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1 Supplementary information file 1
2 Annex 1. Search terms and review process Review 1: BMI data Pubmed, google scholar, article reference lists, author contacts and country statistical databases were searched for the relevant data. Organisation s online databases such as World Health Organization BMI database was also searched for relevant data. The following search terms were used. Obesity OR BMI OR body mass index OR overweight OR weight AND Middle East OR Bahrain OR Egypt OR Iran OR Jordan OR Kuwait OR Lebanon OR Oman OR Saudi Arabia OR Turkey OR Israel OR Morocco OR Tunisia OR United Arab Emirates. Data were included if they were presented using World Health Organization cut-offs for normal weight ( 25 kg/m 2 ), overweight (BMI kg/m 2 ) and obesity (BMI 30 kg/m 2 ) by age and sex group for a specific country. Review 2: Disease data Pubmed, google scholar, article reference lists, author contacts and country statistical databases were searched for the relevant data. Organisation s online databases such as Globocan, World Health Organization infobase, international diabetes federation were also searched for relevant data. The most current data were used. Incidence and mortality data were included if they were presented by age and sex. The following search terms were used: Non-communicable disease OR NCD OR coronary heart disease OR stroke OR cardiovascular OR hypertension OR cancer OR diabetes OR type 2 diabetes OR osteoarthritis OR arthritis AND Incidence OR prevalence OR survival OR mortality For each diseases incidence, prevalence or mortality data were included if they were presented by age or sex. Survival was rarely presented by age and sex and so a single figure was used for all age groups. Where data were not presented by age and sex for a country then proxy country data were included. Only articles written in English or translated by the article s author were included. 2
3 Annex 2. References of disease incidence, mortality and survival data used for each country. Country Disease Incidence Mortality Survival Bahrain Diabetes Iran proxy non-terminal non-terminal CHD Iran proxy WHO 2008 Iran proxy Stroke Iran proxy WHO 2008 Turkey proxy in combination with UK data Hypertension Turkey proxy non-terminal non-terminal Cancers: Breast Globocan 2008 Globocan 2008 Oman proxy Colorectal Globocan 2008 Globocan 2008 Iran proxy Endometrial Globocan 2008 Globocan Kidney Globocan 2008 Globocan Liver Globocan 2008 Globocan 2008 Lebanon proxy Oesophagus Globocan 2008 Globocan Pancreatic Globocan 2008 Globocan Osteoarthritis UK proxy non-terminal non-terminal Egypt Diabetes Iran proxy non-terminal non-terminal CHD Iran proxy WHO 2008 Iran proxy Stroke Iran proxy WHO 2008 Turkey proxy in combination with UK data Hypertension Turkey proxy non-terminal non-terminal Cancers: Breast Globocan 2008 Globocan 2008 Oman proxy Colorectal Globocan 2008 Globocan 2008 Iran proxy Endometrial Globocan 2008 Globocan Kidney Globocan 2008 Globocan Liver Globocan 2008 Globocan 2008 Lebanon proxy Oesophagus Globocan 2008 Globocan Pancreatic Globocan 2008 Globocan Osteoarthritis UK proxy non-terminal non-terminal Iran Diabetes Harati et al 2009 non-terminal non-terminal CHD Unpublished data WHO 2008 Ghadimi et al 2006 Stroke Azarpahooh et al 2010 WHO 2008 Turkey proxy in combination with UK data Hypertension Turkey proxy non-terminal non-terminal Cancers: Breast Globocan 2008 Globocan 2008 Oman proxy Colorectal Globocan 2008 Globocan 2008 Iran proxy Endometrial Globocan 2008 Globocan Kidney Globocan 2008 Globocan Liver Globocan 2008 Globocan 2008 Lebanon proxy Oesophagus Globocan 2008 Globocan Pancreatic Globocan 2008 Globocan Osteoarthritis UK proxy non-terminal non-terminal 3
4 Country Disease Incidence Mortality Survival Jordan Diabetes Iran proxy non-terminal non-terminal CHD Iran proxy WHO 2008 Iran proxy Stroke Iran proxy WHO 2008 Turkey proxy in combination with UK data Hypertension Turkey proxy non-terminal non-terminal Cancers: Breast Globocan 2008 Globocan 2008 Oman proxy Colorectal Globocan 2008 Globocan 2008 Iran proxy Endometrial Globocan 2008 Globocan Kidney Globocan 2008 Globocan Liver Globocan 2008 Globocan 2008 Lebanon proxy Oesophagus Globocan 2008 Globocan Pancreatic Globocan 2008 Globocan Osteoarthritis UK proxy non-terminal non-terminal Kuwait Diabetes Iran proxy non-terminal non-terminal CHD Iran proxy WHO 2008 Iran proxy Stroke Iran proxy WHO 2008 Turkey proxy in combination with UK data Hypertension Turkey proxy non-terminal non-terminal Cancers: Breast Globocan 2008 Globocan 2008 Oman proxy Colorectal Globocan 2008 Globocan 2008 Iran proxy Endometrial Globocan 2008 Globocan Kidney Globocan 2008 Globocan Liver Globocan 2008 Globocan 2008 Lebanon proxy Oesophagus Globocan 2008 Globocan Pancreatic Globocan 2008 Globocan Osteoarthritis UK proxy non-terminal non-terminal Lebanon Diabetes Iran proxy non-terminal non-terminal CHD Iran proxy WHO 2008 Iran proxy Stroke Iran proxy WHO 2008 Turkey proxy in combination with UK data Hypertension Turkey proxy non-terminal non-terminal Cancers: Breast Globocan 2008 Globocan 2008 Oman proxy Colorectal Globocan 2008 Globocan 2008 Iran proxy Endometrial Globocan 2008 Globocan Kidney Globocan 2008 Globocan Liver Globocan 2008 Globocan 2008 Yaghi et al 2006 Oesophagus Globocan 2008 Globocan Pancreatic Globocan 2008 Globocan Osteoarthritis UK proxy non-terminal non-terminal 4
5 Country Disease Incidence Mortality Survival Oman Diabetes Iran proxy non-terminal non-terminal CHD Iran proxy WHO 2008 Iran proxy Stroke Iran proxy WHO 2008 Turkey proxy in combination with UK data Hypertension Turkey proxy non-terminal non-terminal Cancers: 2011 (Oman) Breast Globocan 2008 Globocan 2008 Kumar et al Colorectal Globocan 2008 Globocan 2008 Iran proxy Endometrial Globocan 2008 Globocan Kidney Globocan 2008 Globocan Liver Globocan 2008 Globocan 2008 Lebanon proxy Oesophagus Globocan 2008 Globocan Pancreatic Globocan 2008 Globocan Osteoarthritis UK proxy non-terminal non-terminal Saudi Diabetes Iran proxy non-terminal non-terminal Arabia CHD Iran proxy WHO 2008 Iran proxy Stroke Iran proxy WHO 2008 Turkey proxy in combination with UK data Hypertension Turkey proxy non-terminal non-terminal Cancers: Breast Globocan 2008 Globocan 2008 Oman proxy Colorectal Globocan 2008 Globocan 2008 Iran proxy Endometrial Globocan 2008 Globocan Kidney Globocan 2008 Globocan Liver Globocan 2008 Globocan 2008 Lebanon proxy Oesophagus Globocan 2008 Globocan Pancreatic Globocan 2008 Globocan Osteoarthritis UK proxy non-terminal non-terminal Turkey Diabetes Iran proxy non-terminal non-terminal CHD Iran proxy WHO 2008 Iran proxy Stroke Iran proxy WHO 2008 Samanci et al (2004) in combination with UK Hypertension Arici et al (2010) non-terminal non-terminal Cancers: Breast Globocan 2008 Globocan 2008 Oman proxy Colorectal Globocan 2008 Globocan 2008 Iran proxy Endometrial Globocan 2008 Globocan Kidney Globocan 2008 Globocan Liver Globocan 2008 Globocan 2008 Lebanon proxy Oesophagus Globocan 2008 Globocan Pancreatic Globocan 2008 Globocan Osteoarthritis UK proxy non-terminal non-terminal 5
6 Annex 3 Modelling obesity distribution over time An individual's body mass index (BMI) is defined as:, where and correspond to individual's weight and height, respectively. provides a simple measure of a person's fatness or thinness. Although is measured on a continuous scale, it is grouped in the following five categories: 1) : <18.5 (underweight) 2) from 18.5 to 24.99: (healthy weight) 3) from 25 to 19.99: (overweight) 4) from 30 to 39.99: (obese) 5) : (morbidly obese) Let =1,...,5, denote group (lower and larger values of correspond to groups with lower and larger values of on the continuous scale, respectively). Let be the prevalence of individuals with values that correspond to group at time. To ensure that takes values within [0,1], we model by [ ( )] (1) An alternative way to estimate is through a logistic regression model with percentage for group as the outcome, and time,, as the single explanatory variable: By solving equation (2) for ( ) (2) we obtain ( ) ( ) Equations (1) and (2) are mathematically identical, but (1) provides greater numerical stability. Fitting separate models for the estimation of for each group g, does not guarantee that. Thus, to ensure that the estimates of prevalence of individuals in all groups sum up to 1 for each time, we estimate the prevalence of individuals with values that correspond to group at time by (3) Micro simulation BMI growth model The distribution of BMI in the population is estimated using regression analysis stratified by both sex (S) and age group (A=0-9, 10-19,20-29,,70-79, 80+). The fitted models are extrapolated to forecast the distribution of BMI groups in the future. For each sex-and-agegroup stratum, the set of cross-sectional, time-dependent, discrete distributions of BMI groups,, is used to manufacture BMI growth models for individual members of the population. This is done in a way that guarantees that the cross- 6
7 sectional BMI group distributions obtained by simulation under the growth models match the BMI group distributions of the observed data. The details are as follows; For each such discrete distribution in, there is a continuous counterpart. Let denote BMI in the continuous scale and let be the probability density function of for age group and sex at time. Then (4) Equations (3) and (4) both refer to the same quantity. However, equation (4) uses the definition of a probability density function to express the age-and-sex-specific percentage of individuals in BMI group g at time t. Equation (3) gives an estimate of the this quantity using model (1) for all g=0,..,5. The cumulative distribution function of is (5) At a time t, a person with sex belonging to the age group is said to be on the th percentile of this distribution if Given the cross-sectional information of, it is possible to simulate longitudinal trajectories by forming pseudo cohorts within the population. A key requirement for these sets of longitudinal trajectories is that they reproduce the cross-sectional distribution of BMI groups for any year with available data. The method adopted here and in the earlier Foresight report 1 is based on the assumption that people s BMI changes throughout their lives in such a way that they always stay on the same BMI percentile. As they age, individuals move from one age group to another and their BMI changes so that they remain on the same percentile but of a different distribution. This rule is not too far from the truth; and has as a result that relatively fat people stay relatively fat and relatively thin people stay relatively thin. Crucially it meets the important condition that the cross-sectional BMI group distributions obtained by simulation match the BMI group distributions of the observed data. This can be seen as follows:. When the population s BMI distributions by sex and age are known for all years (by extrapolation of fitted model (3)), a person who is in age group and who grows ten year older will at some time move into the next age group and will have a BMI that was described first by the distribution and then at the later time by the distribution. If the BMI of that individual is on the p th percentile of the BMI distribution, his BMI will change from to so that ( ) (6) ( ) (7) Where is the inverse function of the cumulative distribution function of. (7) guarantees that the transformation taking the random variable to ensures the correct cross-sectional distribution at. The micro simulation both generates individuals from the BMI distributions of the set once generated, grows the individal s BMI in a way that is also determined by the set possible to implement (6) as a suitably fast algorithm. and,. It is 1 Foresight report 7
8 Micro simulation: Birth, disease and death models. Simulated people are generated with the correct demographic statistics in the simulation s start-year. In this year women are stochastically allocated the number and years of birth of their children these are generated from known fertility and mother s age at birth statistics (valid in the start-year). If a woman has children then those children are generated as members of the simulation in the appropriate birth year. In the course of their lives, simulated people can die from one of the BMI related diseases that they might have acquired or from some other cause. The probabilities that a person of a given age and gender dies from a cause other than a BMI related disease are calculated in terms of known death and disease statistics valid in the start-year and are held constant over the course of the simulation. The death rates from BMI related diseases will change as a consequence of the population s changing BMI distribution. 8
9 Annex 4 Projected age-specific BMI distribution for each country Projected BMI distribution for Bahrain Year, BMI Age, sex < >30 < >30 < >30 < >30 < > , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F
10 Projected BMI distribution in Egypt < >30 < >30 < >30 < >30 < > , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F Note: grey highlighting represents simulated data from first previous age group. 10
11 Projected BMI distributions for Iran < >30 < >30 < >30 < >30 < > , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F
12 Projected BMI distribution in Jordan < >30 < >30 < >30 < >30 < > , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F
13 Projected BMI distribution in Kuwait < >30 < >30 < >30 < >30 < > , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F
14 Projected BMI distribution in Lebanon < >30 < >30 < >30 < >30 < > , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F
15 Projected BMI distribution in Oman < >30 < >30 < >30 < >30 < > , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F
16 Projected BMI distribution in Saudi Arabia < >30 < >30 < >30 < >30 < > , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F
17 Projected BMI distribution in Turkey < >30 < >30 < >30 < >30 < > , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F , M , F
18 Annex 5 Incidence, mortality, survival data per 100,000 of the population in 2010 Bahrain incidence per 100,000 breast cancer F colorectal cancer M F coronary heart disease AgeGp >74 M F diabetes AgeGp >64 M F endometrial cancer AgeGp >74 M F hypertension AgeGp >69 M F kidney cancer M F liver cancer AgeGp >74 M F oesophageal cancer AgeGp >74 M F osteoarthritis AgeGp >74 M F pancreatic cancer AgeGp >74 M F stroke AgeGp >84 M F
19 Bahrain mortality per 100,000 breast cancer F colorectal cancer M F coronary heart disease AgeGp >79 M F diabetes endometrial cancer AgeGp >64 M F hypertension kidney cancer AgeGp >74 M F liver cancer AgeGp >74 M F oesophageal cancer AgeGp >74 M F osteoarthritis pancreatic cancer AgeGp >74 M F stroke AgeGp >79 M F
20 Egypt incidence per 100,000 breast cancer F colorectal cancer M F coronary heart disease AgeGp >74 M F diabetes AgeGp >64 M F endometrial cancer F hypertension AgeGp >69 M F kidney cancer M F liver cancer M F oesophageal cancer M F osteoarthritis AgeGp >74 M F pancreatic cancer M F stroke AgeGp >84 M F
21 Egypt mortality per 100,000 breast cancer F colorectal cancer M F coronary heart disease AgeGp >79 M F diabetes endometrial cancer AgeGp >74 M F hypertension kidney cancer M F liver cancer M F oesophageal cancer M F osteoarthritis pancreatic cancer M F stroke AgeGp >79 M F
22 Iran incidence per 100,000 breast cancer F colorectal cancer M F coronary heart disease AgeGp >74 M F diabetes AgeGp >64 M F endometrial cancer F hypertension AgeGp >69 M F kidney cancer AgeGp >74 M F liver cancer M F oesophageal cancer M F osteoarthritis AgeGp >74 M F pancreatic cancer AgeGp >74 M F stroke AgeGp >84 M F
23 Iran mortality per 100,000 breast cancer F colorectal cancer M F coronary heart disease AgeGp >79 M F diabetes endometrial cancer AgeGp >74 M F hypertension kidney cancer M F liver cancer M F oesophageal cancer M F osteoarthritis pancreatic cancer AgeGp >74 M F stroke AgeGp >79 M F
24 Jordan incidence per 100,000 breast cancer F colorectal cancer M F coronary heart disease AgeGp >74 M F diabetes AgeGp >64 M F endometrial cancer F hypertension AgeGp >69 M F kidney cancer M F liver cancer M F oesophageal cancer M F osteoarthritis AgeGp >74 M F pancreatic cancer M F stroke AgeGp >84 M F
25 Jordan mortality per 100,000 breast cancer F colorectal cancer M F coronary heart disease AgeGp >79 M F diabetes endometrial cancer AgeGp >74 M F hypertension kidney cancer M F liver cancer M F oesophageal cancer M F osteoarthritis pancreatic cancer M F stroke AgeGp >79 M F
26 Kuwait incidence per 100,000 breast cancer F colorectal cancer M F coronary heart disease AgeGp >74 M F diabetes AgeGp >64 M F endometrial cancer F hypertension AgeGp >69 M F kidney cancer M F liver cancer M F oesophageal cancer AgeGp >74 M F osteoarthritis AgeGp >74 M F pancreatic cancer M F stroke AgeGp >84 M F
27 Kuwait mortality per 100,000 breast cancer F colorectal cancer M F coronary heart disease AgeGp >79 M F diabetes endometrial cancer AgeGp >64 M F hypertension kidney cancer M F liver cancer M F oesophageal cancer AgeGp >74 M F osteoarthritis pancreatic cancer M F stroke AgeGp >79 M F
28 Lebanon incidence per 100,000 breast cancer F colorectal cancer M F coronary heart disease AgeGp >74 M F diabetes AgeGp >64 M F endometrial cancer AgeGp >74 0 F hypertension AgeGp >69 M F kidney cancer M F liver cancer M F oesophageal cancer M F osteoarthritis AgeGp >74 M F pancreatic cancer M F stroke AgeGp >84 M F
29 Lebanon mortality per 100,000 breast cancer F colorectal cancer M F coronary heart disease AgeGp >79 M F diabetes endometrial cancer AgeGp >74 M F hypertension kidney cancer M F liver cancer M F oesophageal cancer M F osteoarthritis pancreatic cancer M F stroke AgeGp >79 M F
30 Oman incidence per 100,000 breast cancer F colorectal cancer M F coronary heart disease AgeGp >74 M F diabetes AgeGp >64 M F endometrial cancer AgeGp >74 M F hypertension AgeGp >69 M F kidney cancer M F liver cancer M F oesophageal cancer AgeGp >74 M F osteoarthritis AgeGp >74 M F pancreatic cancer M F stroke AgeGp >84 M F
31 Oman mortality per 100,000 breast cancer F colorectal cancer M F coronary heart disease AgeGp >79 M F diabetes endometrial cancer AgeGp >74 M F hypertension kidney cancer AgeGp >74 M F liver cancer M F oesophageal cancer AgeGp >74 M F osteoarthritis pancreatic cancer M F stroke AgeGp >79 M F
32 Saudi Arabia incidence per 100,000 breast cancer F colorectal cancer M F coronary heart disease AgeGp >74 M F diabetes AgeGp >64 M F endometrial cancer F hypertension AgeGp >69 M F kidney cancer M F liver cancer AgeGp >74 M F oesophageal cancer M F osteoarthritis AgeGp >74 M F pancreatic cancer M F stroke AgeGp >84 M F
33 Saudi Arabia mortality per 100,000 breast cancer F colorectal cancer M F coronary heart disease AgeGp >79 M F diabetes endometrial cancer AgeGp >74 M F hypertension kidney cancer M F liver cancer M F oesophageal cancer M F osteoarthritis pancreatic cancer M F stroke AgeGp >79 M F
34 Turkey incidence per 100,000 breast cancer F colorectal cancer M F coronary heart disease AgeGp >74 M F diabetes AgeGp >64 M F endometrial cancer F hypertension AgeGp >69 M F kidney cancer M F liver cancer M F oesophageal cancer M F osteoarthritis AgeGp >74 M F pancreatic cancer M F stroke AgeGp >84 M F
35 Turkey mortality per 100,000 breast cancer F colorectal cancer M F coronary heart disease AgeGp >79 M F diabetes endometrial cancer F hypertension kidney cancer M F liver cancer M F oesophageal cancer M F osteoarthritis pancreatic cancer M F stroke AgeGp >79 M F
36 Middle Eastern survival data breast cancer AgeGp >14 male Rate 0 female Rate male p(5 yr survival) 1 female p(5 yr survival) 0.78 colorectal cancer AgeGp >14 male Rate female Rate male p(5 yr survival) 0.61 female p(5 yr survival) 0.61 coronary heart disease AgeGp >34 male Rate female Rate male p(5 yr survival) female p(5 yr survival) diabetes endometrial cancer AgeGp >79 male Rate female Rate male p(5 yr survival) female p(5 yr survival) hypertension kidney cancer AgeGp >14 male Rate female Rate male p(5 yr survival) 0.5 female p(5 yr survival) 0.49 liver cancer AgeGp >14 male Rate female Rate male p(5 yr survival) female p(5 yr survival) oesophageal cancer AgeGp >14 male Rate female Rate male p(5 yr survival) 0.08 female p(5 yr survival) 0.08 osteoarthritis pancreatic cancer AgeGp >79 male Rate female Rate male p(5 yr survival) female p(5 yr survival)
37 stroke AgeGp >84 male Rate female Rate male p female p male p(5 yr survival) female p(5 yr survival)
38 Annex 6 Population distribution of each country ( 38
39 39
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