Burden of obesity in the Eastern Mediterranean Region: findings from the Global Burden of Disease 2015 study

Burden of obesity in the Eastern Mediterranean Region: findings from the Global Burden of Disease... Int J Public Health (2018) 63 (Suppl 1):S165–S176 https://doi.org/10.1007/s00038-017-1002-5 O R I G IN AL ARTI CL E Burden of obesity in the Eastern Mediterranean Region: findings from the Global Burden of Disease 2015 study GBD 2015 Eastern Mediterranean Region Obesity 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 Conclusions This is the first study to estimate trends in Objectives We used the Global Burden of Disease (GBD) obesity burden for the EMR from 1980 to 2015. We call for 2015 study results to explore the burden of high body mass EMR countries to invest more resources in prevention and index (BMI) in the Eastern Mediterranean Region (EMR). health promotion efforts to reduce this burden. Methods We estimated the prevalence of overweight and obesity among children (2–19 years) and adults Keywords Obesity  Burden of disease  Eastern (C20 years) in 1980 and 2015. The burden of disease Mediterranean Region related to high BMI was calculated using the GBD com- parative risk assessment approach. Results The prevalence of obesity increased for adults Introduction from 15.1% (95% UI 13.4–16.9) in 1980 to 20.7% (95% UI 18.8–22.8) in 2015. It increased from 4.1% (95% UI High body mass index (BMI), or overweight, is associated 2.9–5.5) to 4.9% (95% UI 3.6–6.4) for the same period with increased morbidity and mortality, and is a major risk among children. In 2015, there were 417,115 deaths and factor for diabetes, cancer, cardiovascular diseases, sleep 14,448,548 disability-adjusted life years (DALYs) apnea, and poor physical health (Kim et al. 2016; Yao et al. attributable to high BMI in EMR, which constitute about 2017; Kelly et al. 2017; Mehta et al. 2017). It is also 10 and 6.3% of total deaths and DALYs, respectively, for associated with an increased risk for psychiatric disorders, all ages. including depression (Pratt and Brody 2014; Abou Abbas et al. 2015). A continuous rise in obesity is threatening health improvements in many countries (Sidney et al. 2016; This article is part of the supplement ‘‘The state of health in the GBD 2015 Obesity Collaborators 2017), while controlling Eastern Mediterranean Region, 1990–2015.’’ its spread could drastically improve population health (Maciosek et al. 2017). The members of GBD (Global Burden of Disease) 2015 Eastern Mediterranean Region Obesity Collaborators are listed at the end of The increase in overweight and obesity prevalence is a the article. Ali H. Mokdad, on behalf of GBD 2015 Eastern direct result of lifestyle changes due to the social and Mediterranean Region Obesity Collaborators, is the corresponding demographic transition that started several decades ago author. (Broyles et al. 2015; Mokdad et al. 2016). The Eastern Electronic supplementary material The online version of this Mediterranean Region (EMR) is facing the same chal- article (doi:10.1007/s00038-017-1002-5) contains supplementary lenges due to rapid economic, demographic, and lifestyle material, which is available to authorized users. changes, including changes in food consumption, reduced & GBD 2015 Eastern Mediterranean Region Obesity physical activity, and increased sedentary lifestyle (Mu- Collaborators saiger et al. 2012; Mokdad et al. 2016). The contribution of mokdaa@uw.edu high BMI to total DALYs in the EMR had increased in 2013 (7.5% of DALYs) in comparison to 1990 (3.7% of Institute for Health Metrics and Evaluation, University of DALYs) (Mokdad et al. 2016). Kuwait, Qatar, and Libya, Washington, Seattle, WA, USA 123 S166 GBD 2015 Eastern Mediterranean Region Obesity Collaborators three EMR countries, were among the top ten countries Briefly, Medline was systematically searched for studies with highest prevalence of obesity worldwide in 2013, and providing nationally or subnationally representative esti- the man believed to be the heaviest living person was mates of overweight prevalence, obesity prevalence, or diagnosed in Saudi Arabia (Ng et al. 2014; Terkawi et al. mean body mass index (BMI) published between 1 January 2014). 2014 and 31 December 2015 to update the GBD 2013 The EMR has a population of about 583 million people systematic literature search (Ng et al. 2016). (World Health Organization 2016). Countries in the EMR For adults, 127 out of 2036 abstracts identified met vary significantly in terms of their gross domestic product, inclusion criteria and were extracted. For children, 146 out socio-demographic profiles, health indicators, and health of 971 articles identified were extracted. In total, 816 system capacities and coverage. Despite the heavy burden articles were included in the analysis. Additionally, the of high BMI in the region, no comprehensive and current Global Health Data Exchange (GHDx) database was estimates of the epidemic exist for the EMR. searched for individual-level data from major multinational To quantify the burden of high BMI in the EMR and its survey series or country-specific surveys and identified impact on health, we systematically evaluated the trends in 1026 unique sources meeting the inclusion criteria. Of the prevalence of overweight and obesity as well as the pat- 816 articles and 1026 unique sources, all those pertaining terns of deaths and DALYs related to high BMI by age and to EMR countries were included. The GBD 2015 results sex, using the results of the Global Burden of Disease tool from the GHDx allows readers to view, country by (GBD) 2015 study. We also estimated the country, what data sources have been used to produce these attributable fraction of high BMI to ischemic heart disease, estimates (Institute for Health Metrics and Evaluation stroke, and diabetes mellitus, the three leading non-com- 2016). municable causes of death in EMR, for which high BMI is For adults, overweight was defined as a risk factor. 25.0 B BMI B 30 kg/m , and obesity was defined as BMI C30 kg/m . The International Obesity Task Force defini- tion was used for childhood overweight and obesity (Cole Methods et al. 2000). Children were defined as individuals 2–19 years of age The prevalence of overweight and obesity among children based on the lowest age for which the International Obesity (2–19 years) and adults (C20 years) in 1980 and 2015 was Task Force provides a definition of overweight and obesity, estimated for EMR countries. The EMR countries, based and the age groups used in GBD modeling, which include on the World Health Organization classification, are 19 in the age group 15–19 (Cole et al. 2000; Wang et al. Afghanistan, the Kingdom of Bahrain, Djibouti, the Arab 2016). Republic of Egypt, the Islamic Republic of Iran, the Briefly, a spatiotemporal Gaussian process regression Republic of Iraq, the Hashemite Kingdom of Jordan, the (ST-GPR) was used to estimate the mean prevalence of State of Kuwait, the State of Lebanon, the State of Libya, overweight and obesity (Ng et al. 2014). To improve the Kingdom of Morocco, the Sultanate of Oman, the estimates for countries with sparse data, three country-level Islamic Republic of Pakistan, Palestine, the State of Qatar, covariates with best fit and coefficients in the expected the Kingdom of Saudi Arabia, the Federal Republic of direction were selected: 10-year lag-distributed energy Somalia, the Republic of Sudan, the Syrian Arab Republic, intake per capita, the absolute latitude of the country as the Republic of Tunisia, the United Arab Emirates, and the proxy for income, and the proportion of people living in Republic of Yemen. urban areas. These covariates have been systematically The burden of disease related to high BMI was calcu- evaluated in a previous study (Ng et al. 2014). lated using the GBD comparative risk assessment approach The Bradford Hill criteria for causation and the World between 1990 and 2015 (Forouzanfar et al. 2015, 2016). A Cancer Research Fund evidence grading criteria were used detailed methodology of BMI estimation for GBD 2015 has to systematically evaluate epidemiologic evidence sup- been published elsewhere (GBD 2015 Obesity Collabora- porting the causal relationship between high BMI and tors 2017). Since burden estimations depend on GBD all- various diseases among adults (C20 years of age) (Hill cause mortality, burden of high BMI is only available for 1965; World Cancer Research Fund and American Institute the period 1990–2015. We used all available data surveys for Cancer Research 2007). following a systematic search. The search strategy as well The population-attributable fraction by country, age, as data sources used per country have been published as an sex, and year was calculated to quantify the burden of appendix elsewhere and are available from the Global disease related to high BMI, defined as BMI C25 kg/m , Health Data Exchange (Institute for Health Metrics and for each disease. Deaths and DALYs related to high BMI Evaluation 2016; GBD 2015 Obesity Collaborators 2017). for each country, age, sex, year, and cause were computed 123 Burden of obesity in the Eastern Mediterranean Region: findings from the Global Burden of… S167 by multiplying the population-attributable fraction by the total deaths or DALYs estimated in GBD 2015 for that country, age, sex, year, and cause. The total disease burden of high BMI was calculated as the sum of disease-specific burden. 95% uncertainty intervals (UI) for all results were computed using Monte Carlo simulations, keeping 1000 draws of each quantity of interest to propagate uncertainty into final estimates. Expected estimates were also produced for each country Adults Children based on its Socio-demographic Index (SDI)—a summary 1980 2015 measure of lag-distributed income per capita, average Fig. 1 Prevalence of obesity among adults aged 20 years or older educational attainment over the age of 15 years, and total and children aged 2–19 years in 1980 and 2015 (Global Burden of fertility rate (Forouzanfar et al. 2015). In GBD 2015, SDI Disease 2015 study, Eastern Mediterranean Countries, 1980 and was computed by rescaling each component to a scale of 2015) zero to one, with zero being the lowest observed educa- tional attainment, lowest income per capita, and highest 8.3–12.8) for females (e-Table 1), and in Yemen and Pakistan for children 2–19 years of age: 1.3% (95% UI fertility rate from 1980 to 2015, and one being the highest observed educational attainment, highest income per cap- 0.9–1.8) for males in Yemen and 2.2% (95% UI 1.4–3.3) ita, and lowest fertility rate during that time, and then for females in Pakistan (e-Table 2). Prevalence of obesity taking the geometric mean of these values for each loca- was higher in females than males 20 years or older for all tion-year. countries, with Sudan having the highest difference This study followed the Guidelines for Accurate and between sexes: 11.4% (95% UI 10.0–13.1) for males and Transparent Health Estimates Reporting (GATHER) of the 28.3% (95% UI 25.6–31.2) for females (e-Table 1). The World Health Organization (WHO) regarding documenta- highest difference in obesity prevalence between sexes for tion of data sources, estimation methods, and statistical children was observed in Qatar: 20.8% (95% UI 16.5–25.1) analysis (Stevens et al. 2016). for males and 13.5% (95% UI 10.3–17.1) for females. In children, prevalence of obesity was higher in males for several countries (e-Table 2). Role of the funding source Deaths The Bill & Melinda Gates Foundation had no role in the development of these methods. In 2015, there were 417,115 deaths attributable to high BMI in EMR, which constitute about 10% of total deaths in Results the region for all ages. This is a rate of 120.1 (95% UI 87.5–156.2) deaths per 100,000 population, an 11% The mean BMI increased from 25.2 kg/m [95% uncer- increase since 1990. It contributed to 5.0, 0.9, and 1.9% of tainty interval (UI) 24.9–25.5] in 1980 to 26.0 kg/m (95% all deaths cause by ischemic heart disease, ischemic stroke, UI 25.8–26.3) in 2015 in the EMR among persons aged and diabetes mellitus, respectively. Contribution by speci- fic age groups is detailed in Table 1. In 2015, the rate of 20 years or older. The prevalence of obesity increased from 15.1% (95% UI 13.4–16.9) to 20.7% (95% UI 18.8–22.8) deaths attributable to high BMI was highest in Afghanistan, for the same period and for the same age group (Fig. 1). It 227.6 (95% UI 146.2–319.5), and lowest in Tunisia, 64.8 increased from 4.1% (95% UI 2.9–5.5) to 4.9% (95% UI (95% UI 42.7–92.5) per 100,000 population (Table 2). 3.6–6.4) for the same period among those aged 2–19 years Overall, death rates attributable to high BMI have (Fig. 1). The highest prevalence of obesity among adults increased in 11 countries and decreased in 11 (Table 2). 20 years or older in 2015 was observed in Qatar: 42.5% The largest increase in deaths per 100,000 population (95% UI 40.1–44.8) for males and 52.4% (95% UI attributable to high BMI was observed in Djibouti: from 50.3–54.5) for females (e-Table 1); and the highest for 37.3 (95% UI 15.1–70.6) in 1990 to 101.1 (95% UI children 2–19 years was observed in Kuwait: 22.1% (95% 47.5–192.7) in 2015 (Table 2). The largest decrease was UI 17.8–27.0) for males and 19.2% (95% UI 15.2–23.4) for observed in Lebanon: from 124.8 (95% UI 85.2–169.7) in 1990 to 72.5 (95% UI 47.0–101.0) in 2015 (Table 2). females (e-Table 2). The lowest prevalence of obesity was observed in Somalia among individuals 20 years or older: 2.5% (95% UI 1.5–4.0) for males and 10.5% (95% UI Prevalence % S168 GBD 2015 Eastern Mediterranean Region Obesity Collaborators Table 1 Deaths, with 95% uncertainty intervals (UI), per 100,000 (Global Burden of Disease 2015 study, Eastern Mediterranean population attributable to high body mass index among those who countries, 1990 and 2015) died from ischemic heart disease, stroke, and diabetes, by age groups Age Cause 1990 2015 Males Females Males Females 15–49 Ischemic heart disease 10.1 (6.0–14.7) 7.0 (4.5–9.5) 12.1 (7.6–17.2) 6.9 (4.7–9.3) Stroke 7.1 (4.6–9.8) 8.1 (5.8–10.6) 7.2 (4.7–9.9) 7.3 (5.3–9.6) Diabetes 1.7 (1.2–2.3) 2.0 (1.5–2.6) 2.6 (1.8–3.3) 2.9 (2.2–3.6) 50–69 Ischemic heart disease 118.4 (67.9–177.4) 118.1 (80.0–161.8) 134.4 (82.3–191.6) 107.1 (74.8–142.7) Stroke 58.1 (33.6–85.1) 77.8 (53.9–107.7) 60.6 (38.4–85.5) 68.7 (49.1–91.2) Diabetes 24.8 (15.7–34.4) 36.4 (25.1–50.1) 39.2 (27.2–52.3) 53.4 (40.6–67.8) 70? Ischemic heart disease 283.8 (141.5–460.9) 366.1 (225.5–539.6) 336.9 (183.3–532.9) 361.0 (222.4–520.7) Stroke 117.4 (58.4–198.2) 163.6 (96.9–248.3) 125.8 (65.8–203.8) 150.7 (89.1–223.9) Diabetes 57.7 (30.8–89.2) 81.9 (47.6–130.2) 106.5 (59.1–163.1) 160.3 (103.1–224.8) DALYs for high BMI was higher than expected for most countries in the region based on their SDI levels. Our study calls for In 2015, there were 14,448,548 DALYs attributable to high renewed efforts to reduce the burden of obesity in the BMI in the EMR, which constitutes about 6.3% of total DALYs region. Indeed, with further progression of the epidemio- in the region for all ages. This is a rate of 3452.5 (95% UI logic transition and the growth and aging of the EMR 2599.2–4386.5) DALYs per 100,000 population, a 13.9% population, high BMI will increase the burden of chronic increase since 1990. It contributed to 3.0, 0.5, and 2.3% of all conditions and disability and put financial and resource DALYs caused by ischemic heart disease, stroke, and diabetes strains on the health systems. mellitus, respectively. Contribution by specific age groups is High BMI is observed in some poor and rich countries detailed in Table 3. In 2015, the rate of DALYs attributable to of the EMR. In developed countries such as the United high BMI was highest in Afghanistan, 6576.7 (95% UI States and France, obesity is higher among low socioeco- 4366.1–9219.6), and lowest in Tunisia, 2022.4 (95% UI nomic strata of the population (Drewnowski et al. 2014). 1395.1–2724.3) per 100,000 population. Trends in DALYs These patterns in the West are attributable to poor diet and followed trends in deaths from high BMI in all countries. The lower physical activity levels. Previous studies have largest increase in DALYs per 100,000 population attributable reported similar findings for both socioeconomic status and to high BMI was observed in Djibouti: from 1111.0 (95% UI sex in the EMR (Musaiger 2011a). In the EMR, except for 492.8–2005.8) in 1990 to 2810.8 (95% UI 1432.5–5212.9) in Iran and Lebanon, all high-middle- and high-SDI countries 2015 (Table 4). The largest decrease was observed in Lebanon: had an obesity prevalence equaling or exceeding 25% for from 3552.4 (95% UI 2508.2–4645.4) in 1990 to 2363.8 (95% both males and females 20 years or older. Obesity preva- UI 1707.9–3077.2) in 2015. lence exceeded 10% among children 2–19 years only in these countries as well, except for Bahrain, Jordan, Leba- Expected versus observed non, and Iran. Djibouti and Egypt were the only low- and middle-SDI countries where childhood obesity exceeded Overall, and based on an SDI of 0.55, a death rate of 74.0 10% for females. These country-level estimates might be and a DALYs rate of 2114.3 per 100,000 population were masking variations of the epidemic within each of the expected for the EMR in 2015 for high BMI. Both are countries. Indeed, few studies have been done at the lower than the observed death rate of 120.1 (95% UI country level in the EMR, and these showed a variation in 87.5–156.2) and DALYs rate of 3452.5 (95% UI BMI levels between levels of education and income. High 2599.2–4386.5) for the same year. Expected estimates for BMI was more likely to impact those with low educational each country are detailed in Tables 3 and 4. levels (Sibai et al. 2003; Memish 2014). Unfortunately, our estimations for observed obesity burden were higher than expected for the region, and based Discussion on SDI, which only deals with socioeconomic inequalities between countries. However, the biggest gaps between the This is the first study to provide estimates of trends in included countries are cultural factors and obesogenic obesity prevalence, deaths, and DALYs for the EMR from cultural traditions, as well as political instability (wars, 1980 to 2015. Our study showed that the observed burden civil unrest,) all of which are linked to obesity but not 123 Burden of obesity in the Eastern Mediterranean Region: findings from the Global Burden of… S169 Table 2 Deaths, with 95% uncertainty intervals (UI), per 100,000 population due to high body mass index observed in 1990 and 2015, and expected based on Socio-demographic Index in 2015 (Global Burden of Disease 2015 study, Eastern Mediterranean countries, 1990 and 2015) Country 1990 deaths 2015 deaths Total observed Total expected Males Females Males Females Afghanistan 166.9 (80.9–283.8) 296.3 (170.9–460.1) 160.5 (78.6–268.2) 292.1 (166.4–450.7) 27,827.4 (17,910.5–39,914.8) 5797.5 Bahrain 133.2 (84.8–191.7) 175.2 (129.1–222.4) 97.7 (62.1–141.3) 99.2 (68.9–130.4) 557.5 (421.6–709.1) 443.3 Djibouti 37.1 (9.8–94.0) 36.7 (11.8–82.8) 102.0 (35.3–229.3) 99.1 (37.7–231.1) 467.9 (211.4–929.9) 312.5 Egypt 153.2 (98.2–207.3) 154.4 (116.9–192.2) 176.2 (123.4–228.0) 154.6 (121.5–188.6) 90,774.1 (73,283.6–108,972.5) 48,851.2 Iran 90.7 (44.8–146.5) 98.9 (63.3–138.2) 102.3 (56.9–162.0) 94.4 (59.8–137.7) 49,386.0 (33,333.8–68,369.9) 44,524.9 Iraq 234.4 (158.3–335.3) 243.7 (171.0–324.3) 217.4 (129.1–321.1) 206.3 (136.0–295.7) 30,963.7 (22,483.9–41,095.5) 12,627.7 Jordan 144.8 (93.7–209.6) 198.5 (146.6–258.2) 118.8 (80.1–160.0) 107.3 (80.4–136.1) 3687.0 (2946.9–4478.1) 3082.9 Kuwait 92.2 (64.5–120.0) 115.1 (89.1–142.1) 93.9 (65.2–126.1) 93.6 (70.6–120.7) 1335.8 (1071.0–1657.8) 670.7 Lebanon 127.0 (70.4–198.5) 122.6 (76.9–179.0) 69.4 (35.3–113.1) 75.2 (44.8–109.8) 3565.9 (2320.6–4940.1) 3803.3 Libya 81.8 (49.9–120.0) 103.5 (72.0–137.6) 96.8 (59.6–141.4) 109.7 (78.2–148.0) 3727.3 (2795.3–4725.7) 3261.4 Morocco 85.0 (45.4–132.0) 103.3 (65.8–148.5) 77.4 (40.8–129.9) 102.6 (57.3–158.6) 22,383.7 (14,985.5–31,865.0) 18,621.5 Oman 82.0 (46.3–131.0) 89.4 (51.9–140.9) 92.3 (56.9–131.3) 93.4 (63.0–123.8) 1575.4 (1161.4–2011.7) 1619.5 Pakistan 54.3 (22.0–98.4) 67.1 (33.2–113.4) 105.9 (169.5–54.7) 110.6 (64.32–166.3) 110,546.2 (72,034.6–153,911.6) 67,064.8 Palestine 114.4 (63.3–182.1) 128.0 (77.6–197.6) 149.7 (86.8–225.0) 118.2 (77.7–170.3) 2347.3 (1655.2–3168.4) 1502.5 Qatar 142.7 (101.7–187.8) 209.5 (161.3–263.6) 106.6 (69.3–154.4) 126.1 (88.2–169.1) 567.3 (424.8–747.7) 479.1 Saudi Arabia 75.2 (46.7–107.1) 81.9 (59.0–108.4) 84.5 (57.9–114.5) 68.3 (51.6–87.4) 10,888.8 (8682.8–13,471.6) 11,340.5 Somalia 45.3 (8.4–127.5) 107.8 (24.8–251.0) 43.5 (9.5–112.7) 101.0 (24.2–242.5) 3058.6 (722.6–7430.6) 1761.52 Sudan 96.5 (47.9–163.1) 158.6 (95.9–242.4) 101.0 (48.2–171.3) 146.7 (80.2–230.8) 21,814.0 (14,145.4–31,022.9) 10,515.2 Syria 141.1 (88.4–207.5) 145.8 (99.7–205.0) 112.7 (67.3–164.2) 99.3 (67.9–131.0) 9987.0 (7397.8–12,880.3) 8109.9 Tunisia 72.6 (37.9–111.7) 75.4 (49.4–105.8) 71.9 (37.2–118.8) 58.4 (34.7–87.3) 6256.6 (4163.4–8889.9) 8682.7 United Arab Emirates 128.6 (76.3–188.5) 153.1 (100.1–214.7) 103.9 (60.2–156.2) 103.8 (68.8–148.4) 4460.4 (2941.6–6206.4) 1648.5 Yemen 74.5 (27.2–149.7) 106.8 (43.0–205.8) 78.1 (31.1–158.7) 124.4 (59.2–225.4) 10,936.6 (5668.1–19,184.0) 5848.1 S170 GBD 2015 Eastern Mediterranean Region Obesity Collaborators Table 3 Disability-adjusted life years (DALYs) and years lived with disability (YLDs), with 95% uncertainty intervals (UI), per 100,000 population attributable to high body mass index among DALYs and YLDs due to ischemic heart disease, stroke, and diabetes, by age groups (Global Burden of Disease 2015 study, Eastern Mediterranean countries, 1990 and 2015) Age Cause DALYs YLDs 1990 2015 1990 2015 Males Females Males Females Males Females Males Females 15–49 Ischemic heart 473.0 328.0 560.1 327.5 5.9 (3.0–9.7) 5.3 (3.0–8.5) 9.9 (5.5–15.9) 8.9 (5.3–13.7) disease (278.3–690.8) (211.6–453.7) (352.0–801.7) (222.9–436.6) Stroke 351.1 398.8 353.0 361.8 12.7 (7.0–20.1) 16.8 (10.5–24.9) 16.8 (9.9–25.6) 22.2 (14.2–31.4) (225.4–483.7) (286.0–519.7) (234.5–484.9) (261.7–468.2) Diabetes 251.7 299.3 419.6 483.5 173.6 206.7 303.4 351.3 (163.0–351.2) (216.5–399.8) (284.2–577.4) (351.3–640.4) (103.8–262.9) (133.2–297.0) (187.6–448.4) (229.4–491.8) 50–69 Ischemic heart 3357.1 3270.6 3898.1 3014.3 74.0 73.6 116.6 105.3 disease (1918.0–5002.9) (2220.9–4466.6) (2392.9–5519.0) (2122.1–3987.9) (35.4–126.3) (41.8–114.4) (60.1–192.0) (61.8–157.8) Stroke 1686.2 2247.9 1792.3 2015.4 64.4 87.3 81.1 99.5 (62.9–146.0) (990.7–2460.1) (1556.8–3081.0) (1132.3–2514.2) (1440.7–2648.3) (32.9–106.1) (53.5–133.0) (45.0–126.4) Diabetes 1471.0 2091.7 2417.9 3190.4 787.0 1098.0 1330.1 1736.2 (909.2–2097.8) (1497.4–2815.7) (1604.3–3312.3) (2388.5–4121.2) (431.0–1229.8) (689.5–1605.4) (769.0–1994.0) (1119.0–2482.3) 70? Ischemic heart 4001.4 4951.1 4600.8 4690.5 92.0 110.2 138.4 155.1 disease (2028.0–6420.1) (3092.1–7158.9) (2513.9–7234.6) (2977.1–6737.5) (42.0–167.8) (61.8–180.6) (66.9–240.6) (88.7–248.2) Stroke 1789.7 2449.0 1875.5 2200.4 60.1 78.8 72.2 90.8 (53.2–142.7) (919.0–2943.9) (1480.2–3678.2) (991.2–2972.6) (1362.7–3160.4) (27.2–106.5) (43.8–128.7) (34.3–123.3) Diabetes 1342.6 1933.4 2345.0 3392.0 532.3 802.8 921.4 1307.7 (725.2–2113.3) (1197.4–2848.9) (1336.8–3535.0) (2234.9–4736.3) (258.2–920.5) (468.0–1261.9) (471.6–1515.4) (782.1–1998.0) Burden of obesity in the Eastern Mediterranean Region: findings from the Global Burden of… S171 Table 4 Disability-adjusted life years, with 95% uncertainty intervals (UI) per 100,000 population due to high body mass index observed in 1990 and 2015, and expected based on Socio- demographic Index in 2015 (Global Burden of Disease 2015 study, Eastern Mediterranean countries, 1990 and 2015) Country 1990 2015 Total observed Total expected Males Females Males Females Afghanistan 4767.5 (2400.1–7893.8) 8437.9 (4934.2–12,974.6) 4677.5 (2411.1–7598.2) 8491.9 (5022.7–13,139.8) 965,879.5 (648,967.1–1,371,214.6) 206,389.0 Bahrain 4039.9 (2769.8–5453.6) 4907.8 (3800.7–6091.1) 3109.6 (2127.8–4224.1) 3177.8 (2414.8–4028.7) 29,225.3 (22,628.0–36,438.7) 18,576.5 Djibouti 1189.7 (362.1–2830.1) 1023.3 (378.0–2220.0) 2974.8 (1138.0–6633.1) 2633.4 (1192.2–5883.4) 15,630.0 (7793.1–29,440.4) 10,761.1 Egypt 4317.3 (2880.3–5717.9) 4251.7 (3367.6–5153.0) 5025.6 (3688.3–6374.7) 4435.2 (3623.2–5284.9) 3,037,204.4 (2,478,932.1–3,605,791.4) 1,666,166.5 Iran 2509.4 (1318.4–3910.9) 2733.1 (1877.6–3632.2) 2846.6 (1712.4–4306.3) 2696.7 (1880.0–3776.6) 1,698,192.1 (1,199,771.9–2,259,245.3) 1,493,421.0 Iraq 6502.5 (4537.2–8970.9) 6404.8 (4660.0–8287.8) 6321.0 (4108.7–9096.4) 5726.9 (4046.8–7892.3) 1,090,796.9 (821,609.9–1,421,193.6) 452,426.9 Jordan 4299.2 (2938.7–5943.2) 5362.9 (4166.0–6773.0) 3633.7 (2593.7–4668.3) 3176.4 (2469.7–3918.3) 141,869.1 (112,808.2–172,138.2) 106,862.3 Kuwait 2861.0 (2115.1–3562.8) 3229.1 (2627.2–3868.5) 3026.5 (2239.0–3850.2) 2715.3 (2167.4–3389.3) 69,567.5 (55,117.3–84,812.6) 32,170.1 Lebanon 3664.5 (2191.8–5471.8) 3454.1 (2306.5–4827.3) 2251.5 (1351.6–3307.2) 2480.0 (1658.6–3401.0) 123,560.0 (89,344.0–160,488.7) 110,981.5 Libya 2379.4 (1551.1–3309.5) 3037.5 (2274.4–3882.7) 2904.3 (1926.1–3953.5) 3285.7 (2464.7–4171.7) 139,878.3 (108,615.2–173,319.3) 116,450.8 Morocco 2490.8 (1420.6–3677.6) 2952.1 (1987.8–4068.0) 2336.9 (1332.7–3654.0) 3032.9 (1949.9–4325.84 774,701.5 (555,295.0–1,031,236.8) 600,106.4 Oman 2471.6 (1506.0–3723.1) 2805.3 (1835.8–4077.2) 2927.1 (1965.5–3972.0) 3097.2 (2308.7–3934.5) 75,807.5 (57,042.8–96,463.6) 64,799.1 Pakistan 1543.7 (657.2–2752.2) 1869.4 (995.8–3001.1) 3009.9 (1619.5–4710.8) 3083.7 (1950.6–4398.8) 3,608,879.0 (2,420,204.5–4,972,204.0) 2,240,433.8 Palestine 3235.1 (1896.7–4980.2) 3467.5 (2290.6–5022.7) 4300.8 (2694.9–6204.2) 3206.5 (2240.2–4368.5) 82,929.2 (61,735.5–109,791.4) 54,335.0 Qatar 4074.6 (3099.9–5097.3) 5538.0 (4495.6–6656.9) 3236.7 (2297.7–4272.4) 3669.8 (2825.1–4589.9) 36,660.9 (27,986.6–46,001.8) 23,177.6 Saudi Arabia 2296.3 (1512.6–3138.8) 2558.8 (1946.2–3250.2) 2507.0 (1808.4–3262.0) 2165.4 (1693.7–2699.8) 490,128.6 (383,013.6–601,300.7) 440,497.5 Somalia 1463.9 (330.5–3897.1) 2980.2 (780.2–6721.4) 1325.4 (330.3–3416.1) 2828.7 (834.7–6943.8) 102,092.6 (31,197.6–246,633.4) 59,469.2 Sudan 2883.9 (1513.0–4693.9) 4378.6 (2790.2–6417.8) 3057.9 (1567.6–4893.5) 4128.4 (2480.3–6203.9) 764,380.1 (523,579.9–1,052,574.8) 364,535.7 Syria 4198.1 (2692.6–5980.2) 4090.6 (2917.9–5509.0) 3305.8 (2135.7–4629.7) 2889.2 (2118.5–3677.0) 347,354.1 (267,569.5–437,633.2) 276,598.4 Tunisia 2065.4 (1134.9–3084.8) 2248.3 (1594.2–2947.4) 2165.4 (1242.3–3352.4) 1889.1 (1269.8–2570.8) 217,846.2 (151,390.5–292,471.8) 272,878.3 United Arab Emirates 4028.4 (2596.4–5648.6) 4675.9 (3270.4–6389.7) 3425.1 (2209.4–4864.9) 3491.9 (2522.1–4661.1) 242,427.0 (171,328.5–322,519.2) 87,802.7 Yemen 2247.0 (881.5–4390.2) 3017.4 (1304.3–5529.8) 2373.0 (1016.2–4579.6) 3639.9 (1893.9–6356.4) 393,538.0 (216,892.2–664,897.8) 207,048.3 S172 GBD 2015 Eastern Mediterranean Region Obesity Collaborators measured by SDI (Ulijaszek 2007). Cultural factors and Indeed, estimates from the GBD study show an increase obesogenic cultural traditions disproportionally affect in dietary risk factors and low levels of physical activity women, highlighting cross-cutting gender issues in (Forouzanfar et al. 2016). What the EMR populations women’s health (Shapira 2013). This might explain the consume can be directly affected by national policies, observed gender differences in overweight and obesity. specifically those around food economics. For instance, in Similarly, social determinants of health in countries in many EMR countries such as Egypt, Morocco, and Saudi conflict tend to differ as morbidity and mortality are Arabia, governments subsidize grains, bread, wheat flour, associated with conflict (World Health Organization 2008). sugar, and cooking oil, and impose taxes on imported foods More importantly, wars and civil unrest are closely asso- (Asfaw 2007; Musaiger 2011b). However, fruits and veg- ciated with child malnutrition, which in turn is associated etables are neither subsidized nor exempt from import with increased risk of obesity, hypertension, cardiovascular taxes, making healthier food choices harder for the popu- disease, and type 2 diabetes (Devakumar et al. 2014; lation. As for physical activity, the lack of exercise facil- Charchuk et al. 2015). This leads to the intergenerational ities has been reported, at least in Egypt and Saudi Arabia, effects of war on obesity and the doubled burden of as the main reason for low physical activity (Al-Rafaee and undernutrition in countries affected by wars or experienc- Al-Hazzaa 2001; El-Gilany et al. 2011). ing chronic civil unrest (Devakumar et al. 2014). This study has a few limitations. These include possible Despite this increase in obesity, EMR countries can underestimation of the prevalence of obesity or disease hope to control their epidemic as declines in obesity burden from high BMI as self-reported height and weight prevalence have been reported in other countries due to data were used. However, we corrected these based on health interventions and environmental and policy changes measured data at each age, sex, and country unit (GBD (Schmidt Morgen et al. 2013; Keane et al. 2014). EMR 2015 Obesity Collaborators 2017). Briefly, no significant countries can benefit from implementing an array of proven difference between measured and self-reported data for interventions to control their obesity epidemics. Over the children was found. For adults, self-reported data were last decade, very few EMR ministries of health have adjusted for overweight prevalence, obesity prevalence, focused on health promotion strategies to reduce obesity, and mean BMI using nested hierarchical mixed-effects such as awareness and behavioral changes. Of the 22 regression models, fit using restricted maximum likelihood countries in the EMR, only Bahrain, Qatar, and Saudi separately by sex. Second, estimates for some countries Arabia had substantial information on combating obesity with sparse data were driven by covariates in the statistical on their ministry of health websites (Gharib et al. 2012; modeling. This, in fact, highlights the need for more timely Saudi Ministry of Health 2012). Qatar and Saudi Arabia surveillance data on BMI and risk factors in the EMR. launched national campaigns against obesity in 2011 and Better quality data in the EMR for quantification of mor- 2012, respectively. The Saudi campaign focused primarily bidity and mortality burden and health policy planning is on dietary awareness, promoting healthy eating choices and urgently needed. Third, the attributable effect of BMI on emphasizing variety and balance through a food pyramid ischemic heart disease, stroke, and diabetes was derived (Hamad Al-Dkheel 2012). The Qatar campaign was more from prospective observational studies and meta-analyses, comprehensive, setting up programs to increase physical and hence does not account for differences by ethnicity, or activity, replace school snacks with healthier options, and for underlying diseases (Global BMI Mortality Collabora- pilot a nutrition surveillance system in addition to a dietary tion et al. 2016). While these observational studies are not awareness campaign (Mohammed Al-Thani 2011). Indeed, specifically from the EMR, the attributable effect of BMI and despite the campaign in Qatar, obesity prevalence is on the three diseases has been shown to remain the same one of the highest in the region, suggesting the need for a across different regions from the world (Singh et al. 2013). more aggressive approach. Fourth, BMI presents some drawbacks as it is a measure of Promoting healthier lifestyles around nutrition and excess weight rather excess body fat (Daniels 2009). The physical activity is greatly needed in this region. As relationship between BMI and body fat can be influenced pointed out previously, possible factors determining obe- by age, sex, ethnicity, and muscle mass. In addition, BMI sity in the EMR include nutrition transition, inactivity, does not indicate whether excess weight is due to fat, urbanization, marital status, a shorter duration of breast- muscle, or bone (Daniels 2009). However, BMI is favored feeding, frequent snacking, skipping breakfast, a high for its ease of use in surveys and is the most available intake of sugary beverages, an increase in the incidence of indicator regarding excess weight. More details on this eating outside the home, long periods of time spent view- study’s limitations are available elsewhere (GBD 2015 ing television, massive marketing promotion of high-fat Obesity Collaborators 2017). foods, stunting, perceived body image, cultural elements, This study showed that high BMI creates a major burden and food subsidy policies (Musaiger 2011b). in the EMR. We call for countries in the region to invest 123 Burden of obesity in the Eastern Mediterranean Region: findings from the Global Burden of… S173 Murdoch Childrens Research Institute, The University of Melbourne, more resources in prevention and health promotion efforts Parkville, Victoria, Australia; The University of Melbourne, Mel- to reduce the prevalence of obesity. These programs should bourne, VIC, Australia; The University of Sydney, Sydney, NSW, focus on stopping weight gain as a first step and more Australia. Reza Alizadeh-Navaei, PhD, Gastrointestinal Cancer aggressive programs to reduce weight among those who Research Center, Mazandaran University of Medical Sciences, Sari, Mazandaran, Iran. Ala’a Alkerwi, PhD, Luxembourg Institute of need to do so. These programs should take into account the Health (LIH), Strassen, Luxembourg. Khalid A. Altirkawi, MD, King culture and local environment. Moreover, countries should Saud University, Riyadh, Saudi Arabia. Nelson Alvis-Guzman, PhD, join efforts in their programs and policies and share Universidad de Cartagena, Cartagena de Indias, Colombia. Bernhard experiences and success stories. T. Baune, PhD, School of Medicine, University of Adelaide, Ade- laide, South Australia, Australia. Neeraj Bedi, MD, College of Public Health and Tropical Medicine, Jazan, Saudi Arabia. Derrick A. GBD 2015 Eastern Mediterranean Region Obesity Collabora- Bennett, PhD, Nuffield Department of Population Health, University tors: Ali H. Mokdad, PhD (corresponding author), Institute for of Oxford, Oxford, United Kingdom. Addisu S. Beyene, MPH, Col- Health Metrics and Evaluation, University of Washington, Seattle, lege of Health and Medical Science, Haramaya University, Harar, Washington, United States. Charbel El Bcheraoui, PhD, Institute for Ethiopia. Zulfiqar A. Bhutta, PhD, Centre of Excellence in Women Health Metrics and Evaluation, University of Washington. Ashkan and Child Health, Aga Khan University, Karachi, Pakistan; Centre for Afshin, MD, Institute for Health Metrics and Evaluation, University Global Child Health, The Hospital for Sick Children, Toronto, ON, of Washington, Seattle, WA, United States. Raghid Charara, MD, Canada. Mulugeta M. Birhanu, MS, University of Groningen, UMCG, American University of Beirut, Beirut, Lebanon. Ibrahim Khalil, MD, Groningen, Groningen, Netherlands; Mekelle University, Mekelle, Institute for Health Metrics and Evaluation, University of Washing- Ethiopia. Hadi Danawi, PhD, Walden University, Minneapolis, ton, Seattle, Washington, United States. Maziar Moradi-Lakeh, MD, Minnesota, United States. Seyed-Mohammad Fereshtehnejad, PhD, Department of Community Medicine, Preventive Medicine and Department of Neurobiology, Care Sciences and Society (NVS), Public Health Research Center, Gastrointestinal and Liver Disease Karolinska Institutet, Stockholm, Sweden. Florian Fischer, PhD, Research Center (GILDRC), University of Medical Sciences, Tehran, School of Public Health, Bielefeld University, Bielefeld, Germany. Iran. Nicholas J. Kassebaum, MD, Institute for Health Metrics and Tsegaye Tewelde Gebrehiwot, MPH, Jimma University, Jimma, Evaluation, University of Washington, Seattle, Washington, United Oromia, Ethiopia. Paramjit Singh Gill, DM, Warwick Medical States; Department of Anesthesiology & Pain Medicine, Seattle School, University of Warwick, Coventry, United Kingdom; Children’s Hospital, Seattle, Washington, United States. Michael University of Birmingham, UK, Birmingham, United Kingdom. Collison, BS, Institute for Health Metrics and Evaluation, University Philimon N. Gona, PhD, University of Massachusetts Boston, Boston, of Washington, Seattle, Washington, United States. Farah Daoud, Massachusetts, United States. Vipin Gupta, PhD, Department of BA/BS, Institute for Health Metrics and Evaluation, University of Anthropology, University of Delhi, Delhi, Delhi, India. Tesfa Dejenie Washington, Seattle, Washington, United States. Kristopher J. Krohn, Habtewold, MS, University of Groningen, Groningen, Netherlands; BA, Institute for Health Metrics and Evaluation, University of Debre Berhan University, Debre Berhan, Ethiopia. Randah Ribhi Washington, Seattle, Washington, United States. Adrienne Chew, Hamadeh, DPhil, Arabian Gulf University, Manama, Bahrain. Samer ND, Institute for Health Metrics and Evaluation, University of Hamidi, DrPH, Hamdan Bin Mohammed Smart University, Dubai, Washington, Seattle, Washington, United States. Stan H. Biryukov, United Arab Emirates. Habtamu Abera Hareri, MS, Addis Ababa BS, Institute for Health Metrics and Evaluation, University of University, Addis Ababa, Ethiopia. Masako Horino, MPH, Bureau of Washington, Seattle, Washington, United States. Leslie Cornaby, BS, Child, Family & Community Wellness, Nevada Division of Public Institute for Health Metrics and Evaluation, University of Washing- and Behavioral Health, Carson City, NV, United States. Mohamed ton, Seattle, Washington, United States. Kyle J. Foreman, PhD, Hsairi, MD, Department of Epidemiology, Salah Azaiz Institute, Institute for Health Metrics and Evaluation, University of Washing- Tunis, Tunis, Tunisia. Mehdi Javanbakht, PhD, University of ton, Seattle, Washington, United States; Imperial College London, Aberdeen, Aberdeen, Aberdeen, United Kingdom. Denny John, MPH, London, United Kingdom. Michael Kutz, BS, Institute for Health International Center for Research on Women, New Delhi, Delhi, Metrics and Evaluation, University of Washington, Seattle, Wash- India. Jost B. Jonas, MD, Department of Ophthalmology, Medical ington, United States. Patrick Liu, BA, Institute for Health Metrics Faculty Mannheim, Ruprecht-Karls-University Heidelberg, Man- and Evaluation, University of Washington, Seattle, Washington, nheim, Germany. Vasna Joshua, PhD, National Institute of Epi- United States. Marissa Reitsma, BS, Institute for Health Metrics and demiology, Indian Council of Medical Research, Chennai, India. Evaluation, University of Washington, Seattle, Washington, United Amir Kasaeian, PhD, Hematology-Oncology and Stem Cell Trans- States. Patrick Sur, BA, Institute for Health Metrics and Evaluation, plantation Research Center, Tehran University of Medical Sciences, University of Washington, Seattle, Washington, United States, Seat- Tehran, Iran; Endocrinology and Metabolism Population Sciences tle. Haidong Wang, PhD, Institute for Health Metrics and Evaluation, Institute, Tehran University of Medical Sciences, Tehran, Iran. Ezra University of Washington, Seattle, Washington, United States. Ben B. Ketema, MS, Mekelle University, Mekelle, Ethiopia. Yousef Saleh Zipkin, BS, Institute for Health Metrics and Evaluation, University of Khader, ScD, Department of Community Medicine, Public Health Washington, Seattle, Washington, United States, Seattle. Johan Arn- and Family Medicine, Jordan University of Science and Technology, lo ¨ v, PhD, Department of Neurobiology, Care Sciences and Society, Irbid, Irbid, Jordan. Ejaz Ahmad Khan, MD, Health Services Acad- Division of Family Medicine and Primary Care, Karolinska Institutet, emy, Islamabad, Punjab, Pakistan. Jagdish Khubchandani, PhD, Stockholm, Sweden; School of Health and Social Studies, Dalarna Department of Nutrition and Health Science, Ball State University, University, Falun, Sweden. Cristiana Abbafati, PhD, La Sapienza, Muncie, Indiana, United States. Daniel Kim, DrPH, Department of University of Rome, Rome, Italy. Abdishakur M. Abdulle, PhD, New Health Sciences, Northeastern University, Boston, Massachusetts, York University Abu Dhabi, Abu Dhabi, United Arab Emirates. United States. Yun Jin Kim, PhD, Faculty of Chinese Medicine, Niveen M.E. Abu-Rmeileh, PhD, Institute of Community and Public Southern University College, Skudai, Johor, Malaysia. Yohannes Health, Birzeit University, Ramallah, West Bank, Palestine. Muktar Kinfu, PhD, Centre for Research and Action in Public Health, Beshir Ahmed, MPH, College of Health Sciences, Department of University of Canberra, Canberra, Australian Capital Territory, Epidemiology, ICT and e-Learning Coordinator, Jimma University, Australia. Yoshihiro Kokubo, PhD, Department of Preventive Car- Jimma, Oromiya, Ethiopia. Ziyad Al-Aly, MD, Washington Univer- diology, National Cerebral and Cardiovascular Center, Suita, Osaka, sity in St. Louis, St. Louis, MO, United States. Khurshid Alam, PhD, 123 S174 GBD 2015 Eastern Mediterranean Region Obesity Collaborators Japan. Heidi J. 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Mubarek Abera Japan. Mustafa Z. Younis, DrPH, Jackson State University, Jackson, Mengistie, MS, Jimma University, Jimma, Oromia, Ethiopia. George MS, United States. Bassel Zein, MS, Department of Neuroscience, A. Mensah, MD, Center for Translation Research and Implementation Georgetown University, Washington DC, United States. Aisha O. Science, National Heart, Lung, and Blood Institute, National Insti- Jumaan, PhD, Independent Consultant, Seattle, Washington, United tutes of Health, Bethesda, MD, United States. Felix Akpojene Ogbo, States. Theo Vos, PhD, Institute for Health Metrics and Evaluation, MPH, Centre for Health Research, Western Sydney University, University of Washington, Seattle, Washington, United States. Simon Sydney, New South Wales, Australia. Farshad Pourmalek, PhD, I. Hay, DSc, Oxford Big Data Institute, Li Ka Shing Centre for Health University of British Columbia, Vancouver, British Columbia, Information and Discovery, University of Oxford, Oxford, United Canada. Anwar Rafay, MS, Contech International Health Consultants, Kingdom; Institute for Health Metrics and Evaluation, University of Lahore, Punjab, Pakistan; Contech School of Public Health, Lahore, Washington, Seattle, Washington, United States. Mohsen Naghavi, Punjab, Pakistan. Mostafa Qorbani, PhD, Non-communicable Dis- PhD, Institute for Health Metrics and Evaluation, University of eases Research Center, Alborz University of Medical Sciences, Karaj, Washington, Seattle, Washington, United States. Christopher J. Iran. Vafa Rahimi-Movaghar, MD, Sina Trauma and Surgery L. Murray, DPhil, Institute for Health Metrics and Evaluation, Research Center, Tehran University of Medical Sciences, Tehran, University of Washington, Seattle, Washington, United States. Tehran, Iran. Saleem M. Rana, PhD, Contech School of Public Health, Lahore, Punjab, Pakistan; Contech International Health Compliance with ethical standards Consultants, Lahore, Punjab, Pakistan. Salman Rawaf, MD, Imperial College London, London, United Kingdom. Andre M.N. Renzaho, This manuscript reflects original work that has not previously been PhD, Western Sydney University, Penrith, NSW, NSW, Australia. published in whole or in part and is not under consideration else- Satar Rezaei, PhD, School of Public Health, Kermanshah University where. All authors have read the manuscript and have agreed that the of Medical Sciences, Kermanshah, Iran. Mohammad Sadegh Rezai, work is ready for submission and accept responsibility for its con- MD, Infectious Disease Research Centre with Focus on Nosocomial tents. The authors of this paper have complied with all ethical stan- Infection, Mazandaran University of Medical Sciences, Sari, dards and do not have any conflicts of interest to disclose at the time Mazandaran, Iran. Mahdi Safdarian, MD, Sina Trauma & Surgery of submission. The funding source played no role in the design of the Research Center, Tehran University of Medical Sciences, Tehran, study, the analysis and interpretation of data, and the writing of the Iran. Mohammad Ali Sahraian, MD, MS Research Center, Neuro- paper. The study did not involve human participants and/or animals; science Institute, Tehran University of Medical Sciences, Tehran, therefore, no informed consent was needed. Iran. Payman Salamati, MD, Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran, Tehran, Iran. Funding This research was funded by the Bill & Melinda Gates Abdallah M. Samy, PhD, Ain Shams University, Cairo, Egypt. Juan Foundation. Ramon Sanabria, MD, J Edwards School of Medicine, Marshall Univeristy, Huntington, WV, United States; Case Western Reserve Conflict of interest The authors declare that they have no conflicts of University, Cleveland, OH, United States. Milena M. Santric Mil- interest at this time. icevic, PhD, Institute of Social Medicine, Faculty of Medicine, University of Belgrade, Serbia, Serbia; Centre School of Public Open Access This article is distributed under the terms of the Health and Health Management, Faculty of Medicine, University of Creative Commons Attribution 4.0 International License (http://crea Belgrade, Serbia, Serbia. Benn Sartorius, PhD, Public Health Medi- tivecommons.org/licenses/by/4.0/), which permits unrestricted use, cine, School of Nursing and Public Health, University of KwaZulu- distribution, and reproduction in any medium, provided you give Natal, Durban, South Africa; UKZN Gastrointestinal Cancer appropriate credit to the original author(s) and the source, provide a Research Centre, South African Medical Research Council link to the Creative Commons license, and indicate if changes were (SAMRC), Durban, South Africa. Sadaf G. Sepanlou, PhD, Digestive made. 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Burden of obesity in the Eastern Mediterranean Region: findings from the Global Burden of Disease 2015 study

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Abstract

Int J Public Health (2018) 63 (Suppl 1):S165–S176 https://doi.org/10.1007/s00038-017-1002-5 O R I G IN AL ARTI CL E Burden of obesity in the Eastern Mediterranean Region: findings from the Global Burden of Disease 2015 study GBD 2015 Eastern Mediterranean Region Obesity 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 Conclusions This is the first study to estimate trends in Objectives We used the Global Burden of Disease (GBD) obesity burden for the EMR from 1980 to 2015. We call for 2015 study results to explore the burden of high body mass EMR countries to invest more resources in prevention and index (BMI) in the Eastern Mediterranean Region (EMR). health promotion efforts to reduce this burden. Methods We estimated the prevalence of overweight and obesity among children (2–19 years) and adults Keywords Obesity  Burden of disease  Eastern (C20 years) in 1980 and 2015. The burden of disease Mediterranean Region related to high BMI was calculated using the GBD com- parative risk assessment approach. Results The prevalence of obesity increased for adults Introduction from 15.1% (95% UI 13.4–16.9) in 1980 to 20.7% (95% UI 18.8–22.8) in 2015. It increased from 4.1% (95% UI High body mass index (BMI), or overweight, is associated 2.9–5.5) to 4.9% (95% UI 3.6–6.4) for the same period with increased morbidity and mortality, and is a major risk among children. In 2015, there were 417,115 deaths and factor for diabetes, cancer, cardiovascular diseases, sleep 14,448,548 disability-adjusted life years (DALYs) apnea, and poor physical health (Kim et al. 2016; Yao et al. attributable to high BMI in EMR, which constitute about 2017; Kelly et al. 2017; Mehta et al. 2017). It is also 10 and 6.3% of total deaths and DALYs, respectively, for associated with an increased risk for psychiatric disorders, all ages. including depression (Pratt and Brody 2014; Abou Abbas et al. 2015). A continuous rise in obesity is threatening health improvements in many countries (Sidney et al. 2016; This article is part of the supplement ‘‘The state of health in the GBD 2015 Obesity Collaborators 2017), while controlling Eastern Mediterranean Region, 1990–2015.’’ its spread could drastically improve population health (Maciosek et al. 2017). The members of GBD (Global Burden of Disease) 2015 Eastern Mediterranean Region Obesity Collaborators are listed at the end of The increase in overweight and obesity prevalence is a the article. Ali H. Mokdad, on behalf of GBD 2015 Eastern direct result of lifestyle changes due to the social and Mediterranean Region Obesity Collaborators, is the corresponding demographic transition that started several decades ago author. (Broyles et al. 2015; Mokdad et al. 2016). The Eastern Electronic supplementary material The online version of this Mediterranean Region (EMR) is facing the same chal- article (doi:10.1007/s00038-017-1002-5) contains supplementary lenges due to rapid economic, demographic, and lifestyle material, which is available to authorized users. changes, including changes in food consumption, reduced & GBD 2015 Eastern Mediterranean Region Obesity physical activity, and increased sedentary lifestyle (Mu- Collaborators saiger et al. 2012; Mokdad et al. 2016). The contribution of mokdaa@uw.edu high BMI to total DALYs in the EMR had increased in 2013 (7.5% of DALYs) in comparison to 1990 (3.7% of Institute for Health Metrics and Evaluation, University of DALYs) (Mokdad et al. 2016). Kuwait, Qatar, and Libya, Washington, Seattle, WA, USA 123 S166 GBD 2015 Eastern Mediterranean Region Obesity Collaborators three EMR countries, were among the top ten countries Briefly, Medline was systematically searched for studies with highest prevalence of obesity worldwide in 2013, and providing nationally or subnationally representative esti- the man believed to be the heaviest living person was mates of overweight prevalence, obesity prevalence, or diagnosed in Saudi Arabia (Ng et al. 2014; Terkawi et al. mean body mass index (BMI) published between 1 January 2014). 2014 and 31 December 2015 to update the GBD 2013 The EMR has a population of about 583 million people systematic literature search (Ng et al. 2016). (World Health Organization 2016). Countries in the EMR For adults, 127 out of 2036 abstracts identified met vary significantly in terms of their gross domestic product, inclusion criteria and were extracted. For children, 146 out socio-demographic profiles, health indicators, and health of 971 articles identified were extracted. In total, 816 system capacities and coverage. Despite the heavy burden articles were included in the analysis. Additionally, the of high BMI in the region, no comprehensive and current Global Health Data Exchange (GHDx) database was estimates of the epidemic exist for the EMR. searched for individual-level data from major multinational To quantify the burden of high BMI in the EMR and its survey series or country-specific surveys and identified impact on health, we systematically evaluated the trends in 1026 unique sources meeting the inclusion criteria. Of the prevalence of overweight and obesity as well as the pat- 816 articles and 1026 unique sources, all those pertaining terns of deaths and DALYs related to high BMI by age and to EMR countries were included. The GBD 2015 results sex, using the results of the Global Burden of Disease tool from the GHDx allows readers to view, country by (GBD) 2015 study. We also estimated the country, what data sources have been used to produce these attributable fraction of high BMI to ischemic heart disease, estimates (Institute for Health Metrics and Evaluation stroke, and diabetes mellitus, the three leading non-com- 2016). municable causes of death in EMR, for which high BMI is For adults, overweight was defined as a risk factor. 25.0 B BMI B 30 kg/m , and obesity was defined as BMI C30 kg/m . The International Obesity Task Force defini- tion was used for childhood overweight and obesity (Cole Methods et al. 2000). Children were defined as individuals 2–19 years of age The prevalence of overweight and obesity among children based on the lowest age for which the International Obesity (2–19 years) and adults (C20 years) in 1980 and 2015 was Task Force provides a definition of overweight and obesity, estimated for EMR countries. The EMR countries, based and the age groups used in GBD modeling, which include on the World Health Organization classification, are 19 in the age group 15–19 (Cole et al. 2000; Wang et al. Afghanistan, the Kingdom of Bahrain, Djibouti, the Arab 2016). Republic of Egypt, the Islamic Republic of Iran, the Briefly, a spatiotemporal Gaussian process regression Republic of Iraq, the Hashemite Kingdom of Jordan, the (ST-GPR) was used to estimate the mean prevalence of State of Kuwait, the State of Lebanon, the State of Libya, overweight and obesity (Ng et al. 2014). To improve the Kingdom of Morocco, the Sultanate of Oman, the estimates for countries with sparse data, three country-level Islamic Republic of Pakistan, Palestine, the State of Qatar, covariates with best fit and coefficients in the expected the Kingdom of Saudi Arabia, the Federal Republic of direction were selected: 10-year lag-distributed energy Somalia, the Republic of Sudan, the Syrian Arab Republic, intake per capita, the absolute latitude of the country as the Republic of Tunisia, the United Arab Emirates, and the proxy for income, and the proportion of people living in Republic of Yemen. urban areas. These covariates have been systematically The burden of disease related to high BMI was calcu- evaluated in a previous study (Ng et al. 2014). lated using the GBD comparative risk assessment approach The Bradford Hill criteria for causation and the World between 1990 and 2015 (Forouzanfar et al. 2015, 2016). A Cancer Research Fund evidence grading criteria were used detailed methodology of BMI estimation for GBD 2015 has to systematically evaluate epidemiologic evidence sup- been published elsewhere (GBD 2015 Obesity Collabora- porting the causal relationship between high BMI and tors 2017). Since burden estimations depend on GBD all- various diseases among adults (C20 years of age) (Hill cause mortality, burden of high BMI is only available for 1965; World Cancer Research Fund and American Institute the period 1990–2015. We used all available data surveys for Cancer Research 2007). following a systematic search. The search strategy as well The population-attributable fraction by country, age, as data sources used per country have been published as an sex, and year was calculated to quantify the burden of appendix elsewhere and are available from the Global disease related to high BMI, defined as BMI C25 kg/m , Health Data Exchange (Institute for Health Metrics and for each disease. Deaths and DALYs related to high BMI Evaluation 2016; GBD 2015 Obesity Collaborators 2017). for each country, age, sex, year, and cause were computed 123 Burden of obesity in the Eastern Mediterranean Region: findings from the Global Burden of… S167 by multiplying the population-attributable fraction by the total deaths or DALYs estimated in GBD 2015 for that country, age, sex, year, and cause. The total disease burden of high BMI was calculated as the sum of disease-specific burden. 95% uncertainty intervals (UI) for all results were computed using Monte Carlo simulations, keeping 1000 draws of each quantity of interest to propagate uncertainty into final estimates. Expected estimates were also produced for each country Adults Children based on its Socio-demographic Index (SDI)—a summary 1980 2015 measure of lag-distributed income per capita, average Fig. 1 Prevalence of obesity among adults aged 20 years or older educational attainment over the age of 15 years, and total and children aged 2–19 years in 1980 and 2015 (Global Burden of fertility rate (Forouzanfar et al. 2015). In GBD 2015, SDI Disease 2015 study, Eastern Mediterranean Countries, 1980 and was computed by rescaling each component to a scale of 2015) zero to one, with zero being the lowest observed educa- tional attainment, lowest income per capita, and highest 8.3–12.8) for females (e-Table 1), and in Yemen and Pakistan for children 2–19 years of age: 1.3% (95% UI fertility rate from 1980 to 2015, and one being the highest observed educational attainment, highest income per cap- 0.9–1.8) for males in Yemen and 2.2% (95% UI 1.4–3.3) ita, and lowest fertility rate during that time, and then for females in Pakistan (e-Table 2). Prevalence of obesity taking the geometric mean of these values for each loca- was higher in females than males 20 years or older for all tion-year. countries, with Sudan having the highest difference This study followed the Guidelines for Accurate and between sexes: 11.4% (95% UI 10.0–13.1) for males and Transparent Health Estimates Reporting (GATHER) of the 28.3% (95% UI 25.6–31.2) for females (e-Table 1). The World Health Organization (WHO) regarding documenta- highest difference in obesity prevalence between sexes for tion of data sources, estimation methods, and statistical children was observed in Qatar: 20.8% (95% UI 16.5–25.1) analysis (Stevens et al. 2016). for males and 13.5% (95% UI 10.3–17.1) for females. In children, prevalence of obesity was higher in males for several countries (e-Table 2). Role of the funding source Deaths The Bill & Melinda Gates Foundation had no role in the development of these methods. In 2015, there were 417,115 deaths attributable to high BMI in EMR, which constitute about 10% of total deaths in Results the region for all ages. This is a rate of 120.1 (95% UI 87.5–156.2) deaths per 100,000 population, an 11% The mean BMI increased from 25.2 kg/m [95% uncer- increase since 1990. It contributed to 5.0, 0.9, and 1.9% of tainty interval (UI) 24.9–25.5] in 1980 to 26.0 kg/m (95% all deaths cause by ischemic heart disease, ischemic stroke, UI 25.8–26.3) in 2015 in the EMR among persons aged and diabetes mellitus, respectively. Contribution by speci- fic age groups is detailed in Table 1. In 2015, the rate of 20 years or older. The prevalence of obesity increased from 15.1% (95% UI 13.4–16.9) to 20.7% (95% UI 18.8–22.8) deaths attributable to high BMI was highest in Afghanistan, for the same period and for the same age group (Fig. 1). It 227.6 (95% UI 146.2–319.5), and lowest in Tunisia, 64.8 increased from 4.1% (95% UI 2.9–5.5) to 4.9% (95% UI (95% UI 42.7–92.5) per 100,000 population (Table 2). 3.6–6.4) for the same period among those aged 2–19 years Overall, death rates attributable to high BMI have (Fig. 1). The highest prevalence of obesity among adults increased in 11 countries and decreased in 11 (Table 2). 20 years or older in 2015 was observed in Qatar: 42.5% The largest increase in deaths per 100,000 population (95% UI 40.1–44.8) for males and 52.4% (95% UI attributable to high BMI was observed in Djibouti: from 50.3–54.5) for females (e-Table 1); and the highest for 37.3 (95% UI 15.1–70.6) in 1990 to 101.1 (95% UI children 2–19 years was observed in Kuwait: 22.1% (95% 47.5–192.7) in 2015 (Table 2). The largest decrease was UI 17.8–27.0) for males and 19.2% (95% UI 15.2–23.4) for observed in Lebanon: from 124.8 (95% UI 85.2–169.7) in 1990 to 72.5 (95% UI 47.0–101.0) in 2015 (Table 2). females (e-Table 2). The lowest prevalence of obesity was observed in Somalia among individuals 20 years or older: 2.5% (95% UI 1.5–4.0) for males and 10.5% (95% UI Prevalence % S168 GBD 2015 Eastern Mediterranean Region Obesity Collaborators Table 1 Deaths, with 95% uncertainty intervals (UI), per 100,000 (Global Burden of Disease 2015 study, Eastern Mediterranean population attributable to high body mass index among those who countries, 1990 and 2015) died from ischemic heart disease, stroke, and diabetes, by age groups Age Cause 1990 2015 Males Females Males Females 15–49 Ischemic heart disease 10.1 (6.0–14.7) 7.0 (4.5–9.5) 12.1 (7.6–17.2) 6.9 (4.7–9.3) Stroke 7.1 (4.6–9.8) 8.1 (5.8–10.6) 7.2 (4.7–9.9) 7.3 (5.3–9.6) Diabetes 1.7 (1.2–2.3) 2.0 (1.5–2.6) 2.6 (1.8–3.3) 2.9 (2.2–3.6) 50–69 Ischemic heart disease 118.4 (67.9–177.4) 118.1 (80.0–161.8) 134.4 (82.3–191.6) 107.1 (74.8–142.7) Stroke 58.1 (33.6–85.1) 77.8 (53.9–107.7) 60.6 (38.4–85.5) 68.7 (49.1–91.2) Diabetes 24.8 (15.7–34.4) 36.4 (25.1–50.1) 39.2 (27.2–52.3) 53.4 (40.6–67.8) 70? Ischemic heart disease 283.8 (141.5–460.9) 366.1 (225.5–539.6) 336.9 (183.3–532.9) 361.0 (222.4–520.7) Stroke 117.4 (58.4–198.2) 163.6 (96.9–248.3) 125.8 (65.8–203.8) 150.7 (89.1–223.9) Diabetes 57.7 (30.8–89.2) 81.9 (47.6–130.2) 106.5 (59.1–163.1) 160.3 (103.1–224.8) DALYs for high BMI was higher than expected for most countries in the region based on their SDI levels. Our study calls for In 2015, there were 14,448,548 DALYs attributable to high renewed efforts to reduce the burden of obesity in the BMI in the EMR, which constitutes about 6.3% of total DALYs region. Indeed, with further progression of the epidemio- in the region for all ages. This is a rate of 3452.5 (95% UI logic transition and the growth and aging of the EMR 2599.2–4386.5) DALYs per 100,000 population, a 13.9% population, high BMI will increase the burden of chronic increase since 1990. It contributed to 3.0, 0.5, and 2.3% of all conditions and disability and put financial and resource DALYs caused by ischemic heart disease, stroke, and diabetes strains on the health systems. mellitus, respectively. Contribution by specific age groups is High BMI is observed in some poor and rich countries detailed in Table 3. In 2015, the rate of DALYs attributable to of the EMR. In developed countries such as the United high BMI was highest in Afghanistan, 6576.7 (95% UI States and France, obesity is higher among low socioeco- 4366.1–9219.6), and lowest in Tunisia, 2022.4 (95% UI nomic strata of the population (Drewnowski et al. 2014). 1395.1–2724.3) per 100,000 population. Trends in DALYs These patterns in the West are attributable to poor diet and followed trends in deaths from high BMI in all countries. The lower physical activity levels. Previous studies have largest increase in DALYs per 100,000 population attributable reported similar findings for both socioeconomic status and to high BMI was observed in Djibouti: from 1111.0 (95% UI sex in the EMR (Musaiger 2011a). In the EMR, except for 492.8–2005.8) in 1990 to 2810.8 (95% UI 1432.5–5212.9) in Iran and Lebanon, all high-middle- and high-SDI countries 2015 (Table 4). The largest decrease was observed in Lebanon: had an obesity prevalence equaling or exceeding 25% for from 3552.4 (95% UI 2508.2–4645.4) in 1990 to 2363.8 (95% both males and females 20 years or older. Obesity preva- UI 1707.9–3077.2) in 2015. lence exceeded 10% among children 2–19 years only in these countries as well, except for Bahrain, Jordan, Leba- Expected versus observed non, and Iran. Djibouti and Egypt were the only low- and middle-SDI countries where childhood obesity exceeded Overall, and based on an SDI of 0.55, a death rate of 74.0 10% for females. These country-level estimates might be and a DALYs rate of 2114.3 per 100,000 population were masking variations of the epidemic within each of the expected for the EMR in 2015 for high BMI. Both are countries. Indeed, few studies have been done at the lower than the observed death rate of 120.1 (95% UI country level in the EMR, and these showed a variation in 87.5–156.2) and DALYs rate of 3452.5 (95% UI BMI levels between levels of education and income. High 2599.2–4386.5) for the same year. Expected estimates for BMI was more likely to impact those with low educational each country are detailed in Tables 3 and 4. levels (Sibai et al. 2003; Memish 2014). Unfortunately, our estimations for observed obesity burden were higher than expected for the region, and based Discussion on SDI, which only deals with socioeconomic inequalities between countries. However, the biggest gaps between the This is the first study to provide estimates of trends in included countries are cultural factors and obesogenic obesity prevalence, deaths, and DALYs for the EMR from cultural traditions, as well as political instability (wars, 1980 to 2015. Our study showed that the observed burden civil unrest,) all of which are linked to obesity but not 123 Burden of obesity in the Eastern Mediterranean Region: findings from the Global Burden of… S169 Table 2 Deaths, with 95% uncertainty intervals (UI), per 100,000 population due to high body mass index observed in 1990 and 2015, and expected based on Socio-demographic Index in 2015 (Global Burden of Disease 2015 study, Eastern Mediterranean countries, 1990 and 2015) Country 1990 deaths 2015 deaths Total observed Total expected Males Females Males Females Afghanistan 166.9 (80.9–283.8) 296.3 (170.9–460.1) 160.5 (78.6–268.2) 292.1 (166.4–450.7) 27,827.4 (17,910.5–39,914.8) 5797.5 Bahrain 133.2 (84.8–191.7) 175.2 (129.1–222.4) 97.7 (62.1–141.3) 99.2 (68.9–130.4) 557.5 (421.6–709.1) 443.3 Djibouti 37.1 (9.8–94.0) 36.7 (11.8–82.8) 102.0 (35.3–229.3) 99.1 (37.7–231.1) 467.9 (211.4–929.9) 312.5 Egypt 153.2 (98.2–207.3) 154.4 (116.9–192.2) 176.2 (123.4–228.0) 154.6 (121.5–188.6) 90,774.1 (73,283.6–108,972.5) 48,851.2 Iran 90.7 (44.8–146.5) 98.9 (63.3–138.2) 102.3 (56.9–162.0) 94.4 (59.8–137.7) 49,386.0 (33,333.8–68,369.9) 44,524.9 Iraq 234.4 (158.3–335.3) 243.7 (171.0–324.3) 217.4 (129.1–321.1) 206.3 (136.0–295.7) 30,963.7 (22,483.9–41,095.5) 12,627.7 Jordan 144.8 (93.7–209.6) 198.5 (146.6–258.2) 118.8 (80.1–160.0) 107.3 (80.4–136.1) 3687.0 (2946.9–4478.1) 3082.9 Kuwait 92.2 (64.5–120.0) 115.1 (89.1–142.1) 93.9 (65.2–126.1) 93.6 (70.6–120.7) 1335.8 (1071.0–1657.8) 670.7 Lebanon 127.0 (70.4–198.5) 122.6 (76.9–179.0) 69.4 (35.3–113.1) 75.2 (44.8–109.8) 3565.9 (2320.6–4940.1) 3803.3 Libya 81.8 (49.9–120.0) 103.5 (72.0–137.6) 96.8 (59.6–141.4) 109.7 (78.2–148.0) 3727.3 (2795.3–4725.7) 3261.4 Morocco 85.0 (45.4–132.0) 103.3 (65.8–148.5) 77.4 (40.8–129.9) 102.6 (57.3–158.6) 22,383.7 (14,985.5–31,865.0) 18,621.5 Oman 82.0 (46.3–131.0) 89.4 (51.9–140.9) 92.3 (56.9–131.3) 93.4 (63.0–123.8) 1575.4 (1161.4–2011.7) 1619.5 Pakistan 54.3 (22.0–98.4) 67.1 (33.2–113.4) 105.9 (169.5–54.7) 110.6 (64.32–166.3) 110,546.2 (72,034.6–153,911.6) 67,064.8 Palestine 114.4 (63.3–182.1) 128.0 (77.6–197.6) 149.7 (86.8–225.0) 118.2 (77.7–170.3) 2347.3 (1655.2–3168.4) 1502.5 Qatar 142.7 (101.7–187.8) 209.5 (161.3–263.6) 106.6 (69.3–154.4) 126.1 (88.2–169.1) 567.3 (424.8–747.7) 479.1 Saudi Arabia 75.2 (46.7–107.1) 81.9 (59.0–108.4) 84.5 (57.9–114.5) 68.3 (51.6–87.4) 10,888.8 (8682.8–13,471.6) 11,340.5 Somalia 45.3 (8.4–127.5) 107.8 (24.8–251.0) 43.5 (9.5–112.7) 101.0 (24.2–242.5) 3058.6 (722.6–7430.6) 1761.52 Sudan 96.5 (47.9–163.1) 158.6 (95.9–242.4) 101.0 (48.2–171.3) 146.7 (80.2–230.8) 21,814.0 (14,145.4–31,022.9) 10,515.2 Syria 141.1 (88.4–207.5) 145.8 (99.7–205.0) 112.7 (67.3–164.2) 99.3 (67.9–131.0) 9987.0 (7397.8–12,880.3) 8109.9 Tunisia 72.6 (37.9–111.7) 75.4 (49.4–105.8) 71.9 (37.2–118.8) 58.4 (34.7–87.3) 6256.6 (4163.4–8889.9) 8682.7 United Arab Emirates 128.6 (76.3–188.5) 153.1 (100.1–214.7) 103.9 (60.2–156.2) 103.8 (68.8–148.4) 4460.4 (2941.6–6206.4) 1648.5 Yemen 74.5 (27.2–149.7) 106.8 (43.0–205.8) 78.1 (31.1–158.7) 124.4 (59.2–225.4) 10,936.6 (5668.1–19,184.0) 5848.1 S170 GBD 2015 Eastern Mediterranean Region Obesity Collaborators Table 3 Disability-adjusted life years (DALYs) and years lived with disability (YLDs), with 95% uncertainty intervals (UI), per 100,000 population attributable to high body mass index among DALYs and YLDs due to ischemic heart disease, stroke, and diabetes, by age groups (Global Burden of Disease 2015 study, Eastern Mediterranean countries, 1990 and 2015) Age Cause DALYs YLDs 1990 2015 1990 2015 Males Females Males Females Males Females Males Females 15–49 Ischemic heart 473.0 328.0 560.1 327.5 5.9 (3.0–9.7) 5.3 (3.0–8.5) 9.9 (5.5–15.9) 8.9 (5.3–13.7) disease (278.3–690.8) (211.6–453.7) (352.0–801.7) (222.9–436.6) Stroke 351.1 398.8 353.0 361.8 12.7 (7.0–20.1) 16.8 (10.5–24.9) 16.8 (9.9–25.6) 22.2 (14.2–31.4) (225.4–483.7) (286.0–519.7) (234.5–484.9) (261.7–468.2) Diabetes 251.7 299.3 419.6 483.5 173.6 206.7 303.4 351.3 (163.0–351.2) (216.5–399.8) (284.2–577.4) (351.3–640.4) (103.8–262.9) (133.2–297.0) (187.6–448.4) (229.4–491.8) 50–69 Ischemic heart 3357.1 3270.6 3898.1 3014.3 74.0 73.6 116.6 105.3 disease (1918.0–5002.9) (2220.9–4466.6) (2392.9–5519.0) (2122.1–3987.9) (35.4–126.3) (41.8–114.4) (60.1–192.0) (61.8–157.8) Stroke 1686.2 2247.9 1792.3 2015.4 64.4 87.3 81.1 99.5 (62.9–146.0) (990.7–2460.1) (1556.8–3081.0) (1132.3–2514.2) (1440.7–2648.3) (32.9–106.1) (53.5–133.0) (45.0–126.4) Diabetes 1471.0 2091.7 2417.9 3190.4 787.0 1098.0 1330.1 1736.2 (909.2–2097.8) (1497.4–2815.7) (1604.3–3312.3) (2388.5–4121.2) (431.0–1229.8) (689.5–1605.4) (769.0–1994.0) (1119.0–2482.3) 70? Ischemic heart 4001.4 4951.1 4600.8 4690.5 92.0 110.2 138.4 155.1 disease (2028.0–6420.1) (3092.1–7158.9) (2513.9–7234.6) (2977.1–6737.5) (42.0–167.8) (61.8–180.6) (66.9–240.6) (88.7–248.2) Stroke 1789.7 2449.0 1875.5 2200.4 60.1 78.8 72.2 90.8 (53.2–142.7) (919.0–2943.9) (1480.2–3678.2) (991.2–2972.6) (1362.7–3160.4) (27.2–106.5) (43.8–128.7) (34.3–123.3) Diabetes 1342.6 1933.4 2345.0 3392.0 532.3 802.8 921.4 1307.7 (725.2–2113.3) (1197.4–2848.9) (1336.8–3535.0) (2234.9–4736.3) (258.2–920.5) (468.0–1261.9) (471.6–1515.4) (782.1–1998.0) Burden of obesity in the Eastern Mediterranean Region: findings from the Global Burden of… S171 Table 4 Disability-adjusted life years, with 95% uncertainty intervals (UI) per 100,000 population due to high body mass index observed in 1990 and 2015, and expected based on Socio- demographic Index in 2015 (Global Burden of Disease 2015 study, Eastern Mediterranean countries, 1990 and 2015) Country 1990 2015 Total observed Total expected Males Females Males Females Afghanistan 4767.5 (2400.1–7893.8) 8437.9 (4934.2–12,974.6) 4677.5 (2411.1–7598.2) 8491.9 (5022.7–13,139.8) 965,879.5 (648,967.1–1,371,214.6) 206,389.0 Bahrain 4039.9 (2769.8–5453.6) 4907.8 (3800.7–6091.1) 3109.6 (2127.8–4224.1) 3177.8 (2414.8–4028.7) 29,225.3 (22,628.0–36,438.7) 18,576.5 Djibouti 1189.7 (362.1–2830.1) 1023.3 (378.0–2220.0) 2974.8 (1138.0–6633.1) 2633.4 (1192.2–5883.4) 15,630.0 (7793.1–29,440.4) 10,761.1 Egypt 4317.3 (2880.3–5717.9) 4251.7 (3367.6–5153.0) 5025.6 (3688.3–6374.7) 4435.2 (3623.2–5284.9) 3,037,204.4 (2,478,932.1–3,605,791.4) 1,666,166.5 Iran 2509.4 (1318.4–3910.9) 2733.1 (1877.6–3632.2) 2846.6 (1712.4–4306.3) 2696.7 (1880.0–3776.6) 1,698,192.1 (1,199,771.9–2,259,245.3) 1,493,421.0 Iraq 6502.5 (4537.2–8970.9) 6404.8 (4660.0–8287.8) 6321.0 (4108.7–9096.4) 5726.9 (4046.8–7892.3) 1,090,796.9 (821,609.9–1,421,193.6) 452,426.9 Jordan 4299.2 (2938.7–5943.2) 5362.9 (4166.0–6773.0) 3633.7 (2593.7–4668.3) 3176.4 (2469.7–3918.3) 141,869.1 (112,808.2–172,138.2) 106,862.3 Kuwait 2861.0 (2115.1–3562.8) 3229.1 (2627.2–3868.5) 3026.5 (2239.0–3850.2) 2715.3 (2167.4–3389.3) 69,567.5 (55,117.3–84,812.6) 32,170.1 Lebanon 3664.5 (2191.8–5471.8) 3454.1 (2306.5–4827.3) 2251.5 (1351.6–3307.2) 2480.0 (1658.6–3401.0) 123,560.0 (89,344.0–160,488.7) 110,981.5 Libya 2379.4 (1551.1–3309.5) 3037.5 (2274.4–3882.7) 2904.3 (1926.1–3953.5) 3285.7 (2464.7–4171.7) 139,878.3 (108,615.2–173,319.3) 116,450.8 Morocco 2490.8 (1420.6–3677.6) 2952.1 (1987.8–4068.0) 2336.9 (1332.7–3654.0) 3032.9 (1949.9–4325.84 774,701.5 (555,295.0–1,031,236.8) 600,106.4 Oman 2471.6 (1506.0–3723.1) 2805.3 (1835.8–4077.2) 2927.1 (1965.5–3972.0) 3097.2 (2308.7–3934.5) 75,807.5 (57,042.8–96,463.6) 64,799.1 Pakistan 1543.7 (657.2–2752.2) 1869.4 (995.8–3001.1) 3009.9 (1619.5–4710.8) 3083.7 (1950.6–4398.8) 3,608,879.0 (2,420,204.5–4,972,204.0) 2,240,433.8 Palestine 3235.1 (1896.7–4980.2) 3467.5 (2290.6–5022.7) 4300.8 (2694.9–6204.2) 3206.5 (2240.2–4368.5) 82,929.2 (61,735.5–109,791.4) 54,335.0 Qatar 4074.6 (3099.9–5097.3) 5538.0 (4495.6–6656.9) 3236.7 (2297.7–4272.4) 3669.8 (2825.1–4589.9) 36,660.9 (27,986.6–46,001.8) 23,177.6 Saudi Arabia 2296.3 (1512.6–3138.8) 2558.8 (1946.2–3250.2) 2507.0 (1808.4–3262.0) 2165.4 (1693.7–2699.8) 490,128.6 (383,013.6–601,300.7) 440,497.5 Somalia 1463.9 (330.5–3897.1) 2980.2 (780.2–6721.4) 1325.4 (330.3–3416.1) 2828.7 (834.7–6943.8) 102,092.6 (31,197.6–246,633.4) 59,469.2 Sudan 2883.9 (1513.0–4693.9) 4378.6 (2790.2–6417.8) 3057.9 (1567.6–4893.5) 4128.4 (2480.3–6203.9) 764,380.1 (523,579.9–1,052,574.8) 364,535.7 Syria 4198.1 (2692.6–5980.2) 4090.6 (2917.9–5509.0) 3305.8 (2135.7–4629.7) 2889.2 (2118.5–3677.0) 347,354.1 (267,569.5–437,633.2) 276,598.4 Tunisia 2065.4 (1134.9–3084.8) 2248.3 (1594.2–2947.4) 2165.4 (1242.3–3352.4) 1889.1 (1269.8–2570.8) 217,846.2 (151,390.5–292,471.8) 272,878.3 United Arab Emirates 4028.4 (2596.4–5648.6) 4675.9 (3270.4–6389.7) 3425.1 (2209.4–4864.9) 3491.9 (2522.1–4661.1) 242,427.0 (171,328.5–322,519.2) 87,802.7 Yemen 2247.0 (881.5–4390.2) 3017.4 (1304.3–5529.8) 2373.0 (1016.2–4579.6) 3639.9 (1893.9–6356.4) 393,538.0 (216,892.2–664,897.8) 207,048.3 S172 GBD 2015 Eastern Mediterranean Region Obesity Collaborators measured by SDI (Ulijaszek 2007). Cultural factors and Indeed, estimates from the GBD study show an increase obesogenic cultural traditions disproportionally affect in dietary risk factors and low levels of physical activity women, highlighting cross-cutting gender issues in (Forouzanfar et al. 2016). What the EMR populations women’s health (Shapira 2013). This might explain the consume can be directly affected by national policies, observed gender differences in overweight and obesity. specifically those around food economics. For instance, in Similarly, social determinants of health in countries in many EMR countries such as Egypt, Morocco, and Saudi conflict tend to differ as morbidity and mortality are Arabia, governments subsidize grains, bread, wheat flour, associated with conflict (World Health Organization 2008). sugar, and cooking oil, and impose taxes on imported foods More importantly, wars and civil unrest are closely asso- (Asfaw 2007; Musaiger 2011b). However, fruits and veg- ciated with child malnutrition, which in turn is associated etables are neither subsidized nor exempt from import with increased risk of obesity, hypertension, cardiovascular taxes, making healthier food choices harder for the popu- disease, and type 2 diabetes (Devakumar et al. 2014; lation. As for physical activity, the lack of exercise facil- Charchuk et al. 2015). This leads to the intergenerational ities has been reported, at least in Egypt and Saudi Arabia, effects of war on obesity and the doubled burden of as the main reason for low physical activity (Al-Rafaee and undernutrition in countries affected by wars or experienc- Al-Hazzaa 2001; El-Gilany et al. 2011). ing chronic civil unrest (Devakumar et al. 2014). This study has a few limitations. These include possible Despite this increase in obesity, EMR countries can underestimation of the prevalence of obesity or disease hope to control their epidemic as declines in obesity burden from high BMI as self-reported height and weight prevalence have been reported in other countries due to data were used. However, we corrected these based on health interventions and environmental and policy changes measured data at each age, sex, and country unit (GBD (Schmidt Morgen et al. 2013; Keane et al. 2014). EMR 2015 Obesity Collaborators 2017). Briefly, no significant countries can benefit from implementing an array of proven difference between measured and self-reported data for interventions to control their obesity epidemics. Over the children was found. For adults, self-reported data were last decade, very few EMR ministries of health have adjusted for overweight prevalence, obesity prevalence, focused on health promotion strategies to reduce obesity, and mean BMI using nested hierarchical mixed-effects such as awareness and behavioral changes. Of the 22 regression models, fit using restricted maximum likelihood countries in the EMR, only Bahrain, Qatar, and Saudi separately by sex. Second, estimates for some countries Arabia had substantial information on combating obesity with sparse data were driven by covariates in the statistical on their ministry of health websites (Gharib et al. 2012; modeling. This, in fact, highlights the need for more timely Saudi Ministry of Health 2012). Qatar and Saudi Arabia surveillance data on BMI and risk factors in the EMR. launched national campaigns against obesity in 2011 and Better quality data in the EMR for quantification of mor- 2012, respectively. The Saudi campaign focused primarily bidity and mortality burden and health policy planning is on dietary awareness, promoting healthy eating choices and urgently needed. Third, the attributable effect of BMI on emphasizing variety and balance through a food pyramid ischemic heart disease, stroke, and diabetes was derived (Hamad Al-Dkheel 2012). The Qatar campaign was more from prospective observational studies and meta-analyses, comprehensive, setting up programs to increase physical and hence does not account for differences by ethnicity, or activity, replace school snacks with healthier options, and for underlying diseases (Global BMI Mortality Collabora- pilot a nutrition surveillance system in addition to a dietary tion et al. 2016). While these observational studies are not awareness campaign (Mohammed Al-Thani 2011). Indeed, specifically from the EMR, the attributable effect of BMI and despite the campaign in Qatar, obesity prevalence is on the three diseases has been shown to remain the same one of the highest in the region, suggesting the need for a across different regions from the world (Singh et al. 2013). more aggressive approach. Fourth, BMI presents some drawbacks as it is a measure of Promoting healthier lifestyles around nutrition and excess weight rather excess body fat (Daniels 2009). The physical activity is greatly needed in this region. As relationship between BMI and body fat can be influenced pointed out previously, possible factors determining obe- by age, sex, ethnicity, and muscle mass. In addition, BMI sity in the EMR include nutrition transition, inactivity, does not indicate whether excess weight is due to fat, urbanization, marital status, a shorter duration of breast- muscle, or bone (Daniels 2009). However, BMI is favored feeding, frequent snacking, skipping breakfast, a high for its ease of use in surveys and is the most available intake of sugary beverages, an increase in the incidence of indicator regarding excess weight. More details on this eating outside the home, long periods of time spent view- study’s limitations are available elsewhere (GBD 2015 ing television, massive marketing promotion of high-fat Obesity Collaborators 2017). foods, stunting, perceived body image, cultural elements, This study showed that high BMI creates a major burden and food subsidy policies (Musaiger 2011b). in the EMR. We call for countries in the region to invest 123 Burden of obesity in the Eastern Mediterranean Region: findings from the Global Burden of… S173 Murdoch Childrens Research Institute, The University of Melbourne, more resources in prevention and health promotion efforts Parkville, Victoria, Australia; The University of Melbourne, Mel- to reduce the prevalence of obesity. These programs should bourne, VIC, Australia; The University of Sydney, Sydney, NSW, focus on stopping weight gain as a first step and more Australia. Reza Alizadeh-Navaei, PhD, Gastrointestinal Cancer aggressive programs to reduce weight among those who Research Center, Mazandaran University of Medical Sciences, Sari, Mazandaran, Iran. Ala’a Alkerwi, PhD, Luxembourg Institute of need to do so. These programs should take into account the Health (LIH), Strassen, Luxembourg. Khalid A. Altirkawi, MD, King culture and local environment. Moreover, countries should Saud University, Riyadh, Saudi Arabia. Nelson Alvis-Guzman, PhD, join efforts in their programs and policies and share Universidad de Cartagena, Cartagena de Indias, Colombia. Bernhard experiences and success stories. T. Baune, PhD, School of Medicine, University of Adelaide, Ade- laide, South Australia, Australia. Neeraj Bedi, MD, College of Public Health and Tropical Medicine, Jazan, Saudi Arabia. Derrick A. GBD 2015 Eastern Mediterranean Region Obesity Collabora- Bennett, PhD, Nuffield Department of Population Health, University tors: Ali H. Mokdad, PhD (corresponding author), Institute for of Oxford, Oxford, United Kingdom. Addisu S. Beyene, MPH, Col- Health Metrics and Evaluation, University of Washington, Seattle, lege of Health and Medical Science, Haramaya University, Harar, Washington, United States. Charbel El Bcheraoui, PhD, Institute for Ethiopia. Zulfiqar A. Bhutta, PhD, Centre of Excellence in Women Health Metrics and Evaluation, University of Washington. Ashkan and Child Health, Aga Khan University, Karachi, Pakistan; Centre for Afshin, MD, Institute for Health Metrics and Evaluation, University Global Child Health, The Hospital for Sick Children, Toronto, ON, of Washington, Seattle, WA, United States. Raghid Charara, MD, Canada. Mulugeta M. Birhanu, MS, University of Groningen, UMCG, American University of Beirut, Beirut, Lebanon. Ibrahim Khalil, MD, Groningen, Groningen, Netherlands; Mekelle University, Mekelle, Institute for Health Metrics and Evaluation, University of Washing- Ethiopia. Hadi Danawi, PhD, Walden University, Minneapolis, ton, Seattle, Washington, United States. Maziar Moradi-Lakeh, MD, Minnesota, United States. Seyed-Mohammad Fereshtehnejad, PhD, Department of Community Medicine, Preventive Medicine and Department of Neurobiology, Care Sciences and Society (NVS), Public Health Research Center, Gastrointestinal and Liver Disease Karolinska Institutet, Stockholm, Sweden. Florian Fischer, PhD, Research Center (GILDRC), University of Medical Sciences, Tehran, School of Public Health, Bielefeld University, Bielefeld, Germany. Iran. Nicholas J. Kassebaum, MD, Institute for Health Metrics and Tsegaye Tewelde Gebrehiwot, MPH, Jimma University, Jimma, Evaluation, University of Washington, Seattle, Washington, United Oromia, Ethiopia. Paramjit Singh Gill, DM, Warwick Medical States; Department of Anesthesiology & Pain Medicine, Seattle School, University of Warwick, Coventry, United Kingdom; Children’s Hospital, Seattle, Washington, United States. Michael University of Birmingham, UK, Birmingham, United Kingdom. Collison, BS, Institute for Health Metrics and Evaluation, University Philimon N. Gona, PhD, University of Massachusetts Boston, Boston, of Washington, Seattle, Washington, United States. Farah Daoud, Massachusetts, United States. Vipin Gupta, PhD, Department of BA/BS, Institute for Health Metrics and Evaluation, University of Anthropology, University of Delhi, Delhi, Delhi, India. Tesfa Dejenie Washington, Seattle, Washington, United States. Kristopher J. Krohn, Habtewold, MS, University of Groningen, Groningen, Netherlands; BA, Institute for Health Metrics and Evaluation, University of Debre Berhan University, Debre Berhan, Ethiopia. Randah Ribhi Washington, Seattle, Washington, United States. Adrienne Chew, Hamadeh, DPhil, Arabian Gulf University, Manama, Bahrain. Samer ND, Institute for Health Metrics and Evaluation, University of Hamidi, DrPH, Hamdan Bin Mohammed Smart University, Dubai, Washington, Seattle, Washington, United States. Stan H. Biryukov, United Arab Emirates. Habtamu Abera Hareri, MS, Addis Ababa BS, Institute for Health Metrics and Evaluation, University of University, Addis Ababa, Ethiopia. Masako Horino, MPH, Bureau of Washington, Seattle, Washington, United States. Leslie Cornaby, BS, Child, Family & Community Wellness, Nevada Division of Public Institute for Health Metrics and Evaluation, University of Washing- and Behavioral Health, Carson City, NV, United States. Mohamed ton, Seattle, Washington, United States. Kyle J. Foreman, PhD, Hsairi, MD, Department of Epidemiology, Salah Azaiz Institute, Institute for Health Metrics and Evaluation, University of Washing- Tunis, Tunis, Tunisia. Mehdi Javanbakht, PhD, University of ton, Seattle, Washington, United States; Imperial College London, Aberdeen, Aberdeen, Aberdeen, United Kingdom. Denny John, MPH, London, United Kingdom. Michael Kutz, BS, Institute for Health International Center for Research on Women, New Delhi, Delhi, Metrics and Evaluation, University of Washington, Seattle, Wash- India. Jost B. Jonas, MD, Department of Ophthalmology, Medical ington, United States. Patrick Liu, BA, Institute for Health Metrics Faculty Mannheim, Ruprecht-Karls-University Heidelberg, Man- and Evaluation, University of Washington, Seattle, Washington, nheim, Germany. Vasna Joshua, PhD, National Institute of Epi- United States. Marissa Reitsma, BS, Institute for Health Metrics and demiology, Indian Council of Medical Research, Chennai, India. Evaluation, University of Washington, Seattle, Washington, United Amir Kasaeian, PhD, Hematology-Oncology and Stem Cell Trans- States. Patrick Sur, BA, Institute for Health Metrics and Evaluation, plantation Research Center, Tehran University of Medical Sciences, University of Washington, Seattle, Washington, United States, Seat- Tehran, Iran; Endocrinology and Metabolism Population Sciences tle. Haidong Wang, PhD, Institute for Health Metrics and Evaluation, Institute, Tehran University of Medical Sciences, Tehran, Iran. Ezra University of Washington, Seattle, Washington, United States. Ben B. Ketema, MS, Mekelle University, Mekelle, Ethiopia. Yousef Saleh Zipkin, BS, Institute for Health Metrics and Evaluation, University of Khader, ScD, Department of Community Medicine, Public Health Washington, Seattle, Washington, United States, Seattle. Johan Arn- and Family Medicine, Jordan University of Science and Technology, lo ¨ v, PhD, Department of Neurobiology, Care Sciences and Society, Irbid, Irbid, Jordan. Ejaz Ahmad Khan, MD, Health Services Acad- Division of Family Medicine and Primary Care, Karolinska Institutet, emy, Islamabad, Punjab, Pakistan. Jagdish Khubchandani, PhD, Stockholm, Sweden; School of Health and Social Studies, Dalarna Department of Nutrition and Health Science, Ball State University, University, Falun, Sweden. Cristiana Abbafati, PhD, La Sapienza, Muncie, Indiana, United States. Daniel Kim, DrPH, Department of University of Rome, Rome, Italy. Abdishakur M. Abdulle, PhD, New Health Sciences, Northeastern University, Boston, Massachusetts, York University Abu Dhabi, Abu Dhabi, United Arab Emirates. United States. Yun Jin Kim, PhD, Faculty of Chinese Medicine, Niveen M.E. Abu-Rmeileh, PhD, Institute of Community and Public Southern University College, Skudai, Johor, Malaysia. Yohannes Health, Birzeit University, Ramallah, West Bank, Palestine. Muktar Kinfu, PhD, Centre for Research and Action in Public Health, Beshir Ahmed, MPH, College of Health Sciences, Department of University of Canberra, Canberra, Australian Capital Territory, Epidemiology, ICT and e-Learning Coordinator, Jimma University, Australia. Yoshihiro Kokubo, PhD, Department of Preventive Car- Jimma, Oromiya, Ethiopia. Ziyad Al-Aly, MD, Washington Univer- diology, National Cerebral and Cardiovascular Center, Suita, Osaka, sity in St. Louis, St. Louis, MO, United States. Khurshid Alam, PhD, 123 S174 GBD 2015 Eastern Mediterranean Region Obesity Collaborators Japan. Heidi J. Larson, PhD, Department of Infectious Disease Epi- Anesthesiology, University of Virginia, Charlottesville, VA, United demiology, London School of Hygiene & Tropical Medicine, Lon- States; Department of Anesthesiology, King Fahad Medical City, don, United Kingdom; Institute for Health Metrics and Evaluation, Riyadh, Saudi Arabia; Outcomes Research Consortium, Cleveland University of Washington, Seattle, Washington, United States. Paul Clinic, Cleveland, OH, United States. Tenaw Yimer Tiruye, MPH, H. Lee, PhD, Hong Kong Polytechnic University, Hong Kong, China. Debre Markos University, Debre Markos, Ethiopia. Kingsley Nnanna Raimundas Lunevicius, PhD, Aintree University Hospital National Ukwaja, MD, Department of Internal Medicine, Federal Teaching Health Service Foundation Trust, Liverpool, United Kingdom; School Hospital, Abakaliki, Ebonyi State, Nigeria. Tommi Vasankari, PhD, of Medicine, University of Liverpool, Liverpool, United Kingdom. UKK Institute for Health Promotion Research, Tampere, Finland. Hassan Magdy Abd El Razek, MBBCH, Mansoura Faculty of Med- Vasiliy Victorovich Vlassov, MD, National Research University icine, Mansoura, Egypt. Mohammed Magdy Abd El Razek, MBBCH, Higher School of Economics, Moscow, Russia. Tolassa Wakayo, MS, Aswan University Hospital, Aswan Faculty of Medicine, Aswan, Jimma University, Jimma, Oromia, Ethiopia. Andrea Werdecker, Egypt. Reza Malekzadeh, MD, Digestive Diseases Research Institute, PhD, Competence Center Mortality-Follow-Up of the German Tehran University of Medical Sciences, Tehran, Iran. Kedar K. Mate, National Cohort, Federal Institute for Population Research, Wies- MSc, McGill University, Montreal, Quebec, Canada. Ziad A. Mem- baden, Hessen, Germany. Naohiro Yonemoto, MPH, Department of ish, MD, Saudi Ministry of Health, Riyadh, Saudi Arabia; College of Biostatistics, School of Public Health, Kyoto University, Kyoto, Medicine, Alfaisal University, Riyadh, Saudi Arabia. Mubarek Abera Japan. Mustafa Z. Younis, DrPH, Jackson State University, Jackson, Mengistie, MS, Jimma University, Jimma, Oromia, Ethiopia. George MS, United States. Bassel Zein, MS, Department of Neuroscience, A. Mensah, MD, Center for Translation Research and Implementation Georgetown University, Washington DC, United States. Aisha O. Science, National Heart, Lung, and Blood Institute, National Insti- Jumaan, PhD, Independent Consultant, Seattle, Washington, United tutes of Health, Bethesda, MD, United States. Felix Akpojene Ogbo, States. Theo Vos, PhD, Institute for Health Metrics and Evaluation, MPH, Centre for Health Research, Western Sydney University, University of Washington, Seattle, Washington, United States. Simon Sydney, New South Wales, Australia. Farshad Pourmalek, PhD, I. Hay, DSc, Oxford Big Data Institute, Li Ka Shing Centre for Health University of British Columbia, Vancouver, British Columbia, Information and Discovery, University of Oxford, Oxford, United Canada. Anwar Rafay, MS, Contech International Health Consultants, Kingdom; Institute for Health Metrics and Evaluation, University of Lahore, Punjab, Pakistan; Contech School of Public Health, Lahore, Washington, Seattle, Washington, United States. Mohsen Naghavi, Punjab, Pakistan. Mostafa Qorbani, PhD, Non-communicable Dis- PhD, Institute for Health Metrics and Evaluation, University of eases Research Center, Alborz University of Medical Sciences, Karaj, Washington, Seattle, Washington, United States. Christopher J. Iran. Vafa Rahimi-Movaghar, MD, Sina Trauma and Surgery L. Murray, DPhil, Institute for Health Metrics and Evaluation, Research Center, Tehran University of Medical Sciences, Tehran, University of Washington, Seattle, Washington, United States. Tehran, Iran. Saleem M. Rana, PhD, Contech School of Public Health, Lahore, Punjab, Pakistan; Contech International Health Compliance with ethical standards Consultants, Lahore, Punjab, Pakistan. Salman Rawaf, MD, Imperial College London, London, United Kingdom. Andre M.N. Renzaho, This manuscript reflects original work that has not previously been PhD, Western Sydney University, Penrith, NSW, NSW, Australia. published in whole or in part and is not under consideration else- Satar Rezaei, PhD, School of Public Health, Kermanshah University where. All authors have read the manuscript and have agreed that the of Medical Sciences, Kermanshah, Iran. Mohammad Sadegh Rezai, work is ready for submission and accept responsibility for its con- MD, Infectious Disease Research Centre with Focus on Nosocomial tents. The authors of this paper have complied with all ethical stan- Infection, Mazandaran University of Medical Sciences, Sari, dards and do not have any conflicts of interest to disclose at the time Mazandaran, Iran. Mahdi Safdarian, MD, Sina Trauma & Surgery of submission. The funding source played no role in the design of the Research Center, Tehran University of Medical Sciences, Tehran, study, the analysis and interpretation of data, and the writing of the Iran. Mohammad Ali Sahraian, MD, MS Research Center, Neuro- paper. The study did not involve human participants and/or animals; science Institute, Tehran University of Medical Sciences, Tehran, therefore, no informed consent was needed. Iran. Payman Salamati, MD, Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran, Tehran, Iran. Funding This research was funded by the Bill & Melinda Gates Abdallah M. Samy, PhD, Ain Shams University, Cairo, Egypt. Juan Foundation. Ramon Sanabria, MD, J Edwards School of Medicine, Marshall Univeristy, Huntington, WV, United States; Case Western Reserve Conflict of interest The authors declare that they have no conflicts of University, Cleveland, OH, United States. Milena M. Santric Mil- interest at this time. icevic, PhD, Institute of Social Medicine, Faculty of Medicine, University of Belgrade, Serbia, Serbia; Centre School of Public Open Access This article is distributed under the terms of the Health and Health Management, Faculty of Medicine, University of Creative Commons Attribution 4.0 International License (http://crea Belgrade, Serbia, Serbia. Benn Sartorius, PhD, Public Health Medi- tivecommons.org/licenses/by/4.0/), which permits unrestricted use, cine, School of Nursing and Public Health, University of KwaZulu- distribution, and reproduction in any medium, provided you give Natal, Durban, South Africa; UKZN Gastrointestinal Cancer appropriate credit to the original author(s) and the source, provide a Research Centre, South African Medical Research Council link to the Creative Commons license, and indicate if changes were (SAMRC), Durban, South Africa. Sadaf G. Sepanlou, PhD, Digestive made. 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International Journal of Public HealthSpringer Journals

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