Groupon behalf of the Global Burden of Metabolic Risk Factors of Chronic Diseases Collaborating

Mayuree Rao and Majid EzzatiMariel M. Finucane, Farshad Farzadfar, Gretchen A. Stevens, Leanne M. Riley, Yuan Lu,

Goodarz Danaei, Gitanjali M. Singh, Christopher J. Paciorek, John K. Lin, Melanie J. Cowan,with National Income, Urbanization, and Western Diet in 1980 and 2008

The Global Cardiovascular Risk Transition : Associations of Four Metabolic Risk Factors

Print ISSN: 0009-7322. Online ISSN: 1524-4539 Copyright © 2013 American Heart Association, Inc. All rights reserved.

is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231Circulation doi: 10.1161/CIRCULATIONAHA.113.001470

2013;127:1493-1502; originally published online March 12, 2013;Circulation. 

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1493

Cardiovascular diseases (CVDs) are the leading cause of death and disease burden worldwide. Population aging

leads to an increase in CVD deaths because CVD mortality rises with age. Beyond aging, age-specific mortality rates may increase or decline over time. Age-specific CVD death rates are themselves affected by exposure to risk factors such as excess weight; smoking; high blood pressure, cholesterol, and glucose; and by treatment availability and quality.

Editorial see p 1451 Clinical Perspective on p 1502

Access to treatment tends to rise with income.1 Although the association between CVD risk factors and socioeconomic status has been studied within countries,1 few studies have assessed the cross-country association of CVD risk factors with national macroeconomic variables.2–4 Some studies have postulated that CVD risk factors may rise with national income or urbanization, due to a Westernized diet and lifestyle,5,6 referred to as diseases of affluence or Western diseases paradigm; others have concluded that higher income and urban infrastructure may help reduce CVD risk factors

Background—It is commonly assumed that cardiovascular disease risk factors are associated with affluence and Westernization. We investigated the associations of body mass index (BMI), fasting plasma glucose, systolic blood pressure, and serum total cholesterol with national income, Western diet, and, for BMI, urbanization in 1980 and 2008.

Methods and Results—Country-level risk factor estimates for 199 countries between 1980 and 2008 were from a previous systematic analysis of population-based data. We analyzed the associations between risk factors and per capita national income, a measure of Western diet, and, for BMI, the percentage of the population living in urban areas. In 1980, there was a positive association between national income and population mean BMI, systolic blood pressure, and total cholesterol. By 2008, the slope of the association between national income and systolic blood pressure became negative for women and zero for men. Total cholesterol was associated with national income and Western diet in both 1980 and 2008. In 1980, BMI rose with national income and then flattened at ≈Int$7000; by 2008, the relationship resembled an inverted U for women, peaking at middle-income levels. BMI had a positive relationship with the percentage of urban population in both 1980 and 2008. Fasting plasma glucose had weaker associations with these country macro characteristics, but it was positively associated with BMI.

Conclusions—The changing associations of metabolic risk factors with macroeconomic variables indicate that there will be a global pandemic of hyperglycemia and diabetes mellitus, together with high blood pressure in low-income countries, unless effective lifestyle and pharmacological interventions are implemented. (Circulation. 2013;127:1493-1502.)

Key Words: blood pressure ◼ cholesterol ◼ diabetes mellitus ◼ epidemiology ◼ obesity

© 2013 American Heart Association, Inc.

Circulation is available at http://circ.ahajournals.org DOI: 10.1161/CIRCULATIONAHA.113.001470

Received October 3, 2012; accepted February 12, 2013.From Department of Global Health and Population, Harvard School of Public Health, Boston, MA (G.D., J.K.L., Y.L., M.R.); Department of Epidemiology,

Harvard School of Public Health, Boston, MA (G.D.); Department of Nutrition, Harvard School of Public Health, Boston, MA (G.M.S.); Department of Statistics, University of California, Berkeley (C.J.P.); Department of Chronic Diseases and Health Promotion, World Health Organization, Geneva, Switzerland (M.J.C., L.M.R.); Gladstone Institutes, University of California, San Francisco, CA (M.M.F.); Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences, Tehran, Iran (F.F.); Department of Health Statistics and Informatics, World Health Organization, Geneva, Switzerland (G.A.S.); MRC-HPA Center for Environment and Health, Imperial College London, London, UK (M.E.); and Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK (M.E.).

*Drs Danaei and Singh contributed equally to this work.†A list of Global Burden of Metabolic Risk Factors of Chronic Diseases Collaborating Group members is given in the Appendix.Guest editor for this article was Gregory Y.H. Lip, MD.The online-only Data Supplement is available with this article at http://circ.ahajournals.org/lookup/suppl/doi:10.1161/CIRCULATIONAHA.

113.001470/-/DC1.Correspondence to Majid Ezzati, PhD, Medical Faculty Building, Norfolk Place, London W2 1PG, UK. E-mail majid.ezzati@imperial.ac.uk

The Global Cardiovascular Risk TransitionAssociations of Four Metabolic Risk Factors with National Income,

Urbanization, and Western Diet in 1980 and 2008

Goodarz Danaei, MD, DSc*; Gitanjali M. Singh, PhD*; Christopher J. Paciorek, PhD; John K. Lin, AB; Melanie J. Cowan, MPH; Mariel M. Finucane, PhD; Farshad Farzadfar, MD, DSc; Gretchen A. Stevens, DSc; Leanne M. Riley, MSc; Yuan Lu, MSc; Mayuree Rao, BA; Majid Ezzati,

PhD on behalf of the Global Burden of Metabolic Risk Factors of Chronic Diseases Collaborating Group†

135,8

Epidemiology and Prevention

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through healthier lifestyle or better access to preventive interventions and primary care.7 Even less is known about how these associations have changed over time with the availability of new public health and clinical programs and with the globalization of medicines and foods.8,9 Knowledge of the relationship between socioeconomic factors and CVD risk factors at the population level is essential to understanding the role of risk factors in the epidemiological transition and to informing national and global policies and priorities. Individual-level studies that provide evidence on causal effects cannot assess with population-level patterns and dynamics.

We investigated the population-level associations of major metabolic risk factors – body mass index (BMI), fasting plasma glucose (FPG), systolic blood pressure (SBP), and serum total fasting cholesterol (TC) – with national income, Western diet, and, for BMI only, urbanization in 1980 and 2008. Although some of the associations reported here may be causal, they should not be generally interpreted as such, because factors like national income and urbanization may themselves be cor-related, making inferences about causal effects neither fea-sible nor possibly relevant. Rather, population-level analysis demonstrates how risk factors, whose causal effects on CVD are established in individual-level epidemiological studies, are distributed across countries in relation to the degree of social and economic development, and how these patterns have changed over time.

MethodsRisk Factor Levels by Sex, Country, and YearMean BMI, FPG, SBP, and TC were from a systematic analysis of population-based data, by sex, for 199 countries and territories, as described in detail in previous publications.10–13 In brief, we reviewed and accessed published and unpublished health examination surveys and population-based epidemiological studies to collate comprehen-sive data on these 4 risk factors between 1980 and 2008. There were 960 data sources across countries and years for BMI, 786 for SBP, 321 for TC, and 370 for FPG. Data in some sources were gathered in a single year, whereas others covered 1 year or more. Counting each source as 1 country-year, these numbers are equivalent to 17% of all 5771 country-years for which estimates were made for BMI, 14% for SBP, 5.5% for TC, and 6.5% for FPG. These figures should be compared with a recommended survey frequency of ≈1 in 5 years, ie, 20% of all country-years14; to our knowledge, only Japan has an annual health examination survey. The number of countries with no data ranged from 30 (ie, 15%) for BMI to >100 (ie, >50%) for TC.

For each risk factor, we developed and applied a Bayesian statisti-cal model to impute missing data. The model incorporated the hierar-chical nature of the data, nonlinear time trends and age associations, and national versus subnational and community representativeness of data. With the use of these data and methods, we estimated mean risk factor levels and their uncertainties by age and sex for each country-year. The uncertainties are larger for risk factors, countries, and years without data or with data that were not from a nationally represen-tative survey (see online-only Data Supplement Materials for statis-tical details on how uncertainty was incorporated in all analyses). We estimated age-standardized means by using the World Health Organization standard population to account for the fact that the age composition varies across countries and over time.

National Income, Urbanization, and Western DietNational income was measured as per capita gross domestic product (GDP) converted to international dollars and adjusted for inflation with a base year of 2005.15 Urbanization was measured as the propor-tion of a country’s population who live in urban areas by the use of

data from the Population Division of the Department of Economic and Social Affairs of the United Nations.

We used data on the availability of multiple food types for hu-man consumption from the food balance sheets of the Food and Agriculture Organization of the United Nations. The food balance sheets report the availability of 24 food types for human consump-tion. in kilocalories per capita per day. We used 22 of 24 food types because one (sugar crops) was missing in >70% of country-years, and another (miscellaneous) may not be defined consistently across coun-tries. For the remaining 22 food types, missing values were imputed by using Imputation by Chained Equations (Stata 10.1 software com-mand ice). The average of 100 imputed values was used. We defined outlier values for a single country-year as values that were larger or smaller than both the previous and the subsequent year’s values by 3 standard deviations or 300% and set these outlier values to the aver-age of the 3 previous and 3 subsequent years. There was an abrupt change in the reported values for all food types in the Occupied Palestinian Territory around 1993 and in the values of 1 food type in 1 year in Mongolia, Maldives, Cyprus, and the Netherlands. These discontinuities were likely due to changes in reporting. We adjusted the earlier estimates by the difference between pre- and postdisconti-nuity values, resulting in a smooth trend.

To identify major dietary patterns with the use of all 22 food types, we used principal component analysis, an approach commonly used in dietary pattern studies to objectively aggregate food items based on the correlation structure within the data set.16 We used the first prin-cipal component in our analysis. This component explained >28% of the variance of data10 and had relatively large positive coefficients for alcoholic beverages, animal fats and animal products, eggs, meat, milk, offals, stimulants, sugar and sweeteners, and total calories; and negative coefficients for pulses and cereals (Table). Consistent with studies of dietary patterns in individuals, we considered this pattern

Table. Coefficients of 22 Food Types from Principal Component Analysis, representing a Western Dietary Pattern

Food TypePrincipal Component

Coefficients

Animal products 0.39

Meat 0.36

Animal fats 0.34

Milk, excluding butter 0.34

Offals 0.30

Alcoholic beverages 0.29

Sugar and sweeteners 0.25

Eggs 0.24

Stimulants 0.23

Total calories 0.20

Vegetable oils 0.08

Fish and seafood 0.05

Vegetables 0.03

Tree nuts 0.02

Fruits, excluding wine −0.05

Other aquatic products −0.06

Spices −0.07

Vegetal products −0.09

Oil crops −0.10

Starchy roots −0.10

Cereals, excluding beer −0.16

Pulses −0.19

Food types with large coefficients are the ones that are common in the dietary pattern, and those with small coefficients are uncommon.

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Danaei et al Global Cardiovascular Risk Transition 1495

as representing a Western diet (WD). Because a principal component does not have a directly interpretable scale, we used percentiles for presentation. Percentiles were calculated by using the distribution of data pooled over all 29 years of analysis. Therefore, as diet in a coun-try westernizes over time, its percentile of WD increases.

Statistical MethodsWe investigated the univariate associations of age-standardized mean risk factor levels with national income, Western diet, and ur-banization (for BMI only) with countries as the units of observation. We did not weight the mean risk factor values by country population because our aim was to investigate the associations between mean risk factors and macroeconomic characteristics across countries as opposed to in the global population. We present the scatter plots and the estimated Pearson correlation coefficient and its 95% confidence interval for 1980 and 2008 (Figures 1 through 3). In each graph, we fitted a nonparametric (Loess) regression to visualize the asso-ciation. We also fitted univariate regression models to each pair of variables in Figures 1 through 3 separately by year (1980 and 2008) and report the slopes in these figures. In addition, we analyzed the associations of mean FPG, SBP, and TC with mean BMI (Figure 4), because BMI causally affects the other 3 metabolic risk factors, which also have other determinants, eg, dietary salt and saturated fats and medication use.

All analyses were based on a Bayesian multiple imputation ap-proach as described in online-only Data Supplement Material. We es-timated the uncertainties of the correlation and regression coefficients as described in online-only Data Supplement Material. Analyses were performed in R (v. 2.15.0) and Stata (10.1 StataCorp).

ResultsIn 1980, population mean BMI, SBP, and TC were positively associated with national income, with correlation coefficients ranging between 0.34 and 0.50 (Figure 1). The relationship between BMI and national income flattened at a per capita GDP of ≈Int$7000. The association between national income and FPG in 1980 was weaker than those of the other 3 risk factors, with correlation coefficients below 0.15 and regression slopes close to zero.

In 2008, the relationship between natural logarithm of GDP [Ln(GDP)] and BMI resembled an inverted U for women, peaking at middle-income levels. Middle- and upper-middle-income countries in Oceania, Middle East, and North Africa had higher BMIs than countries with similar incomes in other regions, whereas women living in high-income countries in Asia-Pacific and Western Europe had lower BMIs than women in countries with similar incomes in other regions. The slope of the BMI-Ln(GDP) association and the correlation coeffi-cient in 2008 were about one-third smaller than their 1980 levels for women.

The association between income and SBP reversed between 1980 and 2008, with the slopes becoming negative for women (from 2.76 [95% uncertainty interval (2.36, 3.62)] mm Hg per Ln(GDP) in 1980 to −1.85 [−2.24, −1.27] in 2008) and virtu-ally zero for men. In 2008, TC was still strongly correlated with national income (correlation coefficients of 0.60 [0.53, 0.70] for women and 0.62 [0.53, 0.67] for men). Although the slope of TC-Ln(GDP) association declined slightly between 1980 and 2008, the declines were not statistically significant. The association between mean FPG and income remained weak in 2008.

The associations of risk factors with the percentage of pop-ulation in urban areas, and how they changed between 1980

and 2008, mostly had the same patterns as associations with national income (detailed results available from the authors by request). The exception was the association between BMI and urbanization. The associations between mean BMI and urban population were positive and statistically significant in both 1980 and 2008, with slopes ranging between 0.54 and 0.7 kg/m2 per 10 percentage point increase in urbanization.

In 1980, BMI was associated with WD, with some flatten-ing for men and reversal of association at approximately the 80th percentile for women (Figure 3). In 2008, the BMI-WD curve for men maintained its linear-then-flat shape but was shifted upward, showing higher BMI levels at the same value of WD. For women, the reversal of association occurred at approximately the 60th WD percentile. The association between SBP and WD shifted from positive to negative for women and was substantially weakened for men over the 3 decades of analysis. TC was positively and significantly asso-ciated with Western diet in both 1980 and 2008 with slopes remaining positive and only slightly declining (Figure 3) and correlation coefficients >0.6.

TC was positively associated with BMI in both 1980 and 2008, but there was more flattening of the curve in 2008, and the slope of the TC-BMI relationship declined in both men and women; The SBP-BMI association disappeared in both men and women over time (Figure 4). Although mean FPG had weak associations with national income and Western diet, it was positively correlated with BMI, with correlation coef-ficients increasing from 0.22 (0.02, 0.34) and 0.25 (0.14, 0.43) in 1980 in men and women, respectively, to 0.54 (0.42, 0.64) and 0.52 (0.40, 0.59) in 2008. The FPG-BMI slope ranged between 0.06 and 0.10 mmol/L per kg/m2 over the analysis period for both men and women.

DiscussionRandomized and observational epidemiological studies have established elevated blood pressure and cholesterol, excess body weight, hyperglycemia, and smoking as some of the most important risk factors for CVD. Until now, little has been known about the associations of these risk factors with mac-roeconomic characteristics of countries and how the associa-tions have changed over time. Our analysis suggests that the associations of metabolic risk factors with affluence and West-ern diet are complex and dynamic. In 1980, 3 major metabolic risk factors for CVD, SBP, TC, and BMI, were positively asso-ciated with national income and Western diet at the national level. By 2008, only TC retained a strong positive association with national income, whereas the other associations changed qualitatively. Interestingly, BMI remained positively associ-ated with urbanization over the nearly 3 decades of the analy-sis in both men and women, suggesting that urbanization may have effects on BMI apart from income and Western diet.

Comparable country data for smoking, the other key CVD risk factor, are available only for recent years and hence do not allow analysis for 1980.14 Analysis using 2008 data, which were available, shows that correlation between adult per capita number of cigarettes smoked per day and Ln(GDP) was 0.24 in men and 0.53 in women (Figure 5). The absence of a linear association with income in men could be due to effective tobacco control in high-income

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Figure 1. Mean age-standardized levels of metabolic risk factors in relation to per capita gross domestic product (GDP). GDP is in inter-national dollars, which accounts for differences in purchasing power across countries, and is adjusted for inflation. The thick line shows the mean association and the gray area the 95% uncertainty interval of this relationship, calculated by using a nonparametric (Loess) regression as described in Methods and online-only Data Supplement Material. Individual data points show country means. See online-only Data Supplement Table for country data. BMI indicates body mass index; FPG, fasting plasma glucose; SBP, systolic blood pres-sure; and TC, serum total cholesterol.

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countries and high smoking prevalence in middle-income countries in Eastern Europe and East and Southeast Asia.

The strengths and innovations of this study include the analysis of associations with multiple macroeconomic vari-ables in both 1980 and 2008; the use of a Bayesian hierarchi-cal model for estimating risk factor levels that incorporated their important features such as nonlinear trends and age associations; and systematic analysis of uncertainty. The main limitation of our study is that, despite extensive data seeking, many country-years still lacked data, especially in the 1980s and for FPG and TC. As reported elsewhere, our model per-formed well in external predictive validity tests, ie, in esti-mating risk factor levels in countries and years that had data, but whose data were held back from the analysis to test the model’s predictions.10–13 Furthermore, in the current analysis, this shortage of data is reflected in larger uncertainty intervals in 1980 in comparison with 2008, and in larger uncertainty for the associations of FPG and TC in comparison with BMI. Our results were similar and our conclusions were unchanged in a sensitivity analysis using those countries with at least 1 data point over time.

There are several potential reasons for the changing asso-ciations of metabolic risk factors with affluence and Western diet. The VA Cooperative Studies and subsequent randomized trials demonstrated the benefits of lowering blood pressure at prehypertension levels and led to lower clinical thresholds for treatment with antihypertensives. This may have differen-tially advantaged high-income countries.17 Lower salt intake and higher intake of fresh fruits and vegetables may also be responsible for lower blood pressure in some high-income countries.17,18 There is further evidence from within-country studies that the decline in blood pressure has been even larger in individuals with higher BMI,19,20 which is consistent with the decoupling of these 2 causally-related factors at the popu-lation level.

The large rise in BMI in middle-income and lower-middle-income countries may be occurring because food cost has become (at least until a recent rise in food prices) an increas-ingly smaller share of household expenditure in these coun-tries. The fact that the association of BMI with Western diet has weakened indicates that the rise in BMI in developing

countries may be as much due to increased caloric intake from traditional sources as from a shift to a Western diet. In addi-tion, the persistent positive association of BMI with urbaniza-tion, especially for men, is consistent with a role of physical inactivity in urban populations and in urbanizing countries. The differences in associations of BMI with macroeconomic variables between men and women is consistent with the faster rise in female BMI in low- and middle-income coun-tries in comparison with male BMI.13,21 The continued positive association between serum TC and national income may be due to the relatively high cost of animal products in low- and even middle-income countries. Nonetheless, TC declined in Western countries,12 possibly because of changes in diet and the wider use of statins.22

The changes in metabolic risk factors, especially in relation to national income, may help explain some of the trends in CVD mortality. Age-standardized cardiovascular disease mor-tality in high-income countries has declined substantially over the past few decades.23,24 Faster emergency response times; the use of medicines such as antiplatelet agents, angiotensin-con-verting-enzyme inhibitor, β-blockers, and statins after heart attack or stroke; and medical advances such as angioplasty, defibrillation, and thrombolysis have improved the survival of people with a cardiovascular event.25 However, the contribu-tion of postevent treatment to lower CVD mortality is esti-mated as <50%.26 Rather, the mortality decline is largely a result of lower disease incidence, itself due to preventive inter-ventions, especially population-level improvements in blood pressure and cholesterol and reduction in smoking26 that have occurred despite rising BMI levels. There are fewer data on CVD trends in low- and middle-income countries. The avail-able data nonetheless indicate that relatively soon after the decline in infectious diseases, CVD mortality also declined even in low- and middle-income countries27,28; where this decline has been documented, it has accompanied a decline in blood pressure.29

Given this evidence, the dynamic global epidemiology of metabolic risk factors has important implications for CVD prevention worldwide. First, a key focus of global CVD pre-vention should be to reverse the shifting burden of SBP to low-income countries seen in Figure 1. Lowering salt intake

Figure 2. Mean age-standardized BMI in relation to the proportion of a country’s population that live in urban areas. Associations of other risk factors with urbanization are available from the authors by request. See online-only Data Supplement Table for country data. BMI indicates body mass index.

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Figure 3. Mean age-standardized levels of metabolic risk factors in relation to Western diet (WD). See Methods for how percentiles were calculated. See online-only Data Supplement Table for country data. BMI indicates body mass index; FPG, fasting plasma glucose; SBP, systolic blood pressure; and TC, serum total cholesterol.

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Danaei et al Global Cardiovascular Risk Transition 1499

through regulation and well-designed health education, improving access to fresh fruits and vegetables, and strength-ening primary care to better detect and manage high blood pressure can help lower blood pressure worldwide, replicat-ing the decline in high-income nations.24,30 Second, low- and middle-income countries could implement food policies that encourage the use of polyunsaturated fats instead of satu-rated fats,22 avoiding the high cholesterol levels experienced in Western countries and the rise in Japan and China. Third, high- and middle-income countries can increase the cover-age of statins for lowering serum cholesterol.31 The cost of medicines and screening and follow-up tests may be too high for low-income countries. The availability of generic

drugs and lower-cost tests will help make statins afford-able even in low-income settings, but their implementation requires investment in an equitable and high-quality primary care system. Currently, high- and middle-income countries spend significant healthcare resources on treatment of CVD, diabetes mellitus, and their complications.32–35 Redirecting some of these resources to primary prevention of high blood pressure and cholesterol may lead to a net cost saving.36,37 In low-income countries, additional strengthening and invest-ment in primary care38 and regulatory infrastructures, eg, establishing and enforcing limits on salt in more commonly used packaged foods and staples such as flour and bread, may be needed.

Figure 4. Mean age-standardized SBP, TC, and FPG in relation to mean age-standardized BMI. See online-only Data Supplement Table for country data. BMI indicates body mass index; FPG, fasting plasma glucose; SBP, systolic blood pressure; and TC, serum total cholesterol.

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In contrast to blood pressure, which has become increas-ingly disassociated from BMI at the population level, the per-sistent association between BMI and FPG and the rising BMI levels mean that, unless effective interventions for weight control or improvements in diet and other lifestyle factors are designed and implemented to prevent diabetes mellitus, health systems worldwide will face an increasing burden of hyperglycemia and diabetes mellitus and will have to develop mechanisms for better detection and management of diabe-tes mellitus. A costly global epidemic of hyperglycemia and diabetes mellitus, together with high blood pressure in low-income countries, may be the most salient feature of the global cardiovascular risk transition in the coming decades.

AppendixGlobal Burden of Metabolic Risk Factors of Chronic Diseases Collaborating GroupWriting and Global Analysis Group: Goodarz Danaei,* Gitanjali M. Singh,* Christopher J. Paciorek, John K. Lin, Melanie J. Cowan, Mariel M. Finucane, Farshad Farzadfar, Gretchen A. Stevens, Leanne M. Riley, Yuan Lu, Mayuree Rao, Majid Ezzati.*Contributed equally to the research and manuscript and listed in al-phabetic order.Country Data Group: Geir Aamodt; Ziad Abdeen; Nabila A. Abdella; Hanan F. Abdul Rahim; Juliet Addo; Wichai Aekplakorn; Mustafa M. Afifi; Enrico Agabiti-Rosei; Carlos A. Aguilar Salinas; Charles Agyemang; Mohamed M. Ali; Mohannad Al-Nsour; Abdul R. Al-Nuaim; Ramachandran Ambady; Pertti Aro; Fereidoun Azizi; Carlo M. Barbagallo; Marco Antonio M. Barbieri; Alberto Barceló; Sandhi M. Barreto; Henrique Barros; Leonelo E. Bautista; Athanase Benetos; Peter Bjerregaard; Cecilia Björkelund; Simona Bo; Martin Bobak; Enzo Bonora; Babu V. Bontha; Manuel A. Botana; Pascal Bovet; Juergen Breckenkamp; Monique M. Breteler; Grazyna Broda; Ian J. Brown; Michael Bursztyn; Antonio Cabrera de León; Hannia Campos; Francesco P. Cappuccio; Vincenzo Capuano; Edoardo Casiglia; Maurizio Castellano; Katia Castetbon; Luis Cea; Chih-Jen Chang; Noureddine Chaouki; Somnath Chatterji; Chien-Jen Chen; Zhengming Chen; Chien-Jen Chen; Jin-Su Choi; Lily Chua;

Renata Cífková; Linda J. Cobiac; Richard S. Cooper; Anna Maria Corsi; Michael C. Costanza; Cora L. Craig; Rachel S. Dankner; Saeed Dastgiri; Elias Delgado; Gonul Dinc; Yasufumi Doi; Guang-Hui Dong; Eleonora Dorsi; Nico Dragano; Adam Drewnowski; Robert W. Eggertsen; Paul Elliott; Anders Engeland; Cihangir Erem; Alireza Esteghamati; Caroline H.D. Fall; Jian-Gao Fan; Catterina Ferreccio; Leopold Fezeu; Josélia O. Firmo; Hermes J. Florez; Nélida S. Fornés; F. Gerry R. Fowkes; Guido Franceschini; Fredrik Frisk; Flávio D. Fuchs; Eva L. Fuller; Linn Getz; Simona Giampaoli; Luis F. Gómez; Juan M. Gomez-Zumaquero; Sidsel Graff-Iversen; Janet F. Grant; Ramiro Guerrero Carvajal; Martin C. Gulliford; Rajeev Gupta; Prakash C. Gupta; Oye Gureje; Tine W. Hansen; Jun Hata; Jiang He; Noor Heim; Joachim Heinrich; Tomas Hemmingsson; Anselm Hennis; William H. Herman; Victor M. Herrera; Suzanne Ho; Michelle Holdsworth; Gunilla Hollman Frisman; Wilma M. Hopman; Akhtar Hussain; Abdullatif Husseini; M. Mohsen Ibrahim; Nayu Ikeda; Bjarne K. Jacobsen; Hashem Y. Jaddou; Tazeen H. Jafar; Mohsen Janghorbani; Grazyna Jasienska; Michel R. Joffres; Jost B. Jonas; Othman A. Kadiki; Ofra Kalter-Leibovici; Raoul M. Kamadjeu; Ioannis Karalis; Mika J. Kastarinen; Joanne Katz; Lital Keinan-Boker; Paul Kelly; Omid Khalilzadeh; Young-Ho Khang; Stefan Kiechl; Ki Woong Kim; Yutaka Kiyohara; Junji Kobayashi; Maressa P. Krause; Růžena Kubínová; Pawel Kurjata; Yadlapalli S. Kusuma; Tai Hing Lam; Arnulf Langhammer; Carlene M.M. Lawes; Cai Le; Jeannette Lee; Claire Lévy-Marchal; Yanping Li; Yuqiu Li; Stephen Lim; T.O. Lim; Xu Lin; Cheng-Chieh Lin; Xu Lin; Hsien-Ho Lin; Lars Lind; Lauren Lissner; Xiaoqing Liu; Patricio Lopez-Jaramillo; Roberto Lorbeer; Roberto Lorbeer; Guansheng Ma; Stefan Ma; Francesc Macià; David R. MacLean; Stefania Maggi; Dianna J. Magliano; Marcia Makdisse; Giuseppe Mancia; Toshifumi Mannami; Pedro Marques-Vidal; Jean Claude N. Mbanya; Norma McFarlane-Anderson; Roberto Miccoli; Juhani Miettola; Hoang V. Minh; Juan F. Miquel; J. Jaime Miranda; Mostafa K. Mohamed; V. Mohan; Salim Mohanna; Ali Mokdad; Willem F. Mollentze; Dante D. Morales; Karen Morgan; Lorenza M. Muiesan; Sergio Muntoni; Iraj Nabipour; Tomoko Nakagami; Vinay Nangia; Barbara Nemesure; Martin Neovius; Kjersti A. Nerhus; Flavio Nervi; Hannelore Neuhauser; Minh Nguyen; Toshiharu Ninomiya; Marianna Noale; Sang W. Oh; Takayoshi Ohkubo; Oliviero Olivieri; Ayse Emel Önal; Altan Onat; Myriam Oróstegui; Hermann Ouedraogo; Wen-Harn Pan; Demosthenes B. Panagiotakos; Francesco Panza; Yongsoo Park; Valeria M.A. Passos; Mangesh S. Pednekar; Marco A. Peres; Cynthia Pérez; Román Pérez-Fernández;

Figure 5. Age-standardized per-capita cigarette consumption in relation to per capita gross domes-tic product (GDP) in 2008. Smoking data are from the World Health Organization Global Status Report on Noncommunicable Diseases 2010.14

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Danaei et al Global Cardiovascular Risk Transition 1501

Rafael Pichardo; Hwee Pin Phua; Francesco Pistelli; Pedro Plans; Maria Polakowska; Neil Poulter; Dorairaj Prabhakaran; Qing Qiao; Masoud Rafiei; Olli T. Raitakari; Luiz R. Ramos; Sanjay Rampal; Lekhraj Rampal; Finn Rasmussen; Kanala K.R. Reddy; Josep Redon; Luis Revilla; Victoria Reyes-García; Ragab B. Roaeid; Fernando Rodriguez-Artalejo; Rosalba Rojas-Martinez; Kimmo Ronkainen; Luis Rosero-Bixby; Gregory A. Roth; Harshpal S. Sachdev; José R. Sánchez; Selim Y. Sanisoglu; Susana Sans; Nizal Sarraf-Zadegan; Marcia Scazufca; Beatriz D. Schaan; Norberto Schapochnik; Hedi Schelleman; Ione J.C. Schneider; C. Mary Schooling; Bernhard Schwarz; Cevad Sekuri; Martha S. Sereday; Lluís Serra-Majem; Jonathan Shaw; Abdul S. Shera; Zumin Shi; Rahman Shiri; Xiao Ou Shu; Diego Augusto Santos Silva; Eglé Silva; Leon A. Simons; Margaret Smith; Stefan Söderberg; Suharko Soebardi; Vincenzo Solfrizzi; Emily Sonestedt; Ahmet Soysal; Pär Stattin; Aryeh D. Stein; George S. Stergiou; Jochanan Stessman; Akihiro Sudo; Machi Suka; Valter Sundh; Kristina Sundquist; Johan Sundström; Andrew B. Swai; E. Shyong Tai; Kristian Tambs; Fikru Tesfaye; George N. Thomas; Margaret Thorogood; Reijo S. Tilvis; Martin Tobias; Liv E. Torheim; Peter Trenkwalder; Jaakko O. Tuomilehto; Josep A. Tur; Christophe Tzourio; Ana I. Uhernik; Flora A. Ukoli; Nigel Unwin; Stephen Vander Hoorn; Mark P. Vanderpump; Jose Javier Varo; Marit B. Veierød; Gustavo Velásquez-Meléndez; Monique Verschuren; Lucie Viet; Salvador Villalpando; Jesus Vioque; Peter Vollenweider; Stefano Volpato; Ningli Wang; Ya X. Wang; Mark Ward; Sarwono Waspadji; Lennart X. Welin; Lars Wilhelmsen; Johann Willeit; Mark Woodward; André J. Xavier; Fei Xu; Liang Xu; Akira Yamamoto; Gonghuan Yang; Xiaoguang Yang; Li-Chia Yeh; Jin-Sang Yoon; Qisheng You; Zhijie Yu; Jian Zhang; Lei Zhang; Wei Zheng; Maigeng Zhou.

Acknowledgments Drs Danaei and Ezzati developed the study concept, and Dr Paciorek developed the statistical approach for the analysis of associations. Dr Ezzati wrote the first draft of the article, and Dr Paciorek wrote the first draft of the Statistical Methods. Drs Singh and Finucane and J. K. Lin analyzed data. Other members of the Writing and Global Analysis Group contributed to study design, analysis, and writing of article. The members of the Country Data Group analyzed health examination survey and epidemiological study data. Drs Danaei and Singh had full access to all the data in the study and take responsibil-ity for the integrity of the data and the accuracy of the data analysis. Dr Ezzati oversaw the research and is the study guarantor.

Sources of Funding Dr Ezzati is supported by a strategic award from the UK Medical Research Council (MRC) and by the National Institute for Health Research Comprehensive Biomedical Research Centre at Imperial College Healthcare NHS Trust. Dr Singh is supported by a T32 Training Grant in Academic Nutrition (grant DK007703) from the National Institute of Diabetes and Digestive and Kidney Diseases. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Disclosures Dr Paciorek holds stock in Pfizer. M. Cowan, Dr Stevens, and L. Riley are staff members of the World Health Organization. They alone are responsible for the views expressed in this publication, and they do not necessarily represent the decisions, policy, or views of the World Health Organization. The other authors report no conflicts.

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CLINICAL PERSPECTIvEThe diseases of affluence paradigm suggests that noncommunicable diseases are the modern scourges of the world and are hence more common in developed countries. But until now, little has been known about the associations of these risk factors with the macroeconomic characteristics of countries, and how the associations have changed over time. We examined this paradigm by using data from a global analysis of body mass index, systolic blood pressure (SBP), serum total cholesterol, and fasting plasma glucose. We found that in 1980, body mass index, SBP, and serum total cholesterol were in fact higher in wealthier countries. By 2008, there was either no relationship between SBP and national income (for men) or SBP was lower in wealthier countries (for women). This may be partly due to improved diagnosis and treatment in wealthier countries and perhaps subtle improvements in diet and lifestyle, for example, lower salt intake and year-round availability of fresh fruits and vegetables. The relationship between body mass index and national income in more recent years resembled an inverted U, in part, because overweight and obesity increased substantially in many middle-income countries. Unlike SBP and fast-ing plasma glucose, serum total cholesterol was associated with both income and Western diet throughout this period. We also found that fasting plasma glucose was positively correlated with body mass index but had little association with other national characteristics. If the observed trends continue, developed countries will continue to face an obese population with a high prevalence of diabetes mellitus and hypercholesterolemia, whereas developing countries will be confronted by a com-bination of obesity, hypertension, and diabetes mellitus. The diseases of affluence paradigm seems inadequate for explaining these nuances in the global epidemiology of cardiovascular risk factors and should be replaced with a more refined frame-work that better informs both policy and intervention.

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Supplemental Material

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Supplementary Text

The association of metabolic risk factors with income, urbanization, and western diet:

statistical methods and considerations

All of our graphical and statistical analyses are based on a Bayesian multiple imputation

approach. Specifically, the Bayesian statistical model for estimating risk factor levels borrowed

information based on a hierarchy of countries within subregions and regions, across time and age

groups, and using a small set of country-specific covariates. These covariates included the

country-level characteristics considered in this paper’s analyses, namely metrics of national

income, urbanization, and western diet. This may appear to introduce circularity and potentially

overestimate the association. Below, we provide the statistical justification for this approach and

specify how we took into consideration the fact that the risk factor data were estimated using a

Bayesian statistical model with covariates. In our explanation below, we conceptually divide our

risk factor estimates into those that are essentially “known” based on good data for a given

country-year and those that are imputed in the absence of such data; in reality there is a

continuum of uncertainty.

First, we note that the covariates in the Bayesian model were chosen with the goal of best

predicting country risk factor levels. The predicted risk factor levels used here are based on the

posterior predictive distribution from the Bayesian hierarchical model, which conditions on all

available information, both risk factor data and explanatory variables. As such, the posterior

draws of country risk factor levels are a Bayesian multiple imputation. It is well known in the

statistical literature that a correct multiple imputation procedure should condition on all available

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information1. Further, we note that the imputed country risk factor levels are based on the

estimated relationship with covariates and the variability of country risk factor levels around the

relationship, hence incorporating the variation around the estimated relationship. In other words,

we draw from the posterior predictive distribution, which is the model’s estimate of the

distribution of the country risk factor levels, and therefore includes all of the appropriate sources

of variation.

We can motivate the approach further by considering an example of why it would be incorrect to

exclude the country-level characteristics of interest from the Bayesian hierarchical model.

Suppose that we exclude a country covariate that is correlated with the risk factor of interest, and

that none of the other covariates in the model are correlated with that characteristic. The result

will be that our imputed values for missing country risk factor levels will not be correlated with

the characteristic. When we produce figures such as Figures 1-3, those countries with data-

driven, essentially-known risk factor levels would show a correlation with the characteristic but

the imputed values would not, so the overall association would appear weaker than if we knew

the true country risk factor levels. Now suppose that the characteristic of interest is not

correlated with the risk factor of interest but that we include it in the Bayesian model. In the

Bayesian model, we will estimate a near-zero coefficient based on the available country data, and

the imputed values will be uncorrelated with the characteristic. Then in the subsequent analyses,

neither the data-driven risk factor levels nor the imputed values will be correlated with the

characteristic, and there will not be a circularity problem.

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Given the uncertainty in the country-specific risk factor levels, including in relation to the

covariates, our assessment of associations takes a missing data perspective and is based on a

Bayesian multiple imputation approach1, 2. Specifically, we present the Loess fits, and the

correlation and regression coefficients, in Figures 1-4 based on 500 posterior draws from the

Bayesian model. Uncertainty intervals were calculated as the 2.5th to 97.5th percentiles of these

500 draws.

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Supplementary Table

Table S1: Country data used in Figures 1-4 of the main paper. Data are from a systematic

analysis of population-based data, by sex, for 199 countries and territories, as described in detail

in previous publications3-6 and in the main paper.

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1980 2008Change per 

decade1980 2008

Change per 

decade1980 2008

Change per 

decade1980 (Female) 2008 (Female)

Change per decade (Female)

1980 (Male) 2008 (Male)Change per decade (Male)

1980 (Female) 2008 (Female)Change per decade (Female)

1980 (Male) 2008 (Male)Change per decade (Male)

1980 (Female) 2008 (Female)Change per decade (Female)

1980 (Male) 2008 (Male)Change per decade (Male)

1980 (Female) 2008 (Female)Change per decade (Female)

1980 (Male) 2008 (Male)Change per decade (Male)

Brunei Darussalam 0.60 0.75 0.05 84314 45700 ‐13791 0.60 0.72 0.04 132.4 (120.3, 144.1) 120.9 (111.6, 130.0) ‐4.11 137.7 (126.7, 148.7) 128.0 (119.7, 136.1) ‐3.47 5.5 (4.6, 6.4) 5.2 (4.5, 5.9) ‐0.11 5.5 (4.7, 6.3) 5.1 (4.5, 5.8) ‐0.14 23.0 (19.3, 26.7) 22.9 (19.7, 26.1) ‐0.03 23.2 (20.3, 26.2) 24.2 (21.6, 26.7) 0.34 5.1 (4.1, 6.2) 5.2 (4.3, 6.00) 0.01 5.3 (4.3, 6.3) 5.5 (4.6, 6.4) 0.07Japan 0.60 0.66 0.02 18545 32109 4844 0.67 0.73 0.02 131.2 (128.1, 134.3) 122.0 (118.6, 125.2) ‐3.30 136.8 (133.5, 140.1) 130.5 (126.9, 133.9) ‐2.27 4.8 (4.5, 5.1) 5.2 (4.9, 5.5) 0.13 4.8 (4.6, 5.0) 5.2 (5.0, 5.4) 0.14 21.3 (20.5, 22.1) 21.9 (21.3, 22.4) 0.20 22.1 (21.4, 22.8) 23.5 (23.1, 23.9) 0.50 4.9 (4.3, 5.5) 5.2 (4.9, 5.40) 0.10 5.0 (4.4, 5.6) 5.5 (5.2, 5.7) 0.16Republic of Korea 0.57 0.82 0.09 5244 25525 7243 0.28 0.58 0.11 120.6 (111.9, 128.7) 116.9 (111.6, 121.8) ‐1.33 125.2 (117.3, 133.0) 124.1 (118.9, 129.0) ‐0.37 4.9 (4.1, 5.9) 4.9 (4.5, 5.3) ‐0.03 4.8 (4.1, 5.6) 4.8 (4.5, 5.1) ‐0.02 21.3 (19.7, 22.9) 23.3 (22.6, 24.0) 0.72 22.1 (20.7, 23.4) 24.0 (23.5, 24.5) 0.69 5.3 (4.5, 6.2) 5.2 (4.8, 5.60) ‐0.03 5.5 (4.7, 6.4) 5.4 (5.1, 5.8) ‐0.03Singapore 1.00 1.00 0.00 14623 48389 12059 0.55 0.61 0.02 129.7 (122.4, 136.7) 121.2 (116.6, 125.8) ‐3.06 131.6 (124.6, 139.1) 127.7 (123.1, 132.4) ‐1.39 6.0 (5.3, 6.7) 5.3 (4.8, 5.8) ‐0.25 5.8 (5.3, 6.4) 5.2 (4.8, 5.5) ‐0.24 23.5 (22.2, 24.8) 22.9 (22.1, 23.7) ‐0.23 22.6 (21.6, 23.5) 23.8 (23.2, 24.5) 0.45 5.8 (5.0, 6.5) 5.2 (4.7, 5.60) ‐0.23 5.5 (4.9, 6.2) 5.4 (5.0, 5.8) ‐0.03

Australia 0.86 0.89 0.01 21026 36806 5636 0.96 0.93 ‐0.01 128.8 (122.7, 135.0) 117.4 (111.6, 122.8) ‐4.08 134.3 (128.2, 140.4) 127.2 (121.3, 132.7) ‐2.53 5.8 (5.4, 6.2) 5.2 (4.7, 5.7) ‐0.21 5.8 (5.4, 6.1) 5.1 (4.7, 5.5) ‐0.23 23.6 (22.8, 24.4) 26.9 (26.2, 27.6) 1.16 24.9 (24.3, 25.6) 27.6 (27.0, 28.1) 0.94 5.1 (4.7, 5.6) 5.4 (4.9, 5.80) 0.09 5.3 (4.8, 5.8) 5.7 (5.2, 6.1) 0.12New Zealand 0.83 0.86 0.01 17286 25950 3094 0.97 0.88 ‐0.03 128.1 (121.6, 134.8) 118.7 (110.7, 126.5) ‐3.35 134.2 (127.7, 140.8) 127.2 (120.4, 134.1) ‐2.48 6.0 (5.2, 6.8) 5.2 (4.5, 5.9) ‐0.30 6.1 (5.5, 6.8) 5.2 (4.5, 5.8) ‐0.34 24.2 (22.5, 25.8) 27.4 (26.5, 28.3) 1.14 25.3 (23.9, 26.6) 27.8 (27.1, 28.4) 0.90 5.1 (4.0, 6.1) 5.4 (4.6, 6.30) 0.14 5.4 (4.4, 6.4) 5.7 (4.9, 6.6) 0.11

Andorra 0.92 0.89 ‐0.01 45502 56998 4106 0.91 0.93 0.01 136.6 (126.5, 146.7) 121.8 (112.6, 130.7) ‐5.27 140.9 (131.4, 150.5) 131.6 (123.6, 139.7) ‐3.32 6.1 (5.3, 7.0) 5.5 (4.8, 6.1) ‐0.24 6.2 (5.5, 6.9) 5.6 (4.9, 6.2) ‐0.22 25.7 (22.6, 28.7) 26.4 (23.5, 29.3) 0.27 25.7 (23.3, 27.9) 27.6 (25.4, 29.8) 0.70 5.2 (4.2, 6.2) 5.3 (4.4, 6.10) 0.03 5.4 (4.4, 6.4) 5.6 (4.8, 6.4) 0.08Austria 0.65 0.67 0.01 21754 37304 5554 0.97 0.95 ‐0.01 133.8 (125.4, 142.6) 124.1 (116.1, 132.3) ‐3.45 138.4 (130.0, 146.8) 131.4 (124.1, 138.6) ‐2.51 5.8 (5.1, 6.6) 5.3 (4.6, 5.9) ‐0.20 5.9 (5.3, 6.4) 5.3 (4.7, 5.9) ‐0.20 23.9 (21.9, 26.0) 25.1 (22.7, 27.6) 0.43 24.8 (23.2, 26.4) 26.5 (24.7, 28.2) 0.58 4.8 (4.1, 5.5) 5.1 (4.5, 5.60) 0.09 4.9 (4.2, 5.6) 5.4 (4.8, 6.0) 0.19Belgium 0.95 0.97 0.01 20979 34374 4784 0.97 0.95 ‐0.01 131.2 (124.5, 138.1) 121.8 (114.8, 129.7) ‐3.36 136.5 (129.6, 144.0) 128.5 (122.4, 134.8) ‐2.86 6.0 (5.2, 6.9) 5.3 (4.7, 6.0) ‐0.25 6.1 (5.4, 6.8) 5.4 (4.8, 6.0) ‐0.24 25.0 (24.0, 26.0) 25.1 (23.6, 26.7) 0.06 25.1 (24.4, 25.8) 26.8 (25.6, 28.0) 0.59 5.1 (4.2, 6.1) 5.2 (4.4, 6.10) 0.04 5.3 (4.4, 6.3) 5.6 (4.7, 6.4) 0.09Cyprus 0.59 0.70 0.04 10421 24282 4950 0.66 0.89 0.08 130.5 (121.1, 139.9) 122.6 (114.5, 131.1) ‐2.81 134.3 (125.0, 143.3) 131.2 (123.9, 138.5) ‐1.10 5.5 (4.7, 6.3) 5.2 (4.5, 5.9) ‐0.11 5.4 (4.7, 6.2) 5.2 (4.6, 5.8) ‐0.08 23.8 (21.3, 26.3) 25.9 (24.4, 27.5) 0.76 25.3 (23.6, 27.1) 27.4 (26.2, 28.6) 0.74 5.1 (4.1, 6.1) 5.3 (4.4, 6.10) 0.06 5.4 (4.4, 6.3) 5.6 (4.8, 6.5) 0.10Denmark 0.84 0.86 0.01 21031 34845 4934 0.97 0.99 0.01 130.0 (124.0, 136.0) 119.6 (113.1, 127.0) ‐3.72 136.6 (130.6, 142.8) 129.7 (124.0, 135.8) ‐2.45 6.0 (5.4, 6.5) 5.4 (4.8, 6.0) ‐0.19 5.9 (5.5, 6.4) 5.5 (5.0, 6.1) ‐0.14 23.4 (21.9, 24.9) 25.1 (23.4, 26.9) 0.62 24.6 (23.5, 25.8) 26.1 (24.9, 27.4) 0.56 5.0 (4.0, 6.0) 5.2 (4.3, 6.00) 0.05 5.3 (4.3, 6.3) 5.5 (4.7, 6.3) 0.08Finland 0.72 0.85 0.05 18198 34029 5654 0.96 0.92 ‐0.01 138.4 (132.8, 144.3) 126.6 (121.3, 131.9) ‐4.23 143.1 (137.5, 149.0) 135.3 (130.1, 140.2) ‐2.80 6.2 (5.7, 6.7) 5.3 (5.0, 5.7) ‐0.30 6.1 (5.8, 6.5) 5.2 (4.9, 5.5) ‐0.35 25.0 (23.9, 26.1) 25.6 (24.7, 26.5) 0.21 25.5 (24.6, 26.4) 26.7 (26.0, 27.5) 0.45 5.2 (4.5, 6.0) 5.3 (4.9, 5.80) 0.03 5.7 (5.2, 6.3) 5.7 (5.2, 6.2) ‐0.02France 0.73 0.84 0.04 20703 31331 3796 1.00 0.97 ‐0.01 132.1 (124.9, 139.7) 120.0 (115.4, 124.6) ‐4.31 138.8 (132.1, 145.7) 131.0 (126.6, 135.7) ‐2.76 6.0 (5.3, 6.6) 5.3 (4.9, 5.7) ‐0.23 6.0 (5.5, 6.6) 5.4 (5.0, 5.7) ‐0.24 24.1 (22.6, 25.7) 24.8 (23.9, 25.7) 0.24 24.7 (23.5, 26.0) 25.9 (25.2, 26.5) 0.41 5.1 (4.2, 6.0) 5.1 (4.7, 5.50) 0.01 5.4 (4.5, 6.3) 5.5 (5.0, 5.9) 0.03Germany 0.73 0.74 0.00 20890 33553 4523 0.99 0.95 ‐0.01 135.3 (130.1, 140.2) 124.9 (119.4, 130.1) ‐3.72 140.0 (135.1, 145.0) 132.9 (128.0, 137.6) ‐2.55 5.8 (5.1, 6.4) 5.4 (4.7, 6.0) ‐0.14 5.8 (5.2, 6.4) 5.6 (5.0, 6.2) ‐0.09 24.7 (23.8, 25.7) 25.7 (24.8, 26.7) 0.36 25.5 (24.6, 26.3) 27.2 (26.4, 28.0) 0.61 5.2 (4.3, 6.2) 5.3 (4.7, 5.70) 0.01 5.5 (4.6, 6.4) 5.6 (5.1, 6.1) 0.06Greece 0.58 0.61 0.01 17397 27732 3691 0.74 0.85 0.04 131.7 (123.7, 140.1) 122.9 (116.3, 129.8) ‐3.13 133.6 (125.6, 141.8) 128.5 (122.4, 135.1) ‐1.79 5.6 (4.7, 6.4) 4.9 (4.4, 5.5) ‐0.23 5.5 (4.8, 6.2) 5.0 (4.5, 5.5) ‐0.19 24.1 (21.7, 26.5) 24.9 (23.4, 26.4) 0.30 24.8 (23.3, 26.3) 26.3 (25.2, 27.4) 0.55 5.2 (4.2, 6.2) 5.4 (4.8, 6.00) 0.07 5.4 (4.4, 6.4) 5.6 (5.0, 6.2) 0.06Greenland 0.76 0.84 0.03 42977 57480 5180 0.92 0.93 0.00 137.3 (128.5, 146.3) 125.0 (118.7, 131.1) ‐4.40 141.3 (133.3, 149.6) 133.1 (127.4, 138.9) ‐2.91 6.0 (5.2, 6.8) 5.7 (5.2, 6.1) ‐0.13 6.1 (5.3, 6.8) 5.7 (5.3, 6.1) ‐0.14 25.9 (23.3, 28.5) 26.7 (25.4, 27.9) 0.28 24.5 (22.7, 26.4) 26.0 (25.0, 27.0) 0.55 5.3 (4.3, 6.3) 5.6 (5.2, 6.10) 0.11 5.3 (4.4, 6.3) 5.7 (5.2, 6.2) 0.13Iceland 0.88 0.93 0.02 22621 38813 5783 1.00 1.00 0.00 131.4 (125.3, 138.2) 118.7 (112.2, 125.7) ‐4.52 137.7 (131.6, 144.4) 129.6 (123.7, 135.6) ‐2.91 6.2 (5.6, 6.8) 5.6 (5.0, 6.2) ‐0.22 6.1 (5.7, 6.6) 5.7 (5.1, 6.3) ‐0.16 24.1 (22.6, 25.6) 26.0 (23.9, 28.1) 0.69 24.8 (23.6, 26.1) 27.2 (25.8, 28.7) 0.85 5.1 (4.1, 6.1) 5.3 (4.4, 6.10) 0.06 5.4 (4.4, 6.4) 5.7 (4.8, 6.5) 0.10Ireland 0.55 0.61 0.02 13517 38311 8855 1.00 0.96 ‐0.01 130.4 (121.6, 138.8) 124.8 (120.2, 129.4) ‐2.02 139.4 (131.7, 147.4) 134.9 (130.3, 139.8) ‐1.61 5.9 (5.1, 6.7) 5.3 (4.6, 6.0) ‐0.20 6.0 (5.2, 6.7) 5.4 (4.8, 6.1) ‐0.19 24.6 (22.5, 26.7) 26.6 (25.8, 27.4) 0.71 25.8 (24.3, 27.3) 27.7 (27.1, 28.2) 0.66 5.1 (4.1, 6.0) 5.2 (4.8, 5.70) 0.05 5.4 (4.4, 6.4) 5.5 (5.1, 6.0) 0.05Israel 0.89 0.92 0.01 15597 26084 3746 0.73 0.81 0.03 130.4 (125.4, 135.6) 121.1 (115.2, 127.0) ‐3.33 132.6 (127.7, 137.7) 126.7 (121.4, 132.0) ‐2.09 5.9 (5.2, 6.5) 5.2 (4.6, 5.8) ‐0.25 5.8 (5.3, 6.4) 5.0 (4.5, 5.6) ‐0.28 24.4 (22.5, 26.2) 27.3 (26.3, 28.3) 1.05 24.8 (23.4, 26.2) 27.1 (26.4, 27.9) 0.84 5.3 (4.4, 6.1) 5.5 (4.8, 6.20) 0.09 5.5 (4.6, 6.3) 5.7 (5.0, 6.4) 0.07Italy 0.67 0.68 0.01 19084 28600 3399 0.83 0.86 0.01 135.4 (130.3, 141.1) 123.7 (119.0, 128.2) ‐4.19 136.6 (131.5, 141.8) 130.9 (126.9, 134.7) ‐2.04 5.6 (5.2, 6.0) 5.4 (5.0, 5.7) ‐0.08 5.7 (5.4, 6.0) 5.3 (5.0, 5.6) ‐0.13 25.2 (24.3, 26.1) 24.8 (23.9, 25.6) ‐0.15 25.4 (24.7, 26.1) 26.5 (25.8, 27.2) 0.38 5.2 (4.6, 5.8) 5.1 (4.8, 5.50) ‐0.02 5.3 (4.7, 5.9) 5.6 (5.2, 6.0) 0.10Luxembourg 0.80 0.85 0.02 27681 79507 18510 0.97 0.95 ‐0.01 133.7 (124.2, 143.7) 122.4 (113.7, 131.3) ‐4.05 139.6 (130.7, 148.9) 130.9 (122.8, 138.7) ‐3.11 6.2 (5.6, 6.8) 5.5 (4.8, 6.1) ‐0.27 6.2 (5.7, 6.8) 5.5 (4.9, 6.1) ‐0.25 24.2 (22.2, 26.3) 26.1 (23.5, 28.7) 0.66 25.0 (23.5, 26.6) 27.4 (25.7, 29.3) 0.87 5.0 (4.1, 6.0) 5.3 (4.4, 6.10) 0.08 5.3 (4.3, 6.2) 5.6 (4.8, 6.5) 0.12Malta 0.90 0.94 0.02 9553 22411 4592 0.87 0.92 0.02 135.0 (127.2, 143.2) 124.2 (115.9, 132.2) ‐3.87 136.6 (128.7, 144.7) 131.8 (125.0, 138.6) ‐1.69 6.1 (5.5, 6.8) 5.2 (4.5, 5.9) ‐0.32 5.9 (5.4, 6.5) 5.3 (4.7, 5.9) ‐0.22 25.9 (24.0, 27.6) 27.0 (24.6, 29.5) 0.42 25.7 (24.3, 27.1) 27.7 (25.9, 29.4) 0.71 5.3 (4.8, 5.8) 5.5 (4.7, 6.30) 0.07 5.4 (4.9, 5.9) 5.8 (5.0, 6.6) 0.14Netherlands 0.65 0.82 0.06 23004 39331 5831 0.93 0.90 ‐0.01 131.9 (125.0, 138.8) 121.7 (115.8, 127.6) ‐3.65 138.7 (131.8, 145.9) 131.2 (125.6, 136.7) ‐2.69 5.9 (5.1, 6.7) 5.3 (4.6, 6.0) ‐0.22 5.9 (5.2, 6.6) 5.3 (4.7, 5.9) ‐0.20 24.0 (22.4, 25.7) 25.5 (23.7, 27.2) 0.52 24.1 (22.8, 25.5) 26.0 (24.7, 27.4) 0.67 5.2 (4.3, 6.0) 4.9 (4.3, 5.60) ‐0.08 5.3 (4.5, 6.2) 5.3 (4.6, 5.9) ‐0.03Norway 0.71 0.79 0.03 26015 49613 8428 0.97 0.91 ‐0.02 137.0 (130.4, 143.8) 126.0 (119.6, 132.5) ‐3.91 142.1 (136.0, 148.8) 134.5 (128.7, 140.4) ‐2.73 5.9 (5.1, 6.7) 5.3 (4.7, 6.0) ‐0.22 6.0 (5.3, 6.7) 5.4 (4.8, 6.0) ‐0.21 23.7 (22.0, 25.4) 25.7 (24.7, 26.7) 0.73 24.7 (23.4, 26.0) 26.9 (26.1, 27.7) 0.80 5.1 (4.2, 6.0) 5.4 (4.6, 6.20) 0.09 5.4 (4.4, 6.3) 5.7 (4.9, 6.4) 0.10Portugal 0.43 0.59 0.06 11272 21285 3576 0.71 0.96 0.09 135.3 (127.6, 143.5) 127.3 (121.4, 133.1) ‐2.87 138.1 (130.5, 145.9) 134.6 (128.8, 140.3) ‐1.25 5.6 (4.7, 6.4) 5.2 (4.5, 5.8) ‐0.14 5.5 (4.7, 6.2) 5.2 (4.6, 5.7) ‐0.10 24.8 (22.8, 26.8) 26.2 (25.0, 27.4) 0.50 25.0 (23.5, 26.7) 26.7 (25.7, 27.6) 0.58 5.2 (4.2, 6.2) 5.1 (4.5, 5.80) ‐0.01 5.3 (4.3, 6.3) 5.4 (4.7, 6.1) 0.04Spain 0.73 0.77 0.02 15348 28568 4722 0.79 0.87 0.03 133.5 (127.6, 140.1) 122.0 (117.3, 126.9) ‐4.08 136.6 (130.6, 142.9) 130.4 (125.8, 134.8) ‐2.20 5.6 (4.9, 6.4) 5.1 (4.7, 5.6) ‐0.19 5.6 (5.0, 6.2) 5.2 (4.9, 5.6) ‐0.14 25.2 (23.8, 26.5) 26.3 (25.4, 27.2) 0.41 25.3 (24.2, 26.6) 27.5 (26.8, 28.2) 0.77 5.0 (4.1, 5.8) 5.5 (5.1, 6.00) 0.20 5.1 (4.3, 5.9) 5.7 (5.3, 6.2) 0.22Sweden 0.83 0.85 0.01 21127 35493 5131 0.91 0.88 ‐0.01 132.9 (126.6, 139.0) 122.9 (117.7, 128.5) ‐3.56 138.8 (133.7, 144.1) 131.7 (126.9, 137.0) ‐2.56 6.1 (5.4, 6.8) 5.0 (4.5, 5.4) ‐0.40 6.0 (5.8, 6.3) 5.2 (4.7, 5.6) ‐0.31 24.4 (23.0, 25.7) 25.1 (24.1, 26.2) 0.27 24.7 (23.7, 25.8) 26.4 (25.6, 27.2) 0.59 5.4 (4.6, 6.1) 5.2 (4.5, 5.90) ‐0.05 5.4 (4.7, 6.1) 5.5 (4.9, 6.2) 0.04Switzerland 0.57 0.73 0.06 28943 38922 3564 0.98 0.96 ‐0.01 131.3 (124.1, 139.4) 119.6 (113.6, 126.0) ‐4.16 138.3 (130.9, 146.0) 130.6 (124.8, 136.6) ‐2.76 6.0 (5.1, 6.8) 5.2 (4.7, 5.7) ‐0.26 6.0 (5.2, 6.7) 5.3 (4.9, 5.7) ‐0.24 23.7 (22.1, 25.3) 24.1 (22.5, 25.7) 0.12 25.2 (23.9, 26.5) 26.2 (25.0, 27.4) 0.37 5.0 (4.0, 5.9) 5.2 (4.6, 5.80) 0.07 5.3 (4.3, 6.2) 5.6 (5.0, 6.2) 0.11United Kingdom 0.78 0.79 0.00 18512 34233 5615 0.94 0.92 ‐0.01 131.0 (125.0, 136.6) 124.1 (120.0, 128.1) ‐2.48 136.5 (131.2, 141.8) 131.2 (127.5, 134.9) ‐1.90 6.2 (5.5, 6.9) 5.4 (4.8, 5.9) ‐0.30 6.2 (5.7, 6.8) 5.4 (4.9, 5.9) ‐0.29 24.2 (23.2, 25.2) 26.9 (26.3, 27.6) 0.98 24.7 (23.9, 25.6) 27.4 (26.9, 27.9) 0.95 5.2 (4.4, 6.0) 5.3 (4.8, 5.70) 0.02 5.5 (4.7, 6.3) 5.5 (5.1, 5.9) 0.01

Canada 0.76 0.80 0.02 23325 36448 4687 0.87 0.84 ‐0.01 124.9 (120.2, 129.4) 118.1 (111.3, 124.4) ‐2.43 131.0 (126.2, 135.9) 123.6 (118.0, 129.0) ‐2.64 5.6 (4.9, 6.4) 5.1 (4.4, 5.8) ‐0.18 5.6 (5.1, 6.2) 5.1 (4.5, 5.7) ‐0.20 24.1 (23.3, 24.9) 26.7 (26.0, 27.4) 0.93 25.2 (24.6, 25.8) 27.5 (27.0, 28.0) 0.79 5.1 (4.1, 6.1) 5.4 (4.5, 6.30) 0.12 5.4 (4.4, 6.3) 5.7 (4.8, 6.6) 0.11United States of America 0.74 0.82 0.03 25425 43333 6396 0.89 0.91 0.00 125.4 (121.1, 129.6) 118.5 (115.0, 122.0) ‐2.45 131.2 (127.2, 135.3) 123.3 (119.8, 126.7) ‐2.82 5.7 (5.3, 6.1) 5.1 (4.9, 5.4) ‐0.21 5.6 (5.3, 5.9) 5.1 (4.8, 5.3) ‐0.20 25.0 (24.3, 25.8) 28.3 (27.7, 28.9) 1.19 25.5 (25.0, 26.0) 28.5 (28.0, 28.9) 1.07 5.1 (4.7, 5.6) 5.6 (5.3, 5.90) 0.15 5.3 (4.9, 5.7) 5.9 (5.6, 6.2) 0.19

Armenia 0.66 0.64 ‐0.01 2563 5567 1073 0.67 0.69 0.01 136.2 (125.4, 146.7) 132.8 (126.9, 138.9) ‐1.21 136.5 (126.9, 146.2) 135.3 (129.4, 141.3) ‐0.44 5.3 (4.4, 6.3) 4.8 (4.1, 5.5) ‐0.19 5.2 (4.3, 6.0) 4.7 (4.0, 5.4) ‐0.16 25.8 (22.9, 28.7) 27.1 (26.0, 28.2) 0.48 23.8 (21.5, 26.2) 25.4 (24.5, 26.2) 0.55 5.3 (4.3, 6.4) 5.6 (4.7, 6.50) 0.10 5.4 (4.4, 6.5) 5.7 (4.8, 6.5) 0.08Azerbaijan 0.53 0.52 0.00 4249 8273 1437 0.70 0.59 ‐0.04 132.2 (121.9, 142.8) 128.6 (123.2, 134.2) ‐1.27 134.8 (125.2, 144.5) 132.1 (126.7, 137.7) ‐0.96 5.3 (4.3, 6.2) 4.7 (3.9, 5.4) ‐0.23 5.1 (4.3, 6.0) 4.5 (3.8, 5.2) ‐0.22 26.2 (23.1, 29.3) 27.5 (26.5, 28.6) 0.47 24.5 (22.1, 26.9) 25.7 (24.9, 26.4) 0.41 5.4 (4.3, 6.4) 5.7 (4.8, 6.60) 0.12 5.5 (4.5, 6.5) 5.7 (4.9, 6.6) 0.08Georgia 0.52 0.53 0.00 5048 4748 ‐107 0.69 0.72 0.01 135.1 (124.4, 146.2) 130.6 (122.3, 139.0) ‐1.63 138.5 (127.9, 149.2) 135.4 (127.3, 143.1) ‐1.10 5.4 (4.4, 6.3) 4.7 (3.9, 5.5) ‐0.24 5.2 (4.4, 6.1) 4.6 (3.9, 5.3) ‐0.22 25.8 (22.0, 29.4) 26.5 (23.3, 29.6) 0.24 24.6 (21.8, 27.4) 25.5 (23.1, 27.9) 0.33 5.3 (4.2, 6.3) 5.6 (4.7, 6.50) 0.09 5.5 (4.5, 6.5) 5.7 (4.9, 6.6) 0.07Kazakhstan 0.54 0.58 0.01 6218 10802 1637 0.68 0.86 0.06 134.3 (123.1, 145.7) 128.9 (119.9, 138.2) ‐1.95 137.0 (126.5, 147.8) 134.3 (125.8, 142.8) ‐0.97 5.4 (4.4, 6.4) 4.9 (4.2, 5.7) ‐0.16 5.3 (4.4, 6.2) 4.9 (4.2, 5.5) ‐0.14 25.3 (22.7, 28.0) 26.7 (24.9, 28.4) 0.47 24.6 (22.8, 26.3) 26.3 (24.2, 28.4) 0.61 5.3 (4.2, 6.3) 5.5 (4.6, 6.40) 0.09 5.5 (4.4, 6.5) 5.8 (4.9, 6.6) 0.10Kyrgyzstan 0.39 0.35 ‐0.01 2199 2087 ‐40 0.69 0.66 ‐0.01 130.5 (119.1, 141.7) 129.6 (120.4, 138.9) ‐0.32 133.8 (123.0, 144.8) 133.0 (124.7, 141.3) ‐0.28 5.2 (4.2, 6.1) 4.5 (3.7, 5.3) ‐0.24 5.0 (4.1, 5.9) 4.4 (3.7, 5.0) ‐0.22 25.2 (22.7, 27.7) 25.7 (23.8, 27.5) 0.17 24.4 (22.4, 26.4) 24.7 (22.9, 26.5) 0.14 5.3 (4.2, 6.3) 5.5 (4.6, 6.40) 0.08 5.5 (4.4, 6.5) 5.6 (4.8, 6.5) 0.04Mongolia 0.52 0.61 0.03 1915 3286 490 0.77 0.84 0.02 129.2 (117.8, 140.5) 129.8 (125.5, 133.8) 0.20 135.1 (125.2, 145.1) 136.6 (132.6, 140.5) 0.53 5.2 (4.3, 6.2) 4.7 (4.2, 5.2) ‐0.19 5.1 (4.2, 6.0) 4.7 (4.2, 5.1) ‐0.15 24.4 (21.6, 27.3) 25.7 (25.0, 26.4) 0.48 23.5 (21.2, 25.8) 24.9 (24.4, 25.3) 0.49 5.2 (4.1, 6.3) 5.4 (5.1, 5.70) 0.06 5.5 (4.4, 6.6) 5.7 (5.3, 6.0) 0.08Tajikistan 0.34 0.26 ‐0.03 2611 1790 ‐293 0.71 0.32 ‐0.14 130.5 (118.7, 142.4) 130.0 (120.6, 139.0) ‐0.18 133.9 (122.9, 144.8) 132.4 (123.6, 140.9) ‐0.55 5.2 (4.2, 6.1) 4.3 (3.6, 5.1) ‐0.30 5.0 (4.1, 5.8) 4.2 (3.5, 4.9) ‐0.29 24.2 (21.0, 27.5) 23.8 (22.0, 25.7) ‐0.14 24.2 (21.3, 27.1) 23.8 (21.4, 26.2) ‐0.15 5.2 (4.1, 6.2) 5.4 (4.5, 6.30) 0.08 5.5 (4.4, 6.5) 5.6 (4.7, 6.5) 0.04Turkmenistan 0.47 0.49 0.01 4135 6061 688 0.70 0.53 ‐0.06 132.2 (120.7, 143.5) 129.2 (119.9, 138.1) ‐1.07 135.1 (124.6, 145.6) 133.0 (124.6, 141.1) ‐0.74 5.3 (4.3, 6.2) 4.6 (3.9, 5.4) ‐0.23 5.1 (4.3, 6.0) 4.5 (3.9, 5.2) ‐0.20 24.1 (21.2, 27.0) 24.7 (23.0, 26.3) 0.20 24.4 (21.5, 27.3) 25.2 (22.8, 27.7) 0.32 5.2 (4.1, 6.2) 5.4 (4.6, 6.30) 0.10 5.5 (4.4, 6.5) 5.7 (4.8, 6.6) 0.09Uzbekistan 0.41 0.36 ‐0.02 1779 2367 210 0.69 0.51 ‐0.06 127.0 (115.9, 137.8) 124.3 (117.1, 131.7) ‐0.93 130.4 (120.5, 140.6) 128.4 (121.5, 135.7) ‐0.69 5.2 (4.2, 6.1) 4.4 (3.8, 5.1) ‐0.27 5.0 (4.2, 5.9) 4.3 (3.7, 4.8) ‐0.27 24.1 (21.6, 26.9) 25.4 (24.0, 26.9) 0.46 24.6 (22.3, 26.8) 25.3 (24.2, 26.5) 0.27 5.2 (4.2, 6.3) 5.6 (4.9, 6.30) 0.12 5.6 (4.5, 6.6) 5.8 (5.2, 6.5) 0.10

Albania 0.34 0.50 0.06 3621 6714 1105 0.38 0.77 0.14 132.2 (122.0, 142.6) 129.2 (121.1, 137.5) ‐1.09 132.9 (123.4, 142.4) 133.8 (126.1, 141.5) 0.32 5.0 (4.2, 5.9) 4.9 (4.2, 5.5) ‐0.06 5.0 (4.3, 5.8) 4.9 (4.4, 5.4) ‐0.04 25.2 (22.3, 28.0) 25.7 (23.6, 27.7) 0.17 25.2 (23.1, 27.3) 26.4 (24.7, 28.1) 0.44 5.3 (4.2, 6.3) 5.3 (4.4, 6.20) 0.01 5.4 (4.3, 6.5) 5.5 (4.6, 6.4) 0.04Bosnia and Herzegovina 0.36 0.47 0.04 1039 7163 2187 0.76 0.69 ‐0.03 131.9 (121.5, 142.3) 133.2 (126.4, 140.4) 0.45 131.8 (122.8, 140.9) 133.3 (126.5, 140.0) 0.54 5.0 (4.1, 5.9) 4.8 (4.0, 5.5) ‐0.10 4.9 (4.2, 5.7) 4.7 (4.0, 5.3) ‐0.09 25.0 (22.1, 27.8) 26.4 (25.0, 27.8) 0.49 25.3 (23.1, 27.4) 26.6 (25.5, 27.7) 0.46 5.2 (4.1, 6.3) 5.5 (4.6, 6.40) 0.10 5.4 (4.3, 6.5) 5.7 (4.8, 6.6) 0.09Bulgaria 0.62 0.71 0.03 6342 11516 1848 0.80 0.78 ‐0.01 135.5 (127.8, 143.4) 128.5 (120.4, 136.7) ‐2.52 135.0 (127.3, 142.5) 134.2 (127.1, 141.3) ‐0.27 5.3 (4.5, 6.2) 5.0 (4.3, 5.8) ‐0.11 5.3 (4.5, 6.0) 5.0 (4.3, 5.7) ‐0.10 25.5 (22.9, 28.2) 25.5 (24.2, 26.8) ‐0.01 25.3 (23.4, 27.2) 26.5 (25.6, 27.5) 0.45 5.2 (4.2, 6.3) 5.3 (4.4, 6.20) 0.03 5.4 (4.3, 6.4) 5.5 (4.6, 6.4) 0.07Croatia 0.50 0.57 0.03 12156 16820 1665 0.81 0.80 0.00 136.2 (126.3, 146.1) 130.4 (122.8, 137.8) ‐2.08 138.0 (128.9, 147.0) 135.5 (128.6, 142.7) ‐0.87 5.4 (4.6, 6.3) 5.0 (4.3, 5.7) ‐0.14 5.4 (4.7, 6.1) 5.0 (4.3, 5.6) ‐0.15 25.2 (22.6, 27.8) 25.2 (23.2, 27.2) 0.00 25.3 (23.4, 27.2) 26.6 (25.0, 28.1) 0.47 5.1 (4.1, 6.2) 5.2 (4.4, 6.10) 0.03 5.3 (4.3, 6.4) 5.5 (4.6, 6.4) 0.06Czech Republic 0.75 0.74 ‐0.01 14614 23461 3159 0.78 0.93 0.05 136.0 (128.3, 143.9) 125.7 (121.0, 130.5) ‐3.67 136.4 (129.3, 143.7) 133.7 (129.1, 138.3) ‐0.97 5.9 (5.1, 6.6) 5.1 (4.7, 5.5) ‐0.26 6.0 (5.4, 6.5) 5.1 (4.8, 5.4) ‐0.31 26.8 (25.4, 28.2) 26.5 (25.5, 27.5) ‐0.10 26.3 (25.1, 27.5) 27.9 (27.1, 28.7) 0.57 5.2 (4.6, 5.7) 5.3 (4.9, 5.80) 0.06 5.3 (4.8, 5.8) 5.7 (5.2, 6.1) 0.14Hungary 0.64 0.67 0.01 10635 17874 2585 0.96 0.95 0.00 135.2 (128.8, 141.5) 128.7 (121.3, 136.2) ‐2.34 138.2 (131.5, 144.7) 136.1 (129.8, 142.2) ‐0.75 5.3 (4.5, 6.1) 5.2 (4.4, 6.0) ‐0.04 5.3 (4.7, 5.9) 5.1 (4.5, 5.8) ‐0.06 25.1 (23.9, 26.3) 26.0 (23.7, 28.4) 0.30 25.3 (24.3, 26.3) 27.1 (25.6, 28.7) 0.65 5.1 (4.0, 6.1) 5.2 (4.3, 6.10) 0.05 5.3 (4.3, 6.4) 5.6 (4.7, 6.5) 0.08Macedonia (Former Yugoslav Republic of)

0.53 0.59 0.02 7700 8328 224 0.80 0.80 0.00 134.9 (124.7, 145.3) 129.9 (120.5, 139.1) ‐1.79 136.2 (126.6, 145.8) 134.0 (125.8, 142.2) ‐0.78 5.4 (4.5, 6.2) 4.9 (4.2, 5.6) ‐0.17 5.3 (4.6, 6.0) 4.8 (4.1, 5.4) ‐0.19 25.4 (22.6, 28.2) 25.4 (23.2, 27.5) 0.01 25.4 (22.9, 27.9) 26.3 (24.1, 28.7) 0.34 5.2 (4.1, 6.2) 5.3 (4.4, 6.20) 0.04 5.4 (4.3, 6.4) 5.5 (4.6, 6.4) 0.06

Montenegro 0.37 0.62 0.09 10372 9316 ‐377 0.78 0.82 0.01 134.7 (124.1, 145.8) 129.4 (120.3, 139.1) ‐1.89 137.1 (127.2, 147.3) 135.4 (127.1, 143.7) ‐0.61 5.4 (4.5, 6.2) 5.0 (4.3, 5.7) ‐0.13 5.3 (4.6, 6.1) 4.9 (4.3, 5.6) ‐0.14 26.0 (22.5, 29.3) 25.7 (22.6, 28.8) ‐0.10 25.5 (22.9, 28.1) 26.6 (24.2, 28.9) 0.38 5.2 (4.2, 6.3) 5.2 (4.3, 6.10) 0.00 5.4 (4.4, 6.4) 5.5 (4.6, 6.4) 0.03Poland 0.58 0.61 0.01 8559 16221 2737 0.90 0.86 ‐0.02 137.5 (131.1, 144.4) 129.7 (124.5, 135.3) ‐2.78 136.8 (131.5, 142.0) 135.0 (130.0, 139.9) ‐0.64 5.4 (4.8, 5.9) 5.2 (4.7, 5.7) ‐0.07 5.3 (4.8, 5.8) 5.3 (4.9, 5.7) ‐0.01 25.9 (24.5, 27.2) 25.9 (25.0, 26.9) 0.01 25.1 (24.0, 26.3) 26.7 (26.0, 27.4) 0.55 5.1 (4.2, 6.1) 5.0 (4.5, 5.50) ‐0.04 5.3 (4.3, 6.3) 5.3 (4.8, 5.8) ‐0.02Romania 0.46 0.56 0.04 7607 11219 1290 0.76 0.86 0.04 133.7 (124.5, 143.1) 128.8 (119.8, 137.2) ‐1.78 134.8 (125.8, 143.7) 133.4 (125.6, 141.2) ‐0.49 5.3 (4.5, 6.1) 4.9 (4.2, 5.6) ‐0.15 5.3 (4.6, 5.9) 4.9 (4.2, 5.5) ‐0.13 25.4 (23.3, 27.4) 25.2 (23.5, 27.0) ‐0.06 24.7 (23.1, 26.3) 25.4 (24.0, 26.8) 0.26 5.2 (4.2, 6.3) 5.3 (4.4, 6.20) 0.02 5.3 (4.3, 6.4) 5.5 (4.6, 6.4) 0.05Serbia 0.46 0.55 0.03 11233 9638 ‐570 0.78 0.82 0.01 136.6 (128.7, 144.5) 130.4 (122.3, 138.3) ‐2.24 139.0 (131.3, 146.4) 136.9 (130.0, 143.5) ‐0.75 5.2 (4.6, 5.9) 5.1 (4.3, 6.0) ‐0.05 5.1 (4.6, 5.7) 4.9 (4.2, 5.6) ‐0.07 26.2 (24.4, 28.0) 25.7 (24.0, 27.4) ‐0.19 25.6 (24.1, 27.1) 26.5 (25.2, 27.8) 0.33 5.2 (4.2, 6.3) 5.2 (4.3, 6.10) ‐0.01 5.4 (4.3, 6.4) 5.5 (4.6, 6.4) 0.05Slovakia 0.52 0.55 0.01 10346 20529 3637 0.81 0.84 0.01 135.8 (125.6, 146.4) 129.5 (120.2, 138.8) ‐2.24 137.2 (127.3, 146.9) 135.7 (127.1, 144.1) ‐0.53 5.4 (4.5, 6.3) 5.1 (4.4, 5.8) ‐0.11 5.4 (4.6, 6.1) 5.1 (4.4, 5.7) ‐0.11 26.1 (22.9, 29.4) 26.3 (23.2, 29.4) 0.06 25.5 (23.1, 28.1) 26.9 (24.5, 29.2) 0.49 5.2 (4.2, 6.3) 5.3 (4.5, 6.20) 0.04 5.4 (4.4, 6.4) 5.6 (4.7, 6.4) 0.07Slovenia 0.48 0.50 0.01 13892 27346 4805 0.81 0.89 0.03 136.5 (126.1, 147.3) 129.9 (120.2, 139.4) ‐2.35 138.3 (128.1, 148.1) 136.4 (127.8, 144.7) ‐0.70 5.5 (4.6, 6.3) 5.2 (4.5, 5.9) ‐0.09 5.4 (4.7, 6.2) 5.2 (4.5, 5.9) ‐0.09 26.3 (22.9, 29.7) 26.6 (23.5, 29.8) 0.09 25.7 (23.1, 28.3) 27.4 (25.0, 29.8) 0.62 5.2 (4.2, 6.3) 5.3 (4.4, 6.20) 0.01 5.4 (4.4, 6.4) 5.6 (4.7, 6.5) 0.06

Belarus 0.56 0.74 0.06 5675 11851 2206 0.66 0.87 0.08 134.4 (124.0, 145.0) 129.8 (120.7, 139.3) ‐1.64 138.0 (129.1, 146.9) 136.6 (128.9, 144.2) ‐0.50 5.5 (4.6, 6.4) 5.1 (4.3, 5.9) ‐0.16 5.4 (4.7, 6.2) 5.0 (4.3, 5.7) ‐0.14 26.3 (22.8, 29.7) 26.6 (23.4, 29.8) 0.14 24.9 (22.2, 27.5) 26.2 (23.6, 28.6) 0.45 5.4 (4.2, 6.5) 5.4 (4.5, 6.40) 0.02 5.4 (4.3, 6.5) 5.5 (4.6, 6.4) 0.04Estonia 0.70 0.69 0.00 8797 18585 3496 0.72 0.93 0.08 137.7 (129.3, 146.7) 129.5 (121.8, 137.4) ‐2.93 140.8 (132.4, 149.7) 138.6 (131.3, 145.9) ‐0.80 5.7 (4.8, 6.5) 5.2 (4.4, 6.0) ‐0.17 5.7 (5.1, 6.2) 5.2 (4.5, 5.8) ‐0.18 26.0 (24.1, 28.0) 25.2 (23.3, 27.0) ‐0.29 24.7 (23.2, 26.2) 26.3 (24.9, 27.7) 0.56 5.3 (4.2, 6.4) 5.1 (4.3, 6.00) ‐0.06 5.3 (4.3, 6.4) 5.4 (4.5, 6.2) 0.01Latvia 0.67 0.68 0.00 8957 15525 2346 0.73 0.93 0.07 136.5 (126.0, 147.1) 129.6 (120.1, 138.8) ‐2.48 138.8 (129.2, 148.8) 136.8 (128.5, 145.2) ‐0.72 5.6 (4.8, 6.5) 5.2 (4.4, 6.0) ‐0.16 5.6 (4.8, 6.3) 5.1 (4.4, 5.9) ‐0.15 25.6 (23.0, 28.1) 25.6 (23.7, 27.6) 0.02 25.1 (23.3, 26.9) 26.5 (24.9, 28.0) 0.48 5.3 (4.2, 6.4) 5.3 (4.3, 6.30) 0.00 5.4 (4.3, 6.5) 5.5 (4.6, 6.5) 0.03Lithuania 0.61 0.67 0.02 11174 17128 2127 0.71 0.90 0.07 137.8 (130.7, 145.2) 130.8 (122.7, 138.6) ‐2.48 140.1 (132.9, 147.3) 137.3 (130.3, 144.2) ‐1.00 5.8 (5.1, 6.5) 5.1 (4.3, 5.9) ‐0.24 5.8 (5.3, 6.3) 5.1 (4.4, 5.8) ‐0.23 27.0 (25.2, 28.9) 26.0 (24.1, 27.9) ‐0.36 26.0 (24.6, 27.4) 26.9 (25.3, 28.3) 0.31 5.4 (4.3, 6.5) 5.4 (4.4, 6.30) ‐0.01 5.5 (4.4, 6.6) 5.6 (4.7, 6.5) 0.03Moldova 0.40 0.45 0.02 3640 2699 ‐336 0.73 0.74 0.00 132.3 (121.7, 142.8) 129.9 (120.7, 139.3) ‐0.86 135.9 (125.7, 145.8) 133.8 (125.2, 142.3) ‐0.74 5.4 (4.5, 6.3) 4.7 (3.9, 5.4) ‐0.27 5.3 (4.5, 6.1) 4.6 (3.8, 5.3) ‐0.26 26.9 (23.7, 30.0) 27.1 (25.9, 28.3) 0.04 24.7 (22.1, 27.3) 24.2 (21.7, 26.7) ‐0.18 5.4 (4.3, 6.5) 5.6 (4.6, 6.50) 0.05 5.4 (4.3, 6.5) 5.4 (4.5, 6.3) 0.00Russian Federation 0.70 0.73 0.01 11706 14948 1158 0.69 0.86 0.06 133.8 (128.4, 139.1) 128.5 (123.0, 134.0) ‐1.89 134.6 (129.3, 140.0) 132.3 (127.5, 137.1) ‐0.82 5.8 (5.2, 6.3) 5.1 (4.3, 5.9) ‐0.24 5.6 (5.2, 6.0) 4.9 (4.2, 5.6) ‐0.25 26.8 (25.5, 28.2) 27.2 (26.5, 27.9) 0.16 25.0 (23.9, 26.2) 26.0 (25.5, 26.6) 0.36 5.4 (4.3, 6.5) 5.5 (4.6, 6.40) 0.03 5.4 (4.4, 6.5) 5.5 (4.6, 6.5) 0.04Ukraine 0.62 0.68 0.02 7130 6950 ‐64 0.71 0.82 0.04 136.1 (126.4, 145.4) 131.5 (126.2, 136.8) ‐1.64 139.7 (131.2, 148.3) 137.2 (132.0, 142.4) ‐0.90 5.5 (4.7, 6.4) 4.9 (4.1, 5.7) ‐0.24 5.4 (4.7, 6.2) 4.8 (4.1, 5.5) ‐0.23 26.4 (23.5, 29.2) 26.2 (24.5, 28.0) ‐0.05 24.9 (22.3, 27.5) 25.4 (22.9, 27.9) 0.18 5.4 (4.2, 6.5) 5.4 (4.5, 6.40) 0.02 5.4 (4.3, 6.5) 5.5 (4.6, 6.4) 0.03

Angola 0.24 0.57 0.12 2833 5333 893 0.20 0.25 0.02 130.1 (118.6, 142.0) 130.1 (120.4, 139.8) 0.01 135.2 (124.4, 146.1) 133.6 (125.0, 142.1) ‐0.57 4.8 (3.8, 5.8) 4.5 (3.8, 5.3) ‐0.09 4.5 (3.6, 5.4) 4.4 (3.6, 5.1) ‐0.05 20.1 (16.3, 23.8) 23.5 (20.2, 26.8) 1.22 20.9 (17.5, 24.4) 22.3 (19.7, 24.8) 0.47 4.8 (3.7, 5.9) 5.3 (4.3, 6.20) 0.16 5.0 (4.0, 6.1) 5.2 (4.3, 6.2) 0.07Central African Republic 0.34 0.39 0.02 927 666 ‐93 0.19 0.26 0.03 129.7 (118.1, 141.5) 130.6 (120.5, 140.3) 0.31 133.7 (122.4, 145.3) 133.3 (124.3, 142.1) ‐0.14 4.6 (3.6, 5.7) 4.3 (3.4, 5.0) ‐0.14 4.3 (3.3, 5.2) 4.1 (3.3, 4.8) ‐0.07 19.9 (17.5, 22.3) 22.4 (20.2, 24.6) 0.91 21.0 (17.4, 24.6) 21.0 (18.4, 23.6) 0.01 4.8 (3.7, 5.9) 5.1 (4.2, 6.10) 0.13 5.0 (3.9, 6.1) 5.1 (4.1, 6.0) 0.04Congo 0.48 0.61 0.05 2566 3085 185 0.12 0.31 0.07 132.7 (121.7, 143.8) 131.7 (123.9, 139.8) ‐0.36 136.6 (126.1, 147.6) 134.0 (126.5, 141.4) ‐0.91 4.8 (3.8, 5.9) 4.5 (3.7, 5.3) ‐0.11 4.5 (3.5, 5.4) 4.3 (3.6, 5.0) ‐0.06 19.8 (18.2, 21.5) 23.1 (21.8, 24.4) 1.17 20.6 (17.6, 23.8) 21.9 (20.3, 23.5) 0.44 4.8 (3.6, 5.9) 5.2 (4.3, 6.20) 0.16 5.0 (3.9, 6.1) 5.2 (4.2, 6.1) 0.06Democratic Republic of the Congo

0.29 0.34 0.02 715 275 ‐157 0.01 0.04 0.01 129.7 (120.3, 139.0) 129.4 (121.1, 137.1) ‐0.12 134.0 (124.9, 143.2) 132.7 (125.1, 139.9) ‐0.46 4.6 (3.5, 5.6) 4.0 (3.2, 4.8) ‐0.20 4.2 (3.1, 5.1) 3.8 (3.0, 4.6) ‐0.14 19.8 (16.6, 23.0) 21.7 (20.8, 22.5) 0.67 20.7 (17.6, 24.1) 19.9 (18.2, 21.5) ‐0.31 4.8 (3.6, 5.9) 5.1 (4.1, 6.10) 0.13 5.0 (3.8, 6.1) 5.0 (4.0, 6.0) 0.01

Equatorial Guinea 0.28 0.39 0.04 1384 23152 7774 0.20 0.23 0.01 130.4 (118.9, 141.9) 132.0 (121.5, 142.4) 0.57 135.2 (124.4, 146.4) 136.4 (127.2, 145.8) 0.45 4.8 (3.7, 5.7) 4.8 (4.0, 5.6) 0.02 4.4 (3.4, 5.3) 4.7 (3.9, 5.5) 0.10 20.1 (16.5, 23.8) 24.5 (21.3, 27.9) 1.59 21.1 (17.6, 24.8) 23.8 (21.0, 26.5) 0.96 4.8 (3.6, 5.9) 5.2 (4.3, 6.30) 0.16 5.0 (3.9, 6.1) 5.3 (4.3, 6.3) 0.10Gabon 0.55 0.85 0.11 15292 12463 ‐1011 0.39 0.56 0.06 138.8 (127.0, 151.1) 130.4 (123.8, 137.0) ‐3.02 141.9 (131.3, 153.1) 134.1 (128.1, 139.9) ‐2.78 5.2 (4.2, 6.3) 4.9 (4.1, 5.7) ‐0.12 4.9 (3.9, 6.0) 4.7 (3.9, 5.5) ‐0.07 21.9 (18.9, 24.9) 26.0 (24.9, 27.0) 1.45 22.3 (19.0, 25.8) 24.1 (23.1, 25.0) 0.64 4.8 (3.7, 6.0) 5.4 (4.4, 6.40) 0.20 5.1 (3.9, 6.2) 5.4 (4.4, 6.3) 0.10

Burundi 0.04 0.10 0.02 407 361 ‐17 0.00 0.01 0.00 127.0 (114.9, 138.9) 134.2 (124.2, 144.4) 2.58 129.7 (118.4, 140.8) 135.1 (126.4, 144.0) 1.95 4.4 (3.3, 5.4) 4.1 (3.4, 4.9) ‐0.09 4.0 (3.0, 5.0) 3.9 (3.2, 4.7) ‐0.03 19.2 (15.6, 22.7) 21.3 (18.2, 24.4) 0.75 21.1 (18.0, 24.1) 21.5 (19.0, 24.0) 0.15 4.5 (3.3, 5.7) 4.8 (3.8, 5.80) 0.12 4.7 (3.6, 5.8) 4.9 (4.0, 5.8) 0.07Comoros 0.23 0.28 0.02 1245 1025 ‐79 0.02 0.04 0.01 126.6 (115.4, 137.4) 132.1 (123.0, 141.5) 1.96 131.5 (121.4, 141.6) 134.4 (125.9, 142.8) 1.03 4.7 (3.7, 5.6) 4.3 (3.6, 5.0) ‐0.12 4.4 (3.5, 5.2) 4.2 (3.5, 4.8) ‐0.07 20.8 (18.5, 23.1) 22.4 (20.6, 24.2) 0.57 21.0 (18.2, 23.6) 22.1 (19.7, 24.4) 0.39 4.9 (3.9, 6.0) 5.1 (4.2, 6.00) 0.07 5.1 (4.1, 6.2) 5.2 (4.3, 6.1) 0.03Djibouti 0.72 0.76 0.01 3505 2063 ‐515 0.41 0.36 ‐0.02 130.6 (119.5, 141.7) 129.3 (119.8, 138.8) ‐0.46 134.3 (123.8, 144.6) 133.0 (124.3, 141.8) ‐0.46 5.1 (4.1, 6.1) 4.7 (3.9, 5.4) ‐0.15 5.0 (4.1, 5.8) 4.6 (3.9, 5.3) ‐0.11 22.0 (18.6, 25.3) 24.4 (21.3, 27.4) 0.87 21.2 (18.6, 23.9) 23.4 (21.0, 25.8) 0.77 5.0 (4.0, 6.1) 5.4 (4.5, 6.20) 0.12 5.2 (4.2, 6.2) 5.4 (4.6, 6.3) 0.09Eritrea 0.14 0.21 0.02 496 579 30 0.03 0.07 0.01 117.5 (106.9, 128.3) 125.8 (119.1, 132.5) 2.96 122.3 (112.4, 132.4) 129.1 (122.6, 135.7) 2.40 4.4 (3.4, 5.3) 4.2 (3.5, 4.9) ‐0.07 4.1 (3.2, 5.0) 4.1 (3.4, 4.8) ‐0.01 18.6 (16.4, 20.9) 21.1 (20.0, 22.2) 0.87 20.0 (17.7, 22.3) 20.9 (20.0, 21.8) 0.31 4.8 (3.7, 5.8) 5.1 (4.2, 5.90) 0.10 5.1 (4.0, 6.1) 5.2 (4.3, 6.0) 0.04Ethiopia 0.10 0.16 0.02 559 752 69 0.03 0.04 0.00 116.6 (107.7, 125.1) 126.6 (120.5, 132.6) 3.57 123.1 (115.0, 131.3) 129.6 (124.0, 135.2) 2.32 4.4 (3.5, 5.4) 4.2 (3.5, 4.9) ‐0.09 4.2 (3.4, 5.1) 4.1 (3.4, 4.8) ‐0.04 18.8 (16.2, 21.2) 20.7 (19.8, 21.6) 0.70 19.5 (17.2, 21.8) 20.2 (19.2, 21.3) 0.27 4.8 (3.7, 5.8) 5.0 (4.1, 5.90) 0.09 5.0 (4.0, 6.0) 5.1 (4.3, 5.9) 0.04Kenya 0.16 0.22 0.02 1281 1400 42 0.28 0.29 0.01 123.7 (117.8, 129.6) 129.9 (122.1, 137.8) 2.23 128.2 (122.5, 133.8) 132.1 (125.3, 138.8) 1.39 4.6 (3.6, 5.6) 4.4 (3.7, 5.1) ‐0.09 4.4 (3.5, 5.3) 4.3 (3.7, 5.0) ‐0.03 20.8 (19.0, 22.5) 23.1 (22.3, 23.9) 0.82 20.3 (18.6, 22.0) 21.6 (19.7, 23.5) 0.46 4.9 (3.8, 5.9) 5.1 (4.3, 6.00) 0.09 5.0 (4.0, 6.1) 5.2 (4.3, 6.0) 0.04Madagascar 0.19 0.30 0.04 1223 900 ‐115 0.28 0.17 ‐0.04 126.7 (116.4, 136.5) 132.8 (126.0, 139.6) 2.17 130.6 (121.7, 139.6) 134.2 (127.8, 140.5) 1.25 4.7 (3.7, 5.6) 4.3 (3.6, 5.0) ‐0.12 4.4 (3.6, 5.3) 4.2 (3.5, 4.9) ‐0.07 20.3 (18.1, 22.5) 20.7 (20.1, 21.4) 0.15 20.7 (18.7, 22.8) 21.4 (20.4, 22.4) 0.25 4.9 (3.8, 5.9) 5.0 (4.1, 5.90) 0.04 5.1 (4.1, 6.1) 5.2 (4.4, 6.1) 0.03Malawi 0.09 0.19 0.03 767 687 ‐29 0.01 0.01 0.00 124.9 (114.7, 135.2) 133.9 (129.1, 138.9) 3.22 130.2 (121.2, 139.2) 136.3 (131.6, 140.9) 2.17 4.5 (3.6, 5.5) 4.4 (4.0, 4.7) ‐0.05 4.2 (3.4, 5.0) 4.2 (3.9, 4.5) 0.01 20.7 (18.8, 22.6) 22.9 (22.2, 23.6) 0.81 20.8 (18.6, 23.0) 22.0 (21.4, 22.6) 0.44 5.0 (4.0, 6.1) 5.0 (4.6, 5.40) ‐0.01 5.3 (4.3, 6.3) 5.1 (4.7, 5.5) ‐0.07Mauritius 0.42 0.42 0.00 3533 10742 2575 0.36 0.52 0.06 130.6 (119.7, 141.8) 130.9 (121.0, 140.8) 0.08 133.8 (123.9, 144.2) 134.4 (125.8, 143.4) 0.22 5.0 (4.0, 6.0) 4.9 (4.2, 5.6) ‐0.03 4.9 (4.0, 5.7) 4.9 (4.2, 5.6) 0.03 23.2 (21.7, 24.6) 26.1 (24.5, 27.7) 1.04 22.0 (20.7, 23.1) 25.2 (23.8, 26.5) 1.13 5.2 (4.4, 6.0) 5.5 (4.9, 6.00) 0.09 5.4 (4.7, 6.2) 5.7 (5.1, 6.3) 0.10Mozambique 0.13 0.37 0.08 433 778 123 0.07 0.08 0.00 127.8 (117.4, 137.8) 135.4 (129.2, 141.7) 2.70 133.4 (124.0, 142.5) 137.5 (131.4, 143.6) 1.45 4.6 (3.7, 5.6) 4.4 (3.9, 4.9) ‐0.09 4.3 (3.5, 5.2) 4.3 (3.9, 4.7) 0.00 20.6 (18.3, 22.9) 23.3 (22.3, 24.3) 0.98 20.4 (18.3, 22.6) 21.9 (21.1, 22.8) 0.54 4.9 (3.8, 5.9) 5.2 (4.3, 6.10) 0.11 5.0 (4.0, 6.0) 5.2 (4.4, 6.1) 0.07Rwanda 0.05 0.18 0.05 797 1016 78 0.00 0.01 0.00 129.2 (116.7, 141.9) 135.1 (124.9, 145.3) 2.12 131.8 (119.9, 143.5) 136.0 (126.6, 145.5) 1.47 4.5 (3.4, 5.6) 4.3 (3.5, 5.1) ‐0.07 4.1 (3.1, 5.1) 4.1 (3.4, 4.8) 0.00 20.5 (18.0, 23.1) 22.1 (21.0, 23.2) 0.56 21.6 (18.6, 24.8) 22.6 (20.0, 25.1) 0.33 4.6 (3.4, 5.8) 4.9 (3.9, 5.80) 0.10 4.8 (3.6, 5.9) 5.0 (4.1, 6.0) 0.09Seychelles 0.49 0.54 0.02 10424 22264 4229 0.35 0.53 0.06 130.4 (121.3, 139.2) 128.8 (122.7, 135.0) ‐0.55 135.9 (126.8, 144.5) 135.9 (129.8, 141.9) ‐0.02 5.2 (4.2, 6.1) 5.2 (4.6, 5.7) 0.00 5.0 (4.2, 5.7) 5.2 (4.8, 5.7) 0.09 24.6 (23.0, 26.3) 28.0 (26.8, 29.2) 1.19 22.2 (20.7, 23.6) 25.6 (24.6, 26.5) 1.22 5.2 (4.4, 6.1) 5.8 (5.2, 6.30) 0.19 5.2 (4.3, 6.0) 5.9 (5.3, 6.5) 0.26Somalia 0.27 0.37 0.03 869 634 ‐84 0.14 0.14 0.00 126.9 (116.3, 137.8) 131.5 (122.6, 140.3) 1.67 130.8 (120.7, 141.1) 133.7 (125.3, 142.1) 1.04 4.7 (3.7, 5.6) 4.3 (3.6, 5.0) ‐0.12 4.4 (3.6, 5.3) 4.2 (3.6, 4.9) ‐0.07 20.8 (17.6, 24.0) 22.7 (19.6, 25.8) 0.66 20.9 (18.3, 23.6) 22.0 (19.6, 24.3) 0.37 4.9 (3.8, 5.9) 5.1 (4.2, 6.00) 0.09 5.1 (4.0, 6.1) 5.2 (4.3, 6.0) 0.05Sudan 0.20 0.39 0.07 1242 2074 297 0.39 0.51 0.04 124.5 (113.6, 135.3) 129.7 (120.5, 139.0) 1.86 129.7 (119.4, 139.8) 133.6 (125.4, 141.9) 1.39 4.7 (3.7, 5.7) 4.5 (3.9, 5.2) ‐0.06 4.5 (3.7, 5.3) 4.5 (3.8, 5.2) 0.01 20.8 (17.6, 24.1) 23.2 (20.1, 26.1) 0.83 20.7 (18.1, 23.3) 22.4 (20.1, 24.6) 0.60 4.9 (3.9, 5.9) 5.2 (4.3, 6.00) 0.09 5.1 (4.1, 6.2) 5.3 (4.4, 6.2) 0.06Uganda 0.08 0.13 0.02 531 1085 198 0.11 0.14 0.01 126.3 (114.7, 137.9) 134.5 (124.6, 144.6) 2.93 130.7 (120.3, 141.4) 135.6 (126.7, 144.5) 1.75 4.5 (3.5, 5.5) 4.3 (3.5, 5.0) ‐0.08 4.2 (3.2, 5.1) 4.1 (3.3, 4.8) ‐0.03 20.5 (18.4, 22.6) 22.5 (21.5, 23.5) 0.70 21.3 (18.4, 24.2) 22.4 (19.8, 24.9) 0.39 4.7 (3.6, 5.9) 4.9 (4.0, 5.90) 0.08 4.9 (3.8, 6.0) 5.0 (4.1, 5.9) 0.04United Republic of Tanzania 0.15 0.26 0.04 789 1195 145 0.13 0.10 ‐0.01 122.6 (114.8, 130.5) 130.8 (123.8, 138.1) 2.94 126.4 (117.8, 135.0) 131.6 (125.0, 138.3) 1.86 4.5 (3.6, 5.4) 4.3 (3.7, 5.0) ‐0.05 4.1 (3.4, 4.9) 4.1 (3.5, 4.8) 0.01 20.9 (19.0, 22.8) 23.1 (22.1, 24.1) 0.78 21.3 (19.3, 23.3) 22.5 (21.0, 23.9) 0.43 5.2 (4.4, 6.0) 5.3 (4.5, 6.00) 0.05 5.5 (4.7, 6.2) 5.3 (4.5, 6.0) ‐0.06Zambia 0.40 0.35 ‐0.02 1694 1226 ‐167 0.22 0.15 ‐0.03 127.2 (117.3, 136.8) 132.4 (125.9, 139.2) 1.87 130.7 (121.2, 139.7) 134.3 (127.9, 140.6) 1.29 4.8 (3.8, 5.7) 4.5 (4.0, 4.9) ‐0.11 4.5 (3.7, 5.4) 4.3 (3.9, 4.7) ‐0.07 21.4 (19.5, 23.3) 23.1 (22.3, 23.8) 0.60 19.7 (17.5, 21.8) 20.7 (19.5, 21.9) 0.36 5.1 (4.1, 6.1) 5.2 (4.7, 5.70) 0.02 5.2 (4.2, 6.2) 5.2 (4.6, 5.7) ‐0.02

Botswana 0.16 0.60 0.15 3423 12491 3239 0.33 0.42 0.03 128.9 (118.7, 138.8) 133.1 (127.8, 138.4) 1.52 131.8 (122.8, 141.0) 133.5 (128.6, 138.6) 0.60 4.8 (3.8, 5.8) 4.7 (4.0, 5.5) ‐0.03 4.5 (3.6, 5.5) 4.6 (3.9, 5.3) 0.02 23.3 (20.4, 26.4) 26.1 (25.1, 27.1) 0.98 19.5 (17.2, 22.1) 22.1 (21.4, 22.9) 0.92 5.1 (4.0, 6.2) 5.4 (4.5, 6.30) 0.13 4.9 (3.8, 6.0) 5.2 (4.3, 6.1) 0.10Lesotho 0.11 0.25 0.05 760 1263 180 0.22 0.14 ‐0.03 124.9 (113.5, 135.8) 131.1 (121.8, 140.6) 2.24 128.6 (118.6, 139.5) 131.7 (123.3, 140.0) 1.08 4.6 (3.6, 5.5) 4.3 (3.5, 5.0) ‐0.10 4.3 (3.4, 5.1) 4.1 (3.4, 4.8) ‐0.05 25.2 (22.9, 27.5) 26.8 (25.5, 28.0) 0.58 20.6 (17.6, 23.5) 21.9 (19.4, 24.4) 0.47 5.4 (4.3, 6.5) 5.7 (4.7, 6.60) 0.10 5.2 (4.1, 6.2) 5.3 (4.5, 6.2) 0.07Namibia 0.25 0.37 0.04 4748 5688 336 0.43 0.45 0.01 132.4 (122.0, 142.3) 133.4 (127.1, 139.6) 0.35 137.3 (128.0, 146.9) 136.1 (130.1, 142.0) ‐0.42 5.0 (4.0, 6.0) 4.6 (3.8, 5.3) ‐0.14 4.7 (3.8, 5.6) 4.5 (3.7, 5.2) ‐0.10 23.0 (20.9, 25.2) 25.1 (24.3, 26.0) 0.76 21.1 (18.8, 23.4) 22.7 (21.8, 23.5) 0.56 5.0 (3.9, 6.1) 5.4 (4.5, 6.30) 0.13 5.1 (4.0, 6.1) 5.3 (4.4, 6.2) 0.08South Africa 0.48 0.61 0.04 7725 9032 467 0.61 0.61 0.00 133.2 (127.1, 139.4) 131.0 (126.9, 135.2) ‐0.80 135.4 (129.2, 141.7) 133.8 (129.9, 137.6) ‐0.57 5.2 (4.3, 6.2) 4.7 (4.1, 5.4) ‐0.18 4.9 (4.2, 5.7) 4.5 (3.9, 5.1) ‐0.16 27.0 (24.9, 29.2) 29.5 (28.7, 30.2) 0.90 23.1 (21.0, 25.3) 26.9 (26.2, 27.5) 1.33 5.4 (4.5, 6.3) 5.7 (5.0, 6.30) 0.11 5.5 (4.6, 6.4) 5.7 (5.1, 6.4) 0.08Swaziland 0.18 0.22 0.01 2324 4397 740 0.51 0.35 ‐0.06 128.8 (117.6, 139.9) 131.9 (122.5, 141.2) 1.09 133.6 (122.9, 144.4) 134.1 (125.5, 142.7) 0.17 4.9 (3.9, 5.9) 4.5 (3.8, 5.3) ‐0.12 4.6 (3.7, 5.5) 4.4 (3.6, 5.1) ‐0.07 25.9 (22.7, 29.0) 28.4 (27.4, 29.5) 0.91 21.4 (18.6, 24.3) 23.2 (20.7, 25.6) 0.63 5.3 (4.2, 6.4) 5.7 (4.8, 6.60) 0.12 5.2 (4.1, 6.2) 5.4 (4.5, 6.2) 0.06Zimbabwe 0.22 0.37 0.05 583 315 ‐96 0.29 0.30 0.00 128.7 (119.3, 137.9) 132.0 (124.2, 140.2) 1.15 130.8 (121.5, 140.2) 131.9 (124.7, 139.2) 0.38 4.8 (3.8, 5.8) 4.3 (3.6, 5.1) ‐0.16 4.5 (3.6, 5.3) 4.1 (3.4, 4.9) ‐0.12 25.5 (23.6, 27.5) 24.6 (23.8, 25.5) ‐0.29 21.5 (19.7, 23.3) 22.0 (21.1, 22.9) 0.20 5.4 (4.3, 6.5) 5.4 (4.5, 6.30) 0.01 5.3 (4.2, 6.3) 5.3 (4.4, 6.2) 0.02

Benin 0.27 0.41 0.05 1108 1319 75 0.06 0.02 ‐0.02 126.3 (116.1, 137.1) 130.3 (125.2, 135.2) 1.40 133.6 (124.6, 143.9) 132.8 (128.1, 137.4) ‐0.29 4.6 (3.7, 5.6) 4.2 (3.8, 4.6) ‐0.15 4.2 (3.3, 5.1) 4.1 (3.7, 4.4) ‐0.04 20.9 (18.7, 23.2) 23.7 (23.0, 24.4) 1.01 20.8 (18.6, 23.1) 22.4 (21.8, 23.0) 0.58 4.9 (3.9, 6.0) 5.0 (4.6, 5.40) 0.03 5.2 (4.2, 6.2) 5.1 (4.7, 5.5) ‐0.03Burkina Faso 0.09 0.24 0.05 663 1052 139 0.08 0.11 0.01 124.0 (112.0, 135.6) 130.7 (121.4, 139.8) 2.39 131.1 (120.1, 143.2) 131.6 (123.4, 140.1) 0.17 4.5 (3.5, 5.5) 4.1 (3.4, 4.8) ‐0.14 4.1 (3.2, 5.0) 3.9 (3.2, 4.6) ‐0.07 19.4 (17.4, 21.5) 21.6 (20.5, 22.7) 0.80 20.0 (17.7, 22.4) 21.3 (19.7, 22.9) 0.44 4.8 (3.8, 5.9) 5.3 (4.4, 6.20) 0.15 5.1 (4.1, 6.1) 5.3 (4.5, 6.2) 0.09Cameroon 0.32 0.57 0.09 2030 1939 ‐33 0.12 0.11 0.00 125.9 (116.0, 135.9) 127.3 (121.2, 133.7) 0.51 132.8 (123.4, 143.7) 131.3 (125.5, 137.3) ‐0.55 4.7 (3.6, 5.7) 4.3 (3.6, 5.0) ‐0.13 4.2 (3.2, 5.1) 4.1 (3.4, 4.7) ‐0.05 21.5 (19.2, 23.9) 25.0 (24.0, 26.0) 1.23 21.8 (19.7, 23.9) 23.7 (22.5, 24.8) 0.68 4.9 (3.8, 5.9) 5.6 (5.1, 6.10) 0.25 5.2 (4.2, 6.2) 5.5 (5.0, 5.9) 0.12Cape Verde 0.24 0.60 0.13 1118 3346 796 0.14 0.56 0.15 128.6 (118.2, 138.8) 133.4 (128.3, 138.7) 1.72 136.8 (127.3, 147.0) 138.1 (132.9, 143.2) 0.49 4.6 (3.6, 5.6) 4.3 (3.9, 4.7) ‐0.09 4.2 (3.3, 5.1) 4.2 (3.9, 4.6) 0.01 20.8 (17.9, 23.6) 25.0 (23.9, 26.0) 1.50 20.7 (18.4, 22.8) 23.5 (22.8, 24.2) 1.02 5.0 (3.8, 6.1) 6.0 (5.5, 6.40) 0.35 5.2 (4.1, 6.2) 6.1 (5.6, 6.6) 0.34Chad 0.19 0.27 0.03 686 1316 225 0.11 0.10 ‐0.01 122.6 (111.7, 133.8) 127.9 (120.5, 135.1) 1.88 130.3 (120.3, 141.2) 131.2 (124.6, 137.8) 0.32 4.5 (3.6, 5.6) 4.1 (3.4, 4.8) ‐0.15 4.1 (3.2, 5.0) 3.9 (3.3, 4.6) ‐0.08 19.2 (16.8, 21.5) 22.0 (20.9, 23.0) 0.99 20.2 (17.9, 22.5) 21.5 (20.0, 22.9) 0.45 4.8 (3.7, 5.9) 5.3 (4.4, 6.10) 0.17 5.1 (4.1, 6.1) 5.3 (4.5, 6.2) 0.09Côte d'Ivoire 0.37 0.49 0.04 2183 1446 ‐263 0.17 0.19 0.01 130.3 (119.8, 140.9) 131.5 (124.4, 138.4) 0.45 136.5 (126.5, 146.9) 134.6 (127.9, 141.2) ‐0.68 4.8 (3.8, 5.8) 4.2 (3.6, 4.9) ‐0.19 4.4 (3.5, 5.3) 4.0 (3.4, 4.7) ‐0.12 21.3 (19.2, 23.4) 23.8 (22.5, 25.2) 0.92 21.1 (18.9, 23.5) 22.6 (21.2, 24.0) 0.51 5.0 (3.9, 6.1) 5.4 (4.5, 6.30) 0.15 5.2 (4.1, 6.2) 5.4 (4.6, 6.2) 0.08Gambia 0.28 0.56 0.10 1199 1461 93 0.19 0.30 0.04 126.4 (116.2, 136.6) 130.2 (123.1, 137.3) 1.36 134.3 (124.2, 145.0) 133.7 (126.7, 140.7) ‐0.21 4.6 (3.7, 5.7) 4.2 (3.6, 4.9) ‐0.15 4.3 (3.3, 5.1) 4.0 (3.4, 4.6) ‐0.09 20.0 (17.9, 22.2) 24.8 (23.0, 26.7) 1.71 19.9 (18.2, 21.7) 21.7 (20.2, 23.0) 0.61 5.0 (3.9, 6.0) 5.6 (4.8, 6.40) 0.24 5.2 (4.1, 6.2) 5.5 (4.7, 6.3) 0.11Ghana 0.31 0.50 0.07 912 1329 149 0.05 0.05 0.00 125.7 (115.5, 136.0) 128.3 (124.0, 132.6) 0.92 130.5 (121.5, 141.0) 129.5 (125.3, 133.6) ‐0.37 4.6 (3.7, 5.6) 4.2 (3.6, 4.8) ‐0.16 4.2 (3.2, 5.1) 3.9 (3.2, 4.6) ‐0.11 20.9 (18.9, 22.9) 24.3 (23.8, 24.9) 1.23 21.0 (19.0, 23.1) 22.8 (22.3, 23.4) 0.65 5.0 (3.9, 6.0) 5.5 (4.7, 6.30) 0.19 5.2 (4.2, 6.2) 5.5 (4.7, 6.3) 0.10Guinea‐Bissau 0.18 0.30 0.04 830 913 30 0.21 0.22 0.00 125.9 (114.5, 137.3) 130.8 (121.9, 139.2) 1.74 132.2 (121.4, 143.7) 132.3 (124.2, 140.2) 0.05 4.5 (3.6, 5.6) 4.1 (3.4, 4.7) ‐0.17 4.2 (3.3, 5.0) 3.9 (3.2, 4.5) ‐0.10 20.4 (17.1, 23.6) 22.9 (20.0, 25.9) 0.90 20.7 (18.0, 23.4) 21.6 (19.4, 23.9) 0.33 4.9 (3.8, 5.9) 5.3 (4.5, 6.20) 0.16 5.1 (4.1, 6.1) 5.3 (4.4, 6.2) 0.06Guinea 0.24 0.34 0.04 824 988 58 0.06 0.14 0.03 128.7 (117.4, 140.3) 132.3 (123.5, 140.9) 1.27 132.9 (122.0, 144.4) 132.8 (124.9, 140.6) ‐0.06 4.6 (3.6, 5.6) 4.2 (3.5, 4.8) ‐0.14 4.1 (3.2, 5.1) 3.9 (3.3, 4.6) ‐0.06 20.4 (18.0, 22.9) 22.5 (21.4, 23.5) 0.72 21.1 (18.5, 23.8) 22.5 (20.3, 24.8) 0.49 4.9 (3.7, 5.9) 5.3 (4.4, 6.10) 0.14 5.1 (4.1, 6.1) 5.3 (4.5, 6.2) 0.08Liberia 0.35 0.47 0.04 1506 369 ‐406 0.11 0.16 0.02 129.3 (118.0, 140.7) 131.3 (122.3, 140.0) 0.71 134.2 (123.6, 145.8) 132.8 (124.6, 140.9) ‐0.51 4.7 (3.7, 5.7) 4.1 (3.4, 4.8) ‐0.22 4.3 (3.4, 5.2) 3.8 (3.2, 4.5) ‐0.17 21.4 (18.7, 24.2) 23.2 (22.2, 24.2) 0.65 21.1 (18.5, 23.8) 21.9 (19.6, 24.2) 0.27 5.0 (3.9, 6.0) 5.3 (4.4, 6.30) 0.13 5.2 (4.1, 6.2) 5.3 (4.4, 6.1) 0.04Mali 0.18 0.34 0.06 699 1026 117 0.27 0.27 0.00 122.7 (111.5, 134.2) 128.0 (120.6, 135.2) 1.91 127.9 (117.3, 138.9) 127.5 (120.5, 134.2) ‐0.13 4.6 (3.6, 5.6) 4.1 (3.4, 4.8) ‐0.15 4.2 (3.3, 5.1) 4.0 (3.3, 4.6) ‐0.09 19.1 (16.9, 21.3) 23.1 (22.2, 24.0) 1.43 20.3 (18.3, 22.3) 21.8 (20.6, 23.0) 0.54 4.8 (3.7, 5.9) 5.4 (4.5, 6.20) 0.20 5.1 (4.0, 6.2) 5.4 (4.5, 6.2) 0.09Mauritania 0.27 0.41 0.05 1559 1783 80 0.53 0.54 0.00 126.0 (114.5, 137.3) 130.0 (120.8, 139.1) 1.42 134.1 (123.3, 145.8) 132.7 (124.6, 141.0) ‐0.53 4.8 (3.8, 5.8) 4.3 (3.8, 4.8) ‐0.19 4.5 (3.6, 5.3) 4.2 (3.7, 4.6) ‐0.11 22.5 (19.9, 25.1) 26.3 (25.0, 27.5) 1.36 20.9 (18.6, 23.2) 22.6 (21.1, 24.0) 0.63 5.0 (3.9, 6.1) 5.3 (4.7, 5.80) 0.11 5.1 (4.1, 6.2) 5.2 (4.6, 5.8) 0.02Niger 0.13 0.17 0.01 865 651 ‐76 0.15 0.04 ‐0.04 128.0 (117.0, 139.0) 135.1 (129.4, 140.9) 2.52 140.9 (132.4, 150.5) 139.4 (133.9, 144.9) ‐0.53 4.5 (3.5, 5.6) 4.0 (3.3, 4.7) ‐0.20 4.2 (3.3, 5.1) 3.8 (3.1, 4.5) ‐0.14 19.9 (18.0, 21.9) 22.0 (21.1, 22.7) 0.72 20.5 (18.3, 22.7) 21.2 (20.5, 21.9) 0.25 4.8 (3.7, 5.9) 5.2 (4.3, 6.10) 0.13 5.1 (4.0, 6.1) 5.2 (4.3, 6.1) 0.04Nigeria 0.29 0.48 0.07 1504 1965 165 0.23 0.12 ‐0.04 131.0 (120.5, 141.6) 135.5 (129.3, 142.0) 1.61 134.5 (125.0, 145.1) 132.8 (126.9, 138.8) ‐0.59 4.7 (3.7, 5.7) 4.1 (3.4, 4.8) ‐0.19 4.2 (3.3, 5.1) 3.8 (3.2, 4.4) ‐0.14 22.0 (20.3, 23.7) 23.7 (23.0, 24.4) 0.61 21.0 (19.5, 22.6) 23.0 (21.9, 24.2) 0.72 5.1 (4.0, 6.2) 5.6 (4.8, 6.50) 0.18 5.1 (4.1, 6.2) 5.3 (4.5, 6.0) 0.06São Tomé and Príncipe 0.33 0.61 0.10 1609 1608 0 0.11 0.20 0.03 131.5 (120.3, 142.0) 136.3 (130.7, 141.9) 1.70 138.5 (128.9, 148.8) 137.5 (132.3, 142.6) ‐0.34 4.7 (3.7, 5.7) 4.2 (3.8, 4.5) ‐0.19 4.3 (3.4, 5.2) 3.9 (3.6, 4.3) ‐0.13 21.4 (18.5, 24.3) 24.9 (23.7, 26.0) 1.25 21.3 (19.0, 23.6) 23.5 (22.8, 24.3) 0.80 5.0 (3.9, 6.1) 5.5 (4.6, 6.40) 0.16 5.2 (4.2, 6.2) 5.4 (4.5, 6.3) 0.07Senegal 0.36 0.42 0.02 1333 1593 93 0.20 0.24 0.02 127.7 (116.6, 138.8) 130.3 (121.4, 138.7) 0.93 133.2 (122.7, 144.7) 132.5 (124.4, 140.5) ‐0.26 4.7 (3.7, 5.7) 4.2 (3.5, 4.9) ‐0.17 4.4 (3.5, 5.2) 4.1 (3.4, 4.7) ‐0.10 21.2 (19.9, 22.6) 24.3 (23.3, 25.4) 1.10 20.3 (18.5, 22.1) 21.9 (20.0, 23.8) 0.59 5.1 (4.0, 6.1) 5.5 (4.6, 6.40) 0.16 5.2 (4.2, 6.2) 5.4 (4.6, 6.2) 0.08Sierra Leone 0.29 0.38 0.03 973 762 ‐75 0.15 0.10 ‐0.02 130.1 (119.9, 140.1) 134.7 (130.1, 139.2) 1.66 134.5 (125.1, 144.6) 134.9 (130.5, 139.2) 0.13 4.6 (3.6, 5.6) 4.1 (3.4, 4.7) ‐0.20 4.2 (3.3, 5.1) 3.8 (3.2, 4.5) ‐0.14 21.3 (18.4, 24.2) 23.9 (23.2, 24.6) 0.94 21.4 (19.1, 23.7) 22.5 (21.9, 23.2) 0.42 5.0 (4.0, 6.1) 5.4 (4.6, 6.30) 0.16 5.2 (4.2, 6.3) 5.4 (4.6, 6.2) 0.06Togo 0.25 0.42 0.06 1256 753 ‐180 0.04 0.05 0.01 127.5 (116.4, 138.5) 131.0 (122.2, 139.7) 1.27 134.3 (123.8, 145.8) 133.0 (125.1, 140.9) ‐0.48 4.6 (3.6, 5.7) 4.1 (3.4, 4.8) ‐0.20 4.2 (3.3, 5.1) 3.9 (3.2, 4.5) ‐0.13 20.3 (17.9, 22.7) 22.7 (21.1, 24.3) 0.86 20.9 (18.2, 23.7) 21.9 (19.6, 24.2) 0.35 4.9 (3.8, 6.0) 5.3 (4.4, 6.20) 0.16 5.1 (4.1, 6.2) 5.3 (4.5, 6.2) 0.06

Algeria 0.44 0.65 0.08 5522 6588 381 0.33 0.38 0.02 130.8 (120.4, 141.0) 129.9 (122.8, 136.9) ‐0.33 132.4 (122.5, 142.0) 130.0 (123.4, 136.5) ‐0.84 5.0 (4.0, 5.9) 4.8 (4.3, 5.4) ‐0.06 4.9 (4.1, 5.7) 4.7 (4.2, 5.1) ‐0.09 23.7 (20.9, 26.5) 26.4 (24.9, 27.8) 0.96 22.3 (20.3, 24.3) 24.6 (23.5, 25.8) 0.84 5.1 (4.1, 6.1) 5.4 (4.8, 6.00) 0.11 5.4 (4.4, 6.4) 5.4 (4.8, 6.0) 0.02Bahrain 0.86 0.89 0.01 23853 29774 2115 0.45 0.40 ‐0.02 138.6 (128.2, 148.8) 129.9 (122.0, 137.6) ‐3.09 135.3 (125.2, 146.0) 130.5 (123.3, 137.7) ‐1.71 5.3 (4.4, 6.2) 5.2 (4.5, 5.8) ‐0.06 5.3 (4.4, 6.1) 5.1 (4.5, 5.7) ‐0.07 25.2 (22.8, 27.4) 28.8 (27.2, 30.4) 1.29 24.0 (22.1, 25.7) 27.8 (26.6, 29.1) 1.38 5.1 (4.1, 6.2) 5.7 (4.9, 6.50) 0.19 5.4 (4.4, 6.4) 5.9 (5.1, 6.7) 0.17Egypt 0.44 0.43 0.00 2301 5088 995 0.26 0.27 0.00 124.6 (115.3, 133.6) 125.1 (119.8, 130.4) 0.19 125.7 (116.6, 134.9) 125.9 (120.7, 130.9) 0.07 4.8 (3.9, 5.7) 4.9 (4.5, 5.3) 0.05 4.7 (3.9, 5.4) 4.6 (4.2, 4.9) ‐0.04 25.4 (23.5, 27.3) 30.1 (29.5, 30.7) 1.67 24.3 (22.2, 26.2) 26.7 (25.8, 27.7) 0.88 5.2 (4.1, 6.4) 5.1 (4.6, 5.60) ‐0.03 5.6 (4.5, 6.8) 5.1 (4.6, 5.6) ‐0.18Iran (Islamic Republic of) 0.50 0.69 0.07 6314 10379 1452 0.33 0.40 0.03 128.0 (118.8, 137.3) 125.2 (121.2, 129.2) ‐1.02 131.6 (123.0, 140.2) 128.2 (124.2, 132.1) ‐1.22 5.0 (4.0, 5.9) 5.3 (4.9, 5.6) 0.09 5.0 (4.2, 5.8) 5.0 (4.7, 5.3) 0.00 23.4 (21.0, 25.8) 27.2 (26.6, 27.9) 1.38 23.2 (21.3, 25.2) 25.3 (24.8, 25.8) 0.77 5.0 (4.1, 5.9) 5.5 (5.2, 5.80) 0.18 5.3 (4.4, 6.3) 5.4 (5.1, 5.8) 0.03Iraq 0.66 0.66 0.00 8878 3500 ‐1921 0.45 0.49 0.01 130.4 (119.6, 141.1) 126.0 (116.9, 135.0) ‐1.59 130.8 (120.4, 141.4) 127.4 (119.2, 135.5) ‐1.19 5.1 (4.1, 6.0) 4.9 (4.5, 5.3) ‐0.07 5.0 (4.1, 5.8) 4.8 (4.5, 5.2) ‐0.05 26.2 (23.4, 29.0) 28.4 (27.3, 29.6) 0.77 25.0 (22.9, 26.9) 26.7 (25.9, 27.5) 0.62 5.3 (4.3, 6.3) 5.7 (5.1, 6.40) 0.16 5.5 (4.5, 6.5) 5.8 (5.2, 6.4) 0.11Jordan 0.60 0.78 0.07 4279 4923 230 0.39 0.52 0.05 126.0 (116.5, 136.1) 121.4 (116.6, 126.3) ‐1.64 127.3 (118.3, 136.3) 125.2 (120.3, 130.1) ‐0.74 5.0 (4.0, 5.9) 5.0 (4.6, 5.4) 0.01 5.0 (4.2, 5.8) 4.9 (4.6, 5.2) ‐0.02 27.5 (25.3, 29.7) 29.2 (28.7, 29.8) 0.60 24.7 (22.9, 26.3) 27.5 (26.8, 28.1) 1.01 5.4 (4.3, 6.5) 6.3 (5.8, 6.80) 0.29 5.4 (4.4, 6.4) 6.2 (5.7, 6.7) 0.29Kuwait 0.95 0.98 0.01 45297 44733 ‐201 0.80 0.71 ‐0.03 136.1 (125.7, 146.7) 123.4 (117.9, 128.9) ‐4.54 135.6 (126.2, 145.3) 127.4 (122.2, 132.7) ‐2.93 5.6 (4.7, 6.5) 5.2 (4.7, 5.6) ‐0.15 5.6 (4.8, 6.4) 5.2 (4.8, 5.5) ‐0.16 28.1 (25.8, 30.3) 31.2 (30.2, 32.1) 1.10 25.5 (24.2, 26.8) 29.2 (28.4, 29.9) 1.31 5.2 (4.2, 6.2) 5.9 (5.5, 6.40) 0.25 5.4 (4.4, 6.4) 6.2 (5.7, 6.7) 0.28Lebanon 0.74 0.87 0.05 11079 11733 234 0.57 0.63 0.02 131.8 (120.8, 142.4) 127.5 (118.6, 136.7) ‐1.53 132.0 (121.4, 142.5) 128.9 (120.7, 136.7) ‐1.11 5.1 (4.2, 6.0) 5.0 (4.3, 5.6) ‐0.05 5.0 (4.2, 5.8) 4.8 (4.1, 5.4) ‐0.09 24.3 (21.9, 26.6) 27.7 (26.0, 29.4) 1.23 23.9 (22.1, 25.7) 27.2 (25.9, 28.5) 1.19 5.1 (4.1, 6.1) 5.6 (4.8, 6.40) 0.17 5.4 (4.4, 6.4) 5.8 (5.0, 6.6) 0.16Libyan Arab Jamahiriya 0.70 0.78 0.03 31276 15869 ‐5502 0.67 0.51 ‐0.06 138.4 (127.3, 148.9) 133.6 (128.6, 138.4) ‐1.70 141.7 (131.5, 150.9) 136.6 (131.9, 141.3) ‐1.81 5.3 (4.4, 6.2) 4.7 (4.3, 5.0) ‐0.22 5.2 (4.5, 6.0) 4.6 (4.3, 4.9) ‐0.23 26.8 (24.1, 29.4) 29.2 (28.3, 30.1) 0.87 24.4 (22.5, 26.4) 26.5 (25.9, 27.2) 0.77 5.2 (4.2, 6.2) 5.9 (5.5, 6.30) 0.24 5.2 (4.2, 6.2) 6.0 (5.6, 6.4) 0.26Morocco 0.41 0.57 0.06 2272 3823 554 0.27 0.32 0.02 126.5 (115.7, 137.8) 126.5 (117.7, 135.3) 0.02 127.8 (117.4, 138.5) 127.1 (119.2, 134.9) ‐0.26 4.8 (3.9, 5.7) 4.7 (4.0, 5.3) ‐0.04 4.7 (3.9, 5.5) 4.5 (3.9, 5.1) ‐0.07 23.6 (21.6, 25.6) 26.2 (25.0, 27.4) 0.93 23.2 (20.6, 25.7) 25.6 (23.4, 27.9) 0.86 5.0 (4.0, 6.0) 5.6 (4.9, 6.30) 0.19 5.3 (4.3, 6.3) 5.6 (4.9, 6.2) 0.10

Occupied Palestinian Territory 0.62 0.74 0.04 2114 2217 37 0.36 0.36 0.00 127.1 (116.2, 138.0) 127.3 (120.0, 134.5) 0.07 128.9 (117.8, 140.0) 129.1 (122.1, 136.1) 0.07 4.8 (3.9, 5.7) 4.7 (4.0, 5.3) ‐0.06 4.7 (3.8, 5.5) 4.5 (3.8, 5.1) ‐0.08 25.2 (22.5, 27.8) 29.0 (27.5, 30.5) 1.38 24.3 (22.2, 26.3) 26.6 (25.4, 27.8) 0.81 5.2 (4.2, 6.2) 5.9 (5.2, 6.60) 0.25 5.4 (4.4, 6.5) 5.9 (5.3, 6.6) 0.18

Oman 0.48 0.73 0.09 9785 23216 4797 0.42 0.47 0.02 130.3 (120.9, 140.5) 126.9 (120.1, 133.6) ‐1.22 131.7 (122.0, 141.4) 129.6 (123.0, 136.3) ‐0.74 5.0 (4.1, 6.0) 5.0 (4.4, 5.7) 0.00 5.0 (4.1, 5.8) 4.9 (4.3, 5.6) ‐0.01 24.9 (23.0, 26.9) 26.7 (25.3, 28.1) 0.61 23.0 (21.5, 24.5) 26.2 (25.2, 27.3) 1.14 5.1 (4.2, 6.0) 5.7 (5.0, 6.30) 0.20 5.4 (4.5, 6.3) 5.7 (5.0, 6.4) 0.13Qatar 0.89 0.96 0.02 98083 84944 ‐4692 0.46 0.46 0.00 139.5 (128.4, 150.6) 126.3 (120.8, 131.7) ‐4.69 139.3 (129.3, 149.1) 130.6 (125.5, 135.6) ‐3.07 5.6 (4.6, 6.5) 5.3 (4.6, 5.9) ‐0.11 5.6 (4.7, 6.4) 5.2 (4.5, 5.8) ‐0.14 27.2 (24.3, 30.2) 28.9 (27.9, 29.9) 0.62 25.0 (23.0, 27.1) 28.1 (27.4, 28.9) 1.13 5.4 (4.3, 6.4) 5.6 (5.1, 6.10) 0.08 5.6 (4.5, 6.6) 5.8 (5.3, 6.3) 0.09Saudi Arabia 0.66 0.82 0.06 31506 21774 ‐3476 0.55 0.55 0.00 135.2 (124.9, 145.5) 126.8 (121.3, 132.3) ‐2.98 136.8 (127.0, 146.5) 129.5 (124.2, 135.0) ‐2.60 5.4 (4.4, 6.3) 4.8 (4.3, 5.3) ‐0.19 5.3 (4.5, 6.1) 4.6 (4.2, 5.0) ‐0.24 26.3 (24.8, 27.8) 29.6 (28.7, 30.5) 1.18 25.0 (23.8, 26.3) 27.9 (27.2, 28.6) 1.02 5.2 (4.0, 6.3) 6.6 (6.0, 7.20) 0.48 5.3 (4.2, 6.4) 6.6 (6.0, 7.2) 0.46Syrian Arab Republic 0.47 0.55 0.03 3342 4197 305 0.55 0.59 0.01 129.0 (118.6, 140.1) 127.0 (118.2, 135.9) ‐0.72 129.6 (119.4, 140.7) 128.5 (120.6, 136.6) ‐0.40 4.9 (4.0, 5.8) 4.8 (4.2, 5.5) ‐0.04 4.8 (4.0, 5.6) 4.7 (4.0, 5.3) ‐0.06 25.8 (23.2, 28.4) 28.9 (27.5, 30.3) 1.11 24.3 (22.5, 26.2) 26.9 (25.9, 28.0) 0.92 5.3 (4.2, 6.3) 5.8 (4.9, 6.60) 0.17 5.5 (4.4, 6.5) 5.8 (5.0, 6.7) 0.12Tunisia 0.51 0.67 0.06 3607 7242 1298 0.30 0.40 0.04 130.0 (120.6, 139.2) 128.6 (121.6, 135.5) ‐0.48 129.8 (120.2, 138.9) 128.9 (122.1, 135.4) ‐0.32 4.8 (3.9, 5.7) 4.9 (4.2, 5.6) 0.03 4.5 (3.8, 5.2) 4.6 (4.0, 5.3) 0.06 24.6 (22.2, 26.8) 27.9 (26.3, 29.7) 1.20 22.2 (20.4, 24.0) 25.2 (23.8, 26.4) 1.06 5.1 (4.4, 5.8) 5.8 (5.1, 6.50) 0.23 5.3 (4.7, 6.0) 5.7 (5.0, 6.4) 0.14Turkey 0.44 0.69 0.09 6042 11479 1942 0.40 0.48 0.03 127.1 (118.0, 137.0) 124.1 (120.1, 128.0) ‐1.09 125.9 (117.2, 134.8) 123.6 (119.3, 127.7) ‐0.79 4.9 (3.9, 5.7) 4.8 (4.4, 5.2) ‐0.02 4.8 (4.0, 5.6) 4.7 (4.4, 5.0) ‐0.04 26.1 (24.1, 28.1) 28.2 (27.6, 28.9) 0.78 23.7 (21.9, 25.4) 26.7 (26.3, 27.1) 1.09 5.2 (4.2, 6.1) 5.5 (5.1, 5.80) 0.10 5.3 (4.4, 6.3) 5.5 (5.1, 5.9) 0.06United Arab Emirates 0.81 0.83 0.01 74010 48685 ‐9045 0.87 0.62 ‐0.09 134.5 (124.3, 145.0) 121.7 (114.7, 128.8) ‐4.57 137.7 (127.5, 147.9) 128.1 (121.3, 135.0) ‐3.43 5.7 (4.7, 6.6) 5.3 (4.6, 5.9) ‐0.14 5.7 (4.8, 6.5) 5.2 (4.5, 5.8) ‐0.17 27.2 (24.9, 29.5) 29.6 (28.2, 31.0) 0.86 24.9 (23.1, 26.7) 28.1 (26.9, 29.1) 1.13 5.4 (4.2, 6.5) 6.0 (5.5, 6.60) 0.24 5.4 (4.3, 6.4) 6.0 (5.5, 6.6) 0.23Yemen 0.17 0.31 0.05 1468 2242 276 0.23 0.27 0.01 123.5 (112.5, 134.9) 126.4 (117.3, 135.3) 1.02 127.8 (117.1, 138.7) 129.0 (121.0, 137.3) 0.44 4.7 (3.7, 5.6) 4.6 (3.9, 5.3) ‐0.04 4.6 (3.8, 5.4) 4.4 (3.8, 5.1) ‐0.06 23.5 (20.1, 27.0) 26.1 (23.0, 29.2) 0.94 22.9 (20.2, 25.6) 24.4 (22.0, 26.8) 0.55 5.1 (3.9, 6.1) 5.6 (4.7, 6.40) 0.18 5.3 (4.2, 6.3) 5.6 (4.8, 6.5) 0.11

Afghanistan 0.16 0.22 0.02 680 866 66 0.15 0.18 0.01 122.1 (110.6, 133.5) 125.1 (115.4, 134.8) 1.07 125.2 (114.3, 136.5) 125.7 (116.6, 134.6) 0.17 4.6 (3.7, 5.6) 4.2 (3.6, 4.9) ‐0.14 4.6 (3.7, 5.4) 4.1 (3.4, 4.7) ‐0.17 20.4 (16.9, 24.0) 21.1 (17.8, 24.2) 0.23 21.5 (18.7, 24.2) 20.6 (18.1, 23.1) ‐0.31 4.9 (3.9, 5.9) 5.4 (4.5, 6.20) 0.16 5.3 (4.2, 6.3) 5.4 (4.5, 6.2) 0.03Bangladesh 0.15 0.27 0.04 650 1297 231 0.02 0.04 0.01 120.3 (108.8, 132.3) 125.0 (115.4, 134.3) 1.67 123.9 (112.6, 135.4) 125.8 (117.1, 134.4) 0.67 4.6 (3.6, 5.5) 4.4 (3.6, 5.1) ‐0.07 4.5 (3.6, 5.3) 4.3 (3.6, 4.9) ‐0.08 18.5 (16.2, 20.9) 20.5 (19.8, 21.3) 0.74 20.5 (18.6, 22.6) 20.4 (19.0, 21.9) ‐0.04 4.8 (3.8, 5.8) 5.5 (4.9, 6.10) 0.23 5.2 (4.2, 6.2) 5.4 (4.8, 6.0) 0.08Bhutan 0.10 0.33 0.08 900 4316 1220 0.15 0.19 0.01 119.1 (107.9, 130.1) 125.1 (117.7, 132.4) 2.17 122.7 (112.2, 133.7) 127.2 (120.3, 134.1) 1.61 4.5 (3.5, 5.4) 4.5 (4.0, 5.0) 0.01 4.4 (3.5, 5.2) 4.5 (4.1, 4.9) 0.04 20.2 (17.0, 23.2) 22.9 (21.1, 24.7) 0.98 21.8 (19.1, 24.4) 22.8 (21.4, 24.3) 0.36 5.1 (4.0, 6.0) 5.8 (5.3, 6.30) 0.27 5.3 (4.2, 6.3) 5.8 (5.2, 6.3) 0.17India 0.23 0.29 0.02 927 2833 681 0.09 0.18 0.03 120.9 (111.7, 129.8) 123.1 (119.2, 127.1) 0.80 122.1 (113.4, 131.2) 123.9 (119.9, 127.9) 0.67 4.6 (3.7, 5.4) 4.5 (4.1, 4.8) ‐0.03 4.6 (3.8, 5.3) 4.3 (4.0, 4.6) ‐0.08 20.5 (18.9, 22.2) 21.3 (20.8, 21.8) 0.30 21.1 (19.7, 22.7) 21.0 (20.5, 21.5) ‐0.05 5.1 (4.2, 5.9) 5.6 (5.2, 5.90) 0.17 5.2 (4.3, 6.0) 5.6 (5.3, 6.0) 0.16Nepal 0.06 0.18 0.04 608 1077 167 0.11 0.05 ‐0.02 119.5 (107.9, 131.2) 125.6 (116.1, 135.3) 2.15 123.8 (112.8, 134.9) 125.3 (116.4, 134.0) 0.54 4.5 (3.6, 5.5) 4.3 (3.6, 4.9) ‐0.10 4.5 (3.6, 5.3) 4.1 (3.5, 4.8) ‐0.12 18.7 (16.5, 21.0) 20.7 (19.8, 21.6) 0.73 21.0 (18.6, 23.4) 20.8 (19.0, 22.6) ‐0.07 4.8 (3.9, 5.7) 5.4 (4.7, 6.10) 0.22 5.1 (4.2, 6.0) 5.5 (4.8, 6.2) 0.13Pakistan 0.28 0.35 0.03 1284 2355 383 0.31 0.47 0.06 123.3 (113.7, 132.7) 126.5 (119.6, 134.0) 1.16 125.1 (115.4, 135.0) 127.0 (120.0, 134.0) 0.71 4.7 (3.8, 5.7) 4.5 (4.0, 5.1) ‐0.06 4.7 (3.9, 5.5) 4.5 (4.0, 4.9) ‐0.08 21.8 (19.8, 23.8) 23.4 (21.9, 24.9) 0.60 21.5 (19.8, 23.2) 22.3 (21.0, 23.6) 0.27 5.0 (4.0, 6.0) 5.9 (5.4, 6.60) 0.33 5.2 (4.2, 6.2) 5.7 (5.2, 6.3) 0.18

China (Hong Kong SAR) 0.91 1.00 0.03 13919 39565 9159 0.27 0.29 0.01 131.1 (122.7, 140.0) 119.3 (111.3, 127.3) ‐4.21 134.4 (125.9, 143.8) 127.6 (120.2, 135.2) ‐2.43 5.1 (4.2, 6.0) 5.2 (4.5, 5.9) 0.05 5.1 (4.4, 5.8) 5.1 (4.5, 5.7) 0.02 21.7 (19.7, 23.5) 23.7 (21.6, 25.9) 0.73 21.6 (20.1, 23.1) 25.1 (23.5, 26.7) 1.22 5.3 (4.3, 6.3) 5.4 (4.5, 6.20) 0.02 5.4 (4.4, 6.4) 5.6 (4.7, 6.4) 0.06China (Macao SAR) 0.99 1.00 0.01 14233 53943 14182 0.25 0.28 0.01 133.3 (121.7, 144.9) 121.8 (111.3, 132.0) ‐4.10 134.3 (123.8, 145.4) 127.8 (118.7, 136.9) ‐2.32 5.2 (4.3, 6.1) 5.2 (4.5, 5.9) 0.01 5.1 (4.3, 5.9) 5.1 (4.4, 5.8) ‐0.01 23.7 (20.1, 27.3) 24.9 (21.5, 28.4) 0.41 22.7 (19.9, 25.4) 25.7 (23.1, 28.3) 1.09 5.5 (4.4, 6.5) 5.4 (4.5, 6.30) ‐0.02 5.5 (4.5, 6.5) 5.6 (4.7, 6.5) 0.04China 0.19 0.45 0.09 561 5941 1922 0.15 0.62 0.17 125.7 (119.9, 132.1) 124.3 (121.3, 127.4) ‐0.51 129.1 (123.2, 135.4) 127.7 (124.7, 130.7) ‐0.50 4.3 (3.6, 5.0) 4.6 (4.2, 5.0) 0.11 4.2 (3.7, 4.8) 4.5 (4.2, 4.8) 0.10 21.9 (21.2, 22.7) 22.9 (22.3, 23.5) 0.35 21.6 (21.0, 22.2) 22.9 (22.5, 23.4) 0.48 5.3 (4.8, 5.9) 5.4 (5.2, 5.70) 0.03 5.5 (5.0, 6.1) 5.5 (5.2, 5.8) ‐0.01Democratic People's Republic of Korea

0.57 0.60 0.01 2284 1185 ‐392 0.19 0.19 0.00 126.1 (115.1, 137.0) 122.1 (112.2, 132.2) ‐1.42 129.1 (119.0, 139.4) 125.2 (115.9, 134.4) ‐1.40 4.7 (3.8, 5.6) 4.4 (3.7, 5.2) ‐0.12 4.7 (3.9, 5.4) 4.2 (3.5, 4.9) ‐0.15 21.9 (18.4, 25.3) 21.2 (17.9, 24.7) ‐0.23 21.8 (19.1, 24.5) 22.0 (19.4, 24.8) 0.08 5.3 (4.3, 6.3) 5.1 (4.2, 6.00) ‐0.07 5.4 (4.4, 6.4) 5.2 (4.3, 6.1) ‐0.08

Taiwan 0.63 0.69 0.02 7685 29241 7698 0.25 0.28 0.01 124.1 (116.3, 132.3) 117.6 (112.9, 122.5) ‐2.31 128.9 (120.9, 137.3) 125.2 (120.5, 129.9) ‐1.32 4.9 (4.0, 5.7) 4.8 (4.5, 5.2) ‐0.02 4.9 (4.1, 5.7) 4.8 (4.5, 5.1) ‐0.03 22.4 (20.7, 24.0) 23.0 (22.2, 23.9) 0.25 22.4 (21.0, 23.8) 24.4 (23.9, 25.0) 0.73 5.4 (4.6, 6.2) 5.1 (4.8, 5.50) ‐0.10 5.6 (4.8, 6.5) 5.3 (4.9, 5.7) ‐0.11

Cambodia 0.09 0.20 0.04 567 1840 455 0.03 0.21 0.06 112.6 (101.0, 124.3) 117.6 (112.7, 122.6) 1.77 119.4 (109.2, 129.4) 123.2 (118.7, 127.9) 1.37 4.4 (3.5, 5.4) 4.6 (4.2, 4.9) 0.04 4.4 (3.6, 5.2) 4.4 (4.1, 4.7) ‐0.01 19.6 (17.0, 22.2) 21.7 (20.8, 22.6) 0.77 19.7 (17.1, 22.3) 20.8 (18.5, 23.1) 0.39 5.0 (4.0, 6.1) 4.7 (4.3, 5.10) ‐0.10 5.2 (4.2, 6.3) 4.7 (4.3, 5.2) ‐0.17Indonesia 0.22 0.44 0.08 1489 3742 805 0.07 0.23 0.06 122.5 (112.0, 132.7) 126.4 (120.7, 132.0) 1.41 127.2 (117.2, 137.0) 128.9 (123.1, 134.7) 0.61 4.6 (3.6, 5.6) 4.7 (4.1, 5.3) 0.04 4.6 (3.8, 5.4) 4.5 (4.0, 5.1) ‐0.03 20.1 (18.2, 21.9) 23.0 (22.1, 23.9) 1.04 19.9 (18.5, 21.3) 21.9 (21.2, 22.6) 0.71 5.2 (4.6, 5.8) 5.1 (4.6, 5.60) ‐0.02 5.3 (4.7, 6.0) 5.1 (4.5, 5.6) ‐0.09Lao People's Democratic Republic

0.12 0.31 0.07 753 2136 494 0.08 0.23 0.05 116.8 (105.2, 128.1) 123.0 (115.9, 130.2) 2.22 122.3 (111.7, 132.6) 126.2 (119.5, 132.6) 1.40 4.4 (3.4, 5.4) 4.5 (3.8, 5.3) 0.05 4.4 (3.6, 5.3) 4.4 (3.7, 5.0) ‐0.01 19.6 (16.8, 22.5) 22.0 (20.9, 23.2) 0.85 19.6 (17.4, 21.8) 21.1 (19.6, 22.5) 0.53 5.1 (4.0, 6.1) 5.1 (4.3, 6.00) 0.01 5.1 (4.1, 6.1) 5.1 (4.2, 5.9) ‐0.01

Malaysia 0.42 0.70 0.10 4669 12695 2866 0.50 0.58 0.03 124.6 (114.7, 134.4) 123.9 (118.0, 129.7) ‐0.27 127.3 (117.9, 137.2) 126.9 (120.9, 132.9) ‐0.15 4.9 (3.9, 5.9) 5.1 (4.4, 5.8) 0.09 4.9 (4.1, 5.6) 5.0 (4.3, 5.6) 0.04 22.1 (19.5, 24.8) 25.4 (24.7, 26.2) 1.19 21.6 (19.7, 23.4) 24.7 (24.1, 25.4) 1.13 5.2 (4.2, 6.2) 5.6 (5.1, 6.20) 0.14 5.3 (4.3, 6.2) 5.7 (5.1, 6.2) 0.15Maldives 0.22 0.38 0.05 1225 5018 1355 0.14 0.68 0.19 119.6 (107.9, 131.3) 123.3 (113.1, 133.1) 1.33 124.4 (113.4, 135.6) 127.8 (119.0, 136.9) 1.21 4.6 (3.6, 5.5) 4.9 (4.2, 5.7) 0.13 4.5 (3.6, 5.3) 4.6 (3.9, 5.4) 0.06 21.7 (18.9, 24.3) 26.4 (24.8, 28.1) 1.68 20.4 (17.8, 23.1) 23.2 (20.6, 25.8) 0.99 5.2 (4.2, 6.2) 5.1 (4.5, 5.70) ‐0.01 5.1 (4.1, 6.2) 5.2 (4.5, 5.8) 0.01Myanmar 0.24 0.32 0.03 312 1037 259 0.04 0.24 0.07 116.3 (104.4, 128.7) 123.1 (114.1, 132.2) 2.45 120.2 (108.4, 132.1) 125.5 (117.1, 133.8) 1.89 4.4 (3.4, 5.3) 4.5 (3.8, 5.2) 0.06 4.3 (3.5, 5.1) 4.3 (3.7, 5.0) 0.00 19.7 (16.3, 23.2) 22.5 (19.4, 25.6) 1.00 19.7 (17.1, 22.2) 21.4 (19.1, 23.8) 0.64 5.0 (4.0, 6.0) 5.1 (4.6, 5.60) 0.02 5.2 (4.2, 6.2) 5.0 (4.4, 5.5) ‐0.07Philippines 0.37 0.49 0.04 2462 3146 244 0.31 0.41 0.04 122.2 (112.1, 132.4) 122.3 (116.3, 128.3) 0.04 126.6 (116.9, 136.4) 126.4 (120.6, 132.5) ‐0.06 4.8 (3.8, 5.7) 4.9 (4.4, 5.5) 0.06 4.8 (4.0, 5.6) 4.7 (4.3, 5.1) ‐0.03 21.2 (19.3, 23.0) 23.5 (22.4, 24.5) 0.82 21.2 (19.8, 22.7) 22.9 (22.1, 23.7) 0.59 5.1 (4.1, 6.0) 5.0 (4.5, 5.50) ‐0.03 5.2 (4.3, 6.2) 5.1 (4.5, 5.6) ‐0.06Sri Lanka 0.22 0.14 ‐0.03 1516 4128 933 0.04 0.15 0.04 121.6 (111.0, 132.4) 124.9 (119.0, 130.9) 1.16 126.5 (116.3, 136.6) 128.3 (122.5, 134.0) 0.67 4.6 (3.6, 5.6) 4.7 (4.0, 5.4) 0.03 4.5 (3.7, 5.3) 4.5 (3.8, 5.1) ‐0.01 20.9 (18.3, 23.5) 23.1 (22.2, 24.0) 0.79 20.4 (18.5, 22.3) 22.0 (21.3, 22.6) 0.57 5.2 (4.2, 6.2) 5.3 (4.8, 5.80) 0.02 5.2 (4.2, 6.2) 5.4 (4.9, 6.0) 0.09Thailand 0.27 0.33 0.02 2313 7638 1902 0.24 0.52 0.10 118.3 (108.8, 127.8) 120.5 (114.8, 126.4) 0.78 122.8 (112.9, 132.7) 124.7 (118.9, 130.6) 0.69 4.7 (3.8, 5.7) 5.1 (4.6, 5.7) 0.14 4.7 (3.9, 5.4) 5.0 (4.6, 5.4) 0.12 21.5 (19.6, 23.3) 24.4 (23.4, 25.3) 1.04 20.3 (18.9, 21.7) 23.0 (22.2, 23.8) 0.98 5.2 (4.6, 5.8) 5.1 (4.8, 5.40) ‐0.03 5.1 (4.5, 5.7) 5.2 (4.8, 5.5) 0.02Timor‐Leste 0.16 0.27 0.04 1148 1274 45 0.13 0.21 0.03 116.6 (104.7, 129.0) 122.7 (113.4, 132.3) 2.19 122.5 (111.1, 133.7) 126.0 (117.4, 134.5) 1.25 4.4 (3.4, 5.4) 4.4 (3.7, 5.1) 0.00 4.4 (3.5, 5.2) 4.2 (3.5, 4.9) ‐0.05 19.5 (16.0, 22.8) 21.5 (18.5, 24.6) 0.73 19.9 (17.2, 22.6) 20.6 (18.1, 23.1) 0.24 5.0 (3.9, 6.0) 5.0 (4.1, 5.80) 0.01 5.0 (4.0, 6.1) 5.0 (4.1, 5.8) ‐0.02

Asia, Central

Europe, Central

Europe, Eastern

Sub‐Saharan Africa, Central

Sub‐Saharan Africa, East

Sub‐Saharan Africa, Southern

Sub‐Saharan Africa, West

North Africa and Middle East

Asia, South

Asia, East

Asia, Southeast

North America high‐income

Country

Proportion urban Per‐capita GDP (2005 Int $) Percentile of Western Diet SBP (mmHg) TC (mmol/L) BMI (kg/m²) FPG (mmol/L)

Asia‐Pacific high‐income

Australasia

Europe, Western

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Change per 

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Change per decade (Female)

1980 (Male) 2008 (Male)Change per decade (Male)

1980 (Female) 2008 (Female)Change per decade (Female)

1980 (Male) 2008 (Male)Change per decade (Male)

1980 (Female) 2008 (Female)Change per decade (Female)

1980 (Male) 2008 (Male)Change per decade (Male)

1980 (Female) 2008 (Female)Change per decade (Female)

1980 (Male) 2008 (Male)Change per decade (Male)

Country

Proportion urban Per‐capita GDP (2005 Int $) Percentile of Western Diet SBP (mmHg) TC (mmol/L) BMI (kg/m²) FPG (mmol/L)

Viet Nam 0.19 0.29 0.04 581 2622 729 0.08 0.40 0.11 117.1 (106.6, 128.1) 121.9 (115.9, 128.0) 1.72 123.2 (112.8, 133.6) 126.6 (120.6, 132.6) 1.22 4.5 (3.5, 5.5) 4.7 (4.0, 5.4) 0.07 4.4 (3.6, 5.3) 4.5 (3.9, 5.2) 0.03 18.6 (17.4, 19.8) 21.1 (20.1, 22.0) 0.88 19.0 (18.1, 20.0) 20.9 (20.1, 21.7) 0.68 5.3 (4.4, 6.2) 5.2 (4.5, 5.90) ‐0.03 5.0 (4.1, 5.9) 5.2 (4.5, 5.9) 0.07

Cook Islands 0.54 0.74 0.07 4225 17882 4878 0.45 0.47 0.01 124.7 (114.3, 135.2) 126.5 (119.3, 133.4) 0.65 130.9 (120.3, 140.8) 134.2 (127.1, 141.2) 1.17 5.0 (4.5, 5.5) 5.3 (4.6, 6.0) 0.11 5.0 (4.6, 5.5) 5.2 (4.6, 5.9) 0.07 26.3 (23.0, 29.4) 33.9 (32.5, 35.5) 2.72 26.2 (23.6, 28.6) 32.7 (31.5, 33.8) 2.31 5.4 (4.9, 6.0) 6.5 (5.6, 7.40) 0.39 5.6 (5.1, 6.2) 6.5 (5.6, 7.4) 0.31Fiji 0.38 0.51 0.05 3319 4267 338 0.52 0.58 0.02 124.4 (113.9, 134.7) 127.5 (120.1, 134.3) 1.11 128.9 (118.5, 138.9) 129.6 (122.5, 136.8) 0.26 4.9 (3.9, 5.7) 5.0 (4.4, 5.7) 0.06 5.1 (4.2, 5.8) 5.2 (4.6, 5.7) 0.03 23.5 (20.5, 26.6) 29.3 (27.8, 30.8) 2.07 22.3 (19.9, 24.8) 26.5 (25.4, 27.7) 1.52 5.3 (4.9, 5.7) 6.1 (5.5, 6.70) 0.28 5.6 (5.1, 6.0) 5.9 (5.2, 6.5) 0.10French Polynesia 0.57 0.52 ‐0.02 17781 25602 2793 0.71 0.84 0.04 128.7 (116.9, 140.5) 123.9 (113.9, 133.8) ‐1.71 133.9 (122.5, 145.0) 131.5 (122.7, 140.6) ‐0.86 5.3 (4.4, 6.3) 5.4 (4.7, 6.2) 0.03 5.5 (4.7, 6.4) 5.5 (4.8, 6.2) ‐0.02 26.3 (22.5, 30.1) 32.1 (28.8, 35.3) 2.06 25.4 (22.2, 28.6) 30.9 (28.4, 33.2) 1.95 5.5 (5.0, 5.9) 6.3 (5.4, 7.10) 0.28 5.7 (5.2, 6.1) 6.3 (5.4, 7.2) 0.23Kiribati 0.32 0.44 0.04 2935 3258 115 0.29 0.38 0.03 120.4 (109.9, 130.9) 122.0 (115.1, 128.6) 0.57 127.5 (117.4, 137.3) 128.7 (121.9, 135.4) 0.46 4.8 (3.8, 5.6) 4.7 (4.2, 5.3) ‐0.01 4.8 (3.9, 5.6) 4.5 (4.1, 5.0) ‐0.08 25.9 (22.7, 29.1) 31.3 (30.0, 32.7) 1.92 25.5 (23.1, 27.8) 29.2 (28.2, 30.2) 1.34 5.5 (5.1, 5.9) 6.8 (6.2, 7.40) 0.48 5.6 (5.2, 6.0) 6.8 (6.1, 7.4) 0.40Marshall Islands 0.58 0.71 0.05 5133 5970 299 0.43 0.47 0.01 122.4 (111.7, 133.4) 121.5 (114.0, 128.7) ‐0.34 127.4 (117.3, 137.3) 127.6 (120.3, 134.8) 0.06 4.9 (4.1, 5.8) 5.0 (4.4, 5.6) 0.03 5.0 (4.2, 5.8) 4.8 (4.3, 5.3) ‐0.08 25.4 (22.2, 28.6) 31.4 (29.8, 33.0) 2.14 24.7 (22.2, 27.0) 29.4 (28.2, 30.6) 1.68 5.5 (3.9, 6.7) 7.3 (6.5, 8.20) 0.66 5.5 (4.3, 6.6) 6.9 (6.1, 7.7) 0.50Micronesia (Federated States of)

0.26 0.23 ‐0.01 2699 3262 201 0.45 0.49 0.02 121.5 (110.9, 132.1) 125.4 (118.2, 132.8) 1.41 130.0 (119.8, 140.0) 132.1 (125.0, 139.0) 0.75 4.8 (3.8, 5.7) 5.0 (4.4, 5.6) 0.07 4.9 (4.0, 5.7) 5.0 (4.4, 5.5) 0.03 25.8 (22.7, 29.0) 31.3 (29.7, 32.8) 1.96 24.4 (22.0, 26.8) 28.1 (26.9, 29.3) 1.32 5.4 (4.4, 6.3) 6.4 (5.7, 7.10) 0.37 5.5 (4.6, 6.5) 6.0 (5.3, 6.6) 0.16

Nauru 1.00 1.00 0.00 30996 6516 ‐8743 0.52 0.49 ‐0.01 133.4 (123.4, 143.2) 127.4 (120.7, 134.5) ‐2.12 135.5 (125.9, 144.8) 133.9 (127.2, 140.3) ‐0.59 5.2 (4.2, 6.0) 5.1 (4.5, 5.6) ‐0.03 5.3 (4.5, 6.1) 4.8 (4.3, 5.2) ‐0.17 28.3 (24.5, 31.8) 35.0 (33.6, 36.3) 2.41 28.1 (25.0, 30.9) 33.9 (32.8, 35.0) 2.06 5.3 (4.8, 5.9) 6.0 (5.4, 6.60) 0.22 5.7 (5.1, 6.2) 5.8 (5.1, 6.4) 0.03Palau 0.63 0.81 0.07 14794 13603 ‐425 0.44 0.48 0.02 129.5 (117.4, 141.2) 124.5 (114.9, 133.8) ‐1.79 134.4 (122.8, 145.2) 130.8 (122.1, 139.4) ‐1.29 5.2 (4.3, 6.2) 5.2 (4.5, 5.9) ‐0.02 5.4 (4.6, 6.2) 5.1 (4.4, 5.7) ‐0.09 26.3 (22.5, 30.2) 31.8 (28.8, 34.9) 1.98 25.4 (22.3, 28.4) 30.4 (28.0, 32.8) 1.77 5.4 (4.4, 6.3) 6.3 (5.5, 7.20) 0.34 5.6 (4.6, 6.6) 6.3 (5.4, 7.1) 0.23Papua New Guinea 0.13 0.13 0.00 1905 2234 118 0.43 0.48 0.02 110.3 (103.4, 117.2) 120.1 (114.1, 126.1) 3.47 118.7 (111.1, 126.0) 122.6 (117.1, 128.1) 1.39 4.5 (3.7, 5.3) 4.8 (4.1, 5.5) 0.08 4.7 (4.1, 5.4) 4.6 (4.0, 5.3) ‐0.04 20.8 (18.4, 23.2) 25.8 (24.6, 26.9) 1.77 21.8 (19.8, 23.8) 25.0 (24.3, 25.8) 1.16 5.2 (4.4, 6.0) 6.0 (5.6, 6.50) 0.31 5.5 (4.6, 6.3) 6.1 (5.6, 6.6) 0.23Samoa 0.21 0.21 0.00 3586 5501 684 0.34 0.66 0.11 122.9 (112.1, 133.7) 126.4 (119.2, 133.7) 1.23 130.3 (119.7, 140.6) 132.4 (125.1, 139.6) 0.75 4.8 (3.8, 5.7) 4.7 (4.0, 5.4) ‐0.03 4.7 (3.9, 5.5) 4.5 (4.0, 5.0) ‐0.08 26.8 (25.1, 28.6) 33.7 (32.1, 35.2) 2.44 25.8 (24.4, 27.3) 30.4 (29.2, 31.7) 1.64 5.2 (4.2, 6.1) 6.7 (6.0, 7.40) 0.55 5.4 (4.5, 6.3) 6.6 (5.9, 7.3) 0.42Solomon Islands 0.11 0.18 0.03 2466 2433 ‐12 0.19 0.13 ‐0.02 118.3 (107.5, 129.3) 124.0 (117.0, 130.8) 2.06 123.9 (113.6, 134.2) 125.7 (119.0, 132.3) 0.65 4.7 (3.8, 5.6) 4.7 (4.1, 5.2) ‐0.01 4.6 (3.9, 5.4) 4.4 (4.0, 4.9) ‐0.07 23.8 (20.6, 27.0) 28.9 (27.5, 30.3) 1.82 24.0 (21.6, 26.5) 27.2 (26.1, 28.2) 1.13 5.2 (4.1, 6.1) 6.3 (5.7, 6.90) 0.40 5.4 (4.4, 6.3) 6.2 (5.6, 6.8) 0.30Tonga 0.21 0.23 0.01 2701 5771 1096 0.48 0.47 ‐0.01 119.5 (109.1, 130.0) 125.8 (119.4, 132.3) 2.23 126.3 (116.3, 136.2) 130.6 (124.0, 136.9) 1.53 4.6 (3.7, 5.6) 4.9 (4.3, 5.5) 0.09 4.7 (3.9, 5.5) 5.1 (4.6, 5.5) 0.12 27.2 (23.7, 30.4) 34.3 (32.8, 35.7) 2.53 26.4 (23.9, 28.7) 31.0 (30.0, 32.0) 1.65 5.3 (4.4, 6.3) 6.4 (5.8, 7.00) 0.37 5.6 (4.7, 6.6) 6.2 (5.6, 6.8) 0.22Vanuatu 0.15 0.25 0.04 3476 4872 499 0.47 0.34 ‐0.04 124.8 (114.0, 135.1) 132.1 (125.3, 138.8) 2.60 131.9 (121.6, 141.9) 134.2 (127.7, 140.6) 0.80 4.8 (3.8, 5.7) 4.7 (4.1, 5.4) ‐0.01 4.8 (4.0, 5.6) 4.6 (4.0, 5.3) ‐0.08 22.9 (20.3, 25.6) 28.5 (27.1, 29.8) 1.97 23.2 (21.1, 25.4) 26.8 (25.7, 27.8) 1.28 4.8 (3.7, 6.0) 5.4 (4.5, 6.30) 0.21 5.3 (4.2, 6.4) 5.4 (4.6, 6.3) 0.04

Antigua and Barbuda 0.35 0.30 ‐0.01 6385 17863 4099 0.64 0.75 0.04 125.7 (113.5, 137.6) 125.3 (115.8, 134.5) ‐0.13 132.6 (121.2, 144.1) 133.5 (125.1, 141.8) 0.32 5.0 (4.0, 6.0) 4.9 (4.1, 5.7) ‐0.03 4.9 (4.0, 5.8) 4.8 (4.0, 5.5) ‐0.04 24.2 (20.8, 27.5) 27.5 (24.3, 30.6) 1.17 23.3 (20.8, 25.9) 25.8 (23.4, 28.1) 0.88 5.1 (4.1, 6.2) 5.7 (4.8, 6.60) 0.19 5.3 (4.3, 6.4) 5.6 (4.7, 6.5) 0.11Bahamas 0.73 0.84 0.04 21957 28590 2369 0.82 0.79 ‐0.01 131.9 (120.3, 143.8) 124.4 (115.0, 133.5) ‐2.68 136.6 (125.4, 147.6) 132.8 (124.3, 140.9) ‐1.36 5.4 (4.4, 6.4) 5.2 (4.4, 5.9) ‐0.09 5.3 (4.4, 6.2) 5.0 (4.3, 5.7) ‐0.11 25.7 (22.3, 29.1) 29.1 (26.1, 32.2) 1.22 24.2 (21.6, 26.9) 27.2 (24.9, 29.7) 1.08 5.2 (4.1, 6.2) 5.8 (5.0, 6.70) 0.23 5.4 (4.3, 6.4) 5.8 (4.9, 6.6) 0.14Barbados 0.40 0.43 0.01 17630 22090 1593 0.82 0.77 ‐0.02 129.1 (118.3, 140.5) 126.4 (118.9, 133.9) ‐0.98 134.1 (123.1, 145.2) 131.2 (124.0, 138.8) ‐1.03 5.3 (4.3, 6.3) 4.9 (4.2, 5.7) ‐0.13 5.1 (4.2, 6.0) 4.7 (4.0, 5.4) ‐0.15 26.6 (24.7, 28.5) 29.2 (26.8, 31.6) 0.94 24.4 (22.8, 25.9) 26.4 (24.7, 28.1) 0.72 5.5 (4.6, 6.4) 6.0 (5.1, 6.90) 0.17 5.7 (4.7, 6.6) 5.8 (4.9, 6.6) 0.05Belize 0.49 0.51 0.01 3972 7608 1299 0.72 0.62 ‐0.03 121.8 (110.8, 133.0) 120.9 (114.8, 127.2) ‐0.30 127.9 (117.0, 138.4) 127.9 (121.6, 134.5) 0.01 5.0 (4.0, 6.0) 4.7 (4.0, 5.4) ‐0.11 4.9 (4.0, 5.8) 4.5 (3.8, 5.1) ‐0.15 25.7 (22.9, 28.5) 29.8 (28.7, 30.9) 1.45 24.5 (22.4, 26.5) 27.0 (26.1, 27.9) 0.89 5.1 (4.1, 6.2) 5.7 (5.2, 6.30) 0.22 5.4 (4.4, 6.4) 5.3 (4.8, 5.9) ‐0.02Bermuda 1.00 1.00 0.00 50770 77779 9646 0.96 0.85 ‐0.04 136.1 (123.3, 148.0) 124.0 (114.4, 133.6) ‐4.30 139.9 (128.0, 151.3) 133.5 (125.0, 142.3) ‐2.28 5.8 (4.8, 6.8) 5.4 (4.7, 6.2) ‐0.13 5.7 (4.7, 6.6) 5.3 (4.6, 6.0) ‐0.14 27.4 (23.8, 31.0) 30.3 (27.3, 33.5) 1.03 25.1 (22.4, 27.8) 28.4 (26.0, 30.9) 1.19 5.3 (4.3, 6.4) 6.0 (5.1, 6.90) 0.22 5.5 (4.4, 6.6) 5.9 (5.0, 6.8) 0.16British Virgin Islands 0.23 0.41 0.06 7916 73167 23304 0.64 0.67 0.01 127.1 (116.0, 138.7) 127.7 (122.4, 132.9) 0.22 133.2 (122.2, 143.8) 135.2 (130.4, 140.2) 0.72 5.0 (4.0, 6.0) 5.2 (4.3, 6.0) 0.06 4.8 (3.9, 5.7) 5.0 (4.2, 5.8) 0.08 24.8 (21.6, 28.1) 28.9 (27.9, 30.0) 1.49 23.7 (21.4, 26.2) 27.3 (26.6, 28.0) 1.27 5.1 (4.1, 6.3) 5.8 (4.8, 6.70) 0.22 5.4 (4.3, 6.4) 5.8 (4.8, 6.7) 0.16Cuba 0.68 0.75 0.03 5728 8862 1119 0.72 0.44 ‐0.10 126.5 (114.7, 138.5) 126.1 (116.8, 135.6) ‐0.16 130.4 (118.9, 141.8) 129.6 (120.8, 138.4) ‐0.28 5.0 (4.1, 6.1) 4.7 (3.9, 5.4) ‐0.14 4.9 (4.0, 5.8) 4.4 (3.7, 5.1) ‐0.18 23.8 (22.4, 25.2) 26.6 (25.0, 28.2) 0.99 22.7 (21.7, 23.8) 25.1 (23.8, 26.3) 0.83 5.1 (4.1, 6.2) 5.7 (4.9, 6.50) 0.19 5.3 (4.3, 6.3) 5.7 (4.8, 6.5) 0.11Dominica 0.63 0.67 0.01 3426 7745 1542 0.64 0.77 0.05 130.8 (119.3, 141.9) 131.1 (125.5, 136.7) 0.09 134.1 (123.7, 143.9) 135.0 (129.5, 140.2) 0.33 4.9 (4.0, 5.9) 4.9 (4.4, 5.3) ‐0.03 4.7 (3.9, 5.7) 4.5 (4.1, 4.9) ‐0.08 25.0 (22.1, 27.9) 28.8 (27.7, 29.9) 1.36 22.3 (20.3, 24.4) 24.6 (23.8, 25.4) 0.80 5.3 (4.0, 6.5) 6.5 (5.9, 7.10) 0.42 5.2 (4.0, 6.3) 6.1 (5.5, 6.7) 0.34Dominican Republic 0.51 0.68 0.06 3860 8236 1563 0.37 0.57 0.07 128.0 (117.2, 138.7) 126.4 (118.4, 134.2) ‐0.59 130.0 (119.5, 140.9) 130.8 (123.2, 138.6) 0.28 4.8 (3.9, 5.8) 4.7 (4.0, 5.4) ‐0.06 4.5 (3.7, 5.5) 4.4 (3.8, 5.0) ‐0.06 22.6 (20.7, 24.5) 26.9 (25.2, 28.6) 1.51 22.9 (21.2, 24.6) 25.2 (23.8, 26.6) 0.81 4.7 (3.6, 5.9) 5.3 (4.5, 6.10) 0.21 5.0 (3.9, 6.0) 5.2 (4.4, 6.0) 0.08Grenada 0.33 0.39 0.02 3986 10542 2342 0.54 0.64 0.04 125.1 (113.4, 136.7) 125.6 (116.5, 134.4) 0.19 130.3 (119.0, 141.8) 131.6 (123.3, 139.9) 0.45 4.9 (3.9, 5.9) 4.7 (4.0, 5.4) ‐0.05 4.6 (3.7, 5.5) 4.5 (3.8, 5.1) ‐0.07 24.0 (20.8, 27.4) 27.3 (24.3, 30.3) 1.17 23.3 (20.6, 26.0) 25.2 (22.9, 27.5) 0.68 5.1 (4.0, 6.2) 5.7 (4.9, 6.60) 0.20 5.3 (4.3, 6.4) 5.6 (4.8, 6.5) 0.11Guyana 0.30 0.28 ‐0.01 2966 3896 332 0.47 0.37 ‐0.04 122.5 (110.8, 134.4) 125.2 (115.5, 134.5) 0.96 127.1 (115.4, 138.8) 129.1 (120.6, 137.7) 0.72 4.7 (3.7, 5.7) 4.4 (3.7, 5.1) ‐0.12 4.5 (3.6, 5.4) 4.1 (3.4, 4.8) ‐0.15 23.7 (20.3, 27.0) 26.5 (23.3, 29.6) 1.01 22.7 (20.0, 25.3) 23.7 (21.2, 26.1) 0.35 5.2 (4.1, 6.3) 5.8 (4.9, 6.70) 0.20 5.4 (4.3, 6.3) 5.6 (4.7, 6.4) 0.08Haiti 0.21 0.49 0.10 1924 1174 ‐268 0.20 0.27 0.03 123.5 (111.4, 135.5) 125.3 (115.7, 135.0) 0.65 128.3 (117.0, 140.2) 130.0 (121.2, 138.9) 0.62 4.6 (3.6, 5.7) 4.4 (3.6, 5.1) ‐0.10 4.4 (3.5, 5.3) 4.1 (3.4, 4.7) ‐0.12 20.8 (18.6, 23.1) 23.3 (22.4, 24.2) 0.89 22.9 (20.3, 25.6) 23.7 (21.2, 26.1) 0.27 4.8 (3.7, 5.9) 5.4 (4.5, 6.30) 0.21 5.2 (4.1, 6.3) 5.4 (4.6, 6.3) 0.08Jamaica 0.47 0.52 0.02 5950 8005 734 0.57 0.63 0.02 127.0 (115.5, 138.5) 125.4 (116.2, 134.6) ‐0.58 131.3 (119.8, 142.5) 130.8 (122.4, 139.3) ‐0.15 5.0 (4.0, 6.0) 4.7 (4.0, 5.4) ‐0.10 4.8 (3.9, 5.7) 4.4 (3.8, 5.1) ‐0.13 24.7 (22.4, 26.9) 27.2 (25.5, 29.0) 0.90 22.3 (20.6, 24.1) 24.0 (22.6, 25.4) 0.60 5.2 (4.2, 6.1) 5.8 (5.3, 6.30) 0.22 5.3 (4.4, 6.2) 5.6 (5.0, 6.1) 0.10Netherlands Antilles 0.81 0.93 0.04 26948 31276 1546 0.86 0.84 ‐0.01 132.5 (120.8, 144.6) 123.3 (113.6, 132.6) ‐3.28 137.2 (125.7, 148.5) 132.2 (123.5, 140.8) ‐1.80 5.5 (4.5, 6.5) 5.3 (4.5, 6.0) ‐0.09 5.4 (4.5, 6.3) 5.1 (4.4, 5.8) ‐0.10 26.5 (24.1, 28.9) 29.6 (27.8, 31.4) 1.09 24.6 (22.8, 26.5) 27.7 (26.1, 29.3) 1.10 5.3 (4.2, 6.4) 5.9 (5.1, 6.80) 0.22 5.4 (4.4, 6.5) 5.9 (5.1, 6.8) 0.17Puerto Rico 0.67 0.98 0.11 12977 23553 3777 0.65 0.64 0.00 127.8 (116.5, 138.9) 121.1 (113.3, 128.7) ‐2.37 132.1 (121.4, 142.4) 130.2 (123.1, 137.3) ‐0.67 5.2 (4.2, 6.1) 4.9 (4.5, 5.4) ‐0.08 5.0 (4.2, 5.9) 4.9 (4.4, 5.3) ‐0.05 26.0 (23.0, 29.1) 30.2 (27.8, 32.6) 1.50 24.8 (22.4, 27.2) 28.4 (26.4, 30.3) 1.28 5.2 (4.1, 6.2) 6.0 (5.5, 6.60) 0.28 5.4 (4.4, 6.5) 6.2 (5.6, 6.8) 0.26Saint Kitts and Nevis 0.36 0.32 ‐0.01 4360 13821 3379 0.64 0.69 0.02 128.2 (117.3, 139.5) 129.3 (122.7, 136.0) 0.42 134.2 (123.6, 144.8) 135.9 (129.7, 142.2) 0.59 5.0 (4.0, 6.0) 4.9 (4.1, 5.7) ‐0.03 4.8 (3.9, 5.7) 4.7 (4.0, 5.4) ‐0.03 26.4 (23.3, 29.4) 30.5 (29.2, 31.9) 1.46 25.3 (22.9, 27.6) 28.2 (27.2, 29.3) 1.06 5.4 (4.3, 6.5) 5.9 (5.0, 6.90) 0.19 5.6 (4.6, 6.6) 5.9 (5.0, 6.8) 0.11Saint Lucia 0.27 0.28 0.00 5231 10702 1954 0.54 0.80 0.09 125.4 (114.0, 137.4) 125.2 (115.9, 134.4) ‐0.07 131.1 (119.5, 142.3) 132.2 (123.7, 140.6) 0.41 4.9 (3.9, 5.9) 4.8 (4.1, 5.6) ‐0.03 4.7 (3.7, 5.6) 4.6 (3.9, 5.3) ‐0.03 24.0 (21.4, 26.6) 27.2 (24.9, 29.7) 1.16 22.8 (20.7, 24.9) 24.7 (22.7, 26.6) 0.65 5.1 (4.0, 6.1) 5.6 (4.8, 6.50) 0.20 5.2 (4.2, 6.3) 5.5 (4.7, 6.4) 0.11Saint Vincent and the Grenadines

0.36 0.48 0.04 2586 8413 2081 0.39 0.68 0.10 124.4 (112.6, 136.3) 125.4 (116.4, 134.4) 0.34 129.2 (117.8, 140.5) 131.3 (123.0, 139.7) 0.75 4.8 (3.8, 5.8) 4.8 (4.0, 5.5) ‐0.01 4.6 (3.7, 5.5) 4.5 (3.8, 5.2) ‐0.02 24.0 (20.6, 27.3) 27.5 (24.6, 30.6) 1.27 23.1 (20.4, 25.7) 25.4 (23.1, 27.7) 0.85 5.2 (4.1, 6.2) 5.7 (4.9, 6.60) 0.19 5.3 (4.3, 6.4) 5.6 (4.8, 6.5) 0.10

Suriname 0.55 0.68 0.05 7281 8365 387 0.53 0.56 0.01 127.1 (115.9, 138.9) 124.6 (115.4, 134.0) ‐0.88 131.1 (120.0, 142.3) 130.4 (121.9, 138.5) ‐0.26 5.0 (4.0, 6.0) 4.7 (4.0, 5.5) ‐0.10 4.9 (4.0, 5.7) 4.5 (3.8, 5.2) ‐0.12 24.7 (21.4, 28.1) 27.7 (24.8, 30.6) 1.10 23.3 (20.7, 25.9) 25.5 (23.1, 27.9) 0.79 5.3 (4.2, 6.3) 5.8 (4.9, 6.70) 0.21 5.4 (4.4, 6.4) 5.7 (4.8, 6.5) 0.10Trinidad and Tobago 0.11 0.13 0.01 14040 26886 4588 0.64 0.65 0.01 126.1 (115.1, 137.2) 125.0 (116.8, 133.6) ‐0.38 131.6 (120.6, 142.2) 131.0 (123.2, 138.7) ‐0.22 5.0 (4.0, 6.1) 4.8 (4.0, 5.6) ‐0.08 4.9 (4.0, 5.8) 4.6 (3.8, 5.4) ‐0.10 26.1 (23.8, 28.5) 28.3 (25.8, 30.7) 0.77 24.8 (23.0, 26.7) 26.4 (24.5, 28.3) 0.57 5.3 (4.2, 6.4) 5.8 (4.8, 6.80) 0.17 5.5 (4.4, 6.5) 5.7 (4.8, 6.7) 0.09

Bolivia 0.45 0.66 0.07 3391 3901 182 0.60 0.51 ‐0.03 125.1 (113.4, 137.0) 122.4 (112.7, 132.3) ‐0.95 130.2 (119.1, 141.7) 128.2 (118.9, 137.1) ‐0.73 4.9 (3.9, 6.0) 4.7 (4.0, 5.4) ‐0.08 4.8 (3.9, 5.8) 4.6 (3.9, 5.3) ‐0.10 23.6 (21.2, 25.9) 26.9 (26.0, 27.7) 1.18 23.0 (20.1, 25.9) 24.4 (22.9, 26.0) 0.51 5.2 (4.1, 6.3) 5.5 (4.5, 6.30) 0.11 5.2 (4.1, 6.3) 5.3 (4.4, 6.2) 0.05Ecuador 0.47 0.66 0.07 5620 7007 495 0.53 0.63 0.04 127.1 (115.1, 139.2) 123.0 (113.3, 132.5) ‐1.47 131.9 (120.7, 143.2) 129.4 (120.3, 138.2) ‐0.89 5.0 (4.0, 6.0) 4.8 (4.1, 5.5) ‐0.06 4.9 (4.0, 5.8) 4.7 (4.0, 5.3) ‐0.08 24.7 (21.5, 27.8) 27.1 (25.7, 28.4) 0.84 23.8 (20.9, 26.7) 25.6 (23.9, 27.2) 0.64 5.2 (4.1, 6.3) 5.4 (4.5, 6.30) 0.07 5.2 (4.2, 6.3) 5.4 (4.5, 6.3) 0.05Peru 0.65 0.76 0.04 5775 7719 694 0.37 0.34 ‐0.01 126.5 (115.7, 137.4) 119.6 (113.5, 125.6) ‐2.44 129.7 (120.0, 139.7) 125.6 (119.7, 131.4) ‐1.44 5.0 (4.0, 5.9) 4.8 (4.4, 5.1) ‐0.07 4.9 (4.0, 5.8) 4.7 (4.3, 5.0) ‐0.09 25.1 (22.8, 27.1) 26.0 (25.3, 26.6) 0.33 23.0 (20.3, 25.8) 24.8 (24.1, 25.4) 0.62 5.2 (4.1, 6.4) 4.9 (4.5, 5.30) ‐0.10 5.2 (4.1, 6.3) 4.9 (4.5, 5.3) ‐0.10

Colombia 0.62 0.74 0.04 4853 8037 1137 0.55 0.64 0.03 127.6 (118.5, 137.1) 124.0 (119.1, 128.8) ‐1.28 132.3 (123.1, 141.7) 130.3 (125.3, 135.4) ‐0.71 4.9 (3.9, 5.8) 4.9 (4.5, 5.3) ‐0.01 4.9 (4.0, 5.7) 4.8 (4.4, 5.1) ‐0.04 23.4 (21.3, 25.4) 26.2 (25.5, 26.9) 1.00 22.1 (20.3, 23.8) 24.9 (24.3, 25.6) 1.03 5.1 (4.2, 6.1) 4.9 (4.5, 5.40) ‐0.06 5.3 (4.4, 6.3) 5.1 (4.6, 5.5) ‐0.09Costa Rica 0.43 0.63 0.07 7037 10571 1262 0.64 0.70 0.02 125.3 (114.4, 136.6) 121.3 (113.6, 129.1) ‐1.41 131.1 (120.7, 141.7) 129.1 (121.9, 136.4) ‐0.72 5.0 (4.2, 5.9) 4.9 (4.2, 5.6) ‐0.03 4.8 (4.1, 5.5) 4.7 (4.0, 5.3) ‐0.05 23.4 (20.5, 26.3) 27.0 (25.8, 28.3) 1.31 23.7 (21.4, 25.9) 26.5 (25.5, 27.5) 0.99 4.9 (4.1, 5.7) 5.5 (5.0, 6.00) 0.21 5.1 (4.3, 5.9) 5.5 (5.0, 6.0) 0.16El Salvador 0.44 0.63 0.07 4418 6643 795 0.36 0.52 0.06 122.9 (112.1, 134.3) 119.9 (112.5, 127.6) ‐1.07 127.8 (117.4, 138.3) 126.5 (119.0, 133.7) ‐0.47 4.9 (3.9, 5.9) 4.7 (4.0, 5.4) ‐0.05 4.7 (3.9, 5.6) 4.5 (3.8, 5.1) ‐0.10 23.7 (20.9, 26.4) 27.8 (26.9, 28.8) 1.49 23.8 (21.4, 26.1) 26.4 (24.9, 28.0) 0.93 5.2 (4.2, 6.2) 5.5 (4.9, 6.10) 0.13 5.4 (4.4, 6.4) 5.7 (5.0, 6.3) 0.09Guatemala 0.37 0.49 0.04 4475 4995 186 0.30 0.33 0.01 123.0 (113.0, 134.2) 121.0 (114.3, 127.7) ‐0.73 127.8 (117.7, 138.3) 127.0 (120.3, 133.7) ‐0.32 4.8 (3.9, 5.8) 4.5 (3.9, 5.1) ‐0.11 4.7 (3.9, 5.5) 4.2 (3.7, 4.7) ‐0.17 22.9 (20.5, 25.1) 26.8 (25.7, 28.0) 1.41 23.2 (20.8, 25.5) 25.3 (24.1, 26.5) 0.75 5.2 (4.0, 6.2) 5.9 (5.3, 6.50) 0.27 5.4 (4.3, 6.4) 5.7 (5.2, 6.3) 0.13Honduras 0.35 0.50 0.06 3088 3813 259 0.32 0.51 0.07 123.2 (112.1, 134.3) 123.0 (115.4, 130.7) ‐0.07 128.5 (118.1, 139.1) 128.4 (121.0, 135.8) ‐0.02 4.8 (3.8, 5.8) 4.6 (3.9, 5.3) ‐0.06 4.6 (3.8, 5.4) 4.3 (3.6, 4.9) ‐0.10 23.0 (20.0, 26.0) 26.7 (25.8, 27.6) 1.32 23.1 (20.7, 25.6) 25.1 (23.6, 26.7) 0.71 5.1 (4.1, 6.2) 5.3 (4.7, 5.90) 0.07 5.3 (4.3, 6.3) 5.3 (4.7, 6.0) 0.02Mexico 0.66 0.77 0.04 9963 13193 1153 0.57 0.73 0.06 128.2 (118.8, 138.1) 122.1 (117.1, 127.2) ‐2.15 129.7 (120.5, 139.5) 126.7 (121.6, 132.0) ‐1.08 5.0 (4.1, 6.0) 5.2 (4.7, 5.6) 0.05 4.9 (4.1, 5.7) 4.9 (4.6, 5.3) 0.01 24.6 (22.6, 26.4) 28.7 (27.9, 29.6) 1.49 24.5 (22.5, 26.3) 27.4 (26.8, 28.1) 1.06 5.3 (4.3, 6.2) 5.9 (5.4, 6.40) 0.24 5.3 (4.4, 6.2) 5.9 (5.3, 6.3) 0.19Nicaragua 0.50 0.57 0.02 2876 2482 ‐140 0.36 0.35 0.00 126.4 (115.7, 137.2) 123.5 (115.7, 131.1) ‐1.06 131.6 (121.3, 142.5) 129.0 (121.7, 136.4) ‐0.95 5.0 (4.0, 5.9) 4.6 (3.9, 5.2) ‐0.14 4.8 (4.0, 5.7) 4.3 (3.6, 5.0) ‐0.18 24.4 (21.9, 26.7) 27.6 (26.7, 28.4) 1.14 24.0 (21.6, 26.3) 25.8 (24.2, 27.3) 0.62 5.1 (4.1, 6.2) 5.3 (4.7, 5.90) 0.08 5.2 (4.2, 6.2) 5.3 (4.7, 5.9) 0.04Panama 0.50 0.73 0.08 5845 10835 1782 0.60 0.65 0.02 126.8 (115.6, 138.7) 122.7 (113.3, 131.9) ‐1.48 131.9 (120.9, 143.2) 130.0 (121.7, 138.2) ‐0.68 5.0 (4.1, 6.0) 4.9 (4.2, 5.6) ‐0.03 4.9 (4.1, 5.7) 4.7 (4.1, 5.4) ‐0.05 23.9 (21.0, 26.8) 27.7 (26.2, 29.2) 1.34 23.5 (21.3, 25.7) 26.3 (25.1, 27.4) 0.98 5.2 (4.1, 6.2) 5.6 (4.8, 6.50) 0.15 5.3 (4.3, 6.3) 5.6 (4.8, 6.4) 0.10Venezuela (Bolivarian Republic of)

0.79 0.93 0.05 10937 11081 51 0.66 0.65 ‐0.01 132.2 (121.3, 143.0) 123.7 (116.3, 131.3) ‐3.05 136.5 (125.9, 147.2) 132.1 (124.8, 139.7) ‐1.55 5.3 (4.3, 6.2) 4.8 (4.2, 5.5) ‐0.15 5.1 (4.2, 5.9) 4.5 (3.9, 5.1) ‐0.19 24.7 (21.9, 27.4) 28.1 (26.5, 29.8) 1.24 24.6 (22.4, 26.7) 27.4 (26.1, 28.8) 1.02 5.1 (4.1, 6.1) 5.5 (4.8, 6.20) 0.14 5.2 (4.3, 6.2) 5.6 (4.9, 6.3) 0.12

Argentina 0.83 0.92 0.03 9201 12780 1278 0.92 0.85 ‐0.02 124.5 (114.3, 134.4) 118.9 (110.8, 127.7) ‐2.01 131.5 (121.2, 142.0) 128.5 (120.4, 136.7) ‐1.08 5.4 (4.4, 6.4) 5.1 (4.3, 5.8) ‐0.12 5.4 (4.5, 6.3) 5.0 (4.4, 5.7) ‐0.12 23.8 (22.0, 25.6) 27.5 (25.9, 29.0) 1.29 25.4 (24.0, 26.8) 27.5 (26.3, 28.7) 0.76 5.1 (4.0, 6.2) 5.5 (4.7, 6.20) 0.15 5.4 (4.3, 6.5) 5.6 (4.9, 6.4) 0.08Chile 0.81 0.88 0.03 5440 13141 2750 0.58 0.73 0.05 130.1 (118.6, 141.2) 125.5 (119.0, 132.3) ‐1.62 133.4 (122.2, 144.0) 133.5 (126.8, 140.4) 0.03 5.1 (4.1, 6.1) 5.0 (4.4, 5.6) ‐0.04 5.0 (4.2, 5.8) 5.0 (4.5, 5.4) ‐0.02 24.0 (20.8, 27.0) 27.9 (26.7, 29.1) 1.41 24.5 (22.6, 26.4) 27.0 (26.0, 28.0) 0.90 5.1 (4.1, 6.2) 5.4 (4.8, 6.00) 0.08 5.4 (4.3, 6.4) 5.6 (5.0, 6.2) 0.10Uruguay 0.85 0.92 0.02 7016 11412 1570 0.82 0.70 ‐0.04 129.0 (117.2, 140.4) 124.3 (118.1, 130.4) ‐1.70 134.8 (124.0, 145.3) 132.3 (126.0, 138.7) ‐0.90 5.3 (4.3, 6.3) 4.8 (4.3, 5.3) ‐0.17 5.3 (4.4, 6.2) 4.8 (4.4, 5.2) ‐0.17 23.3 (19.9, 26.6) 26.6 (25.4, 27.9) 1.19 24.2 (22.0, 26.5) 26.4 (25.5, 27.3) 0.77 5.0 (4.0, 6.1) 5.4 (4.5, 6.30) 0.15 5.2 (4.2, 6.3) 5.6 (4.7, 6.5) 0.13

Brazil 0.65 0.86 0.07 7581 9523 694 0.53 0.70 0.06 133.6 (124.5, 143.5) 124.9 (120.4, 129.7) ‐3.12 136.6 (127.7, 146.5) 133.4 (129.0, 137.9) ‐1.15 4.9 (4.0, 5.9) 4.9 (4.2, 5.5) ‐0.01 4.9 (4.1, 5.6) 4.8 (4.3, 5.4) ‐0.01 24.1 (22.5, 25.6) 26.0 (25.3, 26.7) 0.69 22.6 (21.2, 24.0) 25.8 (25.2, 26.4) 1.12 5.1 (4.2, 6.0) 5.5 (4.9, 6.00) 0.13 5.0 (4.0, 5.9) 5.6 (5.0, 6.2) 0.21Paraguay 0.42 0.60 0.07 3951 4218 95 0.59 0.72 0.05 128.1 (116.5, 140.4) 123.6 (114.2, 133.6) ‐1.59 132.8 (122.0, 144.5) 130.7 (122.1, 139.8) ‐0.75 4.9 (3.9, 5.9) 4.8 (4.0, 5.5) ‐0.04 4.7 (3.9, 5.6) 4.6 (3.9, 5.3) ‐0.04 23.5 (19.7, 27.2) 25.9 (22.5, 29.4) 0.87 23.4 (20.4, 26.4) 25.5 (22.9, 28.2) 0.76 5.1 (4.0, 6.1) 5.5 (4.6, 6.30) 0.14 5.2 (4.2, 6.2) 5.6 (4.7, 6.5) 0.14

Caribbean

Latin America, Andean

Latin America, Central

Latin America, Southern

Latin America, Tropical

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References

1. Rubin DB. Multiple Imputation for Nonresponse in Surveys. New York: John Wiley & Sons, Inc.; 1987.

2. Little RJA. Regression with Missing X's: A Review. Journal of the American Statistical Association. 1992;87:1227-1237.

3. Danaei G, Finucane MM, Lin JK, Singh GM, Paciorek CJ, Cowan MJ, Farzadfar F, Stevens GA, Lim SS, Riley LM, Ezzati M. National, regional, and global trends in systolic blood pressure since 1980: systematic analysis of health examination surveys and epidemiological studies with 786 country-years and 5.4 million participants. Lancet. 2011;377:568-577.

4. Danaei G, Finucane MM, Lu Y, Singh GM, Cowan MJ, Paciorek CJ, Lin JK, Farzadfar F, Khang YH, Stevens GA, Rao M, Ali MK, Riley LM, Robinson CA, Ezzati M. National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2.7 million participants. Lancet. 2011;378:31-40.

5. Farzadfar F, Finucane MM, Danaei G, Pelizzari PM, Cowan MJ, Paciorek CJ, Singh GM, Lin JK, Stevens GA, Riley LM, Ezzati M. National, regional, and global trends in serum total cholesterol since 1980: systematic analysis of health examination surveys and epidemiological studies with 321 country-years and 3.0 million participants. Lancet. 2011;377:578-586.

6. Finucane MM, Stevens GA, Cowan MJ, Danaei G, Lin JK, Paciorek CJ, Singh GM, Gutierrez HR, Lu Y, Bahalim AN, Farzadfar F, Riley LM, Ezzati M. National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9.1 million participants. Lancet. 2011;377:557-567.

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