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Int J Public Health (2018) 63 (Suppl 1):S123–S136 https://doi.org/10.1007/s00038-017-1023-0 ORIGINAL ARTICLE Trends in HIV/AIDS morbidity and mortality in Eastern Mediterranean countries, 1990–2015: ﬁndings from the Global Burden of Disease 2015 study GBD 2015 Eastern Mediterranean Region HIV/AIDS Collaborators Received: 1 May 2017 / Revised: 19 July 2017 / Accepted: 21 July 2017 / Published online: 3 August 2017 The Author(s) 2017. This article is an open access publication Abstract Conclusions HIV/AIDS morbidity and mortality increased Objectives We used the results of the Global Burden of in the EMR since 1990. To reverse this trend and achieve Disease 2015 study to estimate trends of HIV/AIDS burden epidemic control, EMR countries should strengthen HIV in Eastern Mediterranean Region (EMR) countries between surveillance, and scale up HIV antiretroviral therapy and 1990 and 2015. comprehensive prevention services. Methods Tailored estimation methods were used to pro- duce ﬁnal estimates of mortality. Years of life lost (YLLs) Keywords HIV HIV mortality Eastern Mediterranean were calculated by multiplying the mortality rate by pop- Region Burden of disease ulation by age-speciﬁc life expectancy. Years lived with disability (YLDs) were computed as the prevalence of a sequela multiplied by its disability weight. Introduction Results In 2015, the rate of HIV/AIDS deaths in the EMR was 1.8 (1.4–2.5) per 100,000 population, a 43% increase In 2015, HIV/AIDS was the 12th-leading cause of death from 1990 (0.3; 0.2–0.8). Consequently, the rate of YLLs worldwide after being the eighth in 2005 when the epi- due to HIV/AIDS increased from 15.3 (7.6–36.2) per demic peaked (Institute for Health Metrics and Evaluation 100,000 in 1990 to 81.9 (65.3–114.4) in 2015. The rate of (IHME) 2017). More than 1.2 million people are estimated YLDs increased from 1.3 (0.6–3.1) in 1990 to 4.4 (2.7–6.6) to have died in 2015 due to HIV/AIDS despite the con- in 2015. siderable achievements in HIV care since the late 1980s (Wang et al. 2016b). This reﬂects the challenges faced by public health policymakers and program managers, health This article is part of the supplement ‘‘The state of health in the professionals, and the global community in dealing with Eastern Mediterranean Region, 1990–2015’’. this epidemic. The burden of the HIV/AIDS epidemic has rapidly The members of GBD (Global Burden of Disease) 2015 Eastern Mediterranean Region HIV/AIDS Collaborators are listed at the end changed since the 1990s with the introduction of HIV of the article. Ali H. Mokdad, on behalf of GBD 2015 Eastern antiretroviral therapy (ART) and other effective interven- Mediterranean Region HIV/AIDS Collaborators, is the corresponding tions (UNAIDS 2015). While incidence has declined con- author. tinuously since the mid-1990s, mortality continued to rise Electronic supplementary material The online version of this and peaked in 2005 at 1.8 million deaths worldwide (Wang article (doi:10.1007/s00038-017-1023-0) contains supplementary et al. 2016b). Inspired by the successes of responding to material, which is available to authorized users. AIDS, global leaders have committed to and embarked on & GBD 2015 Eastern Mediterranean Region HIV/AIDS ending the AIDS epidemic as a public health threat by Collaborators 2030, without leaving anyone behind (UNAIDS 2014a). email@example.com Today, there are large variations in incidence and mortality between regions and countries (Wang et al. 2016b). In the Institute for Health Metrics and Evaluation, 2301 5th Avenue, Eastern Mediterranean Region (EMR), and despite recent Suite 600, Seattle, WA 98121, USA 123 S124 GBD 2015 Eastern Mediterranean Region HIV/AIDS Collaborators progress (Institute for Health Metrics and Evaluation A detailed methodology of HIV/AIDS mortality esti- (IHME) 2017), estimates of HIV/AIDS continue to be mation for GBD 2015 has been published elsewhere (Wang challenged with limitations in data availability and by et al. 2016b). We used all available data sources including insufﬁcient epidemiological surveillance among those most vital registration, verbal autopsies, surveys, publications, at-risk of infection (Shawky et al. 2009; Mumtaz et al. and reports. These data sources have been published else- 2014a). The EMR has a population of about 583 million where as an appendix (Wang et al. 2016b), and are avail- people. Countries in the EMR vary signiﬁcantly in terms of able from the Global Health Data Exchange (Institute for their gross domestic product, socio-demographic proﬁles, Health Metrics and Evaluation 2017). Brieﬂy, the GBD health indicators, and health system capacities and cover- estimation framework contains three sources for estimates age (WHO EMRO 2017). of HIV-speciﬁc mortality: estimated HIV mortality from The EMR has several vulnerability factors for HIV (Abu- Spectrum (Brown et al. 2014; Stover et al. 2014); estimated Raddad et al. 2010). The socio-cultural and socioeconomic excess HIV/AIDS mortality in our all-cause mortality fabric as well as the demographic structure of the region is estimation process; and spatiotemporal Gaussian process evolving rapidly (Abu-Raddad et al. 2010). Extensive levels regression smoothed cause-speciﬁc HIV/AIDS mortality of migration, displacement, mobility, and conﬂicts are a from vital registration (VR) systems that were adjusted for hallmark of the region (UNAIDS RST MENA 2008). incompleteness and misclassiﬁcation of causes of death Injection drug use is also a major challenge in a region that (Wang et al. 2016a). Tailored estimation methods were produces most of the world’s supply of heroin and is at the used to produce ﬁnal estimates of mortality depending on crossroads of major drug trade routes (UNODC 2007). age groups and the availability and quality of data for The emerging HIV epidemics among the most at-risk mortality of HIV/AIDS. populations, such as men who have sex with men (MSM) Years of life lost (YLLs) were calculated by multiplying and people who inject drugs (PWID), constitute the main the mortality rate by population by age-speciﬁc life feature of HIV epidemiology in the EMR today within a expectancy from the reference life table used in the GBD context that criminalizes and marginalizes these popula- study. Years lived with disability (YLDs) were computed tions (Simmons 2014; Mumtaz et al. 2014a). The majority as the prevalence of a sequela multiplied by the disability of these epidemics are recent, having emerged within the weight for that sequela without age weighting or dis- last two decades (Mumtaz et al. 2014b). In addition to these counting. The YLDs arising from HIV/AIDS are the sum documented epidemics, there is evidence suggesting hid- of the YLDs for each of the sequelae associated with HIV/ den, undetected epidemics among the most at-risk popu- AIDS. Disability-adjusted life years (DALYs) are com- lations in countries with still weak HIV surveillance puted as the sum of YLLs and YLDs. Detailed methods on systems (Mumtaz et al. 2014a). YLLs, YLDs, and DALYs are published elsewhere (GBD Data on disability and mortality from HIV are crucial in 2015 DALYs and HALE Collaborators 2016; GBD 2015 understanding the regional response to the disease. To Disease and Injury Incidence and Prevalence Collaborators inform HIV policy, programming, and resource allocation 2016; GBD 2015 Risk Factors Collaborators 2016). about the state of the epidemic in EMR countries, we used We estimated incidence and prevalence from the reco- the results of the GBD 2015 study to report the HIV/AIDS ded spectrum model. This model was updated with burden in these countries. assumptions of on-ART and off-ART mortality, as well as other program data available from the UNAIDS country ﬁles. Vital registration systems and sample registration Methods systems provide some of the most reliable sources for estimation of HIV cause-speciﬁc deaths. Later, our cohort The Eastern Mediterranean Region (EMR) countries, based incidence bias adjustment method was used to scale the on the World Health Organization classiﬁcation, are the sizes of each incidence cohort on the basis of the raw Islamic Republic of Afghanistan, the Kingdom of Bahrain, estimates of HIV mortality from spectrum, adjusted for Djibouti, the Arab Republic of Egypt, the Islamic Republic incompleteness and cause misclassiﬁcation using unad- of Iran, the Republic of Iraq, the Hashemite Kingdom of justed incidence curves and those observed in the vital Jordan, the State of Kuwait, Lebanon, the State of Libya, registration system (Wang et al. 2016a). More details about the Kingdom of Morocco, the Sultanate of Oman, the this method have been published previously (Wang et al. Islamic Republic of Pakistan, Palestine, the State of Qatar, 2016b). the Kingdom of Saudi Arabia, the Federal Republic of We also estimated risk factors following the GBD Somalia, the Republic of Sudan, the Syrian Arab Republic, study’s comparative assessment of risk factors detailed the Republic of Tunisia, the United Arab Emirates, and the elsewhere (Forouzanfar et al. 2015). Brieﬂy, this method Republic of Yemen. uses data for excess mortality and disability associated with 123 Trends in HIV/AIDS morbidity and mortality in Eastern Mediterranean countries, 1990–2015… S125 risk factors, data for exposure to risks, and evidence-based AIDS deaths as a percentage of all deaths decreased in assumptions on the desired counterfactual distribution of Kuwait, Lebanon and, Syria at an annualized rate of 3.3, risk exposure. The attributable burden of a risk factor is 1.0, and 0.4%, respectively (Table 2). In 2015, the percent estimated by multiplying DALYs from HIV/AIDS by the of deaths due to HIV/AIDS was highest in Djibouti, and population attributable fraction for HIV/AIDS due to that higher than the regional average, 0.2 (0.1–0.2), in Bahrain, risk factor. Oman, Libya, Lebanon, Saudi Arabia Somalia, Sudan, and We report age-standardized estimates, and 95% uncer- UAE. It was lower than the regional average in all tainty intervals (UI) for each estimate—such as rates or remaining countries (Table 2). numbers of deaths or DALYs. We estimated UIs by taking 1000 samples from the posterior distribution of each Years of life lost quantity and using the 25th- and 975th-ordered draws of the uncertainty distribution (Wang et al. 2016a). For 2015, Years of life lost to HIV/AIDS increased from 49,094 we estimated the expected burden for each of the three (24,960–117,290) in 1990 to 526,030 (416,745–734,351) in measures (mortality, YLLs, and YLDs) as a function of 2015. The rate of YLLs increased as well for the same each country’s Socio-demographic Index (SDI)—a com- period from 15.3 (7.6–36.2) to 81.8 (65.3–114.4) per posite measure based on levels of income—education, and 100,000 population (e-Table 1). E-Table 1 shows these fertility (Wang et al. 2016a). SDI was developed for GBD rates for individual countries. The percent of YLLs due to 2015 to provide an interpretable synthesis of overall HIV/AIDS decreased in Kuwait, Lebanon, Syria, and Qatar development, as measured by lag-dependent income per at annualized rates of 3.4, 1.4, 0.3, and 0.1%, respectively capita, average educational attainment in the population (Table 2). over 15 years of age, and total fertility rates. In GBD 2015, SDI was computed by rescaling each component to a scale Years lived with disability of zero to one, with zero being the lowest observed edu- cational attainment, lowest income per capita, and highest HIV/AIDS accounted for 26,000 (16,440–38,839) YLDs in fertility rate from 1980 to 2015, and one being the highest 2015, a sixfold increase from 3829 (1875–8539) in 1990. observed educational attainment, highest income per cap- The rate increased from 1.3 (0.6–3.1) per 100,000 popu- ita, and lowest fertility rate during that time, and then lation in 1990 to 4.4 (2.7–6.6) in 2015 (e-Table 2). taking the geometric mean of these values for each loca- E-Table 2 shows these rates for individual countries. The tion-year. percent of YLDs due to HIV/AIDS decreased in Lebanon, Qatar, and Yemen by annualized rates of 2.2, 1.3, and Role of the funding source 0.9%, respectively (Table 2). HIV/AIDS caused more YLLs than YLDs at all times The funder of the study had no role in study design, data (e-Fig. 1). collection, data analysis, data interpretation, or writing of the report. Disability-adjusted life years DALYs due to HIV/AIDS increased tenfold between Results 1990—52,923 (26,913–124,169)—and 2015—552,030 (439,956–768,775). The rate increased from 16.6 Mortality (8.4–38.8) to 86.2 (69.2–120.6) per 100,000 population (e- Table 3). E-Table 3 shows these rates for individual The proportion of deaths attributable to HIV/AIDS has countries. The percent of DALYs due to HIV/AIDS increased steadily in the EMR since 1990 by 6.7% annually decreased in Kuwait, Lebanon, Qatar, Syria, and Yemen by (Fig. 1). annualized rates of 3.3, 1.5, 0.2, 0.2, and 0.1%, respectively In 2015, HIV/AIDS caused 10,558 (95% UI (Table 2). 8411–17,775) deaths in the EMR, a tenfold increase from 1990 (936; 470–2226). This equals an increase in age-s- Incidence and prevalence tandardized rate from 0.3 (0.2–0.8) in 1990 to 1.8 (1.4–2.5) per 100,000 population in 2015 (Table 1). HIV/AIDS Incidence and prevalence of HIV/AIDS have increased in mortality among males—2.4 (1.8–3.4) deaths per 100,000 the EMR since 1990 from 2.9 (2.0–4.9) and 9.1 (5.1–16.4), population—was double that among females—1.1 to 5.3 (3.9–7.9) and 28.4 (22.3–39.8) per 100,000 popula- (0.9–1.5) deaths per 100,000 population. It affected mostly tion, respectively. The highest and lowest incidence for infants and those aged 25 years or older (Fig. 2). HIV/ 2015 was observed in Djibouti and Syria, respectively: 90.9 123 S126 GBD 2015 Eastern Mediterranean Region HIV/AIDS Collaborators Global, males 27.79 Global, females 27.48 Global, both sexes 27.17 EMR, males EMR, females EMR, both sexes 17.14 16.18 15.2 6.72 4.73 2.71 1.71 2.15 0.38 1.63 1.31 0.25 1.07 0.13 0.88 1990 1995 2000 2005 2010 2015 Year Fig. 1 Trends of HIV/AIDS age-standardized mortality worldwide, and in the Eastern Mediterranean Region (EMR), 1990–2015. (Global Burden of Disease Study 2015, Global, the Eastern Mediterranean Region, 1990–2015) (55.0–142.4) and 0.4 (0.2–0.5). The highest and lowest Expected YLDs were within the range of uncertainty for prevalence for 2015 were observed in Djibouti and Kuwait, the observed YLDs in Bahrain, Lebanon, Libya, Saudi respectively: 919.7 (714.8–1161.9) and 0.0 (0.0–0.0) per Arabia, and the United Arab Emirates (e-Table 2). 100,000 populations. Table 3 presents estimates of inci- Expected DALYs were within the range of uncertainty for dence and prevalence of HIV/AIDS in EMR countries in the observed DALYs in Djibouti and the United Arab 1990, 2005, and 2015. Emirates (e-Table 3). Risk factors Discussion Unsafe sex and drug use accounted for 74.1 and 18.8% of HIV deaths, 75.3 and 17.5% of HIV YLLs, 71.9 and 21.3% This is the ﬁrst GBD study to comprehensively examine of HIV YLDs, and 75.1 and 17.7% of HIV DALYs, the burden and trends of HIV/AIDS-related mortality in respectively. In Djibouti, where HIV/AIDS mortality was EMR countries from 1990 to 2015. Our estimates show a highest in comparison to all other EMR countries, unsafe tenfold increase in HIV/AIDS mortality rates and other sex and drug use contributed to 94.4 and 0.4% of deaths measures of disease burden for the EMR region with most related to HIV/AIDS, respectively. On the other hand, in of the HIV/AIDS burden is contributed by the three poorest Syria, where HIV/AIDS mortality was lowest, unsafe sex countries Djibouti, Somalia, and Sudan. These results and drug use contributed to 84.5 and 6.4% of deaths related highlight the expanding nature of the epidemic in the EMR, to HIV/AIDS, respectively. Table 4 presents estimates of in contrast to the other global regions (UNAIDS 2016a). risk factors contribution to HIV/AIDS deaths, YLLs, They also afﬁrm the epidemiological evidence indicating YLDs, and DALYs. emerging HIV epidemics within the last two decades such as among MSM in nearly half of EMR countries (Mumtaz Observed versus expected burden et al. 2011, 2014a) and among PWID in over a third of EMR countries (Mumtaz et al. 2014a, b), two populations Despite the increase of HIV/AIDS mortality in EMR that are still being criminalized in this region, making countries over time, all, but Djibouti had lower observed epidemic control harder to reach (Simmons 2014; Aaraj deaths than expected based on SDI (Table 1). Expected and Chrouch 2016). Despite these rapid increases, HIV deaths were within the range of uncertainty for the disease burden in EMR remains at least tenfold lower than observed deaths in Djibouti (Table 1). Only Djibouti had HIV/AIDS mortality at the global level, and at all times higher observed YLLs and YLDs than what would have (Wang et al. 2016b). been expected for 2015 based on SDI (e-Tables 1, 2). Deaths per 100,000 Trends in HIV/AIDS morbidity and mortality in Eastern Mediterranean countries, 1990–2015… S127 Table 1 Rates and 95% uncertainty levels (UL) of age-standardized HIV/AIDS mortality per 100,000 population in Eastern Mediterranean Region countries observed in 1990, 2005, and 2015, and expected in 2015 based on Socio-demographic Index (SDI) Location 1990 2005 2015 Rate (95% UL) Rate (95% UL) Observed rate (95% UL) Expected rate based on SDI (SDI) Eastern Mediterranean Region 0.3 (0.2–0.8) 1.5 (1.2–2.2) 1.8 (1.4–2.5) 35.1 (0.55) Low and lower middle income Afghanistan 0.5 (0.0–2.6) 1.1 (0.1–6.0) 1.0 (0.2–3.5) 45.0 (0.29) Somalia 2.0 (0.7–4.4) 20.4 (13.4–28.3) 19.1 (13.8–25.0) 44.1 (0.15) Djibouti 10.6 (2.7–30.0) 82.2 (53.7–115.9) 45.8 (30.8–60.9) 46.2 (0.46) Egypt 0.0 (0.0–0.0) 0.2 (0.1–0.2) 0.2 (0.2–0.3) 31.2 (0.62) Morocco 0.1 (0.1–0.1) 0.5 (0.4–0.6) 1.1 (0.8–1.4) 43.6 (0.50) Pakistan 0.1 (0.0–0.5) 0.3 (0.0–1.7) 0.9 (0.2–3.2) 46.3 (0.47) Palestine 0.0 (0.0–0.1) 0.3 (0.3–0.4) 0.4 (0.3–0.7) 35.9 (0.57) Sudan 2.5 (0.7–5.5) 12.4 (8.6–16.6) 13.8 (10.5–16.4) 45.7 (0.43) Syria 0.2 (0.2–0.2) 0.1 (0.1–0.1) 0.2 (0.1–0.3) 34.8 (0.58) Tunisia 0.2 (0.0–0.1) 0.3 (0.2–0.3) 0.8 (0.6–1.0) 27.9 (0.65) Yemen 0.8 (0.0–4.5) 1.5 (0.2–9.3) 0.8 (0.2–3.3) 45.5 (0.41) Upper middle and high income Iran 0.1 (0.0–0.1) 0.4 (0.4–0.5) 0.7 (0.5–1.0) 23.5 (0.72) Iraq 0.0 (0.0–0.1) 0.2 (0.2–0.2) 0.4 (0.3–0.5) 34.8 (0.58) Jordan 0.0 (0.0–0.1) 0.2 (0.1–0.2) 0.2 (0.1–0.3) 25.5 (0.70) Lebanon 2.1 (0.1–17.1) 2.0 (0.2–12.7) 1.7 (0.3–8.2) 17.0 (0.75) Libya 0.9 (0.0–5.0) 2.1 (0.0–12.9) 1.7 (0.1–9.0) 28.9 (0.64) Bahrain 0.4 (0.1–0.8) 1.4 (1.0–2.0) 1.3 (0.8–2.2) 15.2 (0.78) Kuwait 0.4 (0.3–0.4) 0.4 (0.4–0.5) 0.2 (0.2–0.2) 7.2 (0.86) Oman 0.1 (0.1–0.1) 1.0 (0.9–1.3) 1.5 (1.1–2.0) 21.9 (0.73) Qatar 0.3 (0.1–0.5) 0.5 (0.3–0.9) 0.3 (0.2–0.6) 12.4 (0.80) Saudi Arabia 0.6 (0.1–3.1) 1.6 (0.6–5.6) 1.5 (0.7–4.0) 16.9 (0.76) United Arab Emirates 1.0 (0.0–6.3) 2.6 (0.1–16.7) 2.2 (0.1–11.7) 7.2 (0.88) Global Burden of Disease 2015 study, Eastern Mediterranean countries, 1990–2015 Fig. 2 Distribution of HIV/ 7 AIDS mortality rate and 95% uncertainty levels in the Eastern Mediterranean Region, by age group, in 2015 (Global Burden of Disease Study 2015, Global, the Eastern Mediterranean Region, 1990–2015) Age (Years) Deaths Lower bound Upper bound Rate per 100,000 populaon S128 GBD 2015 Eastern Mediterranean Region HIV/AIDS Collaborators Table 2 Percent of deaths, YLDs, and YLLs attributable to HIV/AIDS, and their relative annual percent change, 1990–2015, Eastern Mediterranean Region countries Location % Death Annual % % YLLs Annual % % YLDs Annual % % DALYs Annual % change change change change Eastern Mediterranean Region 0.2 (0.1–0.2) 6.7 0.3 (0.2–0.4) 6.7 0.0 (0.0–0.1) 4.7 0.2 (0.2–0.3) 6.6 Low and lower middle income Afghanistan 0.0 (0.0–0.2) 2.7 0.1 (0.0–0.2) 2.5 0.0 (0.0–0.1) 1.4 0.1 (0.0–0.2) 2.4 Somalia 1.1 (0.5–1.9) 9.0 1.5 (0.7–2.6) 9.0 0.3 (0.2–0.6) 6.8 1.3 (0.7–2.0) 8.9 Djibouti 3.7 (2.1–5.5) 5.9 5.9 (3.6–8.8) 5.9 0.9 (0.6–1.4) 3.3 4.6 (3.0–6.6) 5.7 Egypt 0.0 (0.0–0.0) 7.7 0.1 (0.0–0.1) 7.7 0.0 (0.0–0.0) 7.4 0.0 (0.0–0.0) 7.7 Morocco 0.1 (0.1–0.2) 11.4 0.3 (0.2–0.4) 11.2 0.0 (0.0–0.1) 7.7 0.2 (0.1–0.2) 10.8 Pakistan 0.1 (0.0–0.3) 10.3 0.1 (0.0–0.4) 10.2 0.0 (0.0–0.1) 8.1 0.1 (0.0–0.3) 10.1 Palestine 0.1 (0.0–0.1) 9.3 0.1 (0.1–0.2) 9.2 0.0 (0.0–0.0) 8.8 0.1 (0.1–0.1) 9.2 Sudan 1.3 (0.9–1.7) 6.8 2.0 (1.4–2.7) 6.7 0.2 (0.1–0.4) 4.3 1.5 (1.1–1.9) 6.6 Syria 0.0 (0.0–0.0) -0.4 0.0 (0.0–0.1) -0.3 0.0 (0.0–0.0) 0.4 0.0 (0.0–0.0) -0.2 Tunisia 0.1 (0.1–0.2) 6.3 0.3 (0.2–0.4) 8.8 0.0 (0.0–0.0) 6.9 0.2 (0.1–0.2) 8.6 Yemen 0.1 (0.0–0.3) 0.1 0.1 (0.0–0.4) 0.0 0.0 (0.0–0.1) -0.9 0.1 (0.0–0.3) -0.1 Upper middle and high income Iran 0.1 (0.1–0.1) 10.2 0.2 (0.1–0.2) 10.1 0.0 (0.0–0.0) 4.9 0.1 (0.1–0.2) 9.3 Iraq 0.0 (0.0–0.0) 10.1 0.1 (0.0–0.1) 10.1 0.0 (0.0–0.0) 9.8 0.0 (0.0–0.1) 10.1 Jordan 0.0 (0.0–0.1) 6.5 0.1 (0.0–0.1) 6.4 0.0 (0.0–0.0) 5.6 0.0 (0.0–0.1) 6.3 Lebanon 0.3 (0.0–1.4) -1.0 0.6 (0.1–2.7) -1.4 0.0 (0.0–0.1) -2.2 0.3 (0.1–1.4) -1.5 Libya 0.2 (0.0–1.1) 2.7 0.4 (0.0–2.2) 2.6 0.0 (0.0–0.2) 1.2 0.3 (0.0–1.4) 2.5 Bahrain 0.2 (0.1–0.4) 4.3 0.5 (0.3–0.9) 4.1 0.0 (0.0–0.1) 3.7 0.3 (0.2–0.4) 4.1 Kuwait 0.0 (0.0–0.0) -3.3 0.1 (0.1–0.1) -3.4 0.0 (0.0–0.0) 0.4 0.0 (0.0–0.1) -3.3 Oman 0.2 (0.2–0.3) 10.1 0.5 (0.3–0.6) 9.7 0.0 (0.0–0.1) 8.8 0.3 (0.2–0.4) 9.7 Qatar 0.1 (0.0–0.1) 0.3 0.1 (0.1–0.2) -0.1 0.0 (0.0–0.0) -1.3 0.1 (0.0–0.1) -0.2 Saudi Arabia 0.3 (0.1–0.7) 3.6 0.6 (0.3–1.6) 3.4 0.0 (0.0–0.1) 2.1 0.3 (0.2–0.9) 3.3 United Arab Emirates 0.3 (0.0–1.5) 3.3 0.6 (0.0–3.1) 3.2 0.0 (0.0–0.2) 1.2 0.4 (0.0–1.9) 3.0 Global Burden of Disease 2015 study, Eastern Mediterranean countries, 1990–2015 YLDs years lived with disability, YLLs years of life lost These results indicate that EMR countries are not likely appear to be detected through routine screening, such as in to fulﬁll the Joint United Nations Program on HIV/AIDS the context of blood donation, premarital medical tests, and (UNAIDS) ‘‘90-90-90’’ target of diagnosing 90% of all employment, or visa and residency applications (Hermez people living with HIV/AIDS, providing ART for 90% of et al. 2010). Moreover, data on relevant HIV/AIDS indi- those diagnosed, and achieving viral suppression for 90% cators, such as the Global AIDS Response Progress of those treated, all by 2020 (UNAIDS 2014b). EMR Reporting indicators, are limited in many EMR countries, countries are also not likely to reach the fast-track target of although quality integrated bio-behavioral surveillance ending AIDS by 2030 (UNAIDS 2016a). The striking gap surveys (IBBSS) of hard-to-reach populations have already between the expanding disease burden and global targets proven possible in over half of EMRO countries (Abu- for reducing this burden highlights the need for EMR Raddad et al. 2010; Mumtaz et al. 2011, 2014b). Sustain- countries to strengthen HIV/AIDS voluntary counseling ability of IBBSS rounds in countries where they have been and testing among the most at-risk populations, improve conducted, and implementing them in countries where they HIV epidemiological surveillance, and scale up ART and have not been conducted, should be a priority. comprehensive prevention services. These results also afﬁrm the evidence indicating low A major challenge in the EMR is the weak vital regis- ART coverage in EMR and persistent challenges with the tration and epidemiological surveillance systems. People treatment cascade (World Health Organization 2017; living with HIV are being diagnosed at a late stage of UNAIDS 2016b). EMR has the lowest ART coverage disease progression, thus their chances of accessing treat- globally at a median of 17% in 2015 (UNAIDS 2016b), and ment and surviving are decreasing. Most HIV infections did not reach the 2015 midterm regional objective of 50% 123 Trends in HIV/AIDS morbidity and mortality in Eastern Mediterranean countries, 1990–2015… S129 Table 3 Rates and 95% uncertainty levels (UL) of age-standardized incidence and prevalence of HIV/AIDS per 100,000 population Location Incidence (95% UL) Prevalence (95% UL) 1990 2005 2015 1990 2005 2015 Eastern Mediterranean Region 2.9 (2.0–4.9) 4.7 (4.0–5.7) 5.3 (3.9–7.9) 9.1 (5.1–16.4) 23.4 (20.0–28.1) 28.4 (22.3–39.8) Low and lower middle income Afghanistan 2.7 (0.2–20.5) 1.2 (0.4–4.2) 2.6 (0.6–7.3) 10.4 (0.7–57.8) 9.3 (1.9–49.0) 14.9 (4.8–46.7) Somalia 24.7 (13.1–45.8) 47.4 (34.8–62.4) 29.2 (18.7–45.0) 79.5 (41.1–143.5) 359.2 (276.3–466.0) 293.1 (224.8–380.7) Djibouti 134.5 (58.4–281.3) 115.8 (82.6–159.5) 90.9 (55.0–142.4) 577.6 (198.5–1501.1) 1176.9 (932.3–1472.4) 919.7 (714.8–1161.9) Egypt 0.3 (0.2–0.5) 0.5 (0.4–0.8) 1.2 (0.7–1.9) 0.0 (0.0–0.0) 0.5 (0.1–1.1) 3.1 (1.6–4.7) Morocco 1.6 (1.3–1.9) 3.9 (3.2–5.2) 4.7 (3.9–5.9) 0.0 (0.0–0.0) 12.3 (7.6–17.7) 18.6 (10.2–29.5) Pakistan 0.9 (0.4–4.3) 1.8 (0.9–4.5) 5.6 (1.9–15.0) 0.7 (0.0–4.6) 2.6 (0.4–9.4) 22.3 (6.0–59.6) Palestine 0.4 (0.2–0.6) 1.3 (0.9–1.9) 2.0 (1.0–3.4) 0.0 (0.0–0.0) 3.2 (1.6–5.1) 7.0 (3.7–12.0) Sudan 15.8 (6.0–31.2) 29.7 (22.0–38.5) 15.7 (7.2–27.5) 79.2 (25.8–155.2) 197.1 (155.7–249.7) 167.5 (131.9–213.6) Syria 0.2 (0.2–0.3) 0.2 (0.1–0.2) 0.4 (0.2–0.5) 0.0 (0.0–0.0) 0.0 (0.0–0.0) 0.6 (0.1–1.7) Tunisia 0.9 (0.6–1.3) 1.7 (1.1–2.6) 3.0 (1.8–5.0) 0.3 (0.1–0.7) 6.5 (3.3–10.5) 16.0 (7.7–26.9) Yemen 6.4 (3.4–31.1) 4.4 (3.3–8.0) 3.7 (2.1–7.5) 17.3 (2.1–97.8) 11.9 (4.0–41.6) 16.8 (5.0–41.4) Upper middle and high income Iran 2.4 (2.0–3.3) 5.4 (4.6–6.3) 6.9 (5.8–8.2) 0.9 (0.4–1.5) 9.4 (6.6–14.4) 11.4 (7.8–17.8) Iraq 0.2 (0.1–0.4) 0.7 (0.5–1.2) 1.6 (0.4–3.2) 0.0 (0.0–0.0) 2.0 (1.0–3.2) 7.6 (3.8–13.4) Jordan 0.2 (0.1–0.4) 0.3 (0.2–0.5) 0.4 (0.1–0.8) 0.0 (0.0–0.0) 0.2 (0.0–0.5) 0.4 (0.0–0.9) Lebanon 8.8 (1.6–62.0) 2.4 (1.0–7.0) 2.7 (1.0–6.8) 63.0 (4.3–447.4) 34.6 (6.3–175.9) 28.6 (7.0–123.0) Libya 6.8 (2.2–34.0) 5.4 (2.5–16.5) 5.7 (2.4–16.1) 26.0 (0.3–182.8) 24.4 (1.5–134.4) 29.8 (2.8–84.1) Bahrain 2.8 (1.5–6.2) 3.0 (2.2–4.3) 4.0 (1.5–7.6) 9.2 (0.3–20.1) 21.0 (9.1–46.2) 28.5 (12.5–54.1) Kuwait 0.9 (0.5–1.6) 0.4 (0.3–0.6) 0.4 (0.1–0.6) 0.4 (0.0–1.0) 0.0 (0.0–0.0) 0.0 (0.0–0.0) Oman 1.0 (0.7–1.5) 3.5 (2.6–4.9) 3.1 (2.2–4.1) 1.4 (0.6–2.8) 29.3 (19.3–43.1) 33.7 (18.3–50.5) Qatar 1.1 (0.4–3.0) 0.3 (0.2–0.6) 0.4 (0.1–0.8) 5.5 (1.3–9.7) 2.5 (0.8–4.7) 0.9 (0.2–1.8) Saudi Arabia 4.0 (1.4–17.8) 3.3 (2.1–6.3) 3.8 (2.2–6.6) 15.2 (2.5–80.5) 21.1 (11.0–48.4) 28.2 (16.4–51.7) United Arab Emirates 6.1 (0.8–32.3) 4.0 (0.8–18.4) 4.7 (0.8–15.7) 11.2 (0.2–51.8) 26.0 (1.6–97.2) 36.0 (3.6–155.5) Global Burden of Disease 2015 study, Eastern Mediterranean countries, 1990–2015 S130 GBD 2015 Eastern Mediterranean Region HIV/AIDS Collaborators Table 4 Percent and 95% uncertainty levels (95% UL) of HIV/AIDS deaths, YLLs, YLDs, and DALYs attributed to unsafe sex and drug use Location Percent of HIV/AIDS deaths Percent of HIV/AIDS YLLs Percent of HIV/AIDS YLDs Percent of HIV/AIDS DALYs attributable to attributable to attributable to attributable to Unsafe sex (95% Drug use (95% Unsafe sex (95% Drug use (95% Unsafe sex (95% Drug use (95% Unsafe sex (95% Drug use (95% UL) UL) UL) UL) UL) UL) UL) UL) Eastern Mediterranean 74.1 (69.1–79.0) 18.8 (13.6–24.4) 75.3 (70.7–79.8) 17.5 (12.6–22.9) 71.9 (66.9–76.7) 21.3 (16.3–27.0) 75.1 (70.5–79.7) 17.7 (12.8–23.0) Region Low and lower middle income Afghanistan 74.4 (66.5–81.1) 16.3 (9.5–24.5) 74.6 (67.0–80.9) 15.7 (9.1–23.6) 74.4 (66.7–81.2) 16.1 (9.3–24.3) 74.6 (67.0–80.9) 15.7 (9.1–23.6) Somalia 97.3 (95.8–98.3) 0.9 (0.5–1.6) 97.4 (95.8–98.3) 0.9 (0.5–1.5) 97.3 (95.8–98.3) 0.9 (0.5–1.6) 97.4 (95.8–98.3) 0.9 (0.5–1.6) Djibouti 94.4 (93.1–95.5) 0.4 (0.3–0.4) 94.2 (92.8–95.4) 0.3 (0.3–0.4) 94.4 (93.3–95.4) 0.4 (0.3–0.5) 94.2 (92.8–95.4) 0.3 (0.3–0.4) Egypt 74.2 (66.5–80.7) 15.8 (9.0–23.9) 74.4 (66.8–80.5) 15.0 (8.7–23.0) 74.2 (66.4–80.6) 15.7 (9.0–23.9) 74.4 (66.9–80.5) 15.1 (8.7–23.0) Morocco 74.0 (66.1–80.7) 16.1 (9.2–24.2) 74.2 (66.6–80.5) 15.6 (9.0–23.6) 74.0 (66.3–80.5) 15.9 (9.1–24.0) 74.2 (66.6–80.5) 15.6 (9.0–23.6) Pakistan 65.9 (62.5–69.1) 32.6 (29.3–36.1) 67.5 (64.3–70.7) 30.9 (27.7–34.2) 66.2 (62.9–69.4) 32.3 (28.9–35.5) 67.3 (64.2–70.5) 31.0 (27.8–34.2) Palestine 74.4 (66.6–81.1) 16.1 (9.3–24.3) 74.5 (67.0–80.9) 15.4 (9.0–23.5) 74.3 (66.6–80.8) 15.9 (9.1–24.0) 74.5 (66.9–80.9) 15.5 (9.0–23.5) Sudan 74.5 (66.7–81.2) 16.1 (9.3–24.2) 74.6 (66.9–81.1) 15.5 (9.0–23.6) 74.4 (66.6–81.1) 16.1 (9.4–24.4) 74.6 (66.9–81.1) 15.6 (9.0–23.7) Syria 84.5 (82.1–86.5) 6.4 (5.5–7.7) 84.5 (82.0–86.6) 6.1 (5.2–7.2) 84.5 (82.2–86.6) 6.3 (5.4–7.5) 84.5 (82.0–86.6) 6.1 (5.2–7.2) Tunisia 77.3 (74.1–80.0) 15.8 (13.4–18.4) 77.8 (74.7–80.5) 15.0 (12.8–17.4) 78.2 (75.2–80.9) 14.8 (12.5–17.3) 77.8 (74.8–80.5) 15.0 (12.7–17.4) Yemen 91.0 (90.0–92.0) 2.9 (2.5–3.4) 90.8 (89.7–91.8) 2.8 (2.4–3.2) 90.6 (89.5–91.6) 2.9 (2.5–3.3) 90.8 (89.7–91.8) 2.8 (2.4–3.2) Upper middle and high income Iran 14.1 (12.2–16.2) 78.3 (75.8–80.8) 14.3 (12.4–16.5) 77.6 (75.0–80.3) 14.4 (12.4–16.7) 77.3 (74.4–80.0) 14.3 (12.4–16.5) 77.6 (75.0–80.2) Iraq 74.4 (66.7–80.9) 15.9 (9.2–23.9) 74.5 (66.9–80.7) 15.2 (8.8–23.3) 74.3 (66.8–80.8) 15.8 (9.1–23.8) 74.5 (66.8–80.7) 15.3 (8.9–23.3) Jordan 81.8 (79.8–83.6) 9.7 (8.5–11.2) 81.9 (79.8–83.7) 9.2 (8.0–10.5) 81.6 (79.6–83.5) 9.6 (8.3–11.0) 81.8 (79.8–83.7) 9.2 (8.0–10.6) Lebanon 74.2 (66.0–81.0) 16.5 (9.7–24.7) 74.3 (66.6–80.9) 15.9 (9.3–23.8) 74.3 (66.3–81.0) 16.3 (9.4–24.3) 74.3 (66.6–80.9) 15.9 (9.3–23.8) Libya 74.2 (66.6–80.7) 15.9 (9.2–24.0) 74.4 (66.9–80.5) 15.2 (8.9–23.2) 74.1 (66.4–80.6) 15.7 (9.0–23.9) 74.3 (66.9–80.5) 15.3 (8.9–23.2) Bahrain 36.6 (33.2–40.3) 57.0 (53.0–60.5) 37.4 (33.9–41.0) 55.8 (52.0–59.4) 37.0 (33.6–40.8) 56.3 (52.5–59.9) 37.3 (33.9–41.0) 55.9 (52.0–59.4) Kuwait 74.3 (66.8–80.3) 15.3 (8.9–23.1) 74.7 (66.4–80.4) 13.4 (7.4–22.2) 74.2 (66.5–80.6) 15.9 (9.1–24.0) 74.7 (66.5–80.4) 13.5 (7.5–22.2) Oman 82.8 (80.2–85.3) 7.6 (6.2–8.9) 82.7 (80.1–85.2) 7.3 (6.0–8.5) 82.4 (79.8–84.9) 7.5 (6.2–8.8) 82.6 (80.1–85.2) 7.3 (6.1–8.6) Qatar 74.6 (66.6–81.5) 16.6 (9.6–25.0) 74.8 (67.1–81.5) 16.0 (9.2–24.0) 74.6 (66.9–81.5) 16.3 (9.4–24.5) 74.7 (67.1–81.5) 16.0 (9.2–24.1) Saudi Arabia 75.0 (71.4–78.1) 15.5 (13.1–18.5) 75.2 (71.6–78.5) 14.8 (12.5–17.7) 75.0 (71.5–78.1) 15.3 (12.9–18.2) 75.2 (71.6–78.5) 14.9 (12.5–17.7) United Arab Emirates 74.7 (66.6–81.6) 16.4 (9.7–24.9) 74.9 (67.1–81.2) 15.8 (9.1–24.1) 74.7 (67.0–81.5) 16.1 (9.4–24.3) 74.9 (67.1–81.2) 15.8 (9.1–24.1) Global Burden of Disease 2015 study, Eastern Mediterranean countries, 1990–2015 YLDs years lived with disability, YLLs years of life lost, DALYs disability-adjusted life years Trends in HIV/AIDS morbidity and mortality in Eastern Mediterranean countries, 1990–2015… S131 coverage under the World Health Organization’s (WHO) based on similar indicators, and hence is important to use, initiative to end EMR’s HIV treatment crisis (World Health despite its limitations. Organization Regional Ofﬁce for the Eastern Mediter- Meanwhile, the poor management and treatment of ranean 2014). HIV/AIDS patients is also a persistent issue. EMR poli- The effectiveness of highly active ART was manifested cymakers need to devote adequate funds to expand HIV in 1995 and became the new standard for HIV care in 1997, prevention and treatment services even if the leading making HIV a manageable disease (Carpenter et al. 1997; causes of deaths, YLLs, and YLDs in the EMR are non- Palmisano and Vella 2011). Despite this progress, only communicable, such as ischemic heart disease, diabetes, three EMR countries showed a decrease in HIV/AIDS and road injuries (Mokdad et al. 2014, 2016). These ser- mortality. These countries can share lessons with the vices need to be expanded, particularly among the most at- remaining EMR countries to help them control their epi- risk populations. Countries need to put in place active demics. Moreover, our study showed that for most EMR surveillance systems to detect early infections and monitor countries, the increase in YLLs exceeded by far the the epidemic, in addition to delivering health care to those increase in YLDs during the study period. For instance, affected. With drug use playing a signiﬁcant role in HIV while YLDs contributed to 4.7% of HIV/AIDS DALYs in transmission in this region, introducing syringe exchange EMR countries, they contributed to 8.4% in European programs should be considered given its proven effec- countries (Institute for Health Metrics and Evaluation tiveness in preventing HIV transmission (Wilson et al. (IHME) 2017). This indicates that HIV survival is very low 2015). in EMR countries, afﬁrming the weak and challenged HIV/ Our study might be subjected to several limitations AIDS response in this region (Abu-Raddad et al. 2013). around the estimation of HIV/AIDS burden. These limita- Even if HIV/AIDS treatment is available, often it is tions have been previously described (Wang et al. 2016b). interrupted and patients struggle to survive. Most health In short, our study estimates mortality with HIV/AIDS as care providers are also not well trained to manage HIV/ the underlying cause of death without accounting for AIDS patients and/or understand their situations (Khosra- deaths from other non-communicable causes among people vanifard et al. 2012; Wilder 2008; Anonymous 2012; living with HIV. Additionally, data are less available for Thayer 2012; Upham and Mikkelsen 2012; Hedayati- the most recent years, and our models might have missed Moghaddam et al. 2012). recent progress, or lack of it, in some countries. Our esti- Interestingly, the observed burden of HIV/AIDS was mates have not accounted directly for relevant covariates lower than expected in most EMR countries based on their including prevalence of sexually transmitted infections or SDI. On the surface, this might be sound like good news. rates of ART adherence, ART treatment failure, and HIV However, the burden of HIV/AIDS has been increasing testing (Wang et al. 2016b). continuously in the EMR despite the decrease in the rest of Our study showed that HIV/AIDS disease burden is the world. While SDI is known to be a strong indicator of increasing in the vast majority of EMR countries, in con- health outcomes (Wang et al. 2016a), it is possible that the trast to the global declining trend. Increased and coordi- association with HIV/AIDS is modiﬁed by other cultural nated efforts are needed in the region to apply lessons from and social factors in the EMR. SDI only deals with countries that have succeeded in controlling their epidemic socioeconomic inequalities between countries and does not to reduce this burden, reverse its trend, and reach global account for other cultural and social norms. For instance, stipulated targets for HIV/AIDS. More afﬂuent EMR more of the risky behaviors for HIV, such as access to countries must consider ways to bring the region’s more drugs and alcohol, travel, or multiplicity of concurrent disadvantaged countries to the same level of health. These relationships might be more common among higher-SDI ﬁndings highlight the need for EMR countries to strengthen groups in the EMR. Further, some of the EMR countries HIV/AIDS voluntary counseling and testing among the have experienced warfare and conﬂicts, highlighting the most at-risk populations, improve HIV epidemiological difference in the social determinants of HIV in conﬂict surveillance, and scale up ART and comprehensive pre- versus non-conﬂict settings, with HIV morbidity and vention services. mortality closely associated with conﬂicts (Betsi et al. GBD 2015 Eastern Mediterranean Region HIV/AIDS Collabo- 2006; Mowaﬁ 2011; Wirtz et al. 2014; Robertson and rators: Ali H. Mokdad, PhD (corresponding author), Institute for Hoffman 2014; Doocy et al. 2015; Tunc¸alp et al. 2015; Health Metrics and Evaluation, University of Washington, Seattle, Calam 2016). Some of these include sexual violence and Washington, United States. Charbel El Bcheraoui PhD, Institute for human rights abuses in conﬂict settings, interruption of Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States. Haidong Wang, PhD, Institute for Health treatment due to mass displacement, disruption of health Metrics and Evaluation, University of Washington, Seattle, Wash- systems, and resource diversions from health to support ington, United States. Raghid Charara, MD, American University of wars. However, SDI allows comparisons between countries 123 S132 GBD 2015 Eastern Mediterranean Region HIV/AIDS Collaborators Beirut, Beirut, Lebanon. Ibrahim Khalil, MD, Institute for Health Altirkawi, MD, King Saud University, Riyadh, Saudi Arabia. Nelson Metrics and Evaluation, University of Washington, Seattle, Wash- Alvis-Guzman, PhD, Universidad de Cartagena, Cartagena de Indias, ington, United States. Maziar Moradi-Lakeh, MD, Department of Colombia. Walid Ammar, PhD, Ministry of Public Health, Beirut, Community Medicine, Preventive Medicine and Public Health Lebanon. Nahla Anber, PhD, Mansoura University, Mansoura, Egypt. Research Center, Gastrointestinal and Liver Disease Research Center Carl Abelardo T. Antonio, MD, Department of Health Policy and (GILDRC), Iran University of Medical Sciences, Tehran, Iran. Ash- Administration, College of Public Health, University of the Philip- kan Afshin, MD, Institute for Health Metrics and Evaluation, pines Manila, Manila, Philippines. Palwasha Anwari, MD, Self-em- University of Washington, Seattle, Washington, United States. ployed, Kabul, Afghanistan. Hamid Asayesh, PhD, Department of Michael Collison, BS, Institute for Health Metrics and Evaluation, Medical Emergency, School of Paramedic, Qom University of University of Washington, Seattle, Washington, United States. Farah Medical Sciences, Qom, Iran. Rana Jawad Asghar, MD, South Asian Daoud, BA/BS, Institute for Health Metrics and Evaluation, Univer- Public Health Forum, Islamabad, Pakistan. Tesfay Mehari Atey, MS, sity of Washington, Seattle, Washington, United States. Adrienne Mekelle University, Mekelle, Ethiopia. Euripide Frinel G. Arthur Chew, ND, Institute for Health Metrics and Evaluation, University of Avokpaho, MD, Institut de Recherche Clinique du Be´nin (IRCB), Washington, Seattle, Washington, United States. Kristopher J. Krohn, Cotonou, Benin; Laboratoire d’Etudes et de Recherche-Action en BA, Institute for Health Metrics and Evaluation, University of Sante´ (LERAS Afrique), Parakou, Benin. Tadesse Awoke Ayele, MS, Washington, Seattle, Washington, United States. Austin Carter, BS, University of Gondar, Gondar, Ethiopia. Peter Azzopardi, PhD, Institute for Health Metrics and Evaluation, University of Washing- Burnet Institute, Melbourne, Victoria, Australia; Murdoch Childrens ton, Seattle, Washington, United States. Kyle J. Foreman, PhD, Research Institute, Melbourne, VIC, Australia; Department of Pae- Institute for Health Metrics and Evaluation, University of Washing- diatrics, The University of Melbourne, Melbourne, VIC, Australia; ton, Seattle, Washington, United States, Imperial College London, Wardliparingga Aboriginal Research Unit, South Australian Health London, UK. Fei He, PhD, Institute for Health Metrics and Evalua- and Medical Research Institute, Adelaide, South Australia, Australia. tion, University of Washington, Seattle, Washington, United States. Umar Bacha PhD, School of Health Sciences, University of Man- Nicholas J. Kassebaum, MD, Institute for Health Metrics and Eval- agement and Technology, Lahore, Pakistan. Aleksandra Barac, PhD, uation, University of Washington, Seattle, Washington, United States; Faculty of Medicine, University of Belgrade, Belgrade, Serbia. Till Department of Anesthesiology & Pain Medicine, Seattle Children’s Barnighausen, MD, Department of Global Health and Population, Hospital, Seattle, Washington, United States. Michael Kutz, BS, Harvard T. H. Chan School of Public Health, Harvard University, Institute for Health Metrics and Evaluation, University of Washing- Boston, MA, United States; Africa Health Research Institute, Mtu- ton, Seattle, Washington, United States. Mojde Mirareﬁn, MPH, batuba, South Africa; Institute of Public Health, Heidelberg Univer- Institute for Health Metrics and Evaluation, University of Washing- sity, Heidelberg, Germany. Shahrzad Bazargan-Hejazi, PhD, College ton, Seattle, Washington, United States; Hunger Action Los Angeles, of Medicine, Charles R. Drew University of Medicine and Science, Los Angeles, CA, United States. Grant Nguyen, MPH, Institute for Los Angeles, CA, United States; David Geffen School of Medicine, Health Metrics and Evaluation, University of Washington, Seattle, University of California at Los Angeles, Los Angeles, CA, United Washington, United States. Naris Silpakit, BS, Institute for Health States. Neeraj Bedi, MD, College of Public Health and Tropical Metrics and Evaluation, University of Washington, Seattle, Wash- Medicine, Jazan, Saudi Arabia. Isabela M. Bensenor, PhD, University ington, United States. Amber Sligar, MPH, Institute for Health Met- of Sa˜o Paulo, Sa˜o Paulo, Brazil. Adugnaw Berhane PhD, College of rics and Evaluation, University of Washington, Seattle, Washington, Health Sciences, Debre Berhan University, Debre Berhan, Ethiopia. United States. Amanuel Alemu Abajobir, MPH, School of Public Pascal Obong Bessong, PhD, University of Venda, Thohoyandou, Health, University of Queensland, Brisbane, QLD, Australia. Kalki- South Africa. Addisu Shunu Beyene, MPH, College of Health and dan Hassen Abate, MS, Jimma University, Jimma, Ethiopia. Kaja M. Medical Science, Haramaya University, Harar, Ethiopia. Zulﬁqar A. Abbas, PhD, Virginia Tech, Blacksburg, VA, United States. Foad Bhutta, PhD, Centre of Excellence in Women and Child Health, Aga Abd-Allah, MD, Department of Neurology, Cairo University, Cairo, Khan University, Karachi, Pakistan; Centre for Global Child Health, Egypt. Semaw Ferede Abera, MSc, School of Public Health, College The Hospital for Sick Children, Toronto, ON, Canada. Charles Bir- of Health Sciences, Mekelle University, Mekelle, Ethiopia; Food ungi, MS, University College London, London, UK. Zahid A. Butt, Security and Institute for Biological Chemistry and Nutrition, PhD, Al Shifa Trust Eye Hospital, Rawalpindi, Pakistan. Lucero University of Hohenheim, Stuttgart, Germany. Kelemework Adane, Cahuana-Hurtado, PhD, National Institute of Public Health, Cuer- MS, Department of Medical Microbiology and Immunology, College navaca, Mexico. Hadi Danawi, PhD, Walden University, Min- of Health Sciences, Mekelle University, Mekelle, Ethiopia. Arnav neapolis, Minnesota, United States. Jose das Neves, PhD, I3S- Agarwal, BHSc, University of Toronto, Toronto, Ontario, Canada; Instituto de Investigac¸a˜o e Inovac¸a˜o em Sau´de, University of Porto, McMaster University, Hamilton, Ontario, Canada. Aliasghar Ahmad Porto, Portugal; INEB-Instituto de Engenharia Biomedica, University Kiadaliri, PhD, Department of Clinical Sciences Lund, Orthopedics, of Porto, Porto, Portugal. Kebede Deribe, MPH, Brighton and Sussex Clinical Epidemiology Unit, Lund University, Lund, Sweden. Alireza Medical School, Brighton, UK, School of Public Health, Addis Ababa Ahmadi, PhD, Kermanshah University of Medical Sciences, Ker- University, Addis Ababa, Ethiopia. Amare Deribew, PhD, Nufﬁeld manshah, Iran. Muktar Beshir Ahmed, MPH, College of Health Sci- Department of Medicine, University of Oxford, Oxford, UK; KEMRI- ences, Department of Epidemiology, ICT and e-Learning Wellcome Trust Research Programme, Kiliﬁ, Kenya. Don C. Des Coordinator, Jimma University, Jimma, Ethiopia. Faris Hasan Al Jarlais, PhD, Mount Sinai Beth Israel, New York, United States; Icahn Lami, PhD, Baghdad College of Medicine, Baghdad, Iraq. Khurshid School of Medicine at Mount Sinai, New York City, New York, Alam, PhD, Murdoch Childrens Research Institute, The University of United States. Samath D. Dharmaratne, MD, Department of Com- Melbourne, Parkville, Victoria, Australia; The University of Mel- munity Medicine, Faculty of Medicine, University of Peradeniya, bourne, Melbourne, VIC, Australia; The University of Sydney, Syd- Peradeniya, Sri Lanka. Shirin Djalalinia, PhD, Undersecretary for ney, NSW, Australia. Deena Alasfoor, MSc, Ministry of Health, Al Research & Technology, Ministry of Health & Medical Education, Khuwair, Oman. Reza Alizadeh-Navaei, PhD, Gastrointestinal Can- Tehran, Iran. Kerrie E. Doyle, PhD, RMIT University, Bundoora, cer Research Center, Mazandaran University of Medical Sciences, VIC, Australia; Australian National University, Canberra, ACT, Sari, Iran. Fatma Al-Maskari, PhD, College of Medicine & Health Australia. Aman Yesuf Endries, MPH, Arba Minch University, Arba Sciences, United Arab Emirates University, Al-Ain City, Minch, Ethiopia. Babak Eshrati, PhD, Ministry of Health and Medical United Arab Emirates. Rajaa Al-Raddadi, PhD, Joint Program of Education, Tehran, Iran; Arak University of Medical Sciences, Arak, Family and Community Medicine, Jeddah, Saudi Arabia. Khalid A. Iran. 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Theo Vos, PhD, Institute for Health Metrics and MS Research Center, Neuroscience Institute, Tehran University of Evaluation, University of Washington, Seattle, Washington, United Medical Sciences, Tehran, Iran. Payman Salamati, MD, Sina Trauma States. Simon I. Hay, DSc, Oxford Big Data Institute, Li Ka Shing and Surgery Research Center, Tehran University of Medical Sciences, Centre for Health Information and Discovery, University of Oxford, Tehran, Iran. Abdallah M. Samy, PhD, Ain Shams University, Cairo, Oxford, UK; Institute for Health Metrics and Evaluation, University Egypt, United States. Benn Sartorius, PhD, Public Health Medicine, of Washington, Seattle, Washington, United States. Mohsen Naghavi, School of Nursing and Public Health, University of KwaZulu-Natal, PhD, Institute for Health Metrics and Evaluation, University of Durban, South Africa; UKZN Gastrointestinal Cancer Research Washington, Seattle, Washington, United States. Christopher J. Centre, South African Medical Research Council (SAMRC), Durban, L. Murray, DPhil, Institute for Health Metrics and Evaluation, South Africa. Sadaf G. Sepanlou, PhD, Digestive Diseases Research University of Washington, Seattle, Washington, United States. Institute, Tehran University of Medical Sciences, Tehran, Iran. Masood Ali Shaikh, MD, Independent Consultant, Karachi, Pakistan. Compliance with ethical standards Morteza Shamsizadeh, MPH, Department of Medical Surgical Nurs- ing, School of Nursing and Midwifery, Hamadan University of Ethical approval This manuscript reﬂects original work that has not Medical Sciences, Hamadan, Iran. Ephrem Lejore Sibamo Sibamo, previously been published in whole or in part and is not under con- MPH, Wolaita Sodo University, Wolaita Sodo, Ethiopia. Jasvinder A. sideration elsewhere. All authors have read the manuscript and have Singh, MD, University of Alabama at Birmingham and Birmingham agreed that the work is ready for submission and accept responsibility Veterans Affairs Medical Center, Birmingham, Alabama, United for its contents. The authors of this paper have complied with all States. Badr H. A. Sobaih, MD, King Saud University, Riyadh, Saudi ethical standards and do not have any conﬂicts of interest to disclose Arabia. Sergey Soshnikov, PhD, Federal Research Institute for Health at the time of submission. The funding source played no role in the Organization and Informatics, Ministry of Health of the Russian design of the study, the analysis and interpretation of data, and the Federation, Moscow, Russia. Muawiyyah Babale Suﬁyan, MBA, writing of the paper. The study did not involve human participants Ahmadu Bello University, Zaria, Nigeria. Bryan L. Sykes, PhD, and/or animals; therefore, no informed consent was needed. Departments of Criminology, Law & Society, Sociology, and Public Health, University of California, Irvine, Irvine, CA, United States. Funding This research was funded by the Bill & Melinda Gates Nuno Taveira, PhD, Instituto Superior de Cieˆncias da Sau´de Egas Foundation. Moniz, Almada, Portugal; Faulty of Pharmacy, Universidade de Lisboa, Lisboa, Portugal. Teketo Kassaw Tegegne, MPH, Debre Conﬂict of interest The authors declare that they have no conﬂicts of Markos University, Debre Markos, Ethiopia. Arash Tehrani-Bani- interest at this time. hashemi, PhD, Preventive Medicine and Public Health Research Center, Iran University of Medical Sciences, Tehran, Iran. Tesfalidet Open Access This article is distributed under the terms of the Tekelab, MS, Wollega University, Nekemte, Ethiopia; University of Creative Commons Attribution 4.0 International License (http://crea Newcastle, Newcastle, New South Wales, Australia. Girma Temam tivecommons.org/licenses/by/4.0/), which permits unrestricted use, Shifa, MPH, Arba Minch University, Arba Minch, Ethiopia; Addis distribution, and reproduction in any medium, provided you give Ababa University, Addis Ababa, Ethiopia. Mohamad-Hani Temsah, appropriate credit to the original author(s) and the source, provide a MD, King Saud University, Riyadh, Saudi Arabia. Belay Tesssema, link to the Creative Commons license, and indicate if changes were PhD, University of Gondar, Gondar, Ethiopia. Roman Topor-Madry, made. PhD, Institute of Public Health, Faculty of Health Sciences, Jagiel- lonian University Medical College, Krakow, Poland; Faculty of Health Sciences, Wroclaw Medical University, Wroclaw, Poland. Kingsley Nnanna Ukwaja, MD, Department of Internal Medicine, References Federal Teaching Hospital, Abakaliki, Nigeria. Olalekan A. 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