEastern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e114.01\u003c/p\u003e\n \u003cp\u003e(88.68,146.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.29\u003c/p\u003e\n \u003cp\u003e(5.38,7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003cp\u003e(1.6,4.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003cp\u003e(0.59,2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93.38\u003c/p\u003e\n \u003cp\u003e(73.56,117.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.15\u003c/p\u003e\n \u003cp\u003e(2.49,4.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.14\u003c/p\u003e\n \u003cp\u003e(1.95,5.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003cp\u003e(0.63,2.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAustralasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.77\u003c/p\u003e\n \u003cp\u003e(20.78,47.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.72\u003c/p\u003e\n \u003cp\u003e(3.31,14.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.43\u003c/p\u003e\n \u003cp\u003e(1.97,8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003cp\u003e(1.05,3.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.45\u003c/p\u003e\n \u003cp\u003e(29.81,60.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.78\u003c/p\u003e\n \u003cp\u003e(3.4,14.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.34\u003c/p\u003e\n \u003cp\u003e(2.92,9.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.09\u003c/p\u003e\n \u003cp\u003e(1.08,3.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.22\u003c/p\u003e\n \u003cp\u003e(12.55,30.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003cp\u003e(3.79,10.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003cp\u003e(0.97,3.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003cp\u003e(0.82,2.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.21\u003c/p\u003e\n \u003cp\u003e(12.23,28.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.75\u003c/p\u003e\n \u003cp\u003e(4.13,10.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003cp\u003e(1.16,3.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003cp\u003e(0.81,2.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-income North America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.62\u003c/p\u003e\n \u003cp\u003e(46.38,82.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.39\u003c/p\u003e\n \u003cp\u003e(9.53,21.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003cp\u003e(1.41,3.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.73\u003c/p\u003e\n \u003cp\u003e(1.53,4.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e333.05\u003c/p\u003e\n \u003cp\u003e(268.89,399.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.27\u003c/p\u003e\n \u003cp\u003e(37.04,59.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.81\u003c/p\u003e\n \u003cp\u003e(18.76,29.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.33\u003c/p\u003e\n \u003cp\u003e(1.87,5.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthern Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.67\u003c/p\u003e\n \u003cp\u003e(11.19,35.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.25\u003c/p\u003e\n \u003cp\u003e(8.3,25.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003cp\u003e(0.64,2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003cp\u003e(0.43,1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.76\u003c/p\u003e\n \u003cp\u003e(14.55,36.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.77\u003c/p\u003e\n \u003cp\u003e(10.36,28.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003cp\u003e(1.12,2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003cp\u003e(0.55,1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWestern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.08\u003c/p\u003e\n \u003cp\u003e(24.37,44.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.37\u003c/p\u003e\n \u003cp\u003e(4.56,11.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003cp\u003e(1.65,5.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003cp\u003e(0.79,2.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.13\u003c/p\u003e\n \u003cp\u003e(51.9,78.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.42\u003c/p\u003e\n \u003cp\u003e(5.49,12.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.11\u003c/p\u003e\n \u003cp\u003e(2.6,6.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003cp\u003e(0.86,2.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAndean Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.03\u003c/p\u003e\n \u003cp\u003e(11.09,27.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.04\u003c/p\u003e\n \u003cp\u003e(8.06,15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.11\u003c/p\u003e\n \u003cp\u003e(1.24,3.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003cp\u003e(0.55,2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.89\u003c/p\u003e\n \u003cp\u003e(13.55,30.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.41\u003c/p\u003e\n \u003cp\u003e(11.33,20.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.73\u003c/p\u003e\n \u003cp\u003e(2.45,5.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003cp\u003e(0.53,2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaribbean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.07\u003c/p\u003e\n \u003cp\u003e(9.42,25.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.27\u003c/p\u003e\n \u003cp\u003e(4.74,10.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003cp\u003e(0.64,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003cp\u003e(0.92,3.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.05\u003c/p\u003e\n \u003cp\u003e(8.96,23.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.91\u003c/p\u003e\n \u003cp\u003e(6.26,12.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003cp\u003e(1.22,2.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003cp\u003e(0.91,3.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.08\u003c/p\u003e\n \u003cp\u003e(12.23,26.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.83\u003c/p\u003e\n \u003cp\u003e(8.73,15.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003cp\u003e(0.91,2.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003cp\u003e(0.71,2.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.37\u003c/p\u003e\n \u003cp\u003e(10.74,23.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.27\u003c/p\u003e\n \u003cp\u003e(8.28,15.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003cp\u003e(1.37,3.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003cp\u003e(0.67,2.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTropical Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.96\u003c/p\u003e\n \u003cp\u003e(7.99,22.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.31\u003c/p\u003e\n \u003cp\u003e(6.52,15.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.62\u003c/p\u003e\n \u003cp\u003e(1.78,6.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.11\u003c/p\u003e\n \u003cp\u003e(1.12,3.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.23\u003c/p\u003e\n \u003cp\u003e(8.73,21.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.85\u003c/p\u003e\n \u003cp\u003e(13.46,23.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.99\u003c/p\u003e\n \u003cp\u003e(2.09,7.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003cp\u003e(1.15,3.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.59\u003c/p\u003e\n \u003cp\u003e(40.97,70.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003cp\u003e(5.27,13.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003cp\u003e(0.85,2.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003cp\u003e(0.31,1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.06\u003c/p\u003e\n \u003cp\u003e(44.59,72.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.17\u003c/p\u003e\n \u003cp\u003e(4.46,8.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003cp\u003e(1.3,2.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003cp\u003e(0.31,1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.4\u003c/p\u003e\n \u003cp\u003e(47.94,78.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.69\u003c/p\u003e\n \u003cp\u003e(1.67,6.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003cp\u003e(0.76,2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003cp\u003e(0.83,2.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.17\u003c/p\u003e\n \u003cp\u003e(47.39,76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.09\u003c/p\u003e\n \u003cp\u003e(2.73,5.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003cp\u003e(0.95,2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003cp\u003e(0.78,2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoutheast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.29\u003c/p\u003e\n \u003cp\u003e(18.61,36.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003cp\u003e(0.88,2.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003cp\u003e(1.76,5.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003cp\u003e(0.57,1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.71\u003c/p\u003e\n \u003cp\u003e(20.91,38.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003cp\u003e(1.12,2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.57\u003c/p\u003e\n \u003cp\u003e(2.23,5.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003cp\u003e(0.58,1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e132.04\u003c/p\u003e\n \u003cp\u003e(102.25,164.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003cp\u003e(1.21,2.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.7\u003c/p\u003e\n \u003cp\u003e(10.36,18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003cp\u003e(0.5,1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.59\u003c/p\u003e\n \u003cp\u003e(29.18,54.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003cp\u003e(0.29,0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.54\u003c/p\u003e\n \u003cp\u003e(5.42,10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003cp\u003e(0.52,1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.74\u003c/p\u003e\n \u003cp\u003e(16.85,41.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003cp\u003e(0.3,1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.71\u003c/p\u003e\n \u003cp\u003e(1.48,4.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003cp\u003e(0.81,2.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.49\u003c/p\u003e\n \u003cp\u003e(15.36,38.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003cp\u003e(0.32,1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.79\u003c/p\u003e\n \u003cp\u003e(1.54,4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003cp\u003e(0.8,2.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.59\u003c/p\u003e\n \u003cp\u003e(15.9,39.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003cp\u003e(1.02,2.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003cp\u003e(0.58,1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003cp\u003e(0.43,1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.29\u003c/p\u003e\n \u003cp\u003e(18.44,42.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.87\u003c/p\u003e\n \u003cp\u003e(1.12,3.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003cp\u003e(0.66,2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003cp\u003e(0.43,1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEastern sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.22\u003c/p\u003e\n \u003cp\u003e(24.21,54.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003cp\u003e(1.3,3.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003cp\u003e(0.58,1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003cp\u003e(0.54,1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.32\u003c/p\u003e\n \u003cp\u003e(24.3,48.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.48\u003c/p\u003e\n \u003cp\u003e(1.47,4.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003cp\u003e(0.62,2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003cp\u003e(0.52,1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthern sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.57\u003c/p\u003e\n \u003cp\u003e(66.39,113.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.4\u003c/p\u003e\n \u003cp\u003e(12.27,24.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.18\u003c/p\u003e\n \u003cp\u003e(3.83,9.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003cp\u003e(0.78,2.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.33\u003c/p\u003e\n \u003cp\u003e(67.95,103.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.33\u003c/p\u003e\n \u003cp\u003e(15.36,28.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.28\u003c/p\u003e\n \u003cp\u003e(5.63,12.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003cp\u003e(0.79,2.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWestern sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.85\u003c/p\u003e\n \u003cp\u003e(15.63,31.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.66\u003c/p\u003e\n \u003cp\u003e(1.7,4.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003cp\u003e(0.51,1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003cp\u003e(0.35,1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.77\u003c/p\u003e\n \u003cp\u003e(14.3,28.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.64\u003c/p\u003e\n \u003cp\u003e(2.31,5.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e(0.5,1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003cp\u003e(0.34,1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eSDI, sociodemographic index.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn 2021, the burden of disease for cocaine use disorders was concentrated in high SDI areas, with a significant increase in the ASDR compared with 1990 (Table 2; Table 3; Table S2; Table S3). The ASIR of amphetamine use disorder is higher in the middle SDI region and high-middle SDI region, However, the ASDR in the high SDI region increased the most significantly from 1990 to 2021. The overall ASIR of cannabis use disorder remained stable, with an increase in ASDR in high SDI areas in 2021 compared to 1990. It is worth noting that compared to 1990, the ASDR in the middle SDI region and the high- middle SDI region decreased significantly in 2021, while the ASDR in the high SDI region increased significantly. Overall, from 1990\u0026ndash;2021, the ASDR of the four drug use disorders showed an increasing trend in high SDI regions, while it remained stable or decreased in other SDI regions, indicating a close correlation between the disease burden of drug use disorders and SDI regions. Figure 3 intuitively shows the change trend of incidence crude rate and DALYs crude rate of four DUDs in five SDI regions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e3.3. Regional trends\u003c/h2\u003e\n \u003cp\u003eIn 2021, the ASIR of opioid use disorders in Eastern European populations was the highest among 21 regions, at 15.75 (95% CI 8.56\u0026ndash;24.75), and the ASDR was 93.38 (95% CI 73.56-117.14), ranking second (Table 2; Table 3). In 2021, the ASIR of High incoming North America was 8.98 (95% CI 3.78\u0026ndash;17.08), while ASDR grew rapidly, with an AAPC of 5.61 (95% CI 5.37\u0026ndash;5.84) (Table S2; Table S3). In 2021, the highest ASIR values for cocaine and amphetamine use disorders were observed in Southern Sub-Saharan Africa, at 1.52 (95% CI 0.73\u0026ndash;2.60) and 4.54 (95% CI 2.64\u0026ndash;7.12), respectively. It is worth noting that from 1990 to 2021, although the ASIR values for both drug use disorders decreased or slightly increased in High incoming North America, ASDR showed a significant increase, with AAPC values of 3.86 (95% CI 3.47\u0026ndash;4.25) and 8.08 (95% CI 7.04\u0026ndash;9.13), respectively. The disease burden distribution of cannabis use disorder is relatively even in 21 regions, with High incoming North America having the heaviest disease burden and the fastest growth rate of ASDR. It should be pointed out that in East Asia, except for cannabis use disorder which remained stable, the disease burden of the other three DUDs decreased to varying degrees (AAPC\u0026thinsp;\u0026lt;\u0026thinsp;0, p\u0026thinsp;=\u0026thinsp;0). Fig. S3 shows a comparison of the changes in ASIR and ASDR for four DUDs in 21 regions between 1990 and 2021.\u003c/p\u003e\n \u003cp\u003eIn terms of SDI and disease burden in 21 regions, only the disease burden of opioid use disorders was negatively correlated with regional SDI, with a correlation coefficient of \u0026rho;=-0.22, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for ASIR and \u0026rho;=-0.07, P\u0026thinsp;=\u0026thinsp;0.026 for ASDR (Fig. S5). The disease burden of cocaine, amphetamine, and cannabis use disorders was positively correlated with regional SDI.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e3.4. National trends\u003c/h2\u003e\n \u003cp\u003eIn 2021, the three countries with the highest ASIR for opioid use disorders were Kazakhstan, Belarus, and France (Fig. 4; Table S4). The country with the lowest ASIR was the United Kingdom, and the highest AAPC for ASIR was France\u0026apos;s (Table S6). The two countries with the highest ASDR are the United States and Canada (Table S5). The country with the lowest ASDR is the Dominican Republic, and the maximum AAPC of ASDR is 6.00 (95% CI 5.65\u0026ndash;6.36) in the United States (Table S7).\u003c/p\u003e\n \u003cp\u003eThe three countries with the highest ASIR for cocaine use disorders are all located in Africa, namely South Africa, Ghana, and Cameroon. Countries with lower ASIR are mainly located in Asia, with Sri Lanka having the lowest ASIR. The maximum AAPC value for ASIR is 3.80 (95% CI 3.46\u0026ndash;4.13) in Kazakhstan. The country with the highest ASDR is 48.99 (95% CI 39.16-64.00) in the United States, the highest AAPC in Turkmenistan (4.89, 95% CI 4.16\u0026ndash;5.61), and the lowest AAPC in Taiwan, China Province is -8.47 (95% CI 8.86\u0026ndash;8.08).\u003c/p\u003e\n \u003cp\u003eThe two countries with the highest ASIR for drug use disorders of amphetamines are Northern Mariana Islands and South Africa, the lowest country is T\u0026uuml;rkiye, and the maximum AAPC of ASIR is 1.22 (95% CI 1.13\u0026ndash;1.30) in France. The country with the highest ASDR is the United States, with the lowest ASDR is Palestine at 0.51 (95% CI 0.24\u0026ndash;0.99), and the highest AAPC for ASDR is the United States at 8.34 (95% CI 7.33\u0026ndash;9.36).\u003c/p\u003e\n \u003cp\u003eThe two countries with the highest ASIR for cannabis drug use disorders are the United Kingdom and the United States. The maximum AAPC value for ASIR is 0.49 (95% CI 0.44\u0026ndash;0.55) in the United Kingdom. The two countries with the highest ASDR are Canada and the United States. The maximum AAPC value for ASDR is New Zealand at 1.07 (95% CI 0.86\u0026ndash;1.29).\u003c/p\u003e\n \u003cp\u003eIn terms of SDI and disease burden in 204 countries, only the ASIR of opioid use disorder is negatively correlated with regional SDI, while the ASDR of opioid use disorder and the disease burden of cocaine, amphetamine, and cannabis are positively correlated with regional SDI (Fig. 5). Of note, the ASDR in the United States is significantly higher than the fitted curve for all four DUDs.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e3.5. Decomposition analysis\u003c/h2\u003e\n \u003cp\u003eThe decomposition analysis measured the relative contributions of population aging, population growth, and epidemiological changes to the burden of drug use disorders in different regions using DALYs as units (Fig. 6). In opioid use disorders, population growth has led to changes in disease burden in most regions, particularly in low SDI and low to medium SDI areas (Table S8). North America, Western Europe, and East Asia are greatly influenced by epidemiology, with rates of 609.03%, 118.00%, and \u0026minus;\u0026thinsp;125.28%, respectively. In cocaine use disorders, population growth has led to changes in disease burden in most regions, particularly in low SDI, medium low SDI, and medium SDI areas (Table S9). North America, East Asia, and Eastern Europe are significantly affected by epidemiology, with rates of 312.47%, -135.32%, and \u0026minus;\u0026thinsp;56.50%, respectively. Population growth has led to changes in the disease burden in most regions due to methamphetamine use disorders (Table S10). North America is greatly influenced by epidemiology, accounting for 1323.23%. Population growth is the main factor affecting changes in disease burden in cannabis use disorders (Table S11). The negative growth impact of population aging is significant in East Asia and high-income Asia Pacific, with rates of -16.72% and \u0026minus;\u0026thinsp;25.55%, respectively. North America and South Latin America are greatly influenced by epidemiology, accounting for 32.13% and 28.43% respectively. Overall, the negative impact of aging on drug use disorders other than cannabis is relatively small.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e3.6. Predictive analysis\u003c/h2\u003e\n \u003cp\u003eFigure 7 shows the predicted trajectories of DALY numbers and age standardized rates for four types of drug use disorders globally and the top two high burden regions from 2022 to 2035. Analysis shows that the burden of opioid use disorders is still increasing in high-income North America, with a more pronounced growth trend among males, while the disease burden in Eastern Europe is showing a downward trend. In high-income North America, the burden of cocaine and amphetamine use disorders varies more significantly between men and women, with an increasing trend in men and a stabilizing trend in women. However, in sub-Saharan Africa, the burden on both men and women is decreasing. The burden of cannabis use disorders in high-income North America is on the rise, while the burden globally and in Australasia is expected to remain stable in the coming period.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWith the global aging process, the overall burden of DUD disease among the elderly population is still increasing, but there is still insufficient attention to this vulnerable group (GBD 2021 Diseases and Injuries Collaborators, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A variety of chronic and acute diseases and their complications in later life may lead to drug abuse, while drug use disorders will lead to an increased risk of diseases such as diabetes, cardiovascular diseases, neurodegenerative diseases and infectious diseases (Hser et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kaye et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Winhusen et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, due to social factors and insufficient personal cognition, there are challenges in the diagnosis and treatment of medication use disorders in the elderly population (S. Yarnell et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). Therefore, it is necessary to conduct in-depth research on the DUD burden of the elderly population. This study provides the latest data on the incidence rate and DALYs of drug use disorders at the global, regional and national levels from 1990 to 2021, and reveals their distribution patterns in different SDI regions and between men and women through trend, decomposition and prediction analysis.\u003c/p\u003e \u003cp\u003eOn a global scale, opioid use disorder is the heaviest burden among the four DUDs. The adverse reactions of excessive use of opioid drugs include constipation caused by intestinal dysfunction, difficulty urinating, ventricular arrhythmia, respiratory depression, and cognitive dysfunction, all of which can increase the physical damage and risk of death in elderly people (Bateman et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Farmer et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Krantz et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mercadante, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). From 1990 to 2021, ASDR significantly increased in high SDI regions among the five SDI regions, with the United States being a prominent representative. Pain is a significant cause of opioid use disorders in the elderly (Luchting et al., 2019; Pergolizzi et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In the medical field, in the mid to late 1990s, the American Pain Society identified pain as the fifth vital sign and emphasized the patient's right to assess and manage pain, thereby relaxing restrictions on opioid prescriptions for chronic non cancer pain, which may be a factor contributing to increased abuse (Skolnick, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Opioid abuse, represented by OxyContin, has led to a 79% increase in overdose mortality rates since 1996 (Alpert et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). After the successful reduction of excessive use of OxyContin due to the release of anti-abuse patented formulas, stronger side effects of heroin and illegally manufactured fentanyl emerged, leading to a continued increase in the burden of opioid use disorders (Gardner et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition to the evolution of new opioid drugs, adverse psychological factors such as loneliness also increase the risk of addictive psychiatric prescription drugs, including opioid drugs (Vyas et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Social isolation and loneliness are significant issues faced by a considerable number of elderly Americans, with approximately 25% of community residents aged 65 and above feeling isolated and lonely, leading to an increased burden of DUD (National Academies of Sciences et al., 2020). In terms of social factors, individuals with unstable housing are more prone to drug abuse (Adams et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The elderly in the United States is facing an increasingly severe housing burden problem, with nearly 11.2\u0026nbsp;million elderly households experiencing excessive housing cost burdens in 2021 (JCHS, 2023). The biopsychosocial factors mentioned above may all affect the burden of opioid use disorders in the elderly, which is consistent with the results of the decomposition analysis that North America is more affected by epidemiology. However, the higher DUD burden in the United States may also be due to the country's higher diagnosis rate, such as the corresponding diagnostic criteria being more applicable to the local population, and a greater emphasis on the diagnosis and treatment of substance use disorders at the societal and healthcare levels (Castaldelli-Maia et al., 2022).\u003c/p\u003e \u003cp\u003eThe ASIR and ASDR in middle and high-middle SDI regions have significantly decreased from 2019 to 2021, with China being a representative country. China has made a series of efforts in managing drug use disorders. In 2005, the Chinese government promulgated the \u0026ldquo;Regulations on the Administration of Anesthetic Drugs and Psychotropic Substances\u0026rdquo; to regulate the clinical rational use of opioid drugs and prevent illegal abuse (Fang et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Since 2011, the \u0026ldquo;Good Pain Management Program\u0026rdquo; has been launched nationwide, effectively regulating the prescription mode of opioid drugs and controlling opioid abuse caused by pain in the elderly through professional training, release of pain management guidelines, community outreach, and other methods. However, excessive prescription restrictions can also affect the clinical rational use of opioid drugs, making it difficult to meet the treatment needs of elderly patients with chronic pain. In terms of illegal drug management, the Chinese government launched a series of anti-drug policies in the 1990s, strengthened legislation to reduce the supply and circulation of drugs, and mobilized the medical and social security systems to provide rehabilitation and employment opportunities for drug users (W. Wang, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Since 2008, the Anti-Drug Law has stipulated mandatory treatment for drug addicts (X. Wang et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These policies have achieved outstanding results in the epidemiological changes of opioid use disorders, successfully reducing the disease burden, which is consistent with the results of the decomposition analysis. In addition, it should be pointed out that in the Chinese public's perception, DUD is a behavior that violates social morality and cultural norms. The propaganda and cultural constraints of mass media make this concept more firmly held among the elderly. This public stigma helps to alleviate DUD on the one hand, but on the other hand, it can also become an obstacle for drug users to receive treatment and reintegrate into society (X. Li et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Luo et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Meanwhile, as an alternative therapy, acupuncture and moxibustion has been widely concerned and recognized in China in terms of pain management of elderly patients, reducing opioid dependence, improving withdrawal symptoms and reducing relapse rate, which helps to reduce the burden of disease (He et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; T. Li et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tedesco et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe disease burden in low and low-middle SDI regions represented by Africa has remained at a relatively low level, which may be due to the relatively backward medical resources in Africa leading to certain difficulties in obtaining opioid drugs clinically. The prescription sets in Africa lack opioid drugs for pain management, and although African countries have taken some measures to improve drug supply, such as free morphine therapy for cancer patients, clinical supply demand still cannot be met due to supply chain, policy-making, and education barriers (Manjiani et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom a gender perspective, the ASIR of elderly women with opioid use disorders is higher than that of men, and ASDR is on the rise. The interaction of biological gender specific differences, including sex hormones and their effects on endogenous opioid drug systems and systemic inflammation, as well as gender specific genes, may affect the perception and experience of pain (Knouse et al., 2021). Firstly, chronic non-cancer painful diseases are more common in elderly women than in elderly men, leading to a higher likelihood of opioid analgesic use in elderly women (Rochon et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Secondly, estrogen has been shown to have a pain protective effect. Elderly women have lower levels of estrogen in their bodies, which may make chronic pain more common and severe than when they were younger, and they may use opioid drugs more frequently and at higher doses (Walter et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thirdly, women develop substance use disorders faster than men after exposure to addictive substances, and typically require higher doses to achieve the same level of analgesic effect. Fourthly, elderly women are more susceptible to domestic violence compared to men and are more likely to use opioid drugs for self-treatment to cope with negative emotions after exposure to addictive substances (UNODC, 2024). Lastly, women suffer from a stronger sense of shame and have fewer opportunities to receive treatment for medication use disorders, which may exacerbate the progression and deterioration of the condition and cause more serious physical damage.\u003c/p\u003e \u003cp\u003eAccording to the predictive model analysis, the DALYs of opioid use disorders in the global elderly population still show an upward trend, especially in North America where the growth is faster. Therefore, it is necessary to take targeted measures. In the medical field, personalized guidelines can be developed for the treatment of opioid analgesics to alleviate side effects and addiction, in response to the characteristics of elderly people with multiple diseases, atypical symptoms, and multiple medications. Meanwhile, government departments need to find a balance point in the level of control over opioid drugs, to avoid drug abuse due to loose prescription restrictions or situations where medication cannot be used due to overly strict control, and dynamically adjust the prescription restriction level of opioid drugs according to the actual situation. There is significant inequality in the production and distribution of opioid drugs between different SDI regions (Jayawardana et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Implementing unified scheduling and fair distribution of global opioid drugs can reduce supply costs, improve drug productivity, achieve global health strategies, and potentially alleviate the imbalance between global drug supply and demand. In addition, while strengthening local production capacity, it is necessary for low-income areas to seek international cooperation to provide assistance in improving supply chains, quality control, and pain management (Yao et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCocaine, as an excitatory and anesthetic drug that can increase alertness, happiness, reduce anxiety and social disorders, enhance self-esteem, energy, and libido. Compared to the burden of opioid diseases, the burden of cocaine diseases in the elderly is lower, but the abuse of cocaine is associated with complications of cardiovascular, respiratory, digestive, hematological, and psychiatric disorders, and further increases the burden of inflammation in the elderly (Soder et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; S. Yarnell et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). Our research findings indicate a significant upward trend in ASDR in high SDI regions, with the United States being a representative country. The problem of unstable housing in the United States may lead to negative emotions such as anxiety and depression among the elderly; Retired elderly individuals may experience increased psychological stress due to changes in socioeconomic status, worsening of chronic diseases, social isolation, and policy changes, which may be the reasons for the increased burden of cocaine use disorders (Choi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chun et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ghantous et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, long term use of cocaine increases the risk of common cardiovascular and cerebrovascular diseases, psychomotor symptoms, and neurodegenerative diseases in the elderly population (Carbone et al., 2024; O'Keefe et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), there is still a lack of effective drugs for treating cocaine abuse, and social and psychological treatment for the elderly needs to be strengthened (Kampman, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmphetamines, like cocaine, have stimulant effects that can enhance cognitive ability, improve mood, suppress appetite, and more (Sassi et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The abuse of amphetamines can lead to adverse consequences such as anorexia, insomnia, slowed exercise, and memory impairment (Heal et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Our research results show that the ASIR of amphetamine is about twelve times that of cocaine, but the ASDR is only about 70% of that of cocaine. This may be because amphetamine type stimulants (ATS) have more clinical applications and can be used to treat common elderly diseases such as Parkinson's disease, depression in later life, and cognitive syndrome, while their addiction and dependence are not as severe as cocaine (Farrell et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Up to 5% of elderly people in the community suffer from major depressive disorder, which is related to factors such as loss of work relationships, lack of social support, and economic and living security issues (Taylor, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). When other medications are ineffective in treating refractory depression in later life, ATS can serve as an alternative to alleviate symptoms. However, the desire of elderly people for enhanced effects may further lead to an increase in the use of ATS, which often becomes a part of the development of drug abuse processes (O'Donnell et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). When the user\u0026rsquo;s mental health and family relationships deteriorate, this tendency towards excessive use leading to addiction is further exacerbated. Although the use of ATS increases the incidence of cardiovascular disease and mortality, as well as accidental injuries and homicides, elderly drug abusers are unwilling to completely stop using it due to ongoing mental health issues. The negative impact of excessive use of ATS on the cognitive and behavioral abilities of elderly people may prompt them to seek treatment, and the sustainability of such treatment can be continuously driven by good social factors and family relationships. It is worth noting that the majority of participants are multi substance users, often taking various ATS in combination with opioids and other substances such as alcohol, which increases the risk of cardiac toxicity and violent behavior, leading to more serious health outcomes (Farrell et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, it is necessary to provide comprehensive withdrawal treatment for multi drug abuse. Unfortunately, like cocaine, there is currently a lack of effective drug treatments for ATS, and the overall effectiveness of available social and psychological interventions is relatively weak. It is necessary to adopt joint care to address the physical and mental health, welfare, and social care needs of the elderly.\u003c/p\u003e \u003cp\u003eOur research results are consistent with previous literature, indicating that male abuse of cocaine and amphetamines, two excitatory drugs, is much higher than that of females (McHugh et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The increase in disease burden among elderly people worldwide is more pronounced in males than females, and predictive analysis suggests that this trend will continue, which may be related to higher alcohol consumption rates among males (S. C. Yarnell, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Alcohol can affect drug metabolism, leading patients to require higher doses of medication, thereby increasing the risk of drug abuse and adverse reactions. Additionally, compared to women, gender inequality makes it easier for men to achieve higher career and social status before retirement, which may lead to a greater sense of psychological gap after retirement, resulting in more negative emotions and a higher risk of stimulant drug abuse (Griffin et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1989\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCannabis is the third most commonly used controlled substance globally, second only to alcohol and tobacco. Its various pharmacological active ingredients have been found to have anticonvulsant, antianxiety, antipsychotic, anti-inflammatory, and neuroprotective effects (Connor et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Long term use of cannabis by older adults is associated with an increased risk of falls, bronchitis, psychiatric complications, and cardiovascular events (Lin et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Cannabinoid hyperemesis syndrome (CHS) is also a common adverse reaction, and the lack of risk perception among the elderly further increases their susceptibility and harmfulness (Sorensen et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Our research results indicate that the overall trend of burden of cannabis use disorders among elderly people is stable, but the ASDR of cannabis use disorders among elderly people in high SDI areas shows an upward trend. With the process of legalizing cannabis in high SDI regions such as the United States and Canada, the stigmatization of cannabis is decreasing, and the use of cannabis for recreational or medical purposes is gradually increasing (Lin et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). An increasing number of elderly individuals are utilizing cannabis for therapeutic purposes without a physician's prescription, and this over-the-counter drug acquisition may increase the risk of cannabis drug abuse (Baumbusch et al., 2021). Moreover, factors such as chronic pain, sleep disorders, mental health issues, and alcohol consumption among the elderly also increase the risk of cannabis abuse (Han et al., 2020).\u003c/p\u003e \u003cp\u003eDecomposition analysis shows that population growth is the main contributing factor to the burden of cannabis use disorders, indicating that the \u0026ldquo;baby boomer generation\u0026rdquo; is currently entering a peak of aging, and it is necessary for governments around the world to adopt proactive policies to address the related issues caused by the rapid growth of the elderly population. Predictive analysis suggests that the overall burden of cannabis use disorders worldwide will remain stable in the near future, while high-income North America will experience a slight increase. Due to the decline in physical function and increase in underlying diseases among the elderly population, caution should be exercised when using cannabis for medical purposes to consider potential adverse consequences. Appropriate restrictions should be placed on the non-prescription access to cannabis for the elderly to reduce the harm caused by cannabis abuse to this vulnerable group. However, compared to opioid drugs, cannabis has fewer side effects and has a smaller impact on the quality of life of the elderly. Cannabis may play a key role in suppressing opioid abuse, which is also a future research direction(Wiese et al., 2018).\u003c/p\u003e \u003cp\u003eStudies have shown that as the proportion of female cannabis users increases, the gap in cannabis abuse rates between men and women is narrowing, while our study found that the gender difference in disease burden among older adults has remained stable (Cooper et al., 2018). The difference in results may be due to the different purposes of medical cannabis use among different age groups. In recent years, under the influence of multiple pressures such as occupation, social interaction, and childbirth, more and more middle-aged and young women have been using cannabis to relieve anxiety, which may be the reason for the increasing proportion of female cannabis users (Morris et al., 2023). After retirement, the pressure on elderly people from the workplace and other aspects is significantly reduced, and cannabis is mainly used to alleviate physical discomfort caused by chronic diseases. The impact of clinical symptoms caused by the disease itself is relatively fixed, and the gender difference in the burden of cannabis abuse mainly comes from the gender biological differences in the pharmacological effects of cannabis, so the burden difference between genders has remained stable. Study shows that after using cannabis, men have better pain relief effects than women, but there is a higher diagnosis rate of CHS, which may be the reason why older men have a higher abuse rate of cannabis and a higher disability weight after abuse (Cooper et al., 2016; Sorensen et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur research findings provide valuable insights for policy makers. To alleviate the burden of DUD in the elderly, it is necessary to take into account their physiological and pathological characteristics, comprehensively consider drug types, gender differences, national and regional development levels, and policy differences, and pay attention to modifiable risk factors, and take targeted measures. For the elderly population with a need for painkillers, attention should be paid to the underlying diseases that cause pain, and painkillers should be used reasonably. High SDI countries with sufficient drug supply and lax control should strengthen prescription restrictions on analgesic drugs and control the quality and promotion of new drugs before they are launched, in order to avoid the recurrence of medication accidents such as the \u0026ldquo;OxyContin incident\u0026rdquo;. On the contrary, low SDI countries should improve drug productivity and supply capacity to meet reasonable clinical needs. For elderly people with a need for psychostimulatory drugs, the focus should be on starting treatment with drugs with low side effects and low addiction, while strict control should be implemented for highly addictive drugs such as cocaine. Given the high incidence of underlying diseases, diverse types of medication, and weak physical functions among the elderly population, caution should be exercised in legalizing cannabis drugs for this group, and certain restrictive measures should be taken to achieve a protective effect. At the same time, we should improve and perfect the social security system for the elderly in terms of housing, income, medical care, etc., pay attention to the psychological health problems of the elderly, and solve the burden of DUD caused by psychological factors. In addition, it is necessary to strengthen the management of multiple prescription drugs and avoid the interaction of multiple DUDs. Finally, it is necessary to pay attention to the internal physiological, psychological, and external social and cultural differences between genders, analyze the impact of these differences on DUD, and carry out targeted personalized prevention and treatment.\u003c/p\u003e \u003cp\u003eThere are also some limitations to this study: 1. Data collection methods, technologies, and tools vary among countries, which may result in differences in diagnostic rates. Countries with lower medical and economic levels may have insufficient diagnostic rates. 2. The definition of drug use disorders in GBD 2021 follows the DSM-IV-TR and ICD-10 classifications. Different countries and regions may have different understandings of these classifications under different cultural backgrounds, resulting in uneven data quality. 3. This study only included four common DUDs in the elderly population and may overlook the burden of other types of DUDs. Despite these limitations, this study has several advantages. 1. Taking elderly people aged 60\u0026ndash;89 as the research subjects, this study presents the differences in disease burden of four types of drug use disorders in terms of time, region, gender, etc., and combines multiple perspectives of biology, psychology, and society to find and analyze the objective factors behind the differences in disease burden, which makes up for the current lack of epidemiological research on DUD burden for the vulnerable group of elderly people and provides valuable information and suggestions for policy makers. 2. Predicting the disease burden of four drug use disorders over the next 10 years can help develop personalized health policies to address future trends in disease burden. 3. Introducing the concept of SDI is beneficial for the rational allocation of limited medical resources worldwide, thereby promoting global health.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThere are multiple differences in the DUD burden of the elderly, both apparent and intrinsic. In terms of degree and trend, the burden of opioid use disorders is the heaviest and shows an upward trend. The high-income North American region bears a disproportionately high burden. The overall burden of DUD is heavier in males, while the burden of opioid use disorders in females deserves attention. Among the reasons for changes in DUD burden, the impact of epidemiological changes is most prominent in high-income North America, while other regions are mainly affected by the growth of the elderly population. The multiple influencing factors of DUD burden include biological differences between genders, drug types, social traditional beliefs, economic development level, policy control flexibility orientation, and uneven distribution worldwide. Global health policy makers should pay full attention to the DUD burden of the elderly population, comprehensively consider influencing factors, adjust public health policies in a targeted manner, allocate medical resources reasonably, and establish a controlled drug management system for the elderly population to reduce the DUD burden.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAAPC: Average Annual Percentage Change\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;APC: Annual Percentage Change\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;ASDR: Age-Standardized Disability-Adjusted Life Years Rates\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;ASIR: Age-Standardized Incidence Rates\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;ASR: Age-Standardized Rates\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;ATS: Amphetamine Type Stimulants\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;BAPC: Bayesian Age-Period-Cohort\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;CHS: Cannabinoid Hyperemesis Syndrome\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;CI: Confidence Interval\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;DALYs: Disability-Adjusted Life Years\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;DSM-IV-TR: Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;DUDs: Drug Use Disorders\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;GBD: Global Burden of Disease Study\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;ICD-10: International Classification of Diseases, Tenth Revision\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;SDI: Sociodemographic Index\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince the original data of this study came from a public database, the Research Ethics Committee of Guang \u0026apos;anmen Hospital, China Academy of Chinese Medical Sciences determined that no approval was required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\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 GBD repository (https://vizhub.healthdata.org/gbd-results/). Data supporting the findings of this study are available upon reasonable request from corresponding author Yuanhui Hu.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (82204753), Special Scientific Research for Traditional Chinese Medicine of State Administration of Traditional Chinese Medicine of China (201507004) and High Level Chinese Medical Hospital Promotion Project (HLCMHPP2023011).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBCJ, RW, and ZQL wrote the main manuscript text and prepared the methodology, software, and data interpretation sections. MYF curated the data, developed the visualizations, and reviewed and edited the manuscript. MXW and YW supervised the project and validated the results. ZQL and YHH provided overall supervision. All authors reviewed the drafted manuscript and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe highly appreciate the work by the GBD 2021 collaborators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBochao Jia: http://orcid.org/0009-0006-8997-7839\u003c/p\u003e\n\u003cp\u003eRui Wei: http://orcid.org/0009-0005-7056-7250\u003c/p\u003e\n\u003cp\u003eZhiqi Li: http://orcid.org/ 0000-0002-0222-4783\u003c/p\u003e\n\u003cp\u003eZhenquan Liu: https://orcid.org/0000-0002-7156-8723\u003c/p\u003e\n\u003cp\u003eYuanhui Hu: http://orcid.org/0000-0001-6118-8009\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdams EA, Spencer L, Addison M, McGovern W, Alderson H, Adley M, et al. 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BMC Public Health. 2024;24(1):1639.\u003c/li\u003e\n\u003c/ol\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":"Drug use disorders, Elderly, Age-standardized rate, Global burden of disease","lastPublishedDoi":"10.21203/rs.3.rs-5977182/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5977182/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAs the global population ages, the burden of drug use disorders (DUDs) among the elderly is rising. It is imperative to conduct a quantitative analysis of the disease burden affecting this vulnerable population.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUtilize the Global Burden of Disease Study 2021 database to obtain incidence rates and disability-adjusted life years (DALYs) for opioids, cocaine, amphetamines, and cannabis among the elderly (aged 60\u0026ndash;89) across 204 countries and 5 SDI regions from 1990 to 2021. Employ Joinpoint regression analysis to calculate the average annual percentage change (AAPC) of age-standardized incidence rates (ASIR) and age-standardized DALYs rates (ASDR). Use the Das Gupta method to decompose and analyze the impacts of changes in age structure, population growth, and epidemiology on DALYs during this period. Finally, apply the Bayesian Age-Period-Cohort (BAPC) model to predict ASIR and DALYs for global and high-burden regions from 2022 to 2035.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf the four DUDs, opioids have the highest disease burden. Joinpoint analysis indicates that from 1990 to 2021, the ASIR for opioid use disorder decreased with an AAPC of -0.73 (95% CI: -0.79 to 0.67), while the ASDR remained stable. Cocaine use disorder ASIR remained stable, but ASDR increased with an AAPC of 0.94 (95% CI: 0.77\u0026ndash;1.11). The burden of amphetamine and cannabis use disorders generally stabilized. Geographic heterogeneity was evident at regional and national levels, with ASDR for all four DUDs increasing in high-SDI areas while remaining stable or declining in other SDI areas. High-income North America, represented by the United States, shows a higher burden of disease. Decomposition analysis shows that population growth is the main factor affecting the change in the burden of DUDs in most regions, and high-income North America is mainly affected by epidemiological changes. According to the Predictive models, the DALYs of DUDs in the global elderly population is still on the rise, especially in the male group in North America.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe burden of DUDs among the elderly varies across countries, regions, SDI levels, and genders, underscoring the need for targeted public health policy adjustments and strategic allocation of medical resources to mitigate this burden.\u003c/p\u003e","manuscriptTitle":"Global and regional burden of four drug use disorders in the elderly, 1990 to 2021: an analysis of the Global Burden of Disease Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-10 10:28:51","doi":"10.21203/rs.3.rs-5977182/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":"21da2771-2f18-477a-b66e-aaccb13a0db2","owner":[],"postedDate":"February 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-18T04:38:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-10 10:28:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5977182","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5977182","identity":"rs-5977182","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}