Summary
Background
The mental disorders included in the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 were depressive disorders, anxiety disorders, bipolar disorder, schizophrenia, autism spectrum disorders, conduct disorder, attention-deficit hyperactivity disorder, eating disorders, idiopathic developmental intellectual disability, and a residual category of other mental disorders. We aimed to measure the global, regional, and national prevalence, disability-adjusted life-years (DALYS), years lived with disability (YLDs), and years of life lost (YLLs) for mental disorders from 1990 to 2019.
Methods
In this study, we assessed prevalence and burden estimates from GBD 2019 for 12 mental disorders, males and females, 23 age groups, 204 countries and territories, between 1990 and 2019. DALYs were estimated as the sum of YLDs and YLLs to premature mortality. We systematically reviewed PsycINFO, Embase, PubMed, and the Global Health Data Exchange to obtain data on prevalence, incidence, remission, duration, severity, and excess mortality for each mental disorder. These data informed a Bayesian meta-regression analysis to estimate prevalence by disorder, age, sex, year, and location. Prevalence was multiplied by corresponding disability weights to estimate YLDs. Cause-specific deaths were compiled from mortality surveillance databases. The Cause of Death Ensemble modelling strategy was used to estimate death rate by age, sex, year, and location. The death rates were multiplied by the years of life expected to be remaining at death based on a normative life expectancy to estimate YLLs. Deaths and YLLs could be calculated only for anorexia nervosa and bulimia nervosa, since these were the only mental disorders identified as underlying causes of death in GBD 2019.
Findings
Between 1990 and 2019, the global number of DALYs due to mental disorders increased from 80·8 million (95% uncertainty interval [UI] 59·5–105·9) to 125·3 million (93·0–163·2), and the proportion of global DALYs attributed to mental disorders increased from 3·1% (95% UI 2·4–3·9) to 4·9% (3·9–6·1). Age-standardised DALY rates remained largely consistent between 1990 (1581·2 DALYs [1170·9–2061·4] per 100 000 people) and 2019 (1566·2 DALYs [1160·1–2042·8] per 100 000 people). YLDs contributed to most of the mental disorder burden, with 125·3 million YLDs (95% UI 93·0–163·2; 14·6% [12·2–16·8] of global YLDs) in 2019 attributable to mental disorders. Eating disorders accounted for 17 361·5 YLLs (95% UI 15 518·5–21 459·8). Globally, the age-standardised DALY rate for mental disorders was 1426·5 (95% UI 1056·4–1869·5) per 100 000 population among males and 1703·3 (1261·5–2237·8) per 100 000 population among females. Age-standardised DALY rates were highest in Australasia, Tropical Latin America, and high-income North America.
Interpretation
GBD 2019 showed that mental disorders remained among the top ten leading causes of burden worldwide, with no evidence of global reduction in the burden since 1990. The estimated YLLs for mental disorders were extremely low and do not reflect premature mortality in individuals with mental disorders. Research to establish causal pathways between mental disorders and other fatal health outcomes is recommended so that this may be addressed within the GBD study. To reduce the burden of mental disorders, coordinated delivery of effective prevention and treatment programmes by governments and the global health community is imperative.
Funding
Bill & Melinda Gates Foundation, Australian National Health and Medical Research Council, Queensland Department of Health, Australia.
Introduction
The Lancet Commission on global mental health and sustainable development
emphasised mental health as a fundamental human right and essential to the development of all countries. The Commission called for more investment in mental health services as part of universal health coverage, and better integration of these services into the global response to other health priorities.
To meet the men-tal health needs of individual countries in a way that prioritises transformation of health systems, in-depth understanding of the scale of the impact of these disorders is essential,
including their distribution in the population, the health burden imposed, and their broader health consequences.
Evidence before this study
The last comprehensive review of the global burden of mental disorders was published on Nov 9, 2013 using the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2010 findings, and in subsequent years there have been important updates to the burden estimation methodology and epidemiological datasets. GBD 2019 estimated the prevalence and burden due to 12 mental disorders by age, sex, year, and location. High-level findings of GBD 2019 were published in a capstone publication by Vos and colleagues in 2020, which covered all diseases and injuries. We searched PubMed, PsycINFO, Embase, and PROSPERO for papers on the global burden of mental disorders published between Oct 17, 2020 and Oct 6, 2021. We used the following search terms: (((“Mental disorders”[Title/Abstract]) AND (Global[Title/Abstract],)) AND (2019[Title/Abstract])) AND ((((“GBD 2019”[Title/Abstract]) OR (Disability[Title/Abstract])) OR (Prevalence[Title/Abstract])) OR (Burden[Title/Abstract])). For the PROSPERO search the following additional filters were applied: Health area of review: mental health and behavioural conditions; Type and method of review: epidemiologic, systematic review, meta-analysis, and review of reviews. Our search yielded 102 studies, of which 12 were relevant to our research aim. Of these 12 studies, two reported GBD 2019 results for eating disorders in China, and mental disorders in Mexico. We found no publications dedicated to findings of GBD 2019 mental disorders globally or for any other location by age, sex, and year.
Added value of this study
Using the most up-to-date information on the prevalence and burden of mental disorders across the global population, excluding substance use disorders and suicide, for 2019, we observed similar disparities in the burden of mental disorders as in 1990. Mental disorders remained among the leading causes of burden globally. Disability-adjusted life-years (DALYs) for mental disorders were evident across all age groups, emerging before age 5 years in individuals with idiopathic intellectual disability and autism spectrum disorders, and continued to be evident at older ages in people with depressive disorders, anxiety disorders, and schizophrenia. We identified priority areas for improvement of the epidemiological data and burden estimation methodology for mental disorders, and provided recommendations as to how these areas might be addressed.
Implications of all the available evidence
GBD 2019 confirmed that a large proportion of the world’s disease burden is attributable to mental disorders and found no evidence of a global reduction in that burden since 1990, despite research demonstrating that interventions can achieve a reduction in the burden. Our findings highlighted the limitations of measures for estimating years of life lost for determining the effects of mental disorders on premature mortality. Research is needed to improve these measures to provide a more accurate picture of the true burden due to mental disorders.
,
,
Between 1990 and 2019, a reduction in DALYs from communicable, maternal, neonatal, and nutritional diseases has been offset by an increase in burden due to non-communicable diseases, including mental disorders.
,
,
Mental and substance use disorders are a heterogeneous group of disorders. Health systems in many countries organise their services for these disorder groups separately, whereas in resource-poor settings, these disorders are often grouped within essential health care packages and delivery platforms. In this study we focused on mental disorders, which allowed us to present a more detailed analysis of their distribution and burden by age, sex, location, and year than that provided by previous publications.
,
This supplements GBD 2016 findings for substance use disorders published separately.
There have also been significant updates to the burden estimation methodology and epidemiological datasets underpinning GBD findings since this publication.
In this study, we aimed to facilitate access and interpretation of GBD 2019 estimates for stakeholders, including governments and international agencies, researchers, and clinicians involved in the identification, management, and prevention of mental disorders; present and evaluate the methods used to estimate the burden of mental disorders; and highlight priority areas for improvement in the mental disorder burden estimation methodology.
Methods
Overview
GBD 2019 estimated incidence, prevalence, mortality, years lived with disability (YLDs), years of life lost (YLLs), and DALYs for 369 diseases and injuries, for males and females, 23 age groups, 21 regions, 204 countries and territories, from 1990 onwards.
Comprehensive explanations of burden estimation methods have been published elsewhere.
,
,
Here, we summarise the methodology for estimating the health burden due to mental disorders.
Case definitions
Estimation of YLDs
A flowchart presenting the methodology for estimating YLDs is shown in the appendix (p 6).
Data sources
Epidemiological disease models
The epidemiological data obtained from our systematic reviews were analysed in two steps. For step 1, we tested and adjusted for biases in epidemiological estimates reported between studies. For step 2, “gold-standard” (ie, estimates using the desired data-collection methodology and not requiring bias adjustments) and adjusted estimates were modelled within a meta-regression analysis.
For each disorder, we identified the major sources of bias in the extracted data, on the basis of known sources of measurement error such as recall type (point, 12-month, or lifetime prevalence), survey instrument (diagnostic or symptom scale), and survey interviewer (lay or clinician). Estimates with these biases were considered alternative estimates to gold-standard estimates and were adjusted. The adjustment factor was the pooled ratio between gold-standard estimates and these alternative estimates. We compiled studies reporting both the gold-standard estimate and the alternative estimate (eg, point prevalence and 12-month prevalence) and calculated the ratios within these studies. We also looked for pairs of gold-standard and alternative estimates between studies, matched by age (0—99 years), sex, location (across 82 locations), and year (1980 onwards), and calculated the ratio between these estimates.
These pooled ratios were used as an adjustment factor to correct alternative estimates before analysis. More detailed information about the bias correction process is presented elsewhere.
As part of this process, estimates were generated for locations where high-quality raw epidemiological data were unavailable by using the modelled output from surrounding locations.
As per the GBD protocol, an uncertain estimate is preferable to no estimate when data are sparse or not available, because no estimate would result in no health loss from that condition being recorded. DisMod-MR 2.1 also uses location-level covariates to predict prevalence by location. We included location-level covariates for major depressive disorder and anxiety disorders. The first covariate identified the mean mortality rate in the previous ten years due to war and terrorism for each GBD location, considering the known association between conflict and elevated prevalence of major depressive disorder and anxiety disorders.
The second covariate used the Gallup Negative Experience Index, which measures past-day experiences of physical pain, worry, sadness, stress, and anger from population surveys conducted within the Gallup Initiative.
The covariate was included as a method to test for an association between negative emotions at a location level and major depressive disorder and anxiety disorder prevalence. The third covariate used the proportion of major depressive disorder burden caused by two risk factors, intimate partner violence and childhood sexual abuse, to inform the estimation of prevalence. The choice of scale for location-level covariates differed by disorder and covariate. Both the untransformed and log-transformed covariates were tested as part of the modelling process for each disorder. The final decision for scale was determined based on the coefficient, statistical significance, and skew of the location-level covariate. The priors used to inform the DisMod-MR 2.1 models for each disorder are summarised in the appendix (p 8). More information on DisMod-MR 2.1, covariates, and priors is presented elsewhere.
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Severity proportions
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For depressive disorders, anxiety disorders, and other mental disorders, we used individual-level survey data from the US National Epidemiologic Survey on Alcohol and Related Conditions
or the 1997 Australian National Survey of Mental Health and Wellbeing.
,
,
No severity distribution was estimated for eating disorders. Severity proportions were applied to the total prevalent cases estimated by DisMod-MR 2.1 to obtain prevalence estimates for each level of severity (appendix p 9). Further details on severity proportions have been presented elsewhere.
,
Disability weights
We used disability weights derived from community-based surveys in Bangladesh, Indonesia, Peru, Tanzania, the USA, Hungary, Italy, Sweden, and the Netherlands, and an open web-based survey available in English, Spanish, and Mandarin. In these surveys, participants were presented with pairs of health state descriptions and asked to select the “healthier” state. Responses were anchored on a scale ranging from 0 (perfect health) to 1 (death) using additional population health equivalence questions that compared the benefits of lifesaving and disease-prevention programmes for several health states. The analysis of pair-wise comparisons indicated the relative position of health states to each other, and the population health equivalence questions were required to anchor those relative positions as values on a 0 to 1 scale. Sequela-specific health state descriptions and disability weights have been summarised in the appendix (p 9). More information on the disability weights analysis has been presented elsewhere.
Comorbidity adjustments
Estimation of YLLs
The GBD 2019 cause of death database contained vital registration, verbal autopsy, cancer registry, police records, sibling history, surveillance, and survey or census data collected since 1980. The Cause of Death Ensemble modelling strategy was used to model cause of death data by location, age, sex, and year. Deaths were scaled to total mortality. Normative life tables were generated using data on the lowest observed death rates for any age group within all GBD locations with a total population greater than 5 million.
Each death in GBD could be allocated to only one underlying cause as per ICD categorisation of causes of death. Deaths and YLLs could be calculated only for anorexia nervosa and bulimia nervosa, since these were the only mental disorders identified as underlying causes of death. Deaths and YLL estimates are not reflective of all premature mortality in individuals with mental disorders where the direct cause of death is another disease or injury. For example, suicide was categorised separately under injuries and not included within the mental disorders group. A method for capturing the proportion of premature deaths from other diseases or injuries which can be causally attributed to the mental disorder experienced by a person is not yet available for our estimation of YLLs.
Estimation of DALYs
,
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,
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DALYs were calculated as the sum of YLDs and YLLs. For mental disorders not recognised as causes of death, YLLs were not estimated and YLDs approximated DALYs. Age-standardised rates per 100 000 people were estimated using the GBD world population age standard. Change in prevalence and burden across time was estimated by comparing the change in age-standardised rate and the change in total numbers. The GBD 2019 geographical hierarchy included 204 countries and territories aggregated into 21 regions and seven super-regions. YLDs, YLLs, and DALYs were estimated at all levels of this geographical hierarchy, by sex, for 23 age groups (age 0 to 95 years and older), and for every year from 1990 to 2019. We estimated 95% uncertainty intervals (UIs) for all estimates derived from the 25th and 975th ordinals of 1000 draws of the posterior distribution at each step of the burden estimation process. Microsoft Excel or the maptools package in R (version 3.6.3) were used to generate all tables and figures.
Role of the funding source
The funders of this study had no role in study design, data collection, data analysis, data interpretation, or the writing of the report.
Results
Table 1Global prevalence and age-standardised prevalence for mental disorders in 1990 and 2019
95% UI=95% uncertainty interval.
Table 2Age-standardised prevalence per 100 000 people by mental disorder and region, 2019
95% uncertainty intervals are shown in parentheses. Bolding indicates global estimates or GBD super-regions. GBD=Global Burden of Diseases, Injuries, and Risk Factors Study.
Eating disorders were the cause of 318·3 deaths (95% UI 285·7–386·0) worldwide in 2019. Anorexia nervosa accounted for most of these deaths (268·7 deaths [242·5–326·9]). The remaining deaths (49·6 [36·4–72·2]) were due to bulimia nervosa. Eating disorders were the only mental disorders for which YLLs could be estimated.
Mental disorders accounted for 125·3 million DALYs (95% UI 93·0–163·2) in 2019, equating to an age-standardised DALY rate of 1566·2 (1160·1–2042·8) per 100 000 people, or 4·9% (3·9–6·1) of global DALYs. The number and proportion of DALYs due to mental disorders increased from 1990 (80·8 million DALYs [59·5–105·9]; 3·1% [2·4–3·9] of global DALYs), although the age-standardised DALY rates remained largely consistent since 1990 (1581·2 DALYs [1170·9–2061·4] per 100 000 people). Estimated DALYs for mental disorders do not represent fatal burden because they comprised almost entirely YLDs. A total of 125·3 million YLDs (95% UI 93·0–163·2) were attributable to mental disorders, equating to 14·6% (12·2–16·8) of global YLDs in 2019. YLLs were estimated only for eating disorders, which accounted for 17 361·5 YLLs (15 518·5–21 459·8).
Figure 1Global DALYs by mental disorder, sex, and age, 2019
Figure 2Rankings of YLD and DALY rates for mental disorders by all ages and five age groups for both sexes combined, 2019
Mental disorders were ranked out of all Level 3 causes within the Global Burden of Diseases, Injuries, and Risk Factors Study. Disorders are ordered from highest to lowest ranking for the all ages group. Each colour represents a different mental disorder. Grey cells marked NA show disorders for which burden was not estimated within the age group. ADHD=attention-deficit hyperactivity disorder. DALYs=disability-adjusted life-years. YLDs=years lived with disability.
Figure 3Age-standardised DALYs per 100 000 attributable to mental disorders, 2019
Discussion
We found no marked variation in burden by sex for bipolar disorder and schizophrenia. The burden of depressive disorders, anxiety disorders, and eating disorders was greater in females than males. Burden of autism spectrum disorders and ADHD was greater in males than females. In 2019, 80·6% of the burden due to mental disorders occurred among individuals of working age (16–65 years). Around 9·2% of the remaining burden occurred in people aged younger than 16 years. In 2019, 23·2% of children and adolescents worldwide were located in sub-Saharan Africa, where mental disorders in these age groups pose considerable challenges for economies that already have limited resources dedicated to mental health at a developmental stage when the implementation of prevention and early intervention strategies for mental disorders is crucial.
intimate partner violence,
and conflict and war.
However, at the global level, there are substantial shortages in access to these services, and in the resources allocated for their scale-up, as well as various barriers to care such as perceived need for care and stigma surrounding mental health problems.
,
In high-income countries where increases in the uptake of treatment for mental disorders have been observed since 1990, treatment is still not reaching minimally adequate standards or those in the population who need it the most.
To reduce the burden of mental disorders, we need to expand the delivery of effective prevention and treatment programmes with established efficacy
to cover more of the population for the necessary duration.
Efforts to establish the dataset and methodology from which the impact of the COVID-19 pandemic on the burden of mental disorders can be quantified within the GBD study have been summarised elsewhere.
Our findings demonstrated that mental disorders already imposed a substantial burden before the COVID-19 pandemic. Although it is important to consider the impact of COVID-19 on mental health, the existing unmet mental health needs of the population must also be considered as we focus on recovery from this pandemic. Our GBD 2019 results serve as a stark reminder for countries to re-evaluate their mental health service response more broadly.
some estimates continued to rely on sparse datasets, and high-quality survey data remain scarce for many countries. On the basis of burden of disease analyses done since GBD 2010, we remain concerned about the quality of epidemiological data available for mental disorders. Our systematic literature review made use of inclusion criteria imposing minimum standards to data collection methodology across studies. We recommend that these standards be considered by researchers undertaking new mental health surveys, specifically with regard to decisions around case definitions, instruments, sampling strategy, and standard of reporting.
but the epidemiological data informing burden estimates are limited in this respect. The use of DSM and ICD classifications, which ensures consistency in case definitions across studies, might not be sensitive to all cultural contexts.
The cross-cultural applicability of our case definitions and data collection methodology need to be considered in future research. The uncertainty intervals reported here do not incorporate these sources of bias that are difficult to quantify, including measurement bias not captured by our bias corrections, selection bias due to missing data, and model specification bias.
Third, our estimation of severity distributions was derived from few studies, mostly from high-income countries. Imposing severity distributions from high-income countries to all locations is likely to have underestimated burden in countries with little or no access to treatment and needs to be reconsidered. Raw data on the severity distribution of mental disorders by location that would facilitate this work is not available. However, alternative work to model the impact of access to health care on the severity of mental disorders is ongoing within the GBD study.
Fourth, the majority of the epidemiological data within our datasets adhered to DSM-IV and ICD-10 diagnostic classifications. With the emergence of more epidemiological surveys using DSM-5 and ICD-11 classifications, work to account for the impact of changes to diagnostic classifications within our GBD estimates will be undertaken.
Efforts to compile the required datasets and analyses for formal inclusion of these disorders in the GBD study is underway.
,
These deaths are assigned to those causes within the GBD 2019. A method for capturing the proportion of premature deaths from physical health causes that can be causally attributed to the mental disorder experienced by a person is not yet available for our estimation of YLLs. However, where the evidence exists, it is feasible to use comparative risk assessment to quantify the contribution of mental disorders to premature mortality. Supplementary GBD 2010 analyses found that the inclusion of attributable suicide DALYs would have increased the overall burden of mental and substance use disorders from 7·4% to 8·3% of all global DALYs, increasing their global ranking from fifth to third.
An update to this work using GBD 2020 estimates is in progress, and the first publication using the application of meta-regression techniques to summarise the relative-risk of mental disorders as risk factors for suicide is available.
Further work to establish causal pathways between mental disorders and other health outcomes is required to enable replication of this analysis for other fatal outcomes within the GBD study.
Eighth, broader limitations in the GBD study should be acknowledged. Our definition of disability reflects health loss but not welfare loss. Estimates therefore do not capture the full impact of mental disorders on society. Disability weights were derived from brief descriptions of disease states that might not capture the full complexity of symptoms, across settings. Replication of the disability weight survey across more locations, containing more lay descriptions associated with mental disorders, is required to investigate the generalisability of estimates. We assume independent distributions of comorbid conditions when adjusting YLDs for comorbidity within GBD 2019. This assumption is a limitation especially for mental disorders since comorbidity distributions might be dependent on the combination of disorders experienced. Efforts to incorporate dependent comorbidity within the GBD study have been challenging owing to scarcity of data to inform the correlation structure of prevalence consistently for all diseases and injuries. Even within mental disorders, additional research is required as information on dependent comorbidity is available for only a small subset of possible combinations of disorders and is limited to specific age groups and populations.
The findings of GBD 2019 emphasise the large proportion of the global disease burden attributable to mental disorders and the global disparities in that burden. Furthermore, there was no evidence of global reduction in the burden since 1990, despite evidence-based interventions that can reduce the burden across age, sex, and geographical locations. The ongoing impact of the COVID-19 pandemic is likely to increase the global burden of mental disorders. A coordinated response by governments and the global health community is urgently needed to address the present and future mental health treatment gap.
GBD 2019 Mental Disorders Collaborators
Alize J Ferrari, Damian F Santomauro, Ana M Mantilla Herrera, Jamileh Shadid, Charlie Ashbaugh, Holly E Erskine, Fiona J Charlson, Louisa Degenhardt, James G Scott, John J McGrath, Peter Allebeck, Corina Benjet, Nicholas J K Breitborde, Traolach Brugha, Xiaochen Dai, Lalit Dandona, Rakhi Dandona, Florian Fischer, Juanita A Haagsma, Josep Maria Haro, Christian Kieling, Ann Kristin Skrindo Knudsen, G Anil Kumar, Janni Leung, Azeem Majeed, Philip B Mitchell, Modhurima Moitra, Ali H Mokdad, Mariam Molokhia, Scott B Patten, George C Patton, Michael R Phillips, Joan B Soriano, Dan J Stein, Murray B Stein, Cassandra E I Szoeke, Mohsen Naghavi, Simon I Hay, Christopher J L Murray, Theo Vos, and Harvey A Whiteford.
Affiliations
School of Public Health (A J Ferrari PhD, D F Santomauro PhD, A M Mantilla Herrera PhD, J Shadid BSc, F J Charlson PhD, H E Erskine PhD, Prof H A Whiteford PhD), Queensland Brain Institute (Prof J J McGrath MD), and Center for Youth Substance Abuse Research (J Leung PhD), University of Queensland, Brisbane, QLD, Australia; Queensland Centre for Mental Health Research, Wacol, QLD, Australia (A J Ferrari PhD, D F Santomauro PhD, A M Mantilla Herrera PhD, J Shadid BSc, F J Charlson PhD, H E Erskine PhD, Prof J G Scott PhD, Prof J J McGrath MD, Prof H A Whiteford PhD); Institute for Health Metrics and Evaluation (A J Ferrari PhD, D F Santomauro PhD, A M Mantilla Herrera PhD, J Shadid BSc, C Ashbaugh MA, F J Charlson PhD, H E Erskine PhD, Prof L Degenhardt PhD, X Dai PhD, Prof L Dandona MD, Prof R Dandona PhD, M Moitra MPH, Prof A H Mokdad PhD, Prof M Naghavi MD, Prof S I Hay FMedSci, Prof C J L Murray DPhil, Prof T Vos PhD, Prof H A Whiteford PhD) and Department of Health Metrics Sciences, School of Medicine (X Dai PhD, Prof R Dandona PhD, Prof A H Mokdad PhD, Prof M Naghavi MD, Prof S I Hay FMedSci, Prof C J L Murray DPhil, Prof T Vos PhD), University of Washington, Seattle, WA, USA; National Drug and Alcohol Research Centre (Prof L Degenhardt PhD) and School of Psychiatry (Prof P B Mitchell MD), University of New South Wales, Sydney, NSW, Australia; Mental Health Programme, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia (Prof J G Scott PhD); National Centre for Register-based Research, Aarhus University, Aarhus, Denmark (Prof J J McGrath MD); Department of Global Public Health, Karolinska Institute, Stockholm, Sweden (Prof P Allebeck MD); Department of Epidemiology and Psychosocial Research, Ramón de la Fuente Muñiz National Institute of Psychiatry, Mexico City, Mexico (C Benjet PhD); Department of Psychiatry and Behavioral Health (Prof N J K Breitborde PhD) and Department of Psychology (Prof N J K Breitborde PhD), Ohio State University, Columbus, OH, USA; Department of Health Sciences, University of Leicester, Leicester, UK (Prof T Brugha MD); Public Health Foundation of India, Gurugram, India (Prof L Dandona MD, Prof R Dandona PhD, G Kumar PhD); Indian Council of Medical Research, New Delhi, India (Prof L Dandona MD); Institute of Gerontological Health Services and Nursing Research, Ravensburg-Weingarten University of Applied Sciences, Weingarten, Germany (F Fischer PhD); Department of Public Health, Erasmus University Medical Center, Rotterdam, Netherlands (J A Haagsma PhD); Research Unit, University of Barcelona, Barcelona, Spain (J M Haro MD); Biomedical Research Networking Center for Mental Health Network, Barcelona, Spain (J M Haro MD); Department of Psychiatry, Federal University of Rio Grande do Sul, Porto Alegre, Brazil (C Kieling MD); Division of Child and Adolescent Psychiatry, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil (C Kieling MD); Centre for Disease Burden, Norwegian Institute of Public Health, Bergen, Norway (A S Knudsen PhD); Department of Primary Care and Public Health, Imperial College London, London, UK (Prof A Majeed MD); Faculty of Life Sciences and Medicine, King’s College London, London, UK (M Molokhia PhD); Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada (Prof S B Patten PhD); Department of Pediatrics (Prof G C Patton MD), Faculty of Medicine, Dentistry, and Health Sciences (Prof C E I Szoeke PhD), University of Melbourne, Melbourne, VIC, Australia; Population Health Theme, Murdoch Childrens Research Institute, Melbourne, VIC, Australia (Prof G C Patton MD); Shanghai Mental Health Center, Shanghai Jiao Tong University, Shanghai, China (Prof M R Phillips MD); Department of Psychiatry, Columbia University, New York, NY, USA (Prof M R Phillips MD); Hospital Universitario de La Princesa, Autonomous University of Madrid, Madrid, Spain (Prof J B Soriano MD); Center for Biomedical Research in Respiratory Diseases Network, Madrid, Spain (Prof J B Soriano MD); Risk and Resilience in Mental Disorders Unit, South African Medical Research Council, Cape Town, South Africa (Prof D J Stein MD); Department of Psychiatry, University of California San Diego, La Jolla, CA, USA (M B Stein MD); The Brain Institute, Australian Healthy Ageing Organisation, Melbourne, VIC, Australia (Prof C E I Szoeke PhD).
Contributors
Data sharing
Declaration of interests
C Kieling reports grants from MQ: Transforming Mental Health, the Royal Academy of Engineering, the US Academy of Medical Sciences, the US National Institutes of Health, Conselho Nacional de Desenvolvimento Científico e Tecnológico, the UK Medical Research Council, and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul; and consulting fees from the United Nations Children’s Fund, outside the submitted work. P B Mitchell reports grants from the Australian National Health and Medical Research Council; and payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from Janssen Australia, outside the submitted work. G C Patton reports support for the present manuscript from the Australia National Health and Medical Research Council. J B Soriano reports participation in the Institute for Health Metrics and Evaluation’s Tobacco Advisory board, outside the submitted work. D J Stein reports royalties or licenses from Elsevier and the American Psychiatric Press; consulting fees from Johnson & Johnson, Lundbeck, Sanofi, and Vistagen; and payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Servier and Takeda, outside the submitted work. M B Stein reports grants or contracts from the US National Institute of Mental Health, US Department of Defense, and US Department of Veterans Affairs; consulting fees from Aptinyx, Acadia Pharmaceuticals, Bionomics, Boehringer-Ingelheim, Clexio, EmpowerPharm, Engrail, GW Pharmaceuticals, Janssen, Kazz Pharmaceuticals, and Roche/Genentech; stocks from Pfizer; holds stock options in Epivario and Oxeia Biopharmaceuticals, and owns mutual funds that might contain pharmaceutical stocks; and is the Editor-in-Chief of Depression and Anxiety, Deputy Editor of Biological Psychiatry, and Co-Editor-in-Chief of UptoDate (Psychiatry), outside the submitted work. C E I Szoeke acknowledges support for the present manuscript from National Health and Medical Research Council Australia funding (1032350 and 1062133) paid to the University of Melbourne; and acknowledges payment for expert testimony from the Victorian Department of Health, and for leadership or fiduciary role in board, society, committee or advocacy group, paid or unpaid with the American Medical Association, outside the submitted work. All other authors declare no competing interests.
Acknowledgments
The GBD study is funded by the Bill & Melinda Gates Foundation. A J Ferrari, D F Santomauro, A M Mantilla Herrera, J Shadid, H E Erskine, F J Charlson, J G Scott, J J McGrath, and H A Whiteford are affiliated with the Queensland Centre for Mental Health Research, which receives core funding from the Queensland Department of Health. A J Ferrari is supported by an Australian National Health and Medical Research Council Early Career Fellowship grant (APP1121516). H E Erskine is the recipient of an Australian National Health and Medical Research Council (NHMRC) Early Career Fellowship (APP1137969). F J Charlson is supported by an Australian National Health and Medical Research Council (NHMRC) Early Career Fellowship (APP1138488). J J McGrath is supported by the Danish National Research Foundation (Niels Bohr Professorship). C Kieling is a UK Academy of Medical Sciences Newton Advanced Fellow and a Conselho Nacional de Desenvolvimento Científico e Tecnológico researcher. P B Mitchell is supported by the Australian NHMRC investigator grant (1177991). M Molokhia is supported by the National Institute for Health Research Biomedical Research Center at Guy’s and St Thomas’ National Health Service Foundation Trust and King’s College London. S B Patten holds the Cuthbertson and Fischer Chair in Pediatric Mental Health at the University of Calgary. J B Soriano is supported by the Center for Biomedical Research in Respiratory Diseases Network (Madrid, Spain). D J Stein is supported by the South Africa Medical Research Council. We thank everyone who contributed to the production and review of GBD mental disorder estimates.
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Supplementary Material
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Published: January 10, 2022
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- The true global disease burden of mental illness: still elusive
-
In The Lancet Psychiatry, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 Mental Disorders Collaborators1 updated their global, regional, and national burden estimates to 2019. Their analysis suggests that the proportion of global disability adjusted life-years (DALYs) attributable to mental disorders is 4·9%, and that the age-standardised DALY rate has remained basically unchanged in the past 30 years, at 1566·2 DALYs per 100 000.1 The efforts involved in producing DALY estimates (collating, adjusting, pooling, and summarising global health epidemiological data into a single disease burden metric) are complex and commendable.
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