Global burden and trends in mental disorders among older adults in the BRICS countries from 1990 to 2021, with projections to 2050: a cross-sectional study

Global burden and trends in mental disorders among older adults in the BRICS countries from 1990 to 2021, with projections to 2050: a cross-sectional 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 burden and trends in mental disorders among older adults in the BRICS countries from 1990 to 2021, with projections to 2050: a cross-sectional study Ting Yu, Jiao Wang, Yijing Li, Miaomiao Li, Abigail Dove, Yan Jiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7238043/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background Mental disorders are a growing cause of disability among older adults in BRICS countries, where large population and rapid aging present a significant public health challenge. A comprehensive assessment of disease burden is urgently needed. Methods Data from the Global Burden of Disease Study 2021, we estimated prevalence and years lived with disability (YLDs) for depressive disorders, anxiety disorders, bipolar disorder, schizophrenia, and substance use disorders among older adults(1990–2021) in BRICS. Subgroup analyses by age and sex were performed, and Bayesian age-period-cohort models projected trends until 2050. Results In 2021, depressive disorders (33.5 million) and anxiety disorders (23.1 million) were the most prevalent, with higher rates in females. Alcohol use disorders (8.1 million) were far common than drug use disorders (0.78 million), and more prevalent in males. From 1990 to 2021, prevalence of anxiety disorders rose by 262.6%, while drug use disorders declined by 276.3%. YLDs increased substantially for anxiety disorders (254.1%, 95% UI: 47.8–480.9%) and depressive disorders (98.8%, 95% UI: -63.6–275.2%), whereas drug use disorders declined markedly (-398.9%, 95% UI: -655.3% to -99.4%). Projection analyses indicate that the burden of mental disorders among older adults will increase in most BRICS countries during 2021–2050. Conclusions The prevalence and burden of mental disorders on older adults in BRICS countries are rising and will likely continue increasing until 2050. Strengthened mental health systems and targeted interventions are crucial to meet the growing needs of aging populations in these low- and middle-income countries. Mental Disorders Older Adult Global Burden of Disease BRICS Prevalence Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Mental disorders have become a major contributor to global disability, with far-reaching implications for the aging population [ 1 , 2 ] . Mental and substance use disorders have been estimated to account for 10.6% of disability-adjusted life years (DALYs) among people over the age of 60, with depression and anxiety disorders being the main contributors [ 3 , 4 ] . Older adults are at higher risk of disability and reduced quality of life due to multiple vulnerabilities (e.g., multimorbidity, social isolation, and economic stress), and 27.2% of global suicide deaths occur in this population, underscoring the lethality of untreated mental illnesses in this population [ 5 ] . Despite these challenges, mental health services for older people remain insufficient, particularly in low- and middle-income countries (LMICs), with stigma and fragmented healthcare systems further limiting healthcare availability [ 2 , 6 ] . The BRICS countries – including Brazil, Russia Federation, India, China, and South Africa –together account for 42% of the global population and are experiencing rapid demographic change, with the older population projected to double by 2050 [ 7 ] . Due to their unique socio-economic landscapes and ongoing transformations, these countries face multiple overlapping challenges, including population ageing, socio-economic disparities, and underdeveloped mental health infrastructures [ 8 , 9 ] . Understanding the epidemiology of mental disorders among older adults in these countries is crucial for the development of tailored public health intervention strategies. The Global Burden of Disease (GBD) study provides the broadest coverage of epidemiologic data on mental disorders to date. The most recent comprehensive review of the global burden of mental disorders was published based on the findings of GBD 2019 [ 2 ] . However, previous iterations lacked sufficient data for some LMICs and provided limited information specifically related to the mental health of older adults [ 10 ] . GBD 2021 bridges these gaps by incorporating updated prevalence surveys, subnational data, and validated disability weights across different populations [ 3 , 11 , 12 ] . Moreover, the GBD study also provides data on substance use disorders (SUDs), recognized as significant comorbid conditions in older adults with mental disorders [ 13 , 14 ] . Therefore, here we provide an updated analysis of the prevalence and disability from mental disorders in the BRICS countries, using the latest data from GBD 2021. Taking advantage of the more comprehensive data offered in GBD 2021, we aimed to 1) provide a detailed analysis of the distribution and burden of mental disorders from 1990 to 2021 by age and sex, and 2) generate long-term predictions about the future burden of mental disorders among older adults in the BRICS countries in 2050. Methods Overview This study is based on GBD 2021 data, which is publicly accessible via https://vizhub.healthdata.org/gbd-results/ (accessed January 12, 2025). GBD 2021 provides comprehensive data on 371 diseases and injuries in 204 countries and territories and 811 subnational locations (including the BRICS countries) over the period from 1990 to 2021, which identified from vital registration systems, cause-of-death assignments, registries, surveys, and police or surveillance data across all countries and regions. The GBD study utilized deidentified data from a secondary source, which was aggregated by the XXX. GBD 2021 received ethical approval by XXX. Given the use of anonymized data, informed consent was not required. Briefly, in the present study, we estimated the prevalence, years lived with disability (YLDs) and years of life lost (YLLs) for the following mental disorders categorized as level 3 causes according to GBD hierarchy: schizophrenia, anxiety disorders, depressive disorders, and bipolar disorder. We also estimated the prevalence and YLDs for alcohol and drug use disorders, which are also categorized as mental disorders in the International Classification of Diseases. Prevalence Prevalence estimates were modeled for male and female participants across five age groups: 60-64years, 65-69years, 70-74years, 75-79years, and > 80 years, as described by the GBD 2021 Collaborators [ 3 ] . GBD prevalence estimates are constructed through systematic reviews of published studies, information from the websites of governmental and international organizations, published reports, primary data sources, and contributions from GBD collaborators. The acquired data are adjusted for biases using DisMod-MR version 2.1, a tool that pools data from various sources to produce consistent prevalence estimates by age, sex, location, and year. Details regarding bias correction and other adjustments for each individual disorder are available in the GBD 2021 capstone report [ 11 ] , as well as in the GBD 2021 Mental Disorders Collaborator Study [ 12 ] Subgroup analyses were performed to stratify epidemiological indicators related to mental disorders by age and sex. Whenever feasible, estimates were further disaggregated by age and sex. For instance, if the original studies provided prevalence data for broad age groups stratified by sex, alongside narrower age groups with combined-sex prevalence data, we estimated sex-specific values for the age groups using the reported sex ratios and their associated uncertainty intervals (UI). Calculation of Burden Disease burden was considered in terms of YLDs, YLLs and DALYs. YLDs are derived by multiplying sequela-specific prevalence (i.e., the consequences of a disease or injury) by its corresponding disability weight, which quantifies the amount of health loss associated with each sequela, ranging from 0 (perfect health) to 1 (death). These disability weights are derived from community-based surveys conducted in Bangladesh, Indonesia, Peru, Tanzania, the USA, Hungary, Italy, Sweden, and the Netherlands, as well as an open web-based survey available in English, Spanish, and Mandarin [ 3 , 15 ] . YLLs are calculated based on disease-specific mortality rates and the standard life expectancy at the age of death. Finally, DALYs are obtained by summing YLDs and YLLs. For mental disorders not recognized as direct causes of death but rather contribute to non-fatal health burden, YLDs serve as an approximation of DALYs. The 95% UIs for all final estimates were determined using the 2.5th and 97.5th percentiles from 500 draws. Bayesian Age-Period-Cohort Models Analysis We projected sex-specific trends in the global burden of mental disorders from 2022 to 2050 using a Bayesian age-period-cohort (BAPC) model [ 16 ] . Assuming that age, period, and cohort effects vary smoothly over time, the BAPC model applied Bayesian inference with second-order random walks to estimate and forecast posterior rates. Integrated nested Laplace approximations (INLA) were employed to approximate marginal posterior distributions, thereby circumventing the mixing and convergence issues inherent to Markov chain Monte Carlo methods. This approach has been widely adopted in the analysis of chronic disease trends and future burden projections [ 17 , 18 ] . Results Prevalence of Mental Disorders from 1990 to 2021 From 1990 to 2021, trends in the prevalence of various mental disorders in the BRICS countries were heterogeneous. Overall, anxiety disorders showed the greatest increase, rising by 262.6% (95%UI: 68.7% to 478.1%), while drug use disorders showed the most significant decrease, declining by 276.3% (95%UI: -399.7% to -141.1%). In 2021, depressive disorders (33.5 million, 95%UI: 27.9 million to 40.1 million) and anxiety disorders (23.1 million, 95%UI: 17.8 million to 30.0 million) were the most prevalent mental disorders in the BRICS countries, with a significantly higher prevalence in females than in males (Figure 1). The total number of cases of alcohol use disorders (8.1 million, 95%UI: 6.0 million to 10.7 million) was approximately ten times higher than that of drug use disorders (0.8 million, 95%UI: 0.6 million to 1.0 million), with a higher prevalence in males than in females. Interestingly, the prevalence of all mental disorders appears to decline with age (Figure 1 and Table 1). Prevalence estimates varied considerably between the five BRICS countries (Figure 2). Depressive disorder increased the most in China (67.2%, 95%UI: 39.4% to 98.3%), anxiety disorder the most in Brazil (113.7%, 95%UI: 61.5% to 173.1%), schizophrenia the most in India (74.2%, 95%UI: 47.4% to 101.4%). Bipolar disorder decreased across all BRICS countries, with changes ranging from -4.0% to -1.5%. For SUDs, except for Brazil, drug use disorders declined in the other four BRICS countries, with China showing the most significant decrease (-240.7%, 95%UI: -262.9% to -216.1%). Notably, alcohol use disorders increased the most in China (102.2%, 95%UI: 56.9% to 153.4%) (Table 1). YLDs of Mental Disorders from 1990 to 2021 From 1990 to 2021, the disability burden (in YLDs) from mental disorders increased for depressive disorders (98.8%, 95%UI: -63.6% to 275.2%), anxiety disorders (254.1%, 95%UI: 47.8% to 480.9%), and schizophrenia (149.6%, 95%UI: -72.5% to 396.8%), but not bipolar disorder. Additionally, on SUDs, the disability burden from alcohol use disorders increased (102.1%, 95%UI: -128.8% to 361.9%), while the burden from drug use disorders significantly decreased (-398.9%, 95%UI: -655.3% to -99.4%) (Table 2). In 2021, depressive disorders were the mental disorders that accounted for the largest share of YLDs (78.0 million, 95%UI: 63.4 million to 95.4 million), followed by anxiety disorders, schizophrenia, and bipolar disorder. The YLDs attributed to alcohol use disorders (17.9 million, 95%UI: 13.0 million to 24.0 million) were nearly nine times greater than those caused by drug use disorders (2.0 million, 95%UI: 1.5 million to 2.5 million) (Table 2). Additionally, we observed a significant decline in YLDs with increasing age, with the number of YLDs in the 60-64year age group being nearly three times higher than that in the ≥80year group (2.0 million vs 6.4 million, respectively) (Figure 3). Between the different BRICS countries, the YLDs attributable to each mental and SUDs varies (Figure 3). Depressive disorders and anxiety disorders in China ranked first among the BRICS countries in YLDs, with 20.3 million (95%UI: 16.7 million to 24.6 million) and 13.8 million (95%UI: 10.5 million to 18.2 million), respectively. Brazil had the highest YLDs attributable to anxiety disorders (3.1 million, 95%UI: 2.4 million to 4.0 million), while Russia Federation (2.3 million, 95%UI: 1.8 million to 2.9 million), South Africa (0.5 million, 95%UI: 0.4 million to 0.7 million), and India (13.2 million, 95%UI: 10.7 million to 16.2 million) had the highest YLDs attributable to depressive disorders, respectively. Forecasting the Prevalence of Mental Disorders in 2050 The findings indicate that, from 2021 to 2050, the overall burden of mental disorders is projected to increase in all BRICS countries except China. In Brazil, a projected increase in both the total number of incident cases and YLLs is anticipated from 2021 to 2050. Except for alcohol use disorders, the incidence of other disorders is expected to remain consistently higher in females than in males. By 2050, the number of anxiety disorder cases is projected to reach an estimated 1133473 with 1893035 YLLs. A substantial rise in alcohol use disorders is also projected, with the number of incident cases increasing to 2055396 and YLLs reaching 612,977 by 2025 (Figure 4A). Similar patterns are expected in Russia Federation, India, and South Africa, as shown in Figures 4B, 4C, and 4E. However, China demonstrates a distinct trajectory for certain disorders. As illustrated in Figure 4D panels e and k, the number of depressive disorders cases among older females in China is projected to decline significantly after 2021, from 9622864 to 3964852 by 2050, while the number among older males is expected to increase to 7584052. Additionally, a gradual decrease in the number of incident cases and YLLs due to alcohol use disorders is projected from 2021 to 2050, with 782054 incident cases and 164231 YLLs estimated by 2050 (Figure 4D c and i). Discussion To our knowledge, this was the first study to provide a comprehensive assessment of the burden of mental disorders among older adults in the BRICS countries. Our findings based on GBD 2021 demonstrate that older people in the BRICS countries suffer from a substantial and growing burden of mental disorders and SUDs from 1990 to 2021, with notable differences by country, age, and sex. Furthermore, by 2050 both the number of cases of mental disorders and YLLs attributable to such disorders are predicted to increase among older adults in the BRICS. These results underscore the urgent need for targeted public health interventions to address the mental health needs of the aging population in these rapidly transforming LMICs. Our study revealed that depressive disorders and anxiety disorders were the most common mental health issues among older adults in the BRICS countries and the leading causes of disability, with a higher burden in females compared to males. This trend aligns with global patterns, as depressive disorders and anxiety disorders were increasingly recognized as leading contributors to the non-fatal burden of disease worldwide [ 4 , 12 ] . These disorders were not only widespread but also the major contributor to morbidity in older adults, often resulting in a cascade of functional impairments and reduced quality of life [ 19 , 20 ] . This is especially true of depressive disorders, which are anticipated to become the leading cause of global disease burden by 2030 [ 21 ] . However, because older adults tend to suffer from cognitive impairment and other comorbidities, the burden of depressive disorders and anxiety disorders in this population is often overlooked. Therefore, it is crucial to emphasize the importance and urgency of managing them as the measure to prevent premature death among older adults [ 22 , 23 ] . In addition, it was worth noting that from 1990 to 2021, the number and YLDs of older adults with depressive disorders and anxiety disorders in China were the highest, signaling a growing mental health burden among this nation’s rapidly aging population. Over the past 30 years, China has experienced substantial demographic shifts, marked by accelerated urbanization, economic growth, and changing family structures [ 24 , 25 ] . These factors have led to increased social isolation, financial insecurity, and a lack of adequate support systems for the older population, all of which are well-documented risk factors for depressive disorders [ 26 , 27 ] . In contrast, our projection analysis suggests a declining trend in the incidence of depressive disorders among older women in China over the next 30 years, accompanied by a partial decrease in YLLs. In contrast, both the incidence and YLLs among older men are expected to continue rising. This contrasting pattern may reflect China’s broader societal progress, including improved mental health literacy, greater gender equity, and the growing empowerment of women, which may facilitate help-seeking behaviors. In contrast, persistent cultural norms discouraging emotional expression among men may hinder timely recognition and treatment of mental health issues, particularly in older male populations [ 28 , 29 ] . Therefore, it remains essential to further strengthen mental health service infrastructure, expand social support networks, and – in particular – prioritize targeted interventions for the mental health needs of older male populations. We also observed that prior to 2021, the prevalence of alcohol use disorders was substantially higher than that of drug use disorders among older adults in the BRICS countries, particularly in China and the Russian Federation, with markedly higher rates among males compared to females. Alcohol abuse disorders not only pose high risks to physical health and all-cause mortality but may also contributed to cognitive decline and social isolation [ 30 , 31 ] . To address this issue, some countries have strengthened policy interventions and public health initiatives, and integrated alcohol use disorders into mental health management with the establishment of dedicated addiction clinics. These efforts may likely contributed to the gradual decline in the overall burden of alcohol use disorders observed in the future [ 32 – 34 ] . In contrast, the prevalence of drug use disorders among older adults is lower and has shown a decreasing trend, likely due to improved anti-drug policies and greater awareness of SUDs [ 35 ] . Moreover, an important observation from our study is the decline in the prevalence of mental disorders with increasing age, particularly after the age of 80. This trend might reflect survival bias, such that those with severe mental health conditions may have a reduced life expectancy and may die before reaching the age of 80. And these participants are less likely to have dementia or mild cognitive impairment, which may reduce the generalizability of the study findings [ 36 ] . Additionally, as individuals age, the manifestation of mental health disorders may become less detectable due to cognitive decline or other aging-related factors [ 37 , 38 ] . At the same time, the oldest-old population has typically experienced multiple major life events—such as serious illness, chronic stress, and sociocultural transitions—which may foster more mature emotional regulation, greater psychological resilience, and more adaptive coping mechanisms. These factors may in part contribute to the observed decrease in the prevalence of mental disorders among them [ 39 ] . The unique challenges faced by older adults in BRICS countries, including lack of access to mental health services and cultural stigma surrounding mental health, may further contribute to underreporting and undertreatment of these conditions. Some limitations should be acknowledged. First, although the GBD 2021 data is extensive, the quality and completeness of the data may vary across different countries and regions. Specifically, LMICs like South Africa may face challenges in accurately reporting mental health data due to limited resources, underreporting, and the stigma surrounding mental disorders, especially among older populations. This could have contributed to either an over- or under-estimation of the mental health burden in certain areas, highlighting the need for improved primary data collection to enhance the accuracy of research. Second, the lack of mortality data on mental disorders in the GBD study further exacerbates these limitations. While DALYs and YLDs are equivalent for most mental disorders, the absence of mortality estimates may obscure the true burden of mental disorders. These methodological shortcomings underscore the importance of focusing on non-fatal disability as represented by YLDs. Third, this study focused only on major mental disorders such as depression and anxiety, while other conditions, such as dementia and cognitive impairment, were not included in the analysis. Additional studies are warranted to understand the prevalence and burden of other conditions beyond mental disorders that commonly occur in older adults. While limitations exist, they do not diminish the value of the GBD study, which remains the most comprehensive source of global data on mental disorders and continues to improve over time. Mental disorder and SUDs present substantial challenges across all levels of society. Epidemiological data are essential for identifying high-risk groups, setting intervention priorities, and guiding the planning and allocation of mental health services. Conclusions This study highlights the significant and growing burden of mental and SUDs among older adults in the BRICS countries. From 1990 to 2021, depressive disorders and anxiety disorders became the leading causes of disability in this population, especially for females. The overall burden of mental disorders in the BRICS countries is projected to continue rising through 2050, posing substantial challenges to future disease burden management. This is particularly alarming in the context of the rapid population aging in the BRICS countries, coupled with underdeveloped mental health services. Strengthening mental health care systems, improving early detection, and addressing social and economic factors are critical to mitigating the disability burden due to mental and SUDs in LMICs and improving the well-being of older adults in these countries. Abbreviations GBD: Global Burden of Disease Study YLDs: lived with disability DALYs: disability-adjusted life years YLLs: years of life lost SUDs: substance use disorders LMICs: low- and middle-income countries UI: uncertainty intervals BAPC: bayesian age-period-cohort INLA: integrated nested laplace approximations Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The datasets generated and analysed during the current study are available in the Global Health Data Exchange GBD 2021 website (https://ghdx.healthdata.org/gbd-2021). Competing interests The authors declare that they have no competing interests. Funding This work was supported by the National Key Research and Development Program of China (Grant number:2023YFC3605900). Authors' contributions Ting Yu: conceptualisation, methodology; formal analysis; data analysis; writing-manuscript; and editing. 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Depressive symptoms and mild cognitive impairment in the elderly: an ominous combination[J]. Biol Psychiatry, 2012,71(9):762-764. Cai J, Gao Y, Hu T, et al. Impact of lifestyle and psychological resilience on survival among the oldest-old in China: a cohort study. [J]. Front Public Health., 2023,11:1329885. Tables Table 1. Total prevalence and percentage change for mental and substance use disorders BRICS Brazil Russian Federation 1990 prevalence 2021 prevalence Percentage change, 1990–2021 1990 prevalence 2021 prevalence Percentage change, 1990–2021 1990 prevalence 2021 prevalence Percentage change, 1990–2021 Depressive disorders 12033581 (9982137 to 14526675) 33488929 (27945395 to 40147725) 78.4% (-63.3% to 232.7%) 671718 (557471 to 813333) 2012182 (1648730 to 2439696) -7.3% (-43.2% to 31.5%) 1455939 (1168543 to 1780333) 1980386 (1588620 to 2427065) -19.8% (-35.4% to -2.8%) Anxiety disorders 8412056 (6503696 to 10937023) 23122418 (17828036 to 30009712) 262.6% (68.7% to 478.1%) 748476 (592010 to 953070) 2805559 (2183809 to 3592678) 113.7% (61.5% to 173.1%) 1015773 (789989 to 1300626) 1571835 (1219880 to 2015136） 43.0% (18.3% to 71.1%) Schizophrenia 543967 (463563 to 634303) 1428912 (1218367 to 1668268) 156.4% (80.3% to 236.7%) 25847 (21636 to 30618) 77048 (64378 to 91465) 18.5% (6.2% to 32.7%) 46910 (39440 to 55364) 68054 (57238 to 80269） 29.9% (21.4% to 38.9%) Bipolar disorder 743838 (580220 to 933110) 1828677 (1426439 to 2295251) -11.4% (-21.9% to -0.5%) 106224 (84859 to 131522) 309464 (247370 to 382965) -3.1% (-4.9% to -1.3%) 140397 (110091 to 175561) 192419 (150767 to 240850） -4.0% (-5.8% to -2.2%) Drug use disorders 453095 (359043 to 566518) 782709 (619635 to 969297) -276.3% (-399.7% to -141.1%) 23298 (18782 to 28781) 72159 (58970 to 87521) 39.3% (13.5% to 64.1%) 69655 (55256 to 86826) 79636 (64619 to 96259） -49.1% (-73.3% to -19.8%) Alcohol use disorders 3668133 (2671752 to 4904350) 8098706 (5954943 to 10740539) 105.4% (-102.4% to 340.3%) 247345 (180751 to 329701) 882068 (653306 to 1159874) 62.3% (13.4% to 118.2%) 1229438 (931526 to 1583143) 1606914 (1218105 to 2077102） 10.4% (-30.3% to 52.9%) India China South Africa 1990 prevalence 2021 prevalence Percentage change, 1990–2021 1990 prevalence 2021 prevalence Percentage change, 1990–2021 1990 prevalence 2021 prevalence Percentage change, 1990–2021 Depressive disorders 3939670 (3273544 to 4778439) 11349044 (9417163 to 13695101) 19.8% (-6.9% to 46.8%) 5773591 (4822693 to 6923616) 17682943 (14907956 to 21022223) 67.2% (39.4% to 98.3%) 192663 (159887 to 230954) 464375 (382927 to 563639) 18.5% (-17.1% to 58.8%) Anxiety disorders 2012188 (1540508 to 2625090) 6078813 (4672037 to 7843939) 42.4% (13.2% to 74.1%) 4534248 (3502333 to 5927013) 12402505 (9549567 to 16209632) 4.0% (-27.2% to 36.7%) 101371 (78857 to 131225) 263706 (202743 to 348327) 59.5% (3.0% to 123.1%） Schizophrenia 151565 (125618 to 180781) 445719 (372155 to 528829) 74.2% (47.4% to 101.4%) 314324 (272431 to 361202) 825433 (714004 to 952621) 16.9% (2.2% to 31.5%) 5321 (4438 to 6339) 12658 (10592 to 15083) 17.0% (3.1% to 32.2%） Bipolar disorder 251334 (195226 to 314652) 684141 (531340 to 857337) -3.5% (-5.7% to -1.4%) 230217 (177778 to 291817) 606222 (468411 to 768622) -2.2% (-4.6% to 0.5%) 15666 (12267 to 19558) 36431 (28550 to 45477) 1.5% (-1.0% to 4.0%） Drug use disorders 86276 (66309 to 111073) 227742 (176517 to 286488) -18.5% (-46.9% to 12.9%) 265201 (211481 to 329597) 383473 (302889 to 475866) -240.7% (-262.9% to -216.1%) 8665 (7215 to 10240) 19698 (16640 to 23163) -7.3% (-30.0% to 17.7%） Alcohol use disorders 1192979 (843811 to 1633800) 2696995 (1976451 to 3569739) -57.4% (-89.6% to -19.8%) 948102 (678513 to 1291942) 2796285 (2021117 to 3781249) 102.2% (56.9% to 153.4%) 50269 (37150 to 65765) 116443 (85964 to 152575) -12.2% (-52.8% to 35.6%） * Data in parentheses are 95% uncertainty intervals. Table 2. Total YLDs and percentage change for mental and substance use disorders BRICS Brazil Russian Federation 1990 YLDs 2021 YLDs Percentage change, 1990–2019 1990 YLDs 2021 YLDs Percentage change, 1990–2019 1990 YLDs 2021 YLDs Percentage change, 1990–2019 Depressive disorders 28314909 (22882399 to 34841887) 77974058 (63352600 to 95357655) 98.8% (-63.6% to 275.2%) 784630 (633652 to 970144) 2349066 (1871686 to 2904937) -9.2% (-49.4% to 35.1%) 1693427 (1324521 to 2117074) 2300930 (1800624 to 2875875) -24.1% (-43.3% to -3.4%) Anxiety disorders 19060128 (14470960 to 25132184) 51746907 (39230560 to 68041600) 254.1% (47.8% to 480.9%) 830237(647163 to 1070312) 3111364(2388575 to 4031006) 113.2% (58.6% to 174.5%) 1126898 (863568 to 1460355) 1743051 (1333353 to 2264541) 40.5% (13.9% to 71.4%) Schizophrenia 1761610 (1416751 to 2130073) 4579570 (3688877 to 5539804) 149.6% (-72.5% to 396.8%) 40990 (32551 to 50291) 121987 (96627 to 149272) 18.1% (-31.0% to 72.1%) 74421 (59514 to 90893) 107787 (85971 to 131138) 27.2% (-7.1% to 65.5%) Bipolar disorder 1831846 (1382848 to 2366328) 4432064 (3343775 to 5732657) -16.3% (-177.9% to 160.3%) 127296 (98268 to 162210) 370667 (285989 to 472615) -3.1% (-32.5% to 28.4%) 168292 (127636 to 216552) 230515 (174686 to 296929) -5.8% (-29.0% to 18.7%) Drug use disorders 1170485 (888311 to 1512113) 1944062 (1480519 to 2488166) -398.9% (-655.3% to -99.4%) 26397 (20799 to 33195) 81529 (65135 to 100600) 22.9% (-57.1% to 112.6%) 90294 (68788 to 116186) 101578 (79258 to 126545) -71.7% (-110.1% to -25.5%) Alcohol use disorders 8225372 (5907808 to 11107503) 17908555 (12957152 to 23982866) 102.1% (-128.8% to 361.9%) 270176 (194991 to 362888) 963446(704329 to 1277901) 62.1% (8.7% to 121.3%) 1343710 (1004431 to 1746423) 1755973 (1312562 to 2289853) 9.9% (-30.8% to 52.8%) India China South Africa 1990 YLDs 2021 YLDs Percentage change, 1990–2019 1990 YLDs 2021 YLDs Percentage change, 1990–2019 1990 YLDs 2021 YLDs Percentage change, 1990–2019 Depressive disorders 4581814 (3704467 to 5666822) 13206034 (10667692 to 16233507) 25.0% (-4.2% to 54.5%) 6583104 (5381485 to 8034451 ) 20290798 (16719367 to 24608220) 92.7% (61.5% to 127.7%) 224889 (181777 to 275532) 541587 (434750 to 671388) 14.5% (-28.2% to 61.3%) Anxiety disorders 2229585 (1684256 to 2942572) 6737016 (5104536 to 8796812) 46.6% (14.8% to 81.1%) 5040731(3836401 to 6660676) 13776743(10462507 to 18190171) 2.3% (-30.9% to 37.6%) 112525 (86226 to 147389) 292288 (221547 to 390124) 51.4% (-8.5% to 116.3%) Schizophrenia 239566 (188676 to 295179) 705172 (558107 to 864914) 79.7% (35.5% to 127.7%) 504499 (411463 to 601028) 1321255 (1078314 to 1580388) 15.1% (-19.8% to 50.6%) 8475 (6694 to 10448) 20024 (15891 to 24675) 9.4% (-50.1% to 80.9%) Bipolar disorder 300643 (226335 to 386844) 818852 (615382 to 1055578) 0.8% (-27.7% to 31.8%) 276979 (206972 to 361731) 728547 (544943 to 952380) -3.4% (-42.0% to 40.5%) 18801 (14213 to 24192) 43619 (33042 to 56076) -4.8% (-46.7% to 40.8%) Drug use disorders 105635 (78492 to 139286) 280689 (210342 to 362329 ) -14.2% (-70.1% to 54.0%) 341547 (260611 to 438868) 475501 (361878 o 609251) -282.4% (-308.3% to -252.2%) 10516 (8444 to 12818) 23452 (19216 to 28317) -53.5% (-109.8% to 11.7%) Alcohol use disorders 1302811 (910520 to 1797914) 2945091 (2131978 to 3933474) -54.1% (-88.8% to -12.8%) 1038243 (733171 to 1425830) 3059628 (2181629 to 4168644) 100.8% (47.5% to 160.7%) 54945 (40093 to 72506) 127191 (92664 to 168005) -16.7% (-65.4% to 39.9%) * Data in parentheses are 95% uncertainty intervals. YLD = Years lived with disability. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Oct, 2025 Reviews received at journal 01 Oct, 2025 Reviews received at journal 01 Oct, 2025 Reviews received at journal 11 Sep, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviewers agreed at journal 01 Sep, 2025 Reviewers invited by journal 10 Aug, 2025 Editor invited by journal 01 Aug, 2025 Editor assigned by journal 01 Aug, 2025 Submission checks completed at journal 01 Aug, 2025 First submitted to journal 28 Jul, 2025 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. 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University","correspondingAuthor":false,"prefix":"","firstName":"Jiao","middleName":"","lastName":"Wang","suffix":""},{"id":498362551,"identity":"ee0d8534-031c-4fa9-b066-6f6a406d710e","order_by":2,"name":"Yijing Li","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yijing","middleName":"","lastName":"Li","suffix":""},{"id":498362552,"identity":"aed7652e-8822-429d-93c5-ed32bc7ddb1b","order_by":3,"name":"Miaomiao Li","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Miaomiao","middleName":"","lastName":"Li","suffix":""},{"id":498362553,"identity":"c3f9f6da-467b-49ae-b86b-9b1002a3013b","order_by":4,"name":"Abigail Dove","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Abigail","middleName":"","lastName":"Dove","suffix":""},{"id":498362554,"identity":"4c9757f0-5aea-4e6f-94e5-5f6fb8e0df84","order_by":5,"name":"Yan Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBACxgbGxgcfKiTk2Jj5HxxIADL4CWthbjacccbGmJ+dh/HAgzMWxpINBO1hb5PmbUtLlOznYT74sK0icQMhLcwzEpuNec4cTjA4zHvgQOI8CcYNDMwPH93A57AZiY0P51QczjM4zJdwIHGbBLM5A5uxcQ5+Lc0Gb84cLjY4zGAA0sJm2cDDJk1AS5sEb9vhxA1gLXMkeAwOEKFFEuT9mc1AxYkNEhKEtfQ8hAYyM1vCgYRjEgaSzQT8Ytie/hASlfyHD3/8UVNX38/e/PAxXi0TEtCFmPEoBwF5/gMEVIyCUTAKRsEoAACpaVdg1/FzEwAAAABJRU5ErkJggg==","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2025-07-29 02:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7238043/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7238043/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89110938,"identity":"bdd4a195-e44c-4ed9-88f0-86e3124134bc","added_by":"auto","created_at":"2025-08-14 19:22:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":61007,"visible":true,"origin":"","legend":"\u003cp\u003eOverall prevalence of mental and substance use disorders in BRICS countries by sex and age group (2021).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7238043/v1/680f0ae80c759951cd976ee6.jpg"},{"id":89110829,"identity":"68e91194-ef7e-46d7-b5a0-cfc244deed36","added_by":"auto","created_at":"2025-08-14 19:14:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":116440,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of mental and substance use disorders in each of the BRICS countries by sex and age group (2021).\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7238043/v1/c7de833476f6e8cf03b66707.jpg"},{"id":89110832,"identity":"8269966f-a58d-4c4a-b95e-4c9f1049ad99","added_by":"auto","created_at":"2025-08-14 19:14:45","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":108064,"visible":true,"origin":"","legend":"\u003cp\u003eYLDs from mental and substance use disorders by sex and age group (2021).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7238043/v1/3fcaa999bc5481a96dc0d44f.jpg"},{"id":89110940,"identity":"f124bca9-a9c0-449c-85cc-89b8816f1bf4","added_by":"auto","created_at":"2025-08-14 19:22:45","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":179967,"visible":true,"origin":"","legend":"\u003cp\u003eProjects the mental and substance use disorders in BRICS countries from 2021 to 2050.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7238043/v1/34f2c6880e5ad766f2d24bbf.jpg"},{"id":89111533,"identity":"5b0e65b4-463a-43e6-add6-852e226af8ad","added_by":"auto","created_at":"2025-08-14 19:38:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1638907,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7238043/v1/b19a3668-97a9-488e-a3d1-8c6095162885.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global burden and trends in mental disorders among older adults in the BRICS countries from 1990 to 2021, with projections to 2050: a cross-sectional study","fulltext":[{"header":"Background","content":"\u003cp\u003eMental disorders have become a major contributor to global disability, with far-reaching implications for the aging population\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Mental and substance use disorders have been estimated to account for 10.6% of disability-adjusted life years (DALYs) among people over the age of 60, with depression and anxiety disorders being the main contributors\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Older adults are at higher risk of disability and reduced quality of life due to multiple vulnerabilities (e.g., multimorbidity, social isolation, and economic stress), and 27.2% of global suicide deaths occur in this population, underscoring the lethality of untreated mental illnesses in this population\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Despite these challenges, mental health services for older people remain insufficient, particularly in low- and middle-income countries (LMICs), with stigma and fragmented healthcare systems further limiting healthcare availability\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe BRICS countries – including Brazil, Russia Federation, India, China, and South Africa –together account for 42% of the global population and are experiencing rapid demographic change, with the older population projected to double by 2050\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Due to their unique socio-economic landscapes and ongoing transformations, these countries face multiple overlapping challenges, including population ageing, socio-economic disparities, and underdeveloped mental health infrastructures\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Understanding the epidemiology of mental disorders among older adults in these countries is crucial for the development of tailored public health intervention strategies.\u003c/p\u003e\u003cp\u003eThe Global Burden of Disease (GBD) study provides the broadest coverage of epidemiologic data on mental disorders to date. The most recent comprehensive review of the global burden of mental disorders was published based on the findings of GBD 2019\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. However, previous iterations lacked sufficient data for some LMICs and provided limited information specifically related to the mental health of older adults\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. GBD 2021 bridges these gaps by incorporating updated prevalence surveys, subnational data, and validated disability weights across different populations\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Moreover, the GBD study also provides data on substance use disorders (SUDs), recognized as significant comorbid conditions in older adults with mental disorders\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTherefore, here we provide an updated analysis of the prevalence and disability from mental disorders in the BRICS countries, using the latest data from GBD 2021. Taking advantage of the more comprehensive data offered in GBD 2021, we aimed to 1) provide a detailed analysis of the distribution and burden of mental disorders from 1990 to 2021 by age and sex, and 2) generate long-term predictions about the future burden of mental disorders among older adults in the BRICS countries in 2050.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eOverview\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study is based on GBD 2021 data, which is publicly accessible via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vizhub.healthdata.org/gbd-results/\u003c/span\u003e\u003cspan address=\"https://vizhub.healthdata.org/gbd-results/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed January 12, 2025). GBD 2021 provides comprehensive data on 371 diseases and injuries in 204 countries and territories and 811 subnational locations (including the BRICS countries) over the period from 1990 to 2021, which identified from vital registration systems, cause-of-death assignments, registries, surveys, and police or surveillance data across all countries and regions. The GBD study utilized deidentified data from a secondary source, which was aggregated by the XXX. GBD 2021 received ethical approval by XXX. Given the use of anonymized data, informed consent was not required.\u003c/p\u003e\u003cp\u003eBriefly, in the present study, we estimated the prevalence, years lived with disability (YLDs) and years of life lost (YLLs) for the following mental disorders categorized as level 3 causes according to GBD hierarchy: schizophrenia, anxiety disorders, depressive disorders, and bipolar disorder. We also estimated the prevalence and YLDs for alcohol and drug use disorders, which are also categorized as mental disorders in the International Classification of Diseases.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePrevalence\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePrevalence estimates were modeled for male and female participants across five age groups: 60-64years, 65-69years, 70-74years, 75-79years, and \u0026gt; 80 years, as described by the GBD 2021 Collaborators\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. GBD prevalence estimates are constructed through systematic reviews of published studies, information from the websites of governmental and international organizations, published reports, primary data sources, and contributions from GBD collaborators. The acquired data are adjusted for biases using DisMod-MR version 2.1, a tool that pools data from various sources to produce consistent prevalence estimates by age, sex, location, and year. Details regarding bias correction and other adjustments for each individual disorder are available in the GBD 2021 capstone report\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, as well as in the GBD 2021 Mental Disorders Collaborator Study\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eSubgroup analyses were performed to stratify epidemiological indicators related to mental disorders by age and sex. Whenever feasible, estimates were further disaggregated by age and sex. For instance, if the original studies provided prevalence data for broad age groups stratified by sex, alongside narrower age groups with combined-sex prevalence data, we estimated sex-specific values for the age groups using the reported sex ratios and their associated uncertainty intervals (UI).\u003c/p\u003e\u003cp\u003e\u003cb\u003eCalculation of Burden\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDisease burden was considered in terms of YLDs, YLLs and DALYs. YLDs are derived by multiplying sequela-specific prevalence (i.e., the consequences of a disease or injury) by its corresponding disability weight, which quantifies the amount of health loss associated with each sequela, ranging from 0 (perfect health) to 1 (death). These disability weights are derived from community-based surveys conducted in Bangladesh, Indonesia, Peru, Tanzania, the USA, Hungary, Italy, Sweden, and the Netherlands, as well as an open web-based survey available in English, Spanish, and Mandarin\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. YLLs are calculated based on disease-specific mortality rates and the standard life expectancy at the age of death. Finally, DALYs are obtained by summing YLDs and YLLs. For mental disorders not recognized as direct causes of death but rather contribute to non-fatal health burden, YLDs serve as an approximation of DALYs. The 95% UIs for all final estimates were determined using the 2.5th and 97.5th percentiles from 500 draws.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBayesian Age-Period-Cohort Models Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe projected sex-specific trends in the global burden of mental disorders from 2022 to 2050 using a Bayesian age-period-cohort (BAPC) model\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Assuming that age, period, and cohort effects vary smoothly over time, the BAPC model applied Bayesian inference with second-order random walks to estimate and forecast posterior rates. Integrated nested Laplace approximations (INLA) were employed to approximate marginal posterior distributions, thereby circumventing the mixing and convergence issues inherent to Markov chain Monte Carlo methods. This approach has been widely adopted in the analysis of chronic disease trends and future burden projections\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePrevalence of Mental Disorders from 1990 to 2021\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom 1990 to 2021, trends in the prevalence of various mental disorders in the BRICS countries were heterogeneous. Overall, anxiety disorders showed the greatest increase, rising by 262.6% (95%UI: 68.7% to 478.1%), while drug use disorders showed the most significant decrease, declining by 276.3% (95%UI: -399.7% to -141.1%). In 2021, depressive disorders (33.5 million, 95%UI: 27.9 million to 40.1 million) and anxiety disorders (23.1 million, 95%UI: 17.8 million to 30.0 million) were the most prevalent mental disorders in the BRICS countries, with a significantly higher prevalence in females than in males (Figure 1). The total number of cases of alcohol use disorders (8.1 million, 95%UI: 6.0 million to 10.7 million) was approximately ten times higher than that of drug use disorders (0.8 million, 95%UI: 0.6 million to 1.0 million), with a higher prevalence in males than in females. Interestingly, the prevalence of all mental disorders appears to decline with age (Figure 1 and Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrevalence estimates varied considerably between the five BRICS countries (Figure 2). Depressive disorder increased the most in China (67.2%, 95%UI: 39.4% to 98.3%), anxiety disorder the most in Brazil (113.7%, 95%UI: 61.5% to 173.1%), schizophrenia the most in India (74.2%, 95%UI: 47.4% to 101.4%). Bipolar disorder decreased across all BRICS countries, with changes ranging from -4.0% to -1.5%. For SUDs, except for Brazil, drug use disorders declined in the other four BRICS countries, with China showing the most significant decrease (-240.7%, 95%UI: -262.9% to -216.1%). Notably, alcohol use disorders increased the most in China (102.2%, 95%UI: 56.9% to 153.4%) (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYLDs of Mental Disorders from 1990 to 2021\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom 1990 to 2021, the disability burden (in YLDs) from mental disorders increased for depressive disorders (98.8%, 95%UI: -63.6% to 275.2%), anxiety disorders (254.1%, 95%UI: 47.8% to 480.9%), and schizophrenia (149.6%, 95%UI: -72.5% to 396.8%), but not bipolar disorder. Additionally, on SUDs, the disability burden from alcohol use disorders increased (102.1%, 95%UI: -128.8% to 361.9%), while the burden from drug use disorders significantly decreased (-398.9%, 95%UI: -655.3% to -99.4%) (Table 2).\u003c/p\u003e\n\u003cp\u003eIn 2021, depressive disorders were the mental disorders that accounted for the largest share of YLDs (78.0 million, 95%UI: 63.4 million to 95.4 million), followed by anxiety disorders, schizophrenia, and bipolar disorder. The YLDs attributed to alcohol use disorders (17.9 million, 95%UI: 13.0 million to 24.0 million) were nearly nine times greater than those caused by drug use disorders (2.0 million, 95%UI: 1.5 million to 2.5 million) (Table 2). Additionally, we observed a significant decline in YLDs with increasing age, with the number of YLDs in the 60-64year age group being nearly three times higher than that in the ≥80year group (2.0 million vs 6.4 million, respectively) (Figure 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBetween the different BRICS countries, the YLDs attributable to each mental and SUDs varies (Figure 3). Depressive disorders and anxiety disorders in China ranked first among the BRICS countries in YLDs, with 20.3 million (95%UI: 16.7 million to 24.6 million) and 13.8 million (95%UI: 10.5 million to 18.2 million), respectively. Brazil had the highest YLDs attributable to anxiety disorders (3.1 million, 95%UI: 2.4 million to 4.0 million), while Russia Federation (2.3 million, 95%UI: 1.8 million to 2.9 million), South Africa (0.5 million, 95%UI: 0.4 million to 0.7 million), and India (13.2 million, 95%UI: 10.7 million to 16.2 million) had the highest YLDs attributable to depressive disorders, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eForecasting the Prevalence of Mental Disorders in 2050\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings indicate that, from 2021 to 2050, the overall burden of mental disorders is projected to increase in all BRICS countries except China. In Brazil, a projected increase in both the total number of incident cases and YLLs is anticipated from 2021 to 2050. Except for alcohol use disorders, the incidence of other disorders is expected to remain consistently higher in females than in males. By 2050, the number of anxiety disorder cases is projected to reach an estimated 1133473 with 1893035 YLLs. A substantial rise in alcohol use disorders is also projected, with the number of incident cases increasing to 2055396 and YLLs reaching 612,977 by 2025 (Figure 4A). Similar patterns are expected in Russia Federation, India, and South Africa, as shown in Figures 4B, 4C, and 4E. However, China demonstrates a distinct trajectory for certain disorders. As illustrated in Figure 4D panels e and k, the number of depressive disorders cases among older females in China is projected to decline significantly after 2021, from 9622864 to 3964852 by 2050, while the number among older males is expected to increase to 7584052. Additionally, a gradual decrease in the number of incident cases and YLLs due to alcohol use disorders is projected from 2021 to 2050, with 782054 incident cases and 164231 YLLs estimated by 2050 (Figure 4D c and i).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this was the first study to provide a comprehensive assessment of the burden of mental disorders among older adults in the BRICS countries. Our findings based on GBD 2021 demonstrate that older people in the BRICS countries suffer from a substantial and growing burden of mental disorders and SUDs from 1990 to 2021, with notable differences by country, age, and sex. Furthermore, by 2050 both the number of cases of mental disorders and YLLs attributable to such disorders are predicted to increase among older adults in the BRICS. These results underscore the urgent need for targeted public health interventions to address the mental health needs of the aging population in these rapidly transforming LMICs.\u003c/p\u003e\u003cp\u003eOur study revealed that depressive disorders and anxiety disorders were the most common mental health issues among older adults in the BRICS countries and the leading causes of disability, with a higher burden in females compared to males. This trend aligns with global patterns, as depressive disorders and anxiety disorders were increasingly recognized as leading contributors to the non-fatal burden of disease worldwide\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. These disorders were not only widespread but also the major contributor to morbidity in older adults, often resulting in a cascade of functional impairments and reduced quality of life\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. This is especially true of depressive disorders, which are anticipated to become the leading cause of global disease burden by 2030\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. However, because older adults tend to suffer from cognitive impairment and other comorbidities, the burden of depressive disorders and anxiety disorders in this population is often overlooked. Therefore, it is crucial to emphasize the importance and urgency of managing them as the measure to prevent premature death among older adults\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn addition, it was worth noting that from 1990 to 2021, the number and YLDs of older adults with depressive disorders and anxiety disorders in China were the highest, signaling a growing mental health burden among this nation\u0026rsquo;s rapidly aging population. Over the past 30 years, China has experienced substantial demographic shifts, marked by accelerated urbanization, economic growth, and changing family structures\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. These factors have led to increased social isolation, financial insecurity, and a lack of adequate support systems for the older population, all of which are well-documented risk factors for depressive disorders\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. In contrast, our projection analysis suggests a declining trend in the incidence of depressive disorders among older women in China over the next 30 years, accompanied by a partial decrease in YLLs. In contrast, both the incidence and YLLs among older men are expected to continue rising. This contrasting pattern may reflect China\u0026rsquo;s broader societal progress, including improved mental health literacy, greater gender equity, and the growing empowerment of women, which may facilitate help-seeking behaviors. In contrast, persistent cultural norms discouraging emotional expression among men may hinder timely recognition and treatment of mental health issues, particularly in older male populations\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Therefore, it remains essential to further strengthen mental health service infrastructure, expand social support networks, and \u0026ndash; in particular \u0026ndash; prioritize targeted interventions for the mental health needs of older male populations.\u003c/p\u003e\u003cp\u003eWe also observed that prior to 2021, the prevalence of alcohol use disorders was substantially higher than that of drug use disorders among older adults in the BRICS countries, particularly in China and the Russian Federation, with markedly higher rates among males compared to females. Alcohol abuse disorders not only pose high risks to physical health and all-cause mortality but may also contributed to cognitive decline and social isolation\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. To address this issue, some countries have strengthened policy interventions and public health initiatives, and integrated alcohol use disorders into mental health management with the establishment of dedicated addiction clinics. These efforts may likely contributed to the gradual decline in the overall burden of alcohol use disorders observed in the future\u003csup\u003e[\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. In contrast, the prevalence of drug use disorders among older adults is lower and has shown a decreasing trend, likely due to improved anti-drug policies and greater awareness of SUDs\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMoreover, an important observation from our study is the decline in the prevalence of mental disorders with increasing age, particularly after the age of 80. This trend might reflect survival bias, such that those with severe mental health conditions may have a reduced life expectancy and may die before reaching the age of 80. And these participants are less likely to have dementia or mild cognitive impairment, which may reduce the generalizability of the study findings\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Additionally, as individuals age, the manifestation of mental health disorders may become less detectable due to cognitive decline or other aging-related factors\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. At the same time, the oldest-old population has typically experienced multiple major life events\u0026mdash;such as serious illness, chronic stress, and sociocultural transitions\u0026mdash;which may foster more mature emotional regulation, greater psychological resilience, and more adaptive coping mechanisms. These factors may in part contribute to the observed decrease in the prevalence of mental disorders among them\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. The unique challenges faced by older adults in BRICS countries, including lack of access to mental health services and cultural stigma surrounding mental health, may further contribute to underreporting and undertreatment of these conditions.\u003c/p\u003e\u003cp\u003eSome limitations should be acknowledged. First, although the GBD 2021 data is extensive, the quality and completeness of the data may vary across different countries and regions. Specifically, LMICs like South Africa may face challenges in accurately reporting mental health data due to limited resources, underreporting, and the stigma surrounding mental disorders, especially among older populations. This could have contributed to either an over- or under-estimation of the mental health burden in certain areas, highlighting the need for improved primary data collection to enhance the accuracy of research. Second, the lack of mortality data on mental disorders in the GBD study further exacerbates these limitations. While DALYs and YLDs are equivalent for most mental disorders, the absence of mortality estimates may obscure the true burden of mental disorders. These methodological shortcomings underscore the importance of focusing on non-fatal disability as represented by YLDs. Third, this study focused only on major mental disorders such as depression and anxiety, while other conditions, such as dementia and cognitive impairment, were not included in the analysis. Additional studies are warranted to understand the prevalence and burden of other conditions beyond mental disorders that commonly occur in older adults. While limitations exist, they do not diminish the value of the GBD study, which remains the most comprehensive source of global data on mental disorders and continues to improve over time. Mental disorder and SUDs present substantial challenges across all levels of society. Epidemiological data are essential for identifying high-risk groups, setting intervention priorities, and guiding the planning and allocation of mental health services.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study highlights the significant and growing burden of mental and SUDs among older adults in the BRICS countries. From 1990 to 2021, depressive disorders and anxiety disorders became the leading causes of disability in this population, especially for females. The overall burden of mental disorders in the BRICS countries is projected to continue rising through 2050, posing substantial challenges to future disease burden management. This is particularly alarming in the context of the rapid population aging in the BRICS countries, coupled with underdeveloped mental health services. Strengthening mental health care systems, improving early detection, and addressing social and economic factors are critical to mitigating the disability burden due to mental and SUDs in LMICs and improving the well-being of older adults in these countries.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGBD: Global Burden of Disease Study\u003c/p\u003e\n\u003cp\u003eYLDs: lived with disability\u003c/p\u003e\n\u003cp\u003eDALYs: disability-adjusted life years\u003c/p\u003e\n\u003cp\u003eYLLs: years of life lost\u003c/p\u003e\n\u003cp\u003eSUDs: substance use disorders\u003c/p\u003e\n\u003cp\u003eLMICs: low- and middle-income countries\u003c/p\u003e\n\u003cp\u003eUI: uncertainty intervals\u003c/p\u003e\n\u003cp\u003eBAPC: bayesian age-period-cohort\u003c/p\u003e\n\u003cp\u003eINLA: integrated nested laplace approximations\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are available in the Global Health Data Exchange GBD 2021 website (https://ghdx.healthdata.org/gbd-2021).\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 Key Research and Development Program of China (Grant number:2023YFC3605900).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTing Yu: conceptualisation, methodology; formal analysis; data analysis; writing-manuscript; and editing. Jiao Wang: methodology, formal analysis, data collection, writing-manuscript and editing. Yijing Li: writing-review and editing. Miaomiao Li: methodology and data analysis. Abigail Dove:\u0026nbsp;conceptualisation,\u0026nbsp;writing-review and editing. Yan Jiang: conceptualisation, supervision, writing-review and editing. All authors were in agreement with the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChen Q, Huang S, Xu H, et al. The burden of mental disorders in Asian countries, 1990-2019: an analysis for the global burden of disease study 2019[J]. Transl Psychiatry, 2024,14(1):167.\u003c/li\u003e\n\u003cli\u003eGBD Mental Disorders Collaborators. 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How Dietary Patterns are Related to Inflammaging and Mortality in Community-Dwelling Older Chinese Adults in Hong Kong - A Prospective Analysis[J]. J Nutr Health Aging, 2019,23(2):181-194.\u003c/li\u003e\n\u003cli\u003eTan J, Ma C, Zhu C, et al. Prediction models for depression risk among older adults: systematic review and critical appraisal[J]. Ageing Res Rev, 2023,83:101803.\u003c/li\u003e\n\u003cli\u003eDong X, Ng N, Santosa A. Family structure and depressive symptoms among older adults in China: A marginal structural model analysis.[J]. J Affect Disord., 2023,324:364-369.\u003c/li\u003e\n\u003cli\u003eHong C, Xiong X, Li J, et al. Urbanization and depressive symptoms among middle-aged and older adults in China[J]. Front Public Health, 2022,10:1086248.\u003c/li\u003e\n\u003cli\u003eBai R, Dong W, Peng Q, et al. Trends in depression incidence in China, 1990-2019. [J]. J Affect Disord., 2021,296:291-297.\u003c/li\u003e\n\u003cli\u003eLu J, Xu X, Huang Y, Li T, Ma C, Xu G, Yin H, Xu X, Ma Y, Wang L, Huang Z, Yan Y, Wang B, Xiao S, Zhou L, Li L, Zhang Y, Chen H, Zhang T, Yan J, Ding H, Yu Y, Kou C, Shen Z, Jiang L, Wang Z, Sun X, Xu Y, He Y, Guo W, Jiang L, Li S, Pan W, Wu Y, Li G, Jia F, Shi J, Shen Z, Zhang N. Prevalence of depressive disorders and treatment in China: a cross-sectional epidemiological study[J]. Lancet Psychiatry, 2021,8(11):981-990.\u003c/li\u003e\n\u003cli\u003eXu Z, Gahr M, Xiang Y, et al. The state of mental health care in China[J]. Asian J Psychiatr, 2022,69:102975.\u003c/li\u003e\n\u003cli\u003eZhang K, He F, Ma Y. Sex ratios and mental health: Evidence from China[J]. Econ Hum Biol, 2021,42:101014.\u003c/li\u003e\n\u003cli\u003eWang G, Li D Y, Vance D E, et al. Alcohol Use Disorder as a Risk Factor for Cognitive Impairment[J]. J Alzheimers Dis, 2023,94(3):899-907.\u003c/li\u003e\n\u003cli\u003eLu J, Yang Y, Cui J, et al. Alcohol use disorder and its association with quality of life and mortality in Chinese male adults: a population-based cohort study[J]. BMC Public Health, 2022,22(1):789.\u003c/li\u003e\n\u003cli\u003eKaner E F, Beyer F R, Muirhead C, et al. Effectiveness of brief alcohol interventions in primary care populations[J]. Cochrane Database Syst Rev, 2018,2(2):CD4148.\u003c/li\u003e\n\u003cli\u003eCarvalho A F, Heilig M, Perez A, et al. Alcohol use disorders[J]. Lancet, 2019,394(10200):781-792.\u003c/li\u003e\n\u003cli\u003eChen P, Li F, Harmer P. Healthy China 2030: moving from blueprint to action with a new focus on public health[J]. Lancet Public Health, 2019,4(9):e447.\u003c/li\u003e\n\u003cli\u003eZhao Y, Wang T, Li G, et al. Pharmacovigilance in China: development and challenges.[J]. Int J Clin Pharm., 2018,40(4):823-831.\u003c/li\u003e\n\u003cli\u003eCheng A, Leung Y, Harrison F, et al. The prevalence and predictors of anxiety and depression in near-centenarians and centenarians: a systematic review[J]. Int Psychogeriatr, 2019,31(11):1539-1558.\u003c/li\u003e\n\u003cli\u003eIsmail Z, Elbayoumi H, Fischer C E, et al. Prevalence of Depression in Patients With Mild Cognitive Impairment: A Systematic Review and Meta-analysis[J]. JAMA Psychiatry, 2017,74(1):58-67.\u003c/li\u003e\n\u003cli\u003eSteffens D C. Depressive symptoms and mild cognitive impairment in the elderly: an ominous combination[J]. Biol Psychiatry, 2012,71(9):762-764.\u003c/li\u003e\n\u003cli\u003eCai J, Gao Y, Hu T, et al. Impact of lifestyle and psychological resilience on survival among the oldest-old in China: a cohort study. [J]. Front Public Health., 2023,11:1329885.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Total prevalence and percentage change for mental and substance use disorders\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"980\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 305px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBRICS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 287px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBrazil\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 289px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRussian Federation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1990\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eprevalence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eprevalence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage change, 1990\u0026ndash;2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1990\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eprevalence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eprevalence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage change, 1990\u0026ndash;2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1990\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eprevalence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eprevalence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage change, 1990\u0026ndash;2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eDepressive disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e12033581 (9982137 to 14526675)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e33488929 (27945395 to 40147725)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e78.4% (-63.3% to 232.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e671718 (557471 to 813333)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2012182 (1648730 to 2439696)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e-7.3% (-43.2% to 31.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e1455939 (1168543 to 1780333)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1980386 (1588620 to 2427065)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e-19.8% (-35.4% to -2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eAnxiety disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e8412056 (6503696 to 10937023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e23122418 (17828036 to 30009712)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e262.6% (68.7% to 478.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e748476 (592010 to 953070)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2805559 (2183809 to 3592678)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e113.7% (61.5% to 173.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e1015773 (789989 to 1300626)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1571835 (1219880 to 2015136）\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e43.0% (18.3% to 71.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eSchizophrenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e543967 (463563 to 634303)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e1428912 (1218367 to 1668268)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e156.4% (80.3% to 236.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e25847 (21636 to 30618)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e77048 (64378 to 91465)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e18.5% (6.2%\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eto 32.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e46910 (39440 to 55364)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e68054 (57238 to 80269）\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e29.9% (21.4% to 38.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eBipolar disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e743838 (580220 to 933110)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e1828677 (1426439 to \u0026nbsp;2295251)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e-11.4% (-21.9% to -0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e106224 (84859 to 131522)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e309464 (247370 to 382965)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e-3.1% (-4.9%\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eto -1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e140397 (110091 to 175561)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e192419 (150767 to 240850）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e-4.0% (-5.8%\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eto -2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eDrug use disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e453095 (359043 to 566518)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e782709 (619635 to 969297)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e-276.3% (-399.7% to -141.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e23298 (18782 to 28781)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e72159 (58970 to 87521)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e39.3% (13.5%\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eto 64.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e69655 (55256 to 86826)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e79636 (64619 to 96259）\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e-49.1% (-73.3% to -19.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eAlcohol use disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e3668133 (2671752 to 4904350)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e8098706 (5954943 to 10740539)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e105.4% (-102.4% to 340.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e247345 (180751 to 329701)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e882068 (653306 to 1159874)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e62.3% (13.4% to 118.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e1229438 (931526 to 1583143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1606914 (1218105 to 2077102）\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e10.4% (-30.3% to 52.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"984\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 287px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChina\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSouth Africa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1990\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eprevalence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eprevalence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage change, 1990\u0026ndash;2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1990\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eprevalence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eprevalence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage change, 1990\u0026ndash;2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1990\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eprevalence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eprevalence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage change, 1990\u0026ndash;2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eDepressive disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e3939670 (3273544 to 4778439)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e11349044 (9417163 to 13695101)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e19.8% (-6.9% to 46.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e5773591 (4822693 to 6923616)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e17682943 (14907956 to 21022223)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e67.2% (39.4% to 98.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e192663 (159887 to 230954)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e464375 (382927 to 563639)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e18.5% (-17.1% to 58.8%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eAnxiety disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2012188 (1540508 to 2625090)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e6078813 (4672037 to 7843939)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e42.4% (13.2% to 74.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e4534248 (3502333 to 5927013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e12402505 (9549567 to 16209632)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e4.0% (-27.2% to 36.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e101371 (78857 to 131225)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e263706 (202743 to 348327)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e59.5% (3.0%\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eto 123.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eSchizophrenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e151565 (125618 to 180781)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e445719 (372155 to 528829)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e74.2% (47.4% to 101.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e314324 (272431 to 361202)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e825433 (714004 to 952621)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e16.9% (2.2% to 31.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e5321 (4438 to 6339)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e12658 (10592 to 15083)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e17.0% (3.1%\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eto 32.2%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eBipolar disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e251334 (195226 to 314652)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e684141 (531340 to 857337)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-3.5% (-5.7%\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eto -1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e230217 (177778 to 291817)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e606222 (468411 to 768622)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e-2.2% (-4.6% to 0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e15666 (12267 to 19558)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e36431 (28550 to 45477)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1.5% (-1.0% to 4.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eDrug use disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e86276 (66309 to 111073)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e227742 (176517 to 286488)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-18.5% (-46.9% to 12.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e265201 (211481 to 329597)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e383473 (302889 to 475866)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e-240.7% (-262.9% to -216.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e8665 (7215 to 10240)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e19698 (16640 to 23163)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e-7.3% (-30.0% to 17.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eAlcohol use disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1192979 (843811 to 1633800)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e2696995 (1976451 to 3569739)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-57.4% (-89.6% to -19.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e948102 (678513 to 1291942)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2796285 (2021117 to 3781249)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e102.2% (56.9% to 153.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e50269 (37150 to 65765)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e116443 (85964 to 152575)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e-12.2% (-52.8% to 35.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e* Data in parentheses are 95% uncertainty intervals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Total YLDs and percentage change for mental and substance use disorders\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"989\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBRICS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBrazil\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 289px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRussian Federation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1990\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eYLDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eYLDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage change, 1990\u0026ndash;2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1990\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eYLDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eYLDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage change, 1990\u0026ndash;2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1990\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eYLDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eYLDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage change, 1990\u0026ndash;2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eDepressive disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e28314909 (22882399 to 34841887)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e77974058 (63352600 to 95357655)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e98.8% (-63.6% to 275.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e784630 (633652 to 970144)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e2349066 (1871686 to 2904937)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e-9.2% (-49.4% to 35.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1693427 (1324521 to 2117074)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2300930 (1800624 to 2875875)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-24.1% (-43.3% to -3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eAnxiety disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e19060128 (14470960 to 25132184)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e51746907 (39230560 to 68041600)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e254.1% (47.8% to 480.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e830237(647163 to 1070312)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e3111364(2388575 to 4031006)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e113.2% (58.6% to 174.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1126898 (863568 to 1460355)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1743051 (1333353 to 2264541)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e40.5% (13.9% to 71.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eSchizophrenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1761610 (1416751 to 2130073)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4579570 (3688877 to 5539804)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e149.6% (-72.5% to 396.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e40990 (32551 to 50291)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e121987 (96627 to 149272)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e18.1% (-31.0% to 72.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e74421 (59514 to 90893)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e107787 (85971 to 131138)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e27.2% (-7.1% to 65.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eBipolar disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1831846 (1382848 to 2366328)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4432064 (3343775 to 5732657)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e-16.3% (-177.9% to 160.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e127296 (98268 to 162210)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e370667 (285989 to 472615)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e-3.1% (-32.5% to 28.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e168292 (127636 to 216552)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e230515 (174686 to 296929)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-5.8% (-29.0% to 18.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eDrug use disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1170485 (888311 to 1512113)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1944062 (1480519 to 2488166)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e-398.9% (-655.3% to -99.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e26397 (20799 to 33195)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e81529 (65135 to 100600)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e22.9% (-57.1% to 112.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e90294 (68788 to 116186)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e101578 (79258 to 126545)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-71.7% (-110.1% to -25.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eAlcohol use disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e8225372 (5907808 to 11107503)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e17908555 (12957152 to 23982866)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e102.1% (-128.8% to 361.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e270176 (194991 to 362888)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e963446(704329 to 1277901)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e62.1% (8.7% to 121.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1343710 (1004431 to 1746423)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1755973 (1312562 to 2289853)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e9.9% (-30.8% to 52.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"979\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChina\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 290px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSouth Africa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1990\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eYLDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eYLDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage change, 1990\u0026ndash;2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1990\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eYLDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eYLDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage change, 1990\u0026ndash;2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1990\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eYLDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eYLDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage change, 1990\u0026ndash;2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDepressive disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e4581814 (3704467 to 5666822)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e13206034 (10667692 to 16233507)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e25.0% (-4.2% to 54.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e6583104 (5381485 to 8034451 )\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e20290798 (16719367 to 24608220)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e92.7% (61.5% to 127.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e224889 (181777 to 275532)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e541587 (434750 to 671388)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e14.5% (-28.2% to 61.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eAnxiety disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2229585 (1684256 to 2942572)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e6737016 (5104536 to 8796812)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e46.6% (14.8% to 81.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e5040731(3836401 to 6660676)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e13776743(10462507 to 18190171)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e2.3% (-30.9% to 37.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e112525 (86226 to 147389)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e292288 (221547 to 390124)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e51.4% (-8.5% to 116.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eSchizophrenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e239566 (188676 to 295179)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e705172 (558107 to 864914)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e79.7% (35.5% to 127.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e504499\u003c/p\u003e\n \u003cp\u003e(411463 to 601028)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1321255 (1078314 to 1580388)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e15.1% (-19.8% to 50.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e8475 (6694 to 10448)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e20024 (15891 to 24675)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e9.4% (-50.1% to 80.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eBipolar disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e300643 (226335 to 386844)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e818852 (615382 to 1055578)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.8% (-27.7% to 31.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e276979 (206972 to 361731)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e728547 (544943 to 952380)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-3.4% (-42.0% to 40.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e18801 (14213 to 24192)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e43619 (33042 to 56076)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e-4.8% (-46.7% to 40.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDrug use disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e105635 (78492 to 139286)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e280689 (210342 to 362329 )\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-14.2% (-70.1% to 54.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e341547 (260611 to 438868)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e475501\u003c/p\u003e\n \u003cp\u003e(361878 o 609251)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e-282.4% (-308.3% to -252.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e10516 (8444 to 12818)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e23452 (19216 to 28317)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e-53.5% (-109.8% to 11.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eAlcohol use disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1302811 (910520 to 1797914) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e2945091 (2131978 \u0026nbsp;to 3933474)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-54.1% (-88.8% to -12.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1038243 (733171 to 1425830)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e3059628 (2181629 to 4168644)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e100.8% (47.5% to \u0026nbsp;160.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e54945 (40093 to 72506)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e127191 (92664 to 168005)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e-16.7% (-65.4% to 39.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e* Data in parentheses are 95% uncertainty intervals.\u003c/p\u003e\n\u003cp\u003eYLD = Years lived with disability.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mental Disorders, Older Adult, Global Burden of Disease, BRICS, Prevalence","lastPublishedDoi":"10.21203/rs.3.rs-7238043/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7238043/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eMental disorders are a growing cause of disability among older adults in BRICS countries, where large population and rapid aging present a significant public health challenge. A comprehensive assessment of disease burden is urgently needed.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eData from the Global Burden of Disease Study 2021, we estimated prevalence and years lived with disability (YLDs) for depressive disorders, anxiety disorders, bipolar disorder, schizophrenia, and substance use disorders among older adults(1990\u0026ndash;2021) in BRICS. Subgroup analyses by age and sex were performed, and Bayesian age-period-cohort models projected trends until 2050.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIn 2021, depressive disorders (33.5\u0026nbsp;million) and anxiety disorders (23.1\u0026nbsp;million) were the most prevalent, with higher rates in females. Alcohol use disorders (8.1\u0026nbsp;million) were far common than drug use disorders (0.78\u0026nbsp;million), and more prevalent in males. From 1990 to 2021, prevalence of anxiety disorders rose by 262.6%, while drug use disorders declined by 276.3%. YLDs increased substantially for anxiety disorders (254.1%, 95% UI: 47.8\u0026ndash;480.9%) and depressive disorders (98.8%, 95% UI: -63.6\u0026ndash;275.2%), whereas drug use disorders declined markedly (-398.9%, 95% UI: -655.3% to -99.4%). Projection analyses indicate that the burden of mental disorders among older adults will increase in most BRICS countries during 2021\u0026ndash;2050.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe prevalence and burden of mental disorders on older adults in BRICS countries are rising and will likely continue increasing until 2050. Strengthened mental health systems and targeted interventions are crucial to meet the growing needs of aging populations in these low- and middle-income countries.\u003c/p\u003e","manuscriptTitle":"Global burden and trends in mental disorders among older adults in the BRICS countries from 1990 to 2021, with projections to 2050: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-14 19:14:40","doi":"10.21203/rs.3.rs-7238043/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-03T07:18:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-01T19:35:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-01T15:14:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-11T11:54:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"312228739093398005623288506256456923048","date":"2025-09-10T13:28:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236888820612517291506598363593000997971","date":"2025-09-10T12:56:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217580692848975862849000215322824896118","date":"2025-09-02T00:15:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-10T18:57:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-01T09:18:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-01T08:32:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-01T08:31:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-07-29T02:04:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7af2ae94-b438-43bf-8f08-ed083a1b9976","owner":[],"postedDate":"August 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-29T07:08:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-14 19:14:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7238043","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7238043","identity":"rs-7238043","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}