The burden of mental disorders in the Eastern Mediterranean region, 1990–2015: findings from the global burden of disease 2015 study

The burden of mental disorders in the Eastern Mediterranean region, 1990–2015: findings from... Int J Public Health (2018) 63 (Suppl 1):S25–S37 https://doi.org/10.1007/s00038-017-1006-1 ORIGINAL ARTICLE The burden of mental disorders in the Eastern Mediterranean region, 1990–2015: findings from the global burden of disease 2015 study GBD 2015 Eastern Mediterranean Region Mental Health Collaborators Received: 1 May 2017 / Revised: 21 June 2017 / Accepted: 23 June 2017 / Published online: 3 August 2017 The Author(s) 2017. This article is an open access publication Abstract countries, observed mental disorder rates exceeded the Objectives Mental disorders are among the leading causes expected values. of nonfatal burden of disease globally. Conclusions The burden of mental disorders in the EMR is Methods We used the global burden of diseases, injuries, higher than global levels, particularly for women. To and risk factors study 2015 to examine the burden of properly address this burden, EMR governments should mental disorders in the Eastern Mediterranean region implement nationwide quality epidemiological surveillance (EMR). We defined mental disorders according to criteria of mental disorders and provide adequate prevention and proposed in the diagnostic and statistical manual of mental treatment services. disorders IV and the 10th International Classification of Diseases. Keywords Mental health  Eastern Mediterranean region Results Mental disorders contributed to 4.7% (95% Burden of disease  Depressive disorders  Anxiety uncertainty interval (UI) 3.7–5.6%) of total disability-ad- disorders justed life-years (DALYs), ranking as the ninth leading cause of disease burden. Depressive disorders and anxiety disorders were the third and ninth leading causes of non- Introduction fatal burden, respectively. Almost all countries in the EMR had higher age-standardized mental disorder DALYs rates Mental illness is a growing public health concern. Findings compared to the global level, and in half of the EMR from Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015) showed that mental disorders are among the highest ranking causes of nonfatal burden This article is part of the supplement ‘‘The state of health in the globally (GBD 2015 Disease and Injury Incidence and Eastern Mediterranean Region, 1990–2015’’. Prevalence Collaborators 2016). More specifically, depressive disorders and anxiety disorders were the third The members of GBD (Global Burden of Disease) 2015 Eastern Mediterranean Region Mental Health Collaborators are listed at the and ninth leading contributors to years lived with disability end of the article. Ali H. Mokdad, on behalf of GBD 2015 Eastern (YLDs)—a measure of nonfatal burden. The global Mediterranean Region Mental Health Collaborators, is the prevalence and nonfatal burden of mental disorders were corresponding author. 905,733,400 cases and 124,193,900 YLDs, respectively. Electronic supplementary material The online version of this Five percent of global DALYs and 15.7% of global YLDs article (doi:10.1007/s00038-017-1006-1) contains supplementary were due to mental disorders (Kassebaum et al. 2016). One material, which is available to authorized users. DALY represents the loss of a healthy year of life and & GBD 2015 Eastern Mediterranean Region Mental Health aggregates the YLDs with the years of life lost (YLLs) due Collaborators to premature mortality. mokdaa@uw.edu The EMR is a World Health Organization (WHO)-de- fined group of countries comprising Afghanistan, the Arab Institute for Health Metrics and Evaluation, University of Republic of Egypt (Egypt), Bahrain, Djibouti, Iraq, the Washington, Seattle, WA, USA 123 S26 GBD 2015 Eastern Mediterranean Region Mental Health Collaborators Islamic Republic of Iran (Iran), Jordan, the Kingdom of using a wide range of updated and standardized analytical Saudi Arabia (Saudi Arabia), Kuwait, Lebanon, Libya, procedures. Morocco, Oman, Pakistan, Palestine, Qatar, the Republic of Yemen (Yemen), Somalia, Sudan, the Syrian Arab Republic (Syria), Tunisia, and the United Arab Emirates Methods (UAE). Its population was estimated to be 628 million in 2014 (World Health Organization Regional Office for the Case definition Eastern Mediterranean 2014). The EMR is a very hetero- geneous region where member states vary significantly in In this study, we present GBD 2015 results for mental terms of their gross domestic product, sociodemographic disorders, excluding substance use disorders. The GBD profiles, health indicators, and health system capacities and 2015 mental disorders grouping consisted of anxiety dis- coverage (Mandil et al. 2013). orders, autistic spectrum disorders (autism and Asperger’s Over the past two decades, the EMR has undergone syndrome), conduct disorder, eating disorders (anorexia significant improvements in health status, including nervosa and bulimia nervosa), schizophrenia, attention- increased life expectancy and reductions in child mortality deficit/hyperactivity disorder (ADHD), bipolar disorder, (Memish 2014; Mokdad et al. 2014, 2016a; Moradi-Lakeh depressive disorders (major depressive disorder and dys- et al. 2016). As people in the region are living longer, the thymia), and idiopathic developmental intellectual dis- burden of chronic diseases, including mental disorders, is ability (a residual category capturing intellectual disability expected to rise. Increasing mental disorder burden with not attributed to any of the other causes in the study). We population aging, especially in developing countries, has defined mental disorders according to criteria proposed in been described in the literature (Sathyanarayana Rao and the Diagnostic and Statistical Manual of Mental Disorders Shaji 2007; World Health Organization 2016). The IV (DSM-IV) and the International Classification of Dis- demographic and epidemiological changes in the EMR eases 10 (ICD-10) (GBD 2015 Disease and Injury Inci- have had a major impact on the organization and delivery dence and Prevalence Collaborators 2016). The DSM-IV of mental health services. The population in EMR is very and ICD-10 definitions of the mental disorders described in young, where the median age is about 23 years, around this study are published in detail in the GBD 2015 nonfatal 60% of the population is between 15 and 59 years of age, burden capstone study (GBD 2015 Disease and Injury and one third is below 15 years of age (World Health Incidence and Prevalence Collaborators 2016). Organization 2013a). Moreover, 85% of the EMR population is or has been Calculation of burden (YLDs) (in the past quarter century) in a complex emergency sit- uation resulting in a high prevalence of depression, anxiety, The estimation of YLDs for a given disorder is a product of and post-traumatic stress disorder (Ghosh et al. 2004). epidemiological data that accommodates the number of Since 2010, the region has witnessed economic and polit- people affected as well as the severity and disability ical unrest (Mokdad et al. 2016b). The latter were seen in associated with their symptoms. That is, YLDs are calcu- Egypt, Libya, Tunisia, and Yemen. Currently, Syria is in a lated by multiplying the prevalence of a disorder by its state of civil war. Afghanistan, Bahrain, Iraq, Palestine, severity and comorbidity-adjusted disability weight. YLDs Lebanon and Somalia frequently experience disturbances for previous GBD iterations were re-estimated using the as well. Conflict predisposes a population to the develop- same methods to allow meaningful comparisons of changes ment of mental disorders (Murthy and Lakshminarayana over time. 2006). Stressors of war include loss (human or material) and grief, safety concerns, disruption of the fabric of Epidemiologic inputs society, loss of identity, and potential discrimination with displacement (Hassan et al. 2016). Prevalence, incidence, remission or duration, and excess No previous studies have assessed the burden of mental mortality data for mental disorders were captured through a disorders in the EMR collectively. Using data from GBD systematic review of the literature. In GBD 2010, a liter- 2015, we aim to explore the burden of mental disorders in ature search was conducted in three stages involving the EMR by country, age group, sex, type of mental dis- electronic searches of the peer-reviewed literature (via order, and income group from 1990 to 2015. We have Medline, Embase and PubMed), gray literature, and expert previously published a study on the burden of mental dis- consultation. The agreed approach for mental disorders was orders in the EMR between 1990 and 2013 using findings to conduct electronic database searches on a rolling basis. from GBD 2013. We now update the burden estimates All three stages of the GBD 2010 literature review were repeated for GBD 2013 to capture additional data 123 The burden of mental disorders in the eastern Mediterranean region, 1990–2015: findings from… S27 published up to 2013. For GBD 2015, only stages two and internet survey. Respondents considered two hypothetical three of the literature review were conducted, with another individuals with different health states and were asked to electronic database search due for mental disorders in the indicate which person they perceived as healthier. In GBD next iteration of GBD studies. The inclusion criteria for 2013, methodological advances were introduced to dis- epidemiologic studies stipulated that: (1) the publication ability weighting, including new data capturing many year must be from 1980 onward; (2) ‘‘caseness’’ must be newly published or unpublished data sources for the dis- based on clinical threshold as established by the DSM-IV orders included in GBD. A disability weight ranging or ICD-10; (3) sufficient information must be provided on between 0 (equivalent to perfect health) and 1 (equivalent study method and sample characteristics to assess the to death) was generated for 235 health states which toge- quality of the study; and (4) study samples must be rep- ther reflected all causes of nonfatal burden in GBD 2015 resentative of the general population (i.e., inpatient or (Salomon et al. 2015; GBD 2015 Disease and Injury pharmacological treatment samples, case studies, veterans Incidence and Prevalence Collaborators 2016). or refugee samples were excluded). No limitation was set on the language of publication. Methods used for this Severity distributions systematic review have been reported in greater detail elsewhere (GBD 2015 Disease and Injury Incidence and Sequelae were further defined in terms of severity following Prevalence Collaborators 2016). Data from 108 epidemi- the same approach for estimating the distribution of severity ologic studies were used to estimate the burden of mental as in GBD 2013. Details on the severity distributions for disorders in the EMR in GBD 2015; a full list of the studies mental disorders are available elsewhere (GBD 2015 Disease is available in Appendix 1 in supplementary. and Injury Incidence and Prevalence Collaborators 2016). Disease modeling Comorbidity adjustment For each disorder, epidemiological estimates from the litera- GBD 2015 YLD estimates were adjusted for the effect of ture review were pooled using DisMod-MR 2.1, a Bayesian comorbidity between diseases. Details on the process are meta-regression tool. The tool used in GBD 2010 and GBD available elsewhere (GBD 2015 Disease and Injury Inci- 2013, DisMod-MR, is based on a generalized negative bino- dence and Prevalence Collaborators 2016). mial model that: (1) uses an incidence–prevalence–mortality mathematical model to enforce internal consistency between Calculation of burden (DALYs) estimates from different epidemiological parameters; (2) We calculated DALYs as the sum of YLDs and YLLs. estimates data for countries and world regions with few or no available input data based on random effects for country, YLLs were calculated by multiplying the number of deaths regions, and their corresponding super-region groupings; (3) due to the given disorder at a particular age by the standard deals with variability in the data due to measurement bias, or life expectancy at that age. However, death records used in alternatively, ecological factors through the use of study- and GBD 2015 followed ICD-10 rules for categorical attribu- country-level covariates; and (4) propagates uncertainty tion of cause of death to a single underlying cause and, around the raw epidemiological data through to the final therefore, did not document any deaths due to mental estimates. For GBD 2015, the computational engine of Dis- disorders, except for schizophrenia and eating disorders. Mod-MR 2.1 remained unchanged, but we substantially rewrote the code that organizes the flow of data and settings at Socio-demographic index each level of the analytical cascade. Greater detail on DisMod- MR 2.1 is available elsewhere (GBD 2015 Disease and Injury In GBD 2015, we constructed a summary metric referred to Incidence and Prevalence Collaborators 2016). as the Socio-demographic Index (SDI) based on measures of income per capita, average years of schooling among Disability weights people aged 15 years and older, and total fertility rate (Kassebaum et al. 2016). SDI values were scaled to a range Disability weights are the general public’s assessment of of 0–1, with 0 equaling the lowest income, lowest the severity of health loss associated to the cause. Dis- schooling, and highest fertility rate observed from 1980 to ability weights were derived using the GBD 2010 method 2015, and 1 equaling the highest income, highest school- of pairwise comparison questions in population surveys (of ing, and lowest fertility rate observed during that time. The those aged 18 and over) conducted in Bangladesh, final SDI score was computed as the geometric mean of Indonesia, Peru, Tanzania, Hungary, Italy, Sweden, each of the components. We compared observed patterns of Netherlands, and the United States and an open access mental disorder YLDs with those expected on the basis of 123 S28 GBD 2015 Eastern Mediterranean Region Mental Health Collaborators SDI, allowing us to explore where health gains exceeded— males. The age pattern in males and females was different. or lagged behind-corresponding changes in development. In females, DALY rates increased progressively from birth up to age 35–39 (3131 DALYs/100,000) and then Classification of EMR countries decreased progressively with age. In males, DALY rates peaked at age 15–19 (1929 DALYs/100,000) and In an attempt to properly track the health status in the EMR decreased at age 20–24 (1860 DALYs/100,000), only to countries, we divided the region into three categories rise again and peak at age 35–39 (2075 DALYs/100,000). according to the gross national income (GNI) per capita. The The burden associated with depressive disorders and anx- first category represented the low-income countries (LICs) iety disorders rose abruptly in adolescence for both sexes. with an average GNI per capita of $523 (GBD 2015 Disease Depressive disorder burden peaked between 40 and and Injury Incidence and Prevalence Collaborators 2016; 44 years, whereas anxiety disorder burden peaked between GBD 2015 Maternal Mortality Collaborators 2016;GBD 15 and 19 years. For schizophrenia, the burden peaked 2015 Mortality and Causes of Death Collaborators 2016; between 40 and 49 years while bipolar disorder peaked GBD 2015 Risk Factors Collaborators 2016; Kassebaum between 25 and 29 years. Conduct disorder and ADHD et al. 2016;Wang et al. 2016). On the opposite end of the peaked between 10 and 14 years. Supplementary e-Table 1 spectrum were some oil-rich, high-income countries (HICs) details the values of the DALY rates depicted in the figure. with an average GNI per capita of $39,688. The nations that Figure 3 shows the age-standardized rate of mental lied in between were the middle-income countries (MICs) disorder DALYs over time for males, females, and both with an average GNI per capita of $3,251. The three groups combined in the EMR and globally. The rate of DALYs were LICs: Afghanistan, Djibouti, Yemen, and Somalia; remained almost constant from 1990 to 2015 in the EMR MICs: Egypt, Iraq, Iran, Jordan, Lebanon, Libya, Morocco, for both males and females and globally. EMR DALY rates Pakistan, Palestine, Sudan, Syria, and Tunisia; and HICs: in females were consistently higher than their male coun- Bahrain, Saudi Arabia, Kuwait, Oman, Qatar, and the UAE. terparts and in the EMR compared to the global rates from 1990 to 2015. The rate of DALYs in males was lower than the global level. Supplementary e-Table 2 details the val- Results ues of DALY rates depicted in the figure. None of these differences were statistically significant. Table 1 shows the In 2015, the EMR generated a total of 229.2 million rankings (in age-standardized DALY rates) of each mental DALYs (95% uncertainty interval (UI) 194.8–267.6) of disorder in the EMR in 2015 compared to 1990. All mental which 10.7 million DALYs (7.1–15.0) were due to mental disorders ranked higher in 2015. disorders. In other words, mental disorders contributed to Figure 4 shows the rate of mental disorder DALYs in 4.7% (3.7–5.6%) of total DALYs in the EMR in 2015, the EMR in 2015 by income grouping and sex. The DALY ranking as the ninth leading cause of disease burden. rates were similar across income groups, ranging from In 2015, the EMR generated a total of 67.1 million 1725.9 DALYs/100,000 in LIC to 1758.2 DALYs in HIC. YLDs (46.9–91.2) of which 10.7 million YLDs (7.1–14.9 Across these groups, females had higher mental disorder million) were due to mental disorders. In other words, DALY rates compared to males. All EMR income groups mental disorders contributed to 15.9% (15.1–16.4%) of had higher rates of DALYs compared to global levels. total YLDs in the EMR in 2015, ranking second to mus- Supplementary e-Table 3 details the values of DALY rates culoskeletal disorders which contributed to 16.1% depicted in the figure. None of these differences were (14.2–18.2%) of total YLDs. At a more detailed level, statistically significant. depressive disorders and anxiety disorders were the third Figure 5 shows the age-standardized rate of mental and ninth leading causes of YLDs, respectively. disorder DALYs in the EMR and globally over time, by Figure 1 shows the composition of mental disorder income grouping. The DALY rates remained almost con- DALYs by type of disorder for both sexes combined in the stant in the EMR income groups and globally. Differences EMR in 2015. Depressive disorders (42.1%), followed by between income groups and over time were not statistically anxiety disorders (21.5%), were the greatest contributors to significant. mental disorder DALY numbers. e-Figure 1 shows the age-standardized rate of mental Figure 2 shows the composition of mental disorder disorder DALYs in the EMR, by country and sex. The DALY rates by age, sex, and disorder type in the EMR in countries are ranked in increasing order of age-standard- 2015. DALY rates were higher for females across all age ized mental disorder DALY rates for both sexes combined. groups, except for those under age 15. The highest rate of All countries in the EMR, except Egypt, had higher age- DALYs occurred in the 25–49 age group, with a peak in the standardized mental disorder DALY rates compared to the 35–39 age group for both sexes combined, females, and global value. 123 The burden of mental disorders in the eastern Mediterranean region, 1990–2015: findings from… S29 Fig. 1 Distribution of disability-adjusted life-years (DALYs) due to mental disorders in the Eastern Mediterranean Region, 2015. (Global Burden of Disease Study 2015, Eastern Mediterranean Region, 2015) Fig. 2 Age-standardized rate of disability-adjusted life-years (DALYs) per 100,000 population due to mental disorders in the Eastern Mediterranean Region by age, sex, and disorder, 2015. (Global Burden of Disease Study 2015, Eastern Mediterranean Region 2015) e-Figure 2 shows the age-standardized observed mental drop in the EMR (and globally) over the last quarter cen- disorder DALY rates by EMR country compared with the tury, and females continue to suffer a bigger burden of expected values on the basis of each country’s SDI. For the mental illness. The mental disorder burden in the politi- EMR, SDI in 2015 ranged from 0.151 for Somalia to 0.875 for cally and economically stable high-income countries is the UAE. The area between the observed and expected curves comparable to that in low- and middle-income countries represents the discrepancy between both values (D = ob- (LMICs) of the region, which include some countries in served-expected). The countries are ranked in the order of complex emergency situations. Almost all countries in the decreasing D. In half of the EMR countries, observed mental EMR have a bigger burden of mental disorders compared disorder DALYs exceeded the expected values. to the global rate. Epidemiological transition Discussion While the rate of mental disorder DALYs has not signifi- Our study reveals a number of findings that are relevant to cantly changed over time, the ranking of these disorders setting a mental health agenda for the EMR. The burden shows a different picture. Compared to 1990, there was an associated with mental disorders has not seen a significant increase in the amount of burden mental disorders 123 S30 GBD 2015 Eastern Mediterranean Region Mental Health Collaborators Fig. 3 Age-standardized rate of disability-adjusted life-years (DALYs) per 100,000 population due to mental disorders in the Eastern Mediterranean Region and globally, 1990–2015. (Global Burden of Disease Study 2015, Global, Eastern Mediterranean Region 1990–2015) Table 1 Ranking of mental 1990 rank 2015 rank disorders among all level 3 Global Burden of Disease Schizophrenia 69 56 (GBD) causes for age- Idiopathic developmental intellectual disability 105 92 standardized rates of disability- adjusted life-years, 1990–2015 Bipolar disorder 77 65 Conduct disorder 86 74 Autistic spectrum disorders 73 62 Eating disorders 147 140 Anxiety disorders 33 27 Depressive disorders 16 14 Attention-deficit/hyperactivity disorder 150 149 Global Burden of Disease Study 2015, Eastern Mediterranean Region 2015 contribute to the total burden. This epidemiological tran- timing of illness onset, diagnosis, treatment, and adjust- sition is consistent with that seen globally, especially at ment to a chronic illness are to be accounted for (World higher SDIs (GBD 2015 Disease and Injury Incidence and Health Organization 2006). Women in this part of the Prevalence Collaborators 2016). The increasing mental world are particularly vulnerable to developing mental health burden is mainly attributable to population growth illness, namely depressive disorders. With globalization and aging rather than an increase in prevalence rates. This and urbanization of most EMR societies, women may be is an important finding to be considered when setting exposed to numerous stressors. This distress may have public health agendas for the region as more people will be repercussions on the entire household and children in experiencing mental disorders and for longer durations. particular, as adjustments to new social structures become necessary (Eloul et al. 2009). According to the World Women’s mental health Bank, there has been a 47% increase in women’s partici- pation in the labor market of the Middle East and North Females have a higher burden of mental disorders in the Africa region from 1960 to 2000 (World Bank 2003). EMR. Rates of mental disorders, however, only partially Gender roles in the region may partially contribute to a explain gender disparities in mental health. Risk factors, higher burden of mental illness among women. 123 The burden of mental disorders in the eastern Mediterranean region, 1990–2015: findings from… S31 Fig. 4 Age-standardized rate of disability-adjusted life-years (DALYs) due to mental disorders in the Eastern Mediterranean Region and Globally, by income group and sex, 2015. (Global Burden of Disease Study 2015, Global, Eastern Mediterranean Region, 2015) Fig. 5 Age-standardized rate of disability-adjusted life-years (DALYs) due to mental disorders in the Eastern Mediterranean Region and globally by income group, 1990–2015. (Global Burden of Disease Study 2015, Global, Eastern Mediterranean Region, 1990–2015) Socioeconomic disadvantage, low income and income violence in the world (compared to 25.4% in Europe and inequality, low or subordinate social status and rank, and 29.8% in the Americas) (World Health Organization lack of autonomy are some examples of this polarity 2013b). Women are also more likely to be diagnosed with (World Health Organization 2006). The impact of patri- depression compared to men with similar scores on stan- archy and women’s lack of empowerment on mental health dardized measures of depression (Callahan et al. 1997; is thoroughly evaluated in the literature (Niaz and Hassan Stoppe et al. 1999). 2006). Moreover, cultural factors, high birthrates, and young ages at first conception may contribute to higher The burden by disorder type rates of postpartum depression (Eloul et al. 2009). Perinatal mental disorders are particularly concerning for their Depression is the highest contributor to the burden fol- effects on the development of infants and children (World lowed by anxiety disorders. This is because of the higher Health Organization 2010). In addition, the region wit- prevalence of these disorders as well as the disability nesses one of the highest rates (37.0%) of intimate partner associated with them. It is worth mentioning that some 123 S32 GBD 2015 Eastern Mediterranean Region Mental Health Collaborators highly disabling conditions, such as acute schizophrenia, Assessment Instrument for Mental Health Systems (World do not rank high in YLDs owing to their low prevalence Health Organization Regional Office for the Eastern (Ferrari et al. 2014). This latter condition, however, had the Mediterranean 2010) showed that the median-treated highest disability weight (0.778) among mental disorders in prevalence of mental disorders was 0.31%, thereby sug- GBD 2015 (GBD 2015 Disease and Injury Incidence and gesting a big gap in treatment. The unmet needs of children Prevalence Collaborators 2016). Under the age of 15, and adolescents were greater than those of adults. This is males contribute more greatly to the burden of mental alarming in the region where a big proportion of the pop- disorders, primarily due to the higher burden of autism in ulation is under 19 years of age (World Health Organiza- males under age five and conduct disorder in males ages tion 2010). Compared to the $3–$4 USD per capita 5–15. Indeed, epidemiologic studies from the region indi- spending on mental health in the US, the region spends an cate a high male to female ratio of autistic spectrum dis- average of $0.15 USD per capita with only 2% of the orders (Elsabbagh et al. 2012). governments’ health budgets allocated to mental health (which compares to the 5–10% required to match con- Observed vs expected rates temporary comprehensive healthcare systems) (Gater and Saeed 2015). This level of spending is observed across According to GBD 2015 findings, sizeable discrepancies low-, middle- and high-income countries of the EMR. Not occurred for observed and expected YLDs based on SDI only are the resources scarce but also inefficiently used and throughout North Africa and the Middle East, probably inequitably distributed. Stigma further limits the use of reflecting the uneven achievements in development found available resources. Moreover, there is little integration of in this region (GBD 2015 Disease and Injury Incidence and mental health in primary health care in much of the region Prevalence Collaborators 2016). At a regional level, (World Health Organization Regional Office for the East- observed depression YLD rates exceeded expected rates ern Mediterranean 2010). based on SDI. This implies that the EMR’s income per Some governments within the EMR have already taken capita, educational levels and fertility rates were not action to address the problematic increase in mental dis- commensurate with the high burden of depression seen in order burden. Qatar has launched its 2013–2018 Mental the region. High-income countries in the EMR had the Health Strategy which aims to increase availability and highest SDIs in 2015 but had observed depression YLD utilization of mental health services using comprehensive rates that exceeded expected rates based on SDI. This is not standards and guidelines (Supreme Council of Health, State consistent with the global trend where the proportion of life of Qatar and Hamad Medical Corporation, Primary Health expectancy spent with disability declined slightly with Care 2013). Kuwait is taking action to integrate mental health in primary health care in light of the stigma asso- increasing SDI (Kassebaum et al. 2016). ciated with mental illness and its impact on help seeking, Capacity of EMR versus burden especially in the EMR (Almazeedi and Alsuwaidan 2014). A study found that pharmacologic and/or psychosocial This high burden of mental disorders is particularly chal- treatment packages can be offered at low prices in LMICs, lenging to the EMR, where 16 out of the 22 countries in the including Morocco and Iran. It is important to point out region belong to the LMIC group. WHO’s 2014 Mental that the development of psychosocial interventions needs Health Atlas (World Health Organization 2014) described to be tailored to the culture in the EMR. Most interventions the preparedness of the EMR to deal with mental health at used today in the region are simply exported from research a system level. The mental health workforce per 100,000 in more developed nations instead of being adopted in a population in the EMR was 7.3 compared to a value of 9 culturally sensitive fashion (Chisholm et al. 2007). and 43.5 globally and in the WHO European region. A study published in 2005 showed that Lebanon had the Limitations highest provision of psychiatric services with one psychi- atrist per 45,000 population (Al-Krenawi 2005). A more Our study has a number of limitations. First, our findings recent paper in 2012 reported the highest proportions of were based upon best available secondary data and models psychiatrists in Bahrain (5 per 100,000), Qatar (3.4 per that cannot be verified across geographies or time within the 100,000) and Kuwait (3.1 per 100,000) (Okasha et al. same region due to historical circumstances and constraints 2012). Countries like Iraq, Libya, Morocco, Sudan, Syria, of local resources. Most countries in the EMR lack quality and Yemen had fewer than 0.5 psychiatrists per 100,000 epidemiological data to describe the national prevalence and population. It is important to note, however, that most of burden of mental disorders and to provide quality represen- these studies had design weaknesses, meaning all outcomes tative data input for the GBD estimations. Raw prevalence must be interpreted with caution. The region’s WHO- data was available for 10 of the 22 EMR countries: Egypt, 123 The burden of mental disorders in the eastern Mediterranean region, 1990–2015: findings from… S33 Hospital, Seattle, Washington, United States. Michael Collison, BS, Iran, Iraq, Jordan, Lebanon, Pakistan, Palestine, Sudan, the Institute for Health Metrics and Evaluation, University of Washing- UAE, and Yemen. A list of all data points used in this study ton, Seattle, Washington, United States. Kristopher J. Krohn, BA, are available via the Global Health Data Exchange (http:// Institute for Health Metrics and Evaluation, University of Washing- ghdx.healthdata.org). When data were of poor quality or ton, Seattle, Washington, United States. Adrienne Chew, ND, Insti- tute for Health Metrics and Evaluation, University of Washington, unavailable, we relied on modeling techniques to generate Seattle, Washington, United States. Farah Daoud, BA/BS, Institute the estimates using other available variables and the infor- for Health Metrics and Evaluation, University of Washington, Seattle, mation for neighboring countries or countries with a similar Washington, United States. Fiona J. Charlson, PhD, School of Public health profile in the region. While this allowed us to include Health, University of Queensland, Brisbane, Queensland, Australia; Institute for Health Metrics and Evaluation, University of Washing- all EMR countries in our burden of disease analysis and ton, Seattle, Washington, United States; Queensland Centre for generate collective measures for such a heterogeneous group Mental Health Research, Brisbane, Queensland, Australia. Danny of countries, it is important for countries in the region to Colombara, PhD, Institute for Health Metrics and Evaluation, facilitate the collection of high quality epidemiological data University of Washington, Seattle, Washington, United States. Louisa Degenhardt, PhD, National Drug and Alcohol Research Centre, for mental disorders. Note, however, that the limitation of University of New South Wales, Sydney, Australia. Rebecca data availability is partly captured by the estimates of Ehrenkranz, MPH, Institute for Health Metrics and Evaluation, uncertainty presented in our results. Second, disability University of Washington, Seattle, Washington, United States. Holly weights in GBD studies intentionally capture health loss E. Erskine, PhD, Queensland Centre for Mental Health Research, Brisbane, QLD, Australia; School of Public Health, University of while not attempting to capture welfare loss and hence do not Queensland, Brisbane, QLD, Australia; Institute for Health Metrics reflect the economic and familial effects of mental disorders. and Evaluation, University of Washington, Seattle, Washington, In addition, given the subjective nature of the symptoms of United States. Alize J. Ferrari, PhD, School of Public Health, mental illness, many individuals in cultures who express University of Queensland, Brisbane, Queensland, Australia; Queensland Centre for Mental Health Research, Brisbane, Queens- mental disorders differently from the ICD-10 diagnostic land, Australia; Institute for Health Metrics and Evaluation, Univer- criteria were not captured by GBD. Third, deaths that were sity of Washington, Seattle, Washington, United States. Michael causally linked to mental disorders were largely captured Kutz, BS, Institute for Health Metrics and Evaluation, University of under other causes. This is because an outcome could only be Washington, Seattle, Washington, United States. Janni Leung, PhD, School of Public Health, University of Queensland, Brisbane, QLD, listed once in the GBD cause list. Vital registrations rarely Australia; Institute for Health Metrics and Evaluation, University of list a mental disorder as a cause of death. For instance, major Washington, Seattle, Washington, United States. Damian San- depressive disorder-related deaths from suicide or ischemic tomauro, PhD, School of Public Health, University of Queensland, heart disease were captured under intentional injuries and Brisbane, Queensland, Australia; Queensland Centre for Mental Health Research, Brisbane, Queensland, Australia; Institute for cardiovascular disease, respectively. Fourth, DSM-IV and Health Metrics and Evaluation, University of Washington, Seattle, ICD-10 diagnostic criteria, mainly established in developed Washington, United States. Haidong Wang, PhD, Institute for Health nations, may not be sensitive to all cross-cultural presenta- Metrics and Evaluation, University of Washington, Seattle, Wash- tions of mental disorders. Many patients in the EMR attribute ington, United States. Harvey A. Whiteford, PhD, School of Public Health, University of Queensland, Brisbane, Queensland, Australia. their psychiatric symptoms to physical causes, probably Queensland Centre for Mental Health Research, Brisbane, Queens- secondary to stigma. This would, therefore, bias the mental land, Australia; Institute for Health Metrics and Evaluation, Univer- disorder burden estimates for the region. It is important to sity of Washington, Seattle, Washington, United States. Amanuel note here that none of the EMR countries were included in Alemu Abajobir, MPH, School of Public Health, University of Queensland, Brisbane, QLD, Australia. Foad Abd-Allah, MD, the group where surveys were done to estimate disability Department of Neurology, Cairo University, Cairo, Egypt. Haftom weights. Niguse Abraha, MS, Mekelle University, Mekelle, Ethiopia. Laith J. Abu-Raddad, PhD, Infectious Disease Epidemiology Group, Weill GBD 2015 Eastern Mediterranean Region Mental Health Col- Cornell Medical College in Qatar, Doha, Qatar. Aliasghar Ahmad laborators: Ali H. Mokdad, PhD (corresponding author), Institute Kiadaliri, PhD, Department of Clinical Sciences Lund, Orthopedics, for Health Metrics and Evaluation, University of Washington, Seattle, Clinical Epidemiology Unit, Lund University, Lund, Sweden. Alireza Washington, United States. Raghid Charara, MD, American Univer- Ahmadi, MD, Kermanshah University of Medical Sciences, Ker- sity of Beirut, Beirut, Lebanon. Charbel El Bcheraoui, PhD, Institute manshah, Iran. Kedir Yimam, MPH, Debre Markos University, Debre for Health Metrics and Evaluation, University of Washington, Seattle, Markos, Ethiopia. Muktar Beshir, MPH, College of Health Sciences, Washington, United States. Ibrahim Khalil, MD, Institute for Health Department of Epidemiology, ICT and e-Learning Coordinator, Metrics and Evaluation, University of Washington, Seattle, Wash- Jimma University, Jimma, Ethiopia. Faris Hasan Al Lami, PhD, ington, United States. Maziar Moradi-Lakeh, MD, Department of Baghdad College of Medicine, Baghdad, Iraq. Khurshid Alam, PhD, Community Medicine, Preventive Medicine and Public Health Murdoch Childrens Research Institute, The University of Melbourne, Research Center, Gastrointestinal and Liver Disease Research Center Parkville, Victoria, Australia; The University of Melbourne, Mel- (GILDRC), Iran University of Medical Sciences, Tehran, Iran. Ash- bourne, VIC, Australia; The University of Sydney, Sydney, NSW, kan Afshin, MD, Institute for Health Metrics and Evaluation, Australia. Deena Alasfoor, MSc, Ministry of Health, Al Khuwair, University of Washington, Seattle, Washington, United States. Oman. Reza Alizadeh-Navaei, PhD, Gastrointestinal Cancer Research Nicholas J. Kassebaum, MD, Institute for Health Metrics and Eval- Center, Mazandaran University of Medical Sciences, Sari, Iran. Juma uation, University of Washington, Seattle, Washington, United States; M. Alkaabi, MD, College of Medicine and Health Sciences, United Department of Anesthesiology and Pain Medicine, Seattle Children’s Arab Emirates University, Al-Ain City, United Arab Emirates. Fatma 123 S34 GBD 2015 Eastern Mediterranean Region Mental Health Collaborators Al-Maskari, PhD, College of Medicine and Health Sciences, United Fereshtehnejad, PhD, Department of Neurobiology, Care Sciences Arab Emirates University, Al-Ain City, United Arab Emirates. Rajaa and Society (NVS), Karolinska Institutet, Stockholm, Sweden. Flo- Al-Raddadi, PhD, Joint Program of Family and Community Medi- rian Fischer, PhD, School of Public Health, Bielefeld University, cine, Jeddah, Saudi Arabia. Khalid A. Altirkawi, MD, King Saud Bielefeld, Germany. Tsegaye Tewelde Gebrehiwot, MPH, Jimma University, Riyadh, Saudi Arabia. Nahla Anber, PhD, Mansoura University, Jimma, Ethiopia. Ababi Zergaw Giref, PhD, Addis Ababa University, Mansoura, Egypt. Hossein Ansari, PhD, Health Promotion University, Addis Ababa, Ethiopia. Melkamu Dedefo Gishu, MS, Research Center, Department of Epidemiology and Biostatistics, Haramaya University, Dire Dawa, Ethiopia; Kersa Health and Zahedan University of Medical Sciences, Zahedan, Iran. Hamid Demographic Surveillance System, Harar, Ethiopia. Alessandra, Asayesh, PhD, Department of Medical Emergency, School of Para- Carvalho, Goulart, PhD, Center for Clinical and Epidemiological medic, Qom University of Medical Sciences, Qom, Iran. Rana Jawad Research Center- Hospital Universitario-University of Sa˜o Paulo, Sa˜o Asghar, MD, South Asian Public Health Forum, Islamabad, Pakistan. 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Chan School of Public Department of Infectious Disease Epidemiology, London School of Health, Harvard University, Boston, MA, United States; Harvard/ Hygiene and Tropical Medicine, London, United Kingdom; Institute MGH Center on Genomics, Vulnerable Populations, and Health for Health Metrics and Evaluation, University of Washington, Seattle, Disparities, Mongan Institute for Health Policy, Massachusetts Gen- Washington, United States. Asma Abdul Latif, PhD, Department of eral Hospital, Boston, MA, United States. Wubalem Fekadu, MS, Zoology, Lahore College for Women University, Lahore, Punjab, Bahir Dar University, Bahir Dar, Ethiopia. Seyed-Mohammad Pakistan. Paul H. Lee, PhD, Hong Kong Polytechnic University, 123 The burden of mental disorders in the eastern Mediterranean region, 1990–2015: findings from… S35 Hong Kong, China. Cheru Tesema Leshargie, MPH, Debre Markos Center, Tehran University of Medical Sciences, Tehran, Iran. Rajesh University, Debre Markos, Ethiopia. Ricky Leung, PhD, State Kumar Rai, MPH, Society for Health and Demographic Surveillance, University of New York, Albany, Rensselaer, NY, United States. Suri, India. David Laith Rawaf, MD, WHO Collaborating Centre, Loon-Tzian Lo, MD, UnionHealth Associates, LLC, St. Louis, MO, Imperial College London, London, United Kingdom; North Hamp- United States; Alton Mental Health Center, Alton, IL, United States. shire Hospitals, Basingstroke, United Kingdom; University College Raimundas Lunevicius, PhD, Aintree University Hospital National London Hospitals, London, United Kingdom. Salman Rawaf, MD, Health Service Foundation Trust, Liverpool, United Kingdom; School Imperial College London, London, United Kingdom. Amany H. of Medicine, University of Liverpool, Liverpool, United Kingdom. Refaat, PhD, Walden University, Minneapolis, MN, United States, Hassan Magdy Abd El Razek, MBBCH, Mansoura Faculty of Med- Suez Canal University, Ismailia, Egypt. Satar Rezaei, PhD, School of icine, Mansoura, Egypt. Mohammed Magdy Abd El Razek, MBBCH, Public Health, Kermanshah University of Medical Sciences, Ker- Aswan University Hospital, Aswan Faculty of Medicine, Aswan, manshah, Iran. Mohammad Sadegh Rezai, MD, Infectious Diseases Egypt. Reza Majdzadeh, PhD, Knowledge Utilization Research Research Centre with Focus on Nosocomial Infection, Mazandaran Center and Community Based Participatory Research Center, Tehran University of Medical Sciences, Sari, Mazandaran, Iran. Gholamreza University of Medical Sciences, Tehran, Iran. Azeem Majeed, MD, Roshandel, PhD, Golestan Research Center of Gastroenterology and Department of Primary Care and Public Health, Imperial College Hepatology, Golestan University of Medical Sciences, Gorgan, Iran; London, London, United Kingdom. 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The funding source played no role in the 1016/S0140-6736(16)31012-1 design of the study, the analysis and interpretation of data, and the GBD 2015 Risk Factors Collaborators (2016) Global, regional, and writing of the paper. The study did not involve human participants national comparative risk assessment of 79 behavioural, envi- and/or animals; therefore, no informed consent was needed. ronmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Funding This research was funded by the Bill & Melinda Gates Disease Study 2015. Lancet Lond Engl 388:1659–1724. doi:10. Foundation. 1016/S0140-6736(16)31679-8 Ghosh N, Mohit A, Murthy RS (2004) Mental health promotion in Conflict of interest The authors declare that they have no conflicts of post-conflict countries. 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The burden of mental disorders in the Eastern Mediterranean region, 1990–2015: findings from the global burden of disease 2015 study

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Abstract

Int J Public Health (2018) 63 (Suppl 1):S25–S37 https://doi.org/10.1007/s00038-017-1006-1 ORIGINAL ARTICLE The burden of mental disorders in the Eastern Mediterranean region, 1990–2015: findings from the global burden of disease 2015 study GBD 2015 Eastern Mediterranean Region Mental Health Collaborators Received: 1 May 2017 / Revised: 21 June 2017 / Accepted: 23 June 2017 / Published online: 3 August 2017 The Author(s) 2017. This article is an open access publication Abstract countries, observed mental disorder rates exceeded the Objectives Mental disorders are among the leading causes expected values. of nonfatal burden of disease globally. Conclusions The burden of mental disorders in the EMR is Methods We used the global burden of diseases, injuries, higher than global levels, particularly for women. To and risk factors study 2015 to examine the burden of properly address this burden, EMR governments should mental disorders in the Eastern Mediterranean region implement nationwide quality epidemiological surveillance (EMR). We defined mental disorders according to criteria of mental disorders and provide adequate prevention and proposed in the diagnostic and statistical manual of mental treatment services. disorders IV and the 10th International Classification of Diseases. Keywords Mental health  Eastern Mediterranean region Results Mental disorders contributed to 4.7% (95% Burden of disease  Depressive disorders  Anxiety uncertainty interval (UI) 3.7–5.6%) of total disability-ad- disorders justed life-years (DALYs), ranking as the ninth leading cause of disease burden. Depressive disorders and anxiety disorders were the third and ninth leading causes of non- Introduction fatal burden, respectively. Almost all countries in the EMR had higher age-standardized mental disorder DALYs rates Mental illness is a growing public health concern. Findings compared to the global level, and in half of the EMR from Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015) showed that mental disorders are among the highest ranking causes of nonfatal burden This article is part of the supplement ‘‘The state of health in the globally (GBD 2015 Disease and Injury Incidence and Eastern Mediterranean Region, 1990–2015’’. Prevalence Collaborators 2016). More specifically, depressive disorders and anxiety disorders were the third The members of GBD (Global Burden of Disease) 2015 Eastern Mediterranean Region Mental Health Collaborators are listed at the and ninth leading contributors to years lived with disability end of the article. Ali H. Mokdad, on behalf of GBD 2015 Eastern (YLDs)—a measure of nonfatal burden. The global Mediterranean Region Mental Health Collaborators, is the prevalence and nonfatal burden of mental disorders were corresponding author. 905,733,400 cases and 124,193,900 YLDs, respectively. Electronic supplementary material The online version of this Five percent of global DALYs and 15.7% of global YLDs article (doi:10.1007/s00038-017-1006-1) contains supplementary were due to mental disorders (Kassebaum et al. 2016). One material, which is available to authorized users. DALY represents the loss of a healthy year of life and & GBD 2015 Eastern Mediterranean Region Mental Health aggregates the YLDs with the years of life lost (YLLs) due Collaborators to premature mortality. mokdaa@uw.edu The EMR is a World Health Organization (WHO)-de- fined group of countries comprising Afghanistan, the Arab Institute for Health Metrics and Evaluation, University of Republic of Egypt (Egypt), Bahrain, Djibouti, Iraq, the Washington, Seattle, WA, USA 123 S26 GBD 2015 Eastern Mediterranean Region Mental Health Collaborators Islamic Republic of Iran (Iran), Jordan, the Kingdom of using a wide range of updated and standardized analytical Saudi Arabia (Saudi Arabia), Kuwait, Lebanon, Libya, procedures. Morocco, Oman, Pakistan, Palestine, Qatar, the Republic of Yemen (Yemen), Somalia, Sudan, the Syrian Arab Republic (Syria), Tunisia, and the United Arab Emirates Methods (UAE). Its population was estimated to be 628 million in 2014 (World Health Organization Regional Office for the Case definition Eastern Mediterranean 2014). The EMR is a very hetero- geneous region where member states vary significantly in In this study, we present GBD 2015 results for mental terms of their gross domestic product, sociodemographic disorders, excluding substance use disorders. The GBD profiles, health indicators, and health system capacities and 2015 mental disorders grouping consisted of anxiety dis- coverage (Mandil et al. 2013). orders, autistic spectrum disorders (autism and Asperger’s Over the past two decades, the EMR has undergone syndrome), conduct disorder, eating disorders (anorexia significant improvements in health status, including nervosa and bulimia nervosa), schizophrenia, attention- increased life expectancy and reductions in child mortality deficit/hyperactivity disorder (ADHD), bipolar disorder, (Memish 2014; Mokdad et al. 2014, 2016a; Moradi-Lakeh depressive disorders (major depressive disorder and dys- et al. 2016). As people in the region are living longer, the thymia), and idiopathic developmental intellectual dis- burden of chronic diseases, including mental disorders, is ability (a residual category capturing intellectual disability expected to rise. Increasing mental disorder burden with not attributed to any of the other causes in the study). We population aging, especially in developing countries, has defined mental disorders according to criteria proposed in been described in the literature (Sathyanarayana Rao and the Diagnostic and Statistical Manual of Mental Disorders Shaji 2007; World Health Organization 2016). The IV (DSM-IV) and the International Classification of Dis- demographic and epidemiological changes in the EMR eases 10 (ICD-10) (GBD 2015 Disease and Injury Inci- have had a major impact on the organization and delivery dence and Prevalence Collaborators 2016). The DSM-IV of mental health services. The population in EMR is very and ICD-10 definitions of the mental disorders described in young, where the median age is about 23 years, around this study are published in detail in the GBD 2015 nonfatal 60% of the population is between 15 and 59 years of age, burden capstone study (GBD 2015 Disease and Injury and one third is below 15 years of age (World Health Incidence and Prevalence Collaborators 2016). Organization 2013a). Moreover, 85% of the EMR population is or has been Calculation of burden (YLDs) (in the past quarter century) in a complex emergency sit- uation resulting in a high prevalence of depression, anxiety, The estimation of YLDs for a given disorder is a product of and post-traumatic stress disorder (Ghosh et al. 2004). epidemiological data that accommodates the number of Since 2010, the region has witnessed economic and polit- people affected as well as the severity and disability ical unrest (Mokdad et al. 2016b). The latter were seen in associated with their symptoms. That is, YLDs are calcu- Egypt, Libya, Tunisia, and Yemen. Currently, Syria is in a lated by multiplying the prevalence of a disorder by its state of civil war. Afghanistan, Bahrain, Iraq, Palestine, severity and comorbidity-adjusted disability weight. YLDs Lebanon and Somalia frequently experience disturbances for previous GBD iterations were re-estimated using the as well. Conflict predisposes a population to the develop- same methods to allow meaningful comparisons of changes ment of mental disorders (Murthy and Lakshminarayana over time. 2006). Stressors of war include loss (human or material) and grief, safety concerns, disruption of the fabric of Epidemiologic inputs society, loss of identity, and potential discrimination with displacement (Hassan et al. 2016). Prevalence, incidence, remission or duration, and excess No previous studies have assessed the burden of mental mortality data for mental disorders were captured through a disorders in the EMR collectively. Using data from GBD systematic review of the literature. In GBD 2010, a liter- 2015, we aim to explore the burden of mental disorders in ature search was conducted in three stages involving the EMR by country, age group, sex, type of mental dis- electronic searches of the peer-reviewed literature (via order, and income group from 1990 to 2015. We have Medline, Embase and PubMed), gray literature, and expert previously published a study on the burden of mental dis- consultation. The agreed approach for mental disorders was orders in the EMR between 1990 and 2013 using findings to conduct electronic database searches on a rolling basis. from GBD 2013. We now update the burden estimates All three stages of the GBD 2010 literature review were repeated for GBD 2013 to capture additional data 123 The burden of mental disorders in the eastern Mediterranean region, 1990–2015: findings from… S27 published up to 2013. For GBD 2015, only stages two and internet survey. Respondents considered two hypothetical three of the literature review were conducted, with another individuals with different health states and were asked to electronic database search due for mental disorders in the indicate which person they perceived as healthier. In GBD next iteration of GBD studies. The inclusion criteria for 2013, methodological advances were introduced to dis- epidemiologic studies stipulated that: (1) the publication ability weighting, including new data capturing many year must be from 1980 onward; (2) ‘‘caseness’’ must be newly published or unpublished data sources for the dis- based on clinical threshold as established by the DSM-IV orders included in GBD. A disability weight ranging or ICD-10; (3) sufficient information must be provided on between 0 (equivalent to perfect health) and 1 (equivalent study method and sample characteristics to assess the to death) was generated for 235 health states which toge- quality of the study; and (4) study samples must be rep- ther reflected all causes of nonfatal burden in GBD 2015 resentative of the general population (i.e., inpatient or (Salomon et al. 2015; GBD 2015 Disease and Injury pharmacological treatment samples, case studies, veterans Incidence and Prevalence Collaborators 2016). or refugee samples were excluded). No limitation was set on the language of publication. Methods used for this Severity distributions systematic review have been reported in greater detail elsewhere (GBD 2015 Disease and Injury Incidence and Sequelae were further defined in terms of severity following Prevalence Collaborators 2016). Data from 108 epidemi- the same approach for estimating the distribution of severity ologic studies were used to estimate the burden of mental as in GBD 2013. Details on the severity distributions for disorders in the EMR in GBD 2015; a full list of the studies mental disorders are available elsewhere (GBD 2015 Disease is available in Appendix 1 in supplementary. and Injury Incidence and Prevalence Collaborators 2016). Disease modeling Comorbidity adjustment For each disorder, epidemiological estimates from the litera- GBD 2015 YLD estimates were adjusted for the effect of ture review were pooled using DisMod-MR 2.1, a Bayesian comorbidity between diseases. Details on the process are meta-regression tool. The tool used in GBD 2010 and GBD available elsewhere (GBD 2015 Disease and Injury Inci- 2013, DisMod-MR, is based on a generalized negative bino- dence and Prevalence Collaborators 2016). mial model that: (1) uses an incidence–prevalence–mortality mathematical model to enforce internal consistency between Calculation of burden (DALYs) estimates from different epidemiological parameters; (2) We calculated DALYs as the sum of YLDs and YLLs. estimates data for countries and world regions with few or no available input data based on random effects for country, YLLs were calculated by multiplying the number of deaths regions, and their corresponding super-region groupings; (3) due to the given disorder at a particular age by the standard deals with variability in the data due to measurement bias, or life expectancy at that age. However, death records used in alternatively, ecological factors through the use of study- and GBD 2015 followed ICD-10 rules for categorical attribu- country-level covariates; and (4) propagates uncertainty tion of cause of death to a single underlying cause and, around the raw epidemiological data through to the final therefore, did not document any deaths due to mental estimates. For GBD 2015, the computational engine of Dis- disorders, except for schizophrenia and eating disorders. Mod-MR 2.1 remained unchanged, but we substantially rewrote the code that organizes the flow of data and settings at Socio-demographic index each level of the analytical cascade. Greater detail on DisMod- MR 2.1 is available elsewhere (GBD 2015 Disease and Injury In GBD 2015, we constructed a summary metric referred to Incidence and Prevalence Collaborators 2016). as the Socio-demographic Index (SDI) based on measures of income per capita, average years of schooling among Disability weights people aged 15 years and older, and total fertility rate (Kassebaum et al. 2016). SDI values were scaled to a range Disability weights are the general public’s assessment of of 0–1, with 0 equaling the lowest income, lowest the severity of health loss associated to the cause. Dis- schooling, and highest fertility rate observed from 1980 to ability weights were derived using the GBD 2010 method 2015, and 1 equaling the highest income, highest school- of pairwise comparison questions in population surveys (of ing, and lowest fertility rate observed during that time. The those aged 18 and over) conducted in Bangladesh, final SDI score was computed as the geometric mean of Indonesia, Peru, Tanzania, Hungary, Italy, Sweden, each of the components. We compared observed patterns of Netherlands, and the United States and an open access mental disorder YLDs with those expected on the basis of 123 S28 GBD 2015 Eastern Mediterranean Region Mental Health Collaborators SDI, allowing us to explore where health gains exceeded— males. The age pattern in males and females was different. or lagged behind-corresponding changes in development. In females, DALY rates increased progressively from birth up to age 35–39 (3131 DALYs/100,000) and then Classification of EMR countries decreased progressively with age. In males, DALY rates peaked at age 15–19 (1929 DALYs/100,000) and In an attempt to properly track the health status in the EMR decreased at age 20–24 (1860 DALYs/100,000), only to countries, we divided the region into three categories rise again and peak at age 35–39 (2075 DALYs/100,000). according to the gross national income (GNI) per capita. The The burden associated with depressive disorders and anx- first category represented the low-income countries (LICs) iety disorders rose abruptly in adolescence for both sexes. with an average GNI per capita of $523 (GBD 2015 Disease Depressive disorder burden peaked between 40 and and Injury Incidence and Prevalence Collaborators 2016; 44 years, whereas anxiety disorder burden peaked between GBD 2015 Maternal Mortality Collaborators 2016;GBD 15 and 19 years. For schizophrenia, the burden peaked 2015 Mortality and Causes of Death Collaborators 2016; between 40 and 49 years while bipolar disorder peaked GBD 2015 Risk Factors Collaborators 2016; Kassebaum between 25 and 29 years. Conduct disorder and ADHD et al. 2016;Wang et al. 2016). On the opposite end of the peaked between 10 and 14 years. Supplementary e-Table 1 spectrum were some oil-rich, high-income countries (HICs) details the values of the DALY rates depicted in the figure. with an average GNI per capita of $39,688. The nations that Figure 3 shows the age-standardized rate of mental lied in between were the middle-income countries (MICs) disorder DALYs over time for males, females, and both with an average GNI per capita of $3,251. The three groups combined in the EMR and globally. The rate of DALYs were LICs: Afghanistan, Djibouti, Yemen, and Somalia; remained almost constant from 1990 to 2015 in the EMR MICs: Egypt, Iraq, Iran, Jordan, Lebanon, Libya, Morocco, for both males and females and globally. EMR DALY rates Pakistan, Palestine, Sudan, Syria, and Tunisia; and HICs: in females were consistently higher than their male coun- Bahrain, Saudi Arabia, Kuwait, Oman, Qatar, and the UAE. terparts and in the EMR compared to the global rates from 1990 to 2015. The rate of DALYs in males was lower than the global level. Supplementary e-Table 2 details the val- Results ues of DALY rates depicted in the figure. None of these differences were statistically significant. Table 1 shows the In 2015, the EMR generated a total of 229.2 million rankings (in age-standardized DALY rates) of each mental DALYs (95% uncertainty interval (UI) 194.8–267.6) of disorder in the EMR in 2015 compared to 1990. All mental which 10.7 million DALYs (7.1–15.0) were due to mental disorders ranked higher in 2015. disorders. In other words, mental disorders contributed to Figure 4 shows the rate of mental disorder DALYs in 4.7% (3.7–5.6%) of total DALYs in the EMR in 2015, the EMR in 2015 by income grouping and sex. The DALY ranking as the ninth leading cause of disease burden. rates were similar across income groups, ranging from In 2015, the EMR generated a total of 67.1 million 1725.9 DALYs/100,000 in LIC to 1758.2 DALYs in HIC. YLDs (46.9–91.2) of which 10.7 million YLDs (7.1–14.9 Across these groups, females had higher mental disorder million) were due to mental disorders. In other words, DALY rates compared to males. All EMR income groups mental disorders contributed to 15.9% (15.1–16.4%) of had higher rates of DALYs compared to global levels. total YLDs in the EMR in 2015, ranking second to mus- Supplementary e-Table 3 details the values of DALY rates culoskeletal disorders which contributed to 16.1% depicted in the figure. None of these differences were (14.2–18.2%) of total YLDs. At a more detailed level, statistically significant. depressive disorders and anxiety disorders were the third Figure 5 shows the age-standardized rate of mental and ninth leading causes of YLDs, respectively. disorder DALYs in the EMR and globally over time, by Figure 1 shows the composition of mental disorder income grouping. The DALY rates remained almost con- DALYs by type of disorder for both sexes combined in the stant in the EMR income groups and globally. Differences EMR in 2015. Depressive disorders (42.1%), followed by between income groups and over time were not statistically anxiety disorders (21.5%), were the greatest contributors to significant. mental disorder DALY numbers. e-Figure 1 shows the age-standardized rate of mental Figure 2 shows the composition of mental disorder disorder DALYs in the EMR, by country and sex. The DALY rates by age, sex, and disorder type in the EMR in countries are ranked in increasing order of age-standard- 2015. DALY rates were higher for females across all age ized mental disorder DALY rates for both sexes combined. groups, except for those under age 15. The highest rate of All countries in the EMR, except Egypt, had higher age- DALYs occurred in the 25–49 age group, with a peak in the standardized mental disorder DALY rates compared to the 35–39 age group for both sexes combined, females, and global value. 123 The burden of mental disorders in the eastern Mediterranean region, 1990–2015: findings from… S29 Fig. 1 Distribution of disability-adjusted life-years (DALYs) due to mental disorders in the Eastern Mediterranean Region, 2015. (Global Burden of Disease Study 2015, Eastern Mediterranean Region, 2015) Fig. 2 Age-standardized rate of disability-adjusted life-years (DALYs) per 100,000 population due to mental disorders in the Eastern Mediterranean Region by age, sex, and disorder, 2015. (Global Burden of Disease Study 2015, Eastern Mediterranean Region 2015) e-Figure 2 shows the age-standardized observed mental drop in the EMR (and globally) over the last quarter cen- disorder DALY rates by EMR country compared with the tury, and females continue to suffer a bigger burden of expected values on the basis of each country’s SDI. For the mental illness. The mental disorder burden in the politi- EMR, SDI in 2015 ranged from 0.151 for Somalia to 0.875 for cally and economically stable high-income countries is the UAE. The area between the observed and expected curves comparable to that in low- and middle-income countries represents the discrepancy between both values (D = ob- (LMICs) of the region, which include some countries in served-expected). The countries are ranked in the order of complex emergency situations. Almost all countries in the decreasing D. In half of the EMR countries, observed mental EMR have a bigger burden of mental disorders compared disorder DALYs exceeded the expected values. to the global rate. Epidemiological transition Discussion While the rate of mental disorder DALYs has not signifi- Our study reveals a number of findings that are relevant to cantly changed over time, the ranking of these disorders setting a mental health agenda for the EMR. The burden shows a different picture. Compared to 1990, there was an associated with mental disorders has not seen a significant increase in the amount of burden mental disorders 123 S30 GBD 2015 Eastern Mediterranean Region Mental Health Collaborators Fig. 3 Age-standardized rate of disability-adjusted life-years (DALYs) per 100,000 population due to mental disorders in the Eastern Mediterranean Region and globally, 1990–2015. (Global Burden of Disease Study 2015, Global, Eastern Mediterranean Region 1990–2015) Table 1 Ranking of mental 1990 rank 2015 rank disorders among all level 3 Global Burden of Disease Schizophrenia 69 56 (GBD) causes for age- Idiopathic developmental intellectual disability 105 92 standardized rates of disability- adjusted life-years, 1990–2015 Bipolar disorder 77 65 Conduct disorder 86 74 Autistic spectrum disorders 73 62 Eating disorders 147 140 Anxiety disorders 33 27 Depressive disorders 16 14 Attention-deficit/hyperactivity disorder 150 149 Global Burden of Disease Study 2015, Eastern Mediterranean Region 2015 contribute to the total burden. This epidemiological tran- timing of illness onset, diagnosis, treatment, and adjust- sition is consistent with that seen globally, especially at ment to a chronic illness are to be accounted for (World higher SDIs (GBD 2015 Disease and Injury Incidence and Health Organization 2006). Women in this part of the Prevalence Collaborators 2016). The increasing mental world are particularly vulnerable to developing mental health burden is mainly attributable to population growth illness, namely depressive disorders. With globalization and aging rather than an increase in prevalence rates. This and urbanization of most EMR societies, women may be is an important finding to be considered when setting exposed to numerous stressors. This distress may have public health agendas for the region as more people will be repercussions on the entire household and children in experiencing mental disorders and for longer durations. particular, as adjustments to new social structures become necessary (Eloul et al. 2009). According to the World Women’s mental health Bank, there has been a 47% increase in women’s partici- pation in the labor market of the Middle East and North Females have a higher burden of mental disorders in the Africa region from 1960 to 2000 (World Bank 2003). EMR. Rates of mental disorders, however, only partially Gender roles in the region may partially contribute to a explain gender disparities in mental health. Risk factors, higher burden of mental illness among women. 123 The burden of mental disorders in the eastern Mediterranean region, 1990–2015: findings from… S31 Fig. 4 Age-standardized rate of disability-adjusted life-years (DALYs) due to mental disorders in the Eastern Mediterranean Region and Globally, by income group and sex, 2015. (Global Burden of Disease Study 2015, Global, Eastern Mediterranean Region, 2015) Fig. 5 Age-standardized rate of disability-adjusted life-years (DALYs) due to mental disorders in the Eastern Mediterranean Region and globally by income group, 1990–2015. (Global Burden of Disease Study 2015, Global, Eastern Mediterranean Region, 1990–2015) Socioeconomic disadvantage, low income and income violence in the world (compared to 25.4% in Europe and inequality, low or subordinate social status and rank, and 29.8% in the Americas) (World Health Organization lack of autonomy are some examples of this polarity 2013b). Women are also more likely to be diagnosed with (World Health Organization 2006). The impact of patri- depression compared to men with similar scores on stan- archy and women’s lack of empowerment on mental health dardized measures of depression (Callahan et al. 1997; is thoroughly evaluated in the literature (Niaz and Hassan Stoppe et al. 1999). 2006). Moreover, cultural factors, high birthrates, and young ages at first conception may contribute to higher The burden by disorder type rates of postpartum depression (Eloul et al. 2009). Perinatal mental disorders are particularly concerning for their Depression is the highest contributor to the burden fol- effects on the development of infants and children (World lowed by anxiety disorders. This is because of the higher Health Organization 2010). In addition, the region wit- prevalence of these disorders as well as the disability nesses one of the highest rates (37.0%) of intimate partner associated with them. It is worth mentioning that some 123 S32 GBD 2015 Eastern Mediterranean Region Mental Health Collaborators highly disabling conditions, such as acute schizophrenia, Assessment Instrument for Mental Health Systems (World do not rank high in YLDs owing to their low prevalence Health Organization Regional Office for the Eastern (Ferrari et al. 2014). This latter condition, however, had the Mediterranean 2010) showed that the median-treated highest disability weight (0.778) among mental disorders in prevalence of mental disorders was 0.31%, thereby sug- GBD 2015 (GBD 2015 Disease and Injury Incidence and gesting a big gap in treatment. The unmet needs of children Prevalence Collaborators 2016). Under the age of 15, and adolescents were greater than those of adults. This is males contribute more greatly to the burden of mental alarming in the region where a big proportion of the pop- disorders, primarily due to the higher burden of autism in ulation is under 19 years of age (World Health Organiza- males under age five and conduct disorder in males ages tion 2010). Compared to the $3–$4 USD per capita 5–15. Indeed, epidemiologic studies from the region indi- spending on mental health in the US, the region spends an cate a high male to female ratio of autistic spectrum dis- average of $0.15 USD per capita with only 2% of the orders (Elsabbagh et al. 2012). governments’ health budgets allocated to mental health (which compares to the 5–10% required to match con- Observed vs expected rates temporary comprehensive healthcare systems) (Gater and Saeed 2015). This level of spending is observed across According to GBD 2015 findings, sizeable discrepancies low-, middle- and high-income countries of the EMR. Not occurred for observed and expected YLDs based on SDI only are the resources scarce but also inefficiently used and throughout North Africa and the Middle East, probably inequitably distributed. Stigma further limits the use of reflecting the uneven achievements in development found available resources. Moreover, there is little integration of in this region (GBD 2015 Disease and Injury Incidence and mental health in primary health care in much of the region Prevalence Collaborators 2016). At a regional level, (World Health Organization Regional Office for the East- observed depression YLD rates exceeded expected rates ern Mediterranean 2010). based on SDI. This implies that the EMR’s income per Some governments within the EMR have already taken capita, educational levels and fertility rates were not action to address the problematic increase in mental dis- commensurate with the high burden of depression seen in order burden. Qatar has launched its 2013–2018 Mental the region. High-income countries in the EMR had the Health Strategy which aims to increase availability and highest SDIs in 2015 but had observed depression YLD utilization of mental health services using comprehensive rates that exceeded expected rates based on SDI. This is not standards and guidelines (Supreme Council of Health, State consistent with the global trend where the proportion of life of Qatar and Hamad Medical Corporation, Primary Health expectancy spent with disability declined slightly with Care 2013). Kuwait is taking action to integrate mental health in primary health care in light of the stigma asso- increasing SDI (Kassebaum et al. 2016). ciated with mental illness and its impact on help seeking, Capacity of EMR versus burden especially in the EMR (Almazeedi and Alsuwaidan 2014). A study found that pharmacologic and/or psychosocial This high burden of mental disorders is particularly chal- treatment packages can be offered at low prices in LMICs, lenging to the EMR, where 16 out of the 22 countries in the including Morocco and Iran. It is important to point out region belong to the LMIC group. WHO’s 2014 Mental that the development of psychosocial interventions needs Health Atlas (World Health Organization 2014) described to be tailored to the culture in the EMR. Most interventions the preparedness of the EMR to deal with mental health at used today in the region are simply exported from research a system level. The mental health workforce per 100,000 in more developed nations instead of being adopted in a population in the EMR was 7.3 compared to a value of 9 culturally sensitive fashion (Chisholm et al. 2007). and 43.5 globally and in the WHO European region. A study published in 2005 showed that Lebanon had the Limitations highest provision of psychiatric services with one psychi- atrist per 45,000 population (Al-Krenawi 2005). A more Our study has a number of limitations. First, our findings recent paper in 2012 reported the highest proportions of were based upon best available secondary data and models psychiatrists in Bahrain (5 per 100,000), Qatar (3.4 per that cannot be verified across geographies or time within the 100,000) and Kuwait (3.1 per 100,000) (Okasha et al. same region due to historical circumstances and constraints 2012). Countries like Iraq, Libya, Morocco, Sudan, Syria, of local resources. Most countries in the EMR lack quality and Yemen had fewer than 0.5 psychiatrists per 100,000 epidemiological data to describe the national prevalence and population. It is important to note, however, that most of burden of mental disorders and to provide quality represen- these studies had design weaknesses, meaning all outcomes tative data input for the GBD estimations. Raw prevalence must be interpreted with caution. The region’s WHO- data was available for 10 of the 22 EMR countries: Egypt, 123 The burden of mental disorders in the eastern Mediterranean region, 1990–2015: findings from… S33 Hospital, Seattle, Washington, United States. Michael Collison, BS, Iran, Iraq, Jordan, Lebanon, Pakistan, Palestine, Sudan, the Institute for Health Metrics and Evaluation, University of Washing- UAE, and Yemen. A list of all data points used in this study ton, Seattle, Washington, United States. Kristopher J. Krohn, BA, are available via the Global Health Data Exchange (http:// Institute for Health Metrics and Evaluation, University of Washing- ghdx.healthdata.org). When data were of poor quality or ton, Seattle, Washington, United States. Adrienne Chew, ND, Insti- tute for Health Metrics and Evaluation, University of Washington, unavailable, we relied on modeling techniques to generate Seattle, Washington, United States. Farah Daoud, BA/BS, Institute the estimates using other available variables and the infor- for Health Metrics and Evaluation, University of Washington, Seattle, mation for neighboring countries or countries with a similar Washington, United States. Fiona J. Charlson, PhD, School of Public health profile in the region. While this allowed us to include Health, University of Queensland, Brisbane, Queensland, Australia; Institute for Health Metrics and Evaluation, University of Washing- all EMR countries in our burden of disease analysis and ton, Seattle, Washington, United States; Queensland Centre for generate collective measures for such a heterogeneous group Mental Health Research, Brisbane, Queensland, Australia. Danny of countries, it is important for countries in the region to Colombara, PhD, Institute for Health Metrics and Evaluation, facilitate the collection of high quality epidemiological data University of Washington, Seattle, Washington, United States. Louisa Degenhardt, PhD, National Drug and Alcohol Research Centre, for mental disorders. Note, however, that the limitation of University of New South Wales, Sydney, Australia. Rebecca data availability is partly captured by the estimates of Ehrenkranz, MPH, Institute for Health Metrics and Evaluation, uncertainty presented in our results. Second, disability University of Washington, Seattle, Washington, United States. Holly weights in GBD studies intentionally capture health loss E. Erskine, PhD, Queensland Centre for Mental Health Research, Brisbane, QLD, Australia; School of Public Health, University of while not attempting to capture welfare loss and hence do not Queensland, Brisbane, QLD, Australia; Institute for Health Metrics reflect the economic and familial effects of mental disorders. and Evaluation, University of Washington, Seattle, Washington, In addition, given the subjective nature of the symptoms of United States. Alize J. Ferrari, PhD, School of Public Health, mental illness, many individuals in cultures who express University of Queensland, Brisbane, Queensland, Australia; Queensland Centre for Mental Health Research, Brisbane, Queens- mental disorders differently from the ICD-10 diagnostic land, Australia; Institute for Health Metrics and Evaluation, Univer- criteria were not captured by GBD. Third, deaths that were sity of Washington, Seattle, Washington, United States. Michael causally linked to mental disorders were largely captured Kutz, BS, Institute for Health Metrics and Evaluation, University of under other causes. This is because an outcome could only be Washington, Seattle, Washington, United States. Janni Leung, PhD, School of Public Health, University of Queensland, Brisbane, QLD, listed once in the GBD cause list. Vital registrations rarely Australia; Institute for Health Metrics and Evaluation, University of list a mental disorder as a cause of death. For instance, major Washington, Seattle, Washington, United States. Damian San- depressive disorder-related deaths from suicide or ischemic tomauro, PhD, School of Public Health, University of Queensland, heart disease were captured under intentional injuries and Brisbane, Queensland, Australia; Queensland Centre for Mental Health Research, Brisbane, Queensland, Australia; Institute for cardiovascular disease, respectively. Fourth, DSM-IV and Health Metrics and Evaluation, University of Washington, Seattle, ICD-10 diagnostic criteria, mainly established in developed Washington, United States. Haidong Wang, PhD, Institute for Health nations, may not be sensitive to all cross-cultural presenta- Metrics and Evaluation, University of Washington, Seattle, Wash- tions of mental disorders. Many patients in the EMR attribute ington, United States. Harvey A. Whiteford, PhD, School of Public Health, University of Queensland, Brisbane, Queensland, Australia. their psychiatric symptoms to physical causes, probably Queensland Centre for Mental Health Research, Brisbane, Queens- secondary to stigma. This would, therefore, bias the mental land, Australia; Institute for Health Metrics and Evaluation, Univer- disorder burden estimates for the region. It is important to sity of Washington, Seattle, Washington, United States. Amanuel note here that none of the EMR countries were included in Alemu Abajobir, MPH, School of Public Health, University of Queensland, Brisbane, QLD, Australia. Foad Abd-Allah, MD, the group where surveys were done to estimate disability Department of Neurology, Cairo University, Cairo, Egypt. Haftom weights. Niguse Abraha, MS, Mekelle University, Mekelle, Ethiopia. Laith J. Abu-Raddad, PhD, Infectious Disease Epidemiology Group, Weill GBD 2015 Eastern Mediterranean Region Mental Health Col- Cornell Medical College in Qatar, Doha, Qatar. Aliasghar Ahmad laborators: Ali H. Mokdad, PhD (corresponding author), Institute Kiadaliri, PhD, Department of Clinical Sciences Lund, Orthopedics, for Health Metrics and Evaluation, University of Washington, Seattle, Clinical Epidemiology Unit, Lund University, Lund, Sweden. Alireza Washington, United States. Raghid Charara, MD, American Univer- Ahmadi, MD, Kermanshah University of Medical Sciences, Ker- sity of Beirut, Beirut, Lebanon. Charbel El Bcheraoui, PhD, Institute manshah, Iran. Kedir Yimam, MPH, Debre Markos University, Debre for Health Metrics and Evaluation, University of Washington, Seattle, Markos, Ethiopia. Muktar Beshir, MPH, College of Health Sciences, Washington, United States. Ibrahim Khalil, MD, Institute for Health Department of Epidemiology, ICT and e-Learning Coordinator, Metrics and Evaluation, University of Washington, Seattle, Wash- Jimma University, Jimma, Ethiopia. Faris Hasan Al Lami, PhD, ington, United States. Maziar Moradi-Lakeh, MD, Department of Baghdad College of Medicine, Baghdad, Iraq. Khurshid Alam, PhD, Community Medicine, Preventive Medicine and Public Health Murdoch Childrens Research Institute, The University of Melbourne, Research Center, Gastrointestinal and Liver Disease Research Center Parkville, Victoria, Australia; The University of Melbourne, Mel- (GILDRC), Iran University of Medical Sciences, Tehran, Iran. Ash- bourne, VIC, Australia; The University of Sydney, Sydney, NSW, kan Afshin, MD, Institute for Health Metrics and Evaluation, Australia. Deena Alasfoor, MSc, Ministry of Health, Al Khuwair, University of Washington, Seattle, Washington, United States. Oman. Reza Alizadeh-Navaei, PhD, Gastrointestinal Cancer Research Nicholas J. Kassebaum, MD, Institute for Health Metrics and Eval- Center, Mazandaran University of Medical Sciences, Sari, Iran. Juma uation, University of Washington, Seattle, Washington, United States; M. Alkaabi, MD, College of Medicine and Health Sciences, United Department of Anesthesiology and Pain Medicine, Seattle Children’s Arab Emirates University, Al-Ain City, United Arab Emirates. Fatma 123 S34 GBD 2015 Eastern Mediterranean Region Mental Health Collaborators Al-Maskari, PhD, College of Medicine and Health Sciences, United Fereshtehnejad, PhD, Department of Neurobiology, Care Sciences Arab Emirates University, Al-Ain City, United Arab Emirates. Rajaa and Society (NVS), Karolinska Institutet, Stockholm, Sweden. Flo- Al-Raddadi, PhD, Joint Program of Family and Community Medi- rian Fischer, PhD, School of Public Health, Bielefeld University, cine, Jeddah, Saudi Arabia. Khalid A. Altirkawi, MD, King Saud Bielefeld, Germany. Tsegaye Tewelde Gebrehiwot, MPH, Jimma University, Riyadh, Saudi Arabia. Nahla Anber, PhD, Mansoura University, Jimma, Ethiopia. Ababi Zergaw Giref, PhD, Addis Ababa University, Mansoura, Egypt. Hossein Ansari, PhD, Health Promotion University, Addis Ababa, Ethiopia. Melkamu Dedefo Gishu, MS, Research Center, Department of Epidemiology and Biostatistics, Haramaya University, Dire Dawa, Ethiopia; Kersa Health and Zahedan University of Medical Sciences, Zahedan, Iran. Hamid Demographic Surveillance System, Harar, Ethiopia. Alessandra, Asayesh, PhD, Department of Medical Emergency, School of Para- Carvalho, Goulart, PhD, Center for Clinical and Epidemiological medic, Qom University of Medical Sciences, Qom, Iran. Rana Jawad Research Center- Hospital Universitario-University of Sa˜o Paulo, Sa˜o Asghar, MD, South Asian Public Health Forum, Islamabad, Pakistan. Paulo, Brazil; Center of Check of Hospital Sirio Libanes, Sa˜o Paulo, Tesfay Mehari Atey, MS, Mekelle University, Mekelle, Ethiopia. Brazil. Tesfa Dejenie Habtewold, MS, University of Groningen, Tadesse Awoke Ayele, MS, University of Gondar, Gondar, Ethiopia. Groningen, Netherlands; Debre Berhan University, Debre Berhan, Till Ba¨rnighausen, MD, Department of Global Health and Population, Ethiopia. Randah Ribhi Hamadeh, DPhil, Arabian Gulf University, Harvard T. H. Chan School of Public Health, Harvard University, Manama, Bahrain. Mitiku Teshome Hambisa, MPH, College of Boston, MA, United States; Africa Health Research Institute, Mtu- Health and Medical Sciences, Haramaya University, Harar, Ethiopia. batuba, KwaZulu-Natal, South Africa; Institute of Public Health, Samer Hamidi, DrPH, Hamdan Bin Mohammed Smart University, Heidelberg University, Heidelberg, Germany. Umar Bacha, PhD, Dubai, United Arab Emirates. Josep Maria Haro, MD, Parc Sanitari School of Health Sciences, University of Management and Technol- Sant Joan de De´u - CIBERSAM, Sant Boi de Llobregat (Barcelona), ogy, Lahore, Pakistan. Aleksandra Barac, PhD, Faculty of Medicine, Spain. Mohammad Sadegh Hassanvand, PhD, Center for Air Pollu- University of Belgrade, Belgrade, Serbia. Suzanne L. Barker-Collo, tion Research, Institute for Environmental Research, Tehran PhD, School of Psychology, University of Auckland, Auckland, New University of Medical Sciences, Tehran, Iran. Nobuyuki Horita, MD, Zealand. Bernhard T. Baune, PhD, School of Medicine, University of Department of Pulmonology, Yokohama City University Graduate Adelaide, Adelaide, South Australia, Australia. Shahrzad Bazargan- School of Medicine, Yokohama, Japan. Mohamed Hsairi, MD, Hejazi, PhD, College of Medicine, Charles R. Drew University of Department of Epidemiology, Salah Azaiz Institute, Tunis, Tunisia. Medicine and Science, Los Angeles, CA, United States; David Geffen Hsiang Huang, MD, Cambridge Health Alliance, Cambridge, MA, School of Medicine, University of California at Los Angeles, Los United States. Abdullatif Husseini, PhD, Institute of Community and Angeles, CA, United States. Neeraj Bedi, MD, College of Public Public Health, Birzeit University, Birzeit, Palestine. Mihajlo B. Health and Tropical Medicine, Jazan, Saudi Arabia. Isabela M. Jakovljevic, PhD, Faculty of Medical Sciences, University of Bensenor, PhD, University of Sa˜o Paulo, Sa˜o Paulo, Brazil. Adugnaw Kragujevac, Kragujevac, Serbia; The Center for Health Trends and Berhane, PhD, College of Health Sciences, Debre Berhan University, Forecasts, Institute for Health Metrics and Evaluation, University of Debre Berhan, Ethiopia. Addisu Shunu Beyene, MPH, College of Washington, Seattle, Washington, United States. Spencer Lewis Health and Medical Science, Haramaya University, Harar, Ethiopia. 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Jagdish Khubchandani, PhD, Department of Nutrition and Minnesota, United States. Amare Deribew, PhD, Nuffield Department Health Science, Ball State University, Muncie, Indiana, United States. of Medicine, University of Oxford, Oxford, United Kingdom, Daniel Kim, DrPH, Department of Health Sciences, Northeastern KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya. Samath University, Boston, Massachusetts, United States. Yun Jin Kim, PhD, D. Dharmaratne, MD, Department of Community Medicine, Faculty Faculty of Chinese Medicine, Southern University College, Skudai, of Medicine, University of Peradeniya, Peradeniya, Sri Lanka. Kerrie, Malaysia. Yoshihiro Kokubo, PhD, Department of Preventive Car- E, Doyle, PhD, RMIT University, Bundoora, VIC, Australia; Aus- diology, National Cerebral and Cardiovascular Center, Suita, Japan. tralian National University, Canberra, ACT, Australia. Aman Yesuf Ai Koyanagi, MD, Research and Development Unit, Parc Sanitari Endries, MPH, Arba Minch University, Arba Minch, Ethiopia. Sant Joan de Deu (CIBERSAM), Barcelona, Spain. Barthelemy Kuate Emerito Jose Aquino Faraon, MD, College of Public Health, Defo, PhD, Department of Social and Preventive Medicine, School of University of the Philippines Manila, Manila, Philippines; Depart- Public Health, University of Montreal, Montreal, Quebec, Canada; ment of Health, Manila, Philippines. Andre´ Faro, PhD, Federal Department of Demography and Public Health Research Institute, University of Sergipe, Aracaju, Brazil. Maryam S. Farvid, PhD, University of Montreal, Montreal, Canada. Heidi J. Larson, PhD, Department of Nutrition, Harvard T. H. Chan School of Public Department of Infectious Disease Epidemiology, London School of Health, Harvard University, Boston, MA, United States; Harvard/ Hygiene and Tropical Medicine, London, United Kingdom; Institute MGH Center on Genomics, Vulnerable Populations, and Health for Health Metrics and Evaluation, University of Washington, Seattle, Disparities, Mongan Institute for Health Policy, Massachusetts Gen- Washington, United States. Asma Abdul Latif, PhD, Department of eral Hospital, Boston, MA, United States. Wubalem Fekadu, MS, Zoology, Lahore College for Women University, Lahore, Punjab, Bahir Dar University, Bahir Dar, Ethiopia. Seyed-Mohammad Pakistan. Paul H. Lee, PhD, Hong Kong Polytechnic University, 123 The burden of mental disorders in the eastern Mediterranean region, 1990–2015: findings from… S35 Hong Kong, China. Cheru Tesema Leshargie, MPH, Debre Markos Center, Tehran University of Medical Sciences, Tehran, Iran. Rajesh University, Debre Markos, Ethiopia. Ricky Leung, PhD, State Kumar Rai, MPH, Society for Health and Demographic Surveillance, University of New York, Albany, Rensselaer, NY, United States. Suri, India. David Laith Rawaf, MD, WHO Collaborating Centre, Loon-Tzian Lo, MD, UnionHealth Associates, LLC, St. Louis, MO, Imperial College London, London, United Kingdom; North Hamp- United States; Alton Mental Health Center, Alton, IL, United States. shire Hospitals, Basingstroke, United Kingdom; University College Raimundas Lunevicius, PhD, Aintree University Hospital National London Hospitals, London, United Kingdom. Salman Rawaf, MD, Health Service Foundation Trust, Liverpool, United Kingdom; School Imperial College London, London, United Kingdom. Amany H. of Medicine, University of Liverpool, Liverpool, United Kingdom. Refaat, PhD, Walden University, Minneapolis, MN, United States, Hassan Magdy Abd El Razek, MBBCH, Mansoura Faculty of Med- Suez Canal University, Ismailia, Egypt. Satar Rezaei, PhD, School of icine, Mansoura, Egypt. Mohammed Magdy Abd El Razek, MBBCH, Public Health, Kermanshah University of Medical Sciences, Ker- Aswan University Hospital, Aswan Faculty of Medicine, Aswan, manshah, Iran. Mohammad Sadegh Rezai, MD, Infectious Diseases Egypt. Reza Majdzadeh, PhD, Knowledge Utilization Research Research Centre with Focus on Nosocomial Infection, Mazandaran Center and Community Based Participatory Research Center, Tehran University of Medical Sciences, Sari, Mazandaran, Iran. Gholamreza University of Medical Sciences, Tehran, Iran. Azeem Majeed, MD, Roshandel, PhD, Golestan Research Center of Gastroenterology and Department of Primary Care and Public Health, Imperial College Hepatology, Golestan University of Medical Sciences, Gorgan, Iran; London, London, United Kingdom. Reza Malekzadeh, MD, Digestive Digestive Diseases Research Institute, Tehran University of Medical Diseases Research Institute, Tehran University of Medical Sciences, Sciences, Tehran, Iran. Mahdi Safdarian, MD, Sina Trauma and Tehran, Iran. Jose Martinez-Raga, PhD, Hospital Universitario Doctor Surgery Research Center, Tehran University of Medical Sciences, Peset, Valencia, Spain; CEU Cardinal Herrera University, Moncada, Tehran, Iran. Mahdi Safiabadi, MD, Student Research Committee, Spain. Habibolah Masoudi Farid, MD, State Welfare Organisation, Baqiyatallah University of Medical Sciences, Tehran, Iran. Saeid Tehran, Iran. Mohsen Mazidi, PhD, Key State Laboratory of Safiri, PhD, Managerial Epidemiology Research Center, Department Molecular Developmental Biology, Institute of Genetics and Devel- of Public Health, School of Nursing and Midwifery, Maragheh opmental Biology, Chinese Academy of Sciences, Beijing, China. University of Medical Sciences, Maragheh, Iran. 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Lancet Lond Engl 388:1775–1812. doi:10.1016/S0140- Ethical approval This manuscript reflects original work that has not 6736(16)31470-2 previously been published in whole or in part and is not under con- GBD 2015 Mortality and Causes of Death Collaborators (2016) sideration elsewhere. All authors have read the manuscript and have Global, regional, and national life expectancy, all-cause mortal- agreed that the work is ready for submission and accept responsibility ity, and cause-specific mortality for 249 causes of death, for its contents. The authors of this paper have complied with all ethical standards and do not have any conflicts of interest to disclose 1980-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Lond Engl 388:1459–1544. doi:10. at the time of submission. The funding source played no role in the 1016/S0140-6736(16)31012-1 design of the study, the analysis and interpretation of data, and the GBD 2015 Risk Factors Collaborators (2016) Global, regional, and writing of the paper. The study did not involve human participants national comparative risk assessment of 79 behavioural, envi- and/or animals; therefore, no informed consent was needed. ronmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Funding This research was funded by the Bill & Melinda Gates Disease Study 2015. Lancet Lond Engl 388:1659–1724. doi:10. Foundation. 1016/S0140-6736(16)31679-8 Ghosh N, Mohit A, Murthy RS (2004) Mental health promotion in Conflict of interest The authors declare that they have no conflicts of post-conflict countries. 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Journal

International Journal of Public HealthSpringer Journals

Published: Aug 3, 2017

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