APPENDIX. Lower-Income Countries that Face Most Rapid Shift in Noncommunicable Disease Burden Are Also the Least Prepared for It

Bollyky T, Templin T, Cohen M, Dieleman J. Lower-income countries that face the most rapid shift in noncommunicable disease burden are also the least prepared. Health Aff (Millwood). 2017;36(11). APPENDIX Lower-Income Countries that Face Most Rapid Shift in Noncommunicable Disease Burden Are Also the Least Prepared for It Version: October 12, 2017

APPENDIX 1.0 Data 1.1 Introduction 1.2 Data Sources 1.3 Descriptive Statistics 2.0 Statistical Analysis 2.1 Principal Components Analysis 2.2 Health Burden Projections 3.0 Tables

1.0 DATA Part 1.1: INTRODUCTION The objective of this research is to assess the speed and scale of the epidemiological transition from communicable to noncommunicable diseases (NCDs) in low- and middle-income countries from 2015 to 2040. We project what burden patterns will look like 25 years in the future, if past trends in changing burden rates continue. We compare these expected increases in NCDs to health expenditure projections from the Institute for Health Metrics and Evaluation in order to assess the resources available to address increasing NCD burden. Additionally, we produce a novel index of health system capacity for NCDs. NCDs are not often prioritized by country governments and development partners, and thus this research identifies deficiencies in health systems preparedness and health spending while there is still time to change course. The purpose of this appendix is to provide technical details on our data sources and methodology. We include information on all data sources in Section 1; statistical analyses, including projections and principal components analysis, in Section 2; and relevant country groupings in Section 3.

Part 1.2: DATA SOURCES We used data from seven sources to assess the demographic and epidemiological transition, and its impact on health systems. Institute for Health Metrics and Evaluation s Global Burden of Disease Study 2015 Death and disability-adjusted life year (DALY) estimates are from the Global Burden of Disease 2015 study. 1, 2 GBD 2015 reports age- and sex-specific DALY estimates for 315 causes in 195 countries from 1990 to 2015 in five-year increments. GBD 2015 also reports mortality estimates for 249 causes in 195 countries from 1990 to 2015 by age and sex. Using these data, we focused on Level One causes to assess the epidemiological transition. IHME defines three Level One causes: (1) Communicable, maternal, neonatal, and nutritional diseases; (2) Noncommunicable diseases; and (3) Injuries. They are exhaustive and mutually exclusive. Figure 1.2.1 displays NCD DALYs by age group for all World Bank income groups. Figure 1.2.1: Millions of NCD DALYs by age group and income (1990, 2015, 2040)

World Bank World Development Indicators Health systems data were extracted from the World Bank World Development Indicators. 3 Three indicators that we used, and descriptions provided by the World Bank, are included in Table 1.2.1. Table 1.2.1: World Development Indicators used in Health Systems Capacity Index WDI indicator Number of hospital beds (per 1,000 people) Number of physicians (per 1,000 people) Description The number of inpatient beds available in public, private, general, and specialized hospitals and rehabilitation centers, including beds for both acute and chronic care in most cases. The total number of both generalist and specialist medical practitioners. Number of surgical procedures (per 100,000 population) The number of procedures undertaken in an operating theatre. A procedure is defined as the incision, excision, or manipulation of tissue that needs regional or general anesthesia, or profound sedation to control pain. Institute for Health Metrics and Evaluation s Financing Global Health 2016 report The Institute for Health Metrics and Evaluation s Financing Global Health 2016 database provides health expenditure estimates for 184 countries from 1990 to 2016. 4 Health spending projections are available from 2016 to 2040. In our analysis, we examine the expected change in total health spending, which is defined by IHME as the sum of five mutually exclusive categories: government health spending, development assistance for health, out-of-pocket health spending, private insurance, and nongovernmental organization spending. Institute for Health Metrics and Evaluation s GBD 2015 Covariates Database The Institute for Health Metrics and Evaluation s GBD 2015 Covariates database provides access to covariates used in the GBD 2015 modeling process for 195 countries from 1990 to 2015. 5 We extract information on skilled birth attendance for all countries. Skilled birth attendance is defined as the proportion of all births in the country overseen by an individual trained to provide care. World Health Organization Noncommunicable Diseases Progress Monitor 2015 The World Health Organization s Noncommunicable Diseases Progress Monitor 2015 report classifies each country s achievement of tobacco control policies as fully, partially, or not achieved. 6 Four indicators are included into our index: tobacco excise taxes; laws to create smoke-free environments in indoor workplaces, public places, and public transport; existence of tobacco health warnings and mass media campaigns; and bans on tobacco advertising, promotion, and sponsorship. Each indicator is evaluated on an ordinal scale, where increasing numbers indicate greater implementation of the policies.

UN World Population Prospects: 2015 Revision Demographic data were collected from the 2015 revision of the UN World Population Prospects (WPP). 7 The WPP uses a cohort-component model to produce population estimates by sex, country, five-year age bins, and year from 1950 until 2100. From 1950 to 2015, the estimates benchmark against observed data. From 2016 to 2100, WPP uses a hierarchical Bayesian model to estimate life tables according to projected fertility transitions. They first model female life expectancy and prioritize country data if it is available, but otherwise draw on regional data. There is a separate step that models the male-female difference in life expectancy. The data are graphed below to display trends across World Bank income groupings. We used the medium-variant projection, which has the following assumptions: fertility will continue to decline in countries with high total fertility rates; total fertility will eventually stabilize at two children per woman; stated government policies and past trends of migration will continue; and mortality rates line up with estimated life expectancies, according to historical data or statistical models. Figure 1.2.2: UN WPP population estimates by income group (1950 2040)

Part 1.3: DESCRIPTIVE STATISTICS Descriptive statistics of all variables included in the principal components analysis are summarized in Table 1.3.1, and correlations are shown in Table 1.3.2. We use the most recent data available for each variable for inclusion in the principal components analysis. The most recent years are reported in Table 1.3.3. Table 1.3.1: Descriptive statistics of variables included in the principal components analysis Variable Observations Mean Standard Minimum Maximum deviation Surgeries 172 4,923.395 5,141.095 53 30,537 SBA 172 0.879827 0.179395 0.187173 0.996892 Total health spending 172 1,376.19 1,672.58 35 9,237 Physicians 172 1.653211 1.56785 0.014 7.739 Hospital beds 172 2.930756 2.392175 0.1 13.7 Tobacco control policy implementation 172 0.7921512 0.4579009 0 1.75 Table 1.3.2: Correlation matrix of variables included in the principal components analysis Surgeries SBA THE Physicians Hospital beds Tobacco control policy implementation Surgeries Correlation 1 p-value N/A observations 172 SBA Correlation 0.4694* 1 p-value 0 N/A observations 172 172 THE Correlation 0.8954* 0.4356* 1 p-value 0 0 N/A observations 172 172 172 Physicians Correlation 0.5595* 0.5641* 0.6304* 1 p-value 0 0 0 N/A observations 172 172 172 172 Hospital beds Correlation 0.3665* 0.4063* 0.3869* 0.5758* 1 p-value 0 0 0 0 N/A observations 172 172 172 172 172 Tobacco control policy implementation Correlation 0.0792 0.2174* 0.1137 0.2348* 0.0907 1

p-value 0.3015 0.0042 0.1374 0.0019 0.2369 N/A observations 172 172 172 172 172 172 Table 1.3.3: Most recent year of data available by source Location name Hospital beds Physicians Surgeries SBA THE Tobacco control policy implementation Afghanistan 2012 2013 2012 2015 2014 2012 Albania 2012 2013 2012 2015 2014 2012 Algeria 2004 2010 2012 2015 2014 2012 Andorra 2009 2010 2012 2015 2014 2012 Angola 2005 2009 2012 2015 2014 2012 Antigua and Barbuda 2011 1999 2012 2015 2014 2012 Argentina 2012 2013 2012 2015 2014 2012 Armenia 2012 2013 2012 2015 2014 2012 Australia 2010 2011 2012 2015 2014 2012 Austria 2011 2011 2012 2015 2014 2012 Azerbaijan 2012 2013 2012 2015 2014 2012 Bahrain 2012 2012 2012 2015 2014 2012 Bangladesh 2011 2011 2012 2015 2014 2012 Barbados 2012 2010 2012 2015 2014 2012 Belarus 2011 2013 2012 2015 2014 2012 Belgium 2012 2013 2012 2015 2014 2012 Belize 2012 2010 2012 2015 2014 2012 Benin 2010 2010 2012 2015 2014 2012 Bhutan 2012 2012 2012 2015 2014 2012

Location name Hospital beds Physicians Surgeries SBA THE Tobacco control policy implementation Bolivia 2012 2011 2012 2015 2014 2012 Bosnia and Herzegovina 2010 2013 2012 2015 2014 2012 Botswana 2010 2010 2012 2015 2014 2012 Brazil 2012 2013 2012 2015 2014 2012 Bulgaria 2011 2012 2012 2015 2014 2012 Burkina Faso 2010 2010 2012 2015 2014 2012 Burundi 2011 2004 2012 2015 2014 2012 Cambodia 2011 2012 2012 2015 2014 2012 Cameroon 2010 2009 2012 2015 2014 2012 Canada 2010 2010 2012 2015 2014 2012 Central African Republic 2011 2009 2012 2015 2014 2012 Chad 2005 2006 2012 2015 2014 2012 Chile 2011 2010 2012 2015 2014 2012 China 2011 2012 2012 2015 2014 2012 Colombia 2012 2010 2012 2015 2014 2012 Comoros 2006 2004 2012 2015 2014 2012 Congo 2005 2010 2012 2015 2014 2012 Costa Rica 2012 2013 2012 2015 2014 2012 Côte d Ivoire 2006 2010 2012 2015 2014 2012 Croatia 2014 2012 2012 2015 2014 2012 Cuba 2012 2010 2012 2015 2014 2012 Cyprus 2011 2012 2012 2015 2014 2012

Location name Hospital beds Physicians Surgeries SBA THE Tobacco control policy implementation Czech Republic 2011 2011 2012 2015 2014 2012 Democratic Republic of the Congo 2006 2004 2012 2015 2014 2012 Denmark 2010 2010 2012 2015 2014 2012 Djibouti 2012 2010 2012 2015 2014 2012 Dominican Republic 2011 2011 2012 2015 2014 2012 Ecuador 2011 2011 2012 2015 2014 2012 Egypt 2012 2010 2012 2015 2014 2012 El Salvador 2012 2010 2012 2015 2014 2012 Equatorial Guinea 2010 2004 2012 2015 2014 2012 Eritrea 2011 2004 2012 2015 2014 2012 Estonia 2011 2012 2012 2015 2014 2012 Ethiopia 2011 2010 2012 2015 2014 2012 Federated States of Micronesia 2009 2010 2012 2015 2014 2012 Fiji 2009 2010 2012 2015 2014 2012 Finland 2011 2010 2012 2015 2014 2012 France 2011 2013 2012 2015 2014 2012 Gabon 2010 2004 2012 2015 2014 2012 Georgia 2012 2013 2012 2015 2014 2012 Germany 2011 2012 2012 2015 2014 2012 Ghana 2011 2010 2012 2015 2014 2012 Greece 2009 2010 2012 2015 2014 2012

Location name Hospital beds Physicians Surgeries SBA THE Tobacco control policy implementation Grenada 2012 2006 2012 2015 2014 2012 Guatemala 2011 2009 2012 2015 2014 2012 Guinea 2011 2010 2012 2015 2014 2012 Guinea-Bissau 2009 2010 2012 2015 2014 2012 Guyana 2009 2010 2012 2015 2014 2012 Haiti 2007 1998 2012 2015 2014 2012 Honduras 2012 2005 2012 2015 2014 2012 Hungary 2011 2012 2012 2015 2014 2012 Iceland 2012 2012 2012 2015 2014 2012 India 2011 2012 2012 2015 2014 2012 Indonesia 2012 2012 2012 2015 2014 2012 Iran 2012 2010 2012 2015 2014 2012 Ireland 2011 2013 2012 2015 2014 2012 Israel 2012 2012 2012 2015 2014 2012 Italy 2011 2012 2012 2015 2014 2012 Jamaica 2012 2008 2012 2015 2014 2012 Japan 2009 2010 2012 2015 2014 2012 Jordan 2012 2010 2012 2015 2014 2012 Kazakhstan 2012 2013 2012 2015 2014 2012 Kenya 2010 2013 2012 2015 2014 2012 Kiribati 2011 2010 2012 2015 2014 2012 Kuwait 2012 2012 2012 2015 2014 2012

Location name Hospital beds Physicians Surgeries SBA THE Tobacco control policy implementation Kyrgyzstan 2012 2013 2012 2015 2014 2012 Laos 2012 2012 2012 2015 2014 2012 Latvia 2011 2012 2012 2015 2014 2012 Lebanon 2012 2011 2012 2015 2014 2012 Lesotho 2006 2003 2012 2015 2014 2012 Liberia 2010 2010 2012 2015 2014 2012 Libya 2012 2010 2012 2015 2014 2012 Lithuania 2011 2012 2012 2015 2014 2012 Luxembourg 2010 2013 2012 2015 2014 2012 Macedonia 2011 2010 2012 2015 2014 2012 Madagascar 2010 2010 2012 2015 2014 2012 Malawi 2011 2010 2012 2015 2014 2012 Malaysia 2012 2010 2012 2015 2014 2012 Maldives 2009 2010 2012 2015 2014 2012 Mali 2010 2010 2012 2015 2014 2012 Malta 2012 2013 2012 2015 2014 2012 Marshall Islands 2010 2010 2012 2015 2014 2012 Mauritania 2006 2013 2012 2015 2014 2012 Mauritius 2011 2004 2012 2015 2014 2012 Mexico 2011 2011 2012 2015 2014 2012 Moldova 2012 2013 2012 2015 2014 2012 Mongolia 2012 2011 2012 2015 2014 2012

Location name Hospital beds Physicians Surgeries SBA THE Tobacco control policy implementation Montenegro 2011 2013 2012 2015 2014 2012 Morocco 2012 2010 2012 2015 2014 2012 Mozambique 2011 2012 2012 2015 2014 2012 Myanmar 2006 2012 2012 2015 2014 2012 Namibia 2009 2010 2012 2015 2014 2012 Nepal 2006 2004 2012 2015 2014 2012 Netherlands 2009 2010 2012 2015 2014 2012 New Zealand 2011 2010 2012 2015 2014 2012 Nicaragua 2012 2014 2012 2015 2014 2012 Niger 2005 2010 2012 2015 2014 2012 Nigeria 2004 2010 2012 2015 2014 2012 Norway 2011 2012 2012 2015 2014 2012 Oman 2012 2012 2012 2015 2014 2012 Pakistan 2012 2010 2012 2015 2014 2012 Panama 2011 2013 2012 2015 2014 2012 Paraguay 2011 2012 2012 2015 2014 2012 Peru 2012 2012 2012 2015 2014 2012 Philippines 2011 2004 2012 2015 2014 2012 Poland 2011 2012 2012 2015 2014 2012 Portugal 2011 2012 2012 2015 2014 2012 Qatar 2012 2010 2012 2015 2014 2012 Romania 2011 2012 2012 2015 2014 2012

Location name Hospital beds Physicians Surgeries SBA THE Tobacco control policy implementation Russia 2006 2010 2012 2015 2014 2012 Rwanda 2007 2010 2012 2015 2014 2012 Samoa 2005 2010 2012 2015 2014 2012 Sao Tome and Principe 2011 2004 2012 2015 2014 2012 Saudi Arabia 2012 2012 2012 2015 2014 2012 Senegal 2008 2010 2012 2015 2014 2012 Serbia 2009 2010 2012 2015 2014 2012 Seychelles 2011 2012 2012 2015 2014 2012 Sierra Leone 2006 2010 2012 2015 2014 2012 Singapore 2011 2013 2012 2015 2014 2012 Slovakia 2011 2012 2012 2015 2014 2012 Solomon Islands 2012 2010 2012 2015 2014 2012 South Africa 2005 2013 2012 2015 2014 2012 South Korea 2009 2012 2012 2015 2014 2012 Spain 2011 2013 2012 2015 2014 2012 Sri Lanka 2012 2010 2012 2015 2014 2012 Suriname 2010 2004 2012 2015 2014 2012 Swaziland 2011 2009 2012 2015 2014 2012 Sweden 2011 2011 2012 2015 2014 2012 Switzerland 2011 2012 2012 2015 2014 2012 Tajikistan 2011 2013 2012 2015 2014 2012 Tanzania 2010 2012 2012 2015 2014 2012

Location name Hospital beds Physicians Surgeries SBA THE Tobacco control policy implementation Thailand 2010 2010 2012 2015 2014 2012 The Bahamas 2011 2008 2012 2015 2014 2012 The Gambia 2011 2010 2012 2015 2014 2012 Timor-Leste 2010 2011 2012 2015 2014 2012 Togo 2011 2010 2012 2015 2014 2012 Tonga 2010 2010 2012 2015 2014 2012 Trinidad and Tobago 2012 2010 2012 2015 2014 2012 Tunisia 2012 2010 2012 2015 2014 2012 Turkey 2011 2011 2012 2015 2014 2012 Turkmenistan 2012 2010 2012 2015 2014 2012 Uganda 2010 2010 2012 2015 2014 2012 Ukraine 2012 2013 2012 2015 2014 2012 United Arab Emirates 2012 2010 2012 2015 2014 2012 United Kingdom 2011 2013 2012 2015 2014 2012 United States 2011 2011 2012 2015 2014 2012 Uruguay 2012 2010 2012 2015 2014 2012 Uzbekistan 2010 2013 2012 2015 2014 2012 Vanuatu 2008 2010 2012 2015 2014 2012 Venezuela 2011 2001 2012 2015 2014 2012 Vietnam 2010 2013 2012 2015 2014 2012 Yemen 2012 2010 2012 2015 2014 2012 Zambia 2010 2012 2012 2015 2014 2012

2.0 STATISTICAL ANALYSIS Part 2.1: Principal Components Analysis In order to compare NCD burden to capacity to treat that burden, we construct a health systems capacity index. We tailor our indicators to target the capacity for a health system to treat NCDs. Our index is adapted from the WHO list of recommended core indicators for evaluating health system capacity. 8 These indicators are broken down into six categories: health service delivery, health workforce, health information, essential medicines, health financing, and leadership and governance. Previous studies have also adapted the WHO recommended core health system capacity indicators, but no examined studies were found to use indicators specifically targeted for NCDs. We used all recommended core indicators with externally validated data. In order to estimate an indicator for as many countries as possible, we use the most recent data available for an indicator. Available core indicators included hospital beds per 1,000 population (health service delivery), physicians per 1,000 population (health workforce), total health expenditure by percentage of GDP (health financing), and a variable identifying the degree to which each country has implemented tobacco control policies (leadership and governance). An additional health workforce variable, skilled attendants at birth (%), was also added to provide a clearer picture of the workforce in countries with very low numbers of physicians. Number of surgeries per 100,000 population (health service delivery) was added to emphasize the health system capacity to care for surgically treatable NCDs. For example, due to insufficient availability of primary care in low- and middle-income countries, NCDs like heart disease and cancers are often identified at a late stage when surgery is the only treatment option. Externally validated data were not available for two of the recommended core indicator categories: health information and essential medicines. Some work has been completed by WHO to collect information on NCD-specific indicators for these two categories through a voluntary survey of WHO member states, but external validation of these data has not yet been completed. Table 2.1.1: Indicators used to fulfill WHO Recommended Core Indicator Categories WHO Recommended Core Indicator Categories Health service delivery Health workforce Health information Essential medicines Health financing Leadership and governance Indicator Used in NCD Health Capacity Index Hospital beds (per 1,000); Number of surgeries (per 100,000) Physicians (per 1,000); Skilled attendants at birth (%) none available none available Total health expenditure (% GDP) Tobacco control policy implementation

Our country health system capacity indicator was produced using principal components analysis (PCA). Principal components analysis is frequently used to elucidate the underlying patterns in the data and reduce many variables to orthogonal factor loadings, which achieves dimensionality reduction. We estimate the loadings on the first principal component, which captures maximal variance in the data, to calculate the indicator. We report rankings of the first principal component in order to produce a more policy-relevant and interpretable indicator. Table 2.1.2 displays the eigenvalues of the correlation matrix. Table 2.1.2: Eigenvalues of the correlation matrix Component Eigenvalue Difference Proportion of variance explained Cumulative variance explained Component 1 3.19215 2.17293 0.5320 0.5320 Component 2 1.01922 0.220785 0.1699 0.7019 Component 3 0.798431 0.237759 0.1331 0.8350 Component 4 0.560672 0.225671 0.0934 0.9284 Component 5 0.335001 0.240473 0.0558 0.9842 Component 6 0.0945285 0.0158 1.0000 Table 2.1.3 displays the eigenvectors of the correlation matrix for the first component. Table 2.1.3: Eigenvectors of the correlation matrix for Component 1 Variable First component Proportion of unexplained variance Surgeries 0.4713 0.291 Skilled birth attendance 0.4020 0.484 Total health spending (per capita) 0.4819 0.2588 Physicians 0.4751 0.2793 Hospital beds 0.3693 0.5647 Tobacco control policy implementation 0.1481 0.93 Different variables included in the principal components analysis can produce very different results about the underlying structure of the data. For instance, below is a comparison of two principal components analyses. The column on the left is a health system indicator based on antenatal care, distance to facility, total fertility, and skilled birth attendance. We could postulate that this indicator represents capacity to provide care for maternal, newborn, and child health (MNCH). The column on the right is the indicator described above to assess health system capacity for NCDs. The two rankings are different, as shown in Figure 2.1.1.

Figure 2.1.1: Comparison of two country-level health system rankings The health system capacity indicator in the paper is estimated according to the first component of a principal components analysis of the five variables described above. In order to assess uncertainty associated with the model specification, we systematically leave one variable out to create five counterfactual indicators, each based on four of the original variables. Figure 2.1.2 displays the index and accompanying minimum and maximum ranking each country achieved, plotted against GDP per capita.

Figure 2.1.2: Health system rankings (and uncertainty) versus GDP per capita We also conduct a sensitivity analysis to assess the robustness of the rankings if we also include a variable indicating country adoption of an NCD strategy. Data are not as complete for this indicator, thus reducing our sample, and they are not externally verified. Figure 2.1.3 is a recalculated Figure 5 from the main text.

Figure 2.1.3: Change in NCD burden by health system rankings calculated for sensitivity analysis Table 2.1.4 shows the factor scoring coefficients associated with the health system index, and the index calculated for the sub-sample analysis. We see that the factor loadings are almost identical. Table 2.1.4: Eigenvectors of the correlation matrix for Component 1, sensitivity analysis Variable First component First component (sensitivity analysis) Surgeries 0.4713 0.4715 Skilled birth attendance 0.4020 0.3995 Total health spending (per person) 0.4819 0.4812 Physicians 0.4751 0.4754 Hospital beds 0.3693 0.3658 Tobacco control policy implementation 0.1481 0.1200 NCD strategy indicator 0.1111

Part 2.2: Health Burden Projections In order to project health burden, we make two simplifying assumptions: 1) We assume that past trends in health burden change will continue 25 years into the future. 2) We only examine rates of change from 2005 to 2015 in order to capture recent trends in HIV mortality reduction. We then follow a three-step process in order to produce the projections: 1) For each country-age-sex-cause death rate and DALY rate, we calculate the annualized percentage change from 2005 to 2015. 2) We apply the annualized rate of change calculated from the 2005 2015 data to the death and DALY rates and iterate through years to reach 2040. 3) To estimate total deaths and DALYs, we utilize the WPP population projections and multiply by the projected DALY and mortality rates. As a robustness check, we completed our forecasts using a different model. In this alternate model, we use the entire time period, but forecast all causes of mortality except HIV/AIDS. This is an alternative way to circumvent the problem associated with the sudden rise and subsequent fall of the HIV epidemic. This analysis shows qualitatively the same results. Figures 2.2.1 to 2.2.4 display the four figures from the manuscript with these alternate set of forecasts. Figure 2.2.5 displays a scatter comparing the 2040 values of the percent of DALYs due to NCDs from the original and alternate models. In order to evaluate the drivers of our NCD burden forecast, we also decomposed changes due to two factors: (1) demographic changes and (2) epidemiological changes. In the exhibits displayed below, as a second robustness check, we project NCD burden using only the effects of demographic change or epidemiological change. Figures 2.2.6-2.2.13 display the four figures from the manuscript with each of these alternate set of forecasts. Because the projections include only demographic change or epidemiological change, the anticipated burden diverges from what is expected based on past trends. In actuality, these two effects together determine the rise of the NCD burden. Note that in the counterfactual scenarios that display NCD burden as a percent of total burden, the decomposition was also applied to total DALY rates.

Figure 2.2.1: Counterfactual without HIV/AIDS: millions of NCD DALYs by age group (1990, 2015, 2040)

Figure 2.2.2: Counterfactual without HIV/AIDS: change in percent of DALYs due to NCDs (1990 2040)

Figure 2.2.3: Counterfactual without HIV/AIDS: percent of NCD DALYs versus expected spending

Figure 2.2.4: Counterfactual without HIV/AIDS: change in NCD DALY percent versus health system capacity indicator

Figure 2.2.5: Original projections versus counterfactual HIV/AIDS projections for 2040

Figure 2.2.6: Counterfactual projection for only demographic change scenario, millions of NCD DALYs, by age group (1990, 2015, 2040)

Figure 2.2.7: Counterfactual projection for only demographic change scenario, change in percent of DALYs due to NCDs (1990 2040)

Figure 2.2.8: Counterfactual projection for only demographic change scenario, percent of NCD DALYs versus expected spending

Figure 2.2.9: Counterfactual projection for only demographic change scenario, change in NCD DALY percent versus health system capacity indicator

Figure 2.2.10: Counterfactual projection for only epidemiological change scenario, millions of NCD DALYs, by age group (1990, 2015, 2040)

Figure 2.2.11: Counterfactual projection for only epidemiological change scenario, change in percent of DALYs due to NCDs (1990 2040)

Figure 2.2.12: Counterfactual projection for only epidemiological change scenario, percent of NCD DALYs versus expected spending

Figure 2.2.13: Counterfactual projection for only epidemiological change scenario, change in NCD DALY percent versus health system capacity indicator

3.0 TABLES Table 3.1 Classifications of countries by income groups High-income Upper middle-income Lower middle-income Low-income Andorra Albania Armenia Afghanistan Antigua and Barbuda Algeria Bangladesh Benin Argentina American Samoa Bhutan Burkina Faso Australia Angola Bolivia Burundi Austria Azerbaijan Cameroon Cambodia Bahrain Belarus Cape Verde Central African Republic Barbados Belize Congo Chad Belgium Bosnia and Herzegovina Côte d Ivoire Comoros Bermuda Botswana Djibouti Democratic Republic of the Congo Brunei Brazil Egypt Eritrea Canada Bulgaria El Salvador Ethiopia Chile China Federated States of Guinea Micronesia Croatia Colombia Georgia Guinea-Bissau Cyprus Costa Rica Ghana Haiti Czech Republic Cuba Guatemala Liberia Denmark Dominica Guyana Madagascar Equatorial Guinea Dominican Republic Honduras Malawi Estonia Ecuador India Mali Finland Fiji Indonesia Mozambique France Gabon Kenya Nepal Germany Grenada Kiribati Niger Greece Iran Kyrgyzstan North Korea Greenland Iraq Laos Rwanda Guam Jamaica Lesotho Sierra Leone Hungary Jordan Mauritania Somalia Iceland Kazakhstan Moldova South Sudan Ireland Lebanon Morocco Tanzania Israel Libya Myanmar The Gambia Italy Macedonia Nicaragua Togo Japan Malaysia Nigeria Uganda Kuwait Maldives Pakistan Zimbabwe Latvia Marshall Islands Palestine Lithuania Mauritius Papua New Guinea Luxembourg Mexico Philippines Malta Mongolia Samoa Netherlands Montenegro Sao Tome and Principe

High-income Upper middle-income Lower middle-income Low-income New Zealand Namibia Senegal Northern Mariana Islands Panama Solomon Islands Norway Paraguay Sri Lanka Oman Peru Sudan Poland Romania Swaziland Portugal Saint Lucia Syria Puerto Rico Saint Vincent and the Tajikistan Grenadines Qatar Serbia Timor-Leste Russia South Africa Ukraine Saudi Arabia Suriname Uzbekistan Seychelles Thailand Vanuatu Singapore Tonga Vietnam Slovakia Tunisia Yemen Slovenia Turkey Zambia South Korea Turkmenistan Spain Sweden Switzerland Taiwan The Bahamas Trinidad and Tobago United Arab Emirates United Kingdom United States Uruguay Venezuela Virgin Islands, US

Table 3.2 Classifications of countries by Global Burden of Disease geographical regions Central Europe, Eastern Europe, and Central Asia High-income Latin America and Caribbean North Africa and Middle East South Asia Southeast Asia, East Asia, and Oceania Sub-Saharan Africa Albania Andorra Antigua Afghanistan Bangladesh American Angola Samoa Armenia Argentina Barbados Algeria Bhutan Cambodia Benin Azerbaijan Australia Belize Bahrain India China Botswana Belarus Austria Bermuda Egypt Nepal Micronesia Burkina Faso Bosnia Belgium Bolivia Iran Pakistan Fiji Burundi Bulgaria Brunei Brazil Iraq Guam Cameroon Croatia Canada Colombia Jordan Indonesia Cape Verde Czech Chile Costa Rica Kuwait Kiribati Central African Republic Estonia Cyprus Cuba Lebanon Laos Chad Georgia Denmark Dominica Libya Malaysia Comoros Hungary Finland Dominican Morocco Maldives Congo Republic Kazakhstan France Ecuador Oman Marshall Côte d Ivoire Kyrgyzstan Germany El Salvador Palestine Mauritius Congo DR Latvia Greece Grenada Qatar Myanmar Djibouti Lithuania Greenland Guatemala Saudi Arabia North Korea Equatorial Guinea Macedonia Iceland Guyana Sudan Northern Eritrea Mariana Islands Moldova Ireland Haiti Syria Papua New Ethiopia Guinea Mongolia Israel Honduras Tunisia Philippines Gabon Montenegro Italy Jamaica Turkey Samoa Ghana Poland Japan Mexico United Seychelles Guinea Arab Emirates Romania Luxembourg Nicaragua Yemen Solomon Guinea-Bissau Russia Malta Panama Sri Lanka Kenya Serbia Netherlands Paraguay Taiwan Lesotho Slovakia New Zealand Peru Thailand Liberia Slovenia Norway Puerto Rico Timor-Leste Madagascar Tajikistan Portugal St. Lucia Tonga Malawi Turkmenistan Singapore St. Vincent Vanuatu Mali Ukraine South Korea Suriname Vietnam Mauritania Uzbekistan Spain Bahamas Mozambique Sweden Trinidad and Tobago Namibia Switzerland Venezuela Niger

Central Europe, Eastern Europe, and Central Asia High-income United Kingdom United States Uruguay Latin America and Caribbean Virgin Islands, US North Africa and Middle East South Asia Southeast Asia, East Asia, and Oceania Sub-Saharan Africa Nigeria Rwanda Sao Tome Principe Senegal Sierra Leone Somalia South Africa South Sudan Swaziland Tanzania Gambia Togo Uganda Zambia Zimbabwe Table 3.3 Global Burden of Disease cause list for noncommunicable disease causes of death Neoplasms Lip and oral cavity cancer Nasopharynx cancer Other pharynx cancer Esophageal cancer Stomach cancer Colon and rectum cancer Liver cancer Liver cancer due to hepatitis B Liver cancer due to hepatitis C Liver cancer due to alcohol use Liver cancer due to other causes Gallbladder and biliary tract cancer Pancreatic cancer Larynx cancer

Tracheal, bronchus, and lung cancer Malignant skin melanoma Non-melanoma skin cancer Breast cancer Cervical cancer Uterine cancer Ovarian cancer Prostate cancer Testicular cancer Kidney cancer Bladder cancer Non-melanoma skin cancer (squamous-cell carcinoma) Non-melanoma skin cancer (basal cell carcinoma) Brain and nervous system cancer Thyroid cancer Mesothelioma Hodgkin lymphoma Non-Hodgkin lymphoma Multiple myeloma Leukemia Other neoplasms Cardiovascular diseases Rheumatic heart disease Ischemic heart disease Cerebrovascular disease Acute lymphoid leukemia Chronic lymphoid leukemia Acute myeloid leukemia Chronic myeloid leukemia Ischemic stroke Hemorrhagic stroke Hypertensive heart disease Cardiomyopathy and myocarditis Atrial fibrillation and flutter Aortic aneurysm Peripheral vascular disease Endocarditis

Other cardiovascular and circulatory diseases Chronic respiratory diseases Chronic obstructive pulmonary disease Pneumoconiosis Silicosis Asbestosis Coal workers pneumoconiosis Other pneumoconiosis Asthma Interstitial lung disease and pulmonary sarcoidosis Other chronic respiratory diseases Cirrhosis and other chronic liver diseases Cirrhosis and other chronic liver diseases due to hepatitis B Cirrhosis and other chronic liver diseases due to hepatitis C Cirrhosis and other chronic liver diseases due to alcohol use Cirrhosis and other chronic liver diseases due to other causes Digestive diseases Peptic ulcer disease Gastritis and duodenitis Appendicitis Paralytic ileus and intestinal obstruction Inguinal, femoral, and abdominal hernia Inflammatory bowel disease Vascular intestinal disorders Gallbladder and biliary diseases Pancreatitis Other digestive diseases Neurological disorders Alzheimer disease and other dementias Parkinson disease Epilepsy Multiple sclerosis Motor neuron disease Tension-type headache Medication overuse headache Other neurological disorders Mental and substance use disorders Schizophrenia

Alcohol use disorders Drug use disorders Depressive disorders Bipolar disorder Anxiety disorders Eating disorders Opioid use disorders Cocaine use disorders Amphetamine use disorders Cannabis use disorders Other drug use disorders Major depressive disorder Dysthymia Anorexia nervosa Bulimia nervosa Autistic spectrum disorders Autism Asperger syndrome Attention-deficit/hyperactivity disorder Conduct disorder Idiopathic intellectual disability Other mental and substance use disorders Diabetes, urogenital, blood, and endocrine diseases Diabetes mellitus Acute glomerulonephritis Chronic kidney disease Chronic kidney disease due to diabetes mellitus Chronic kidney disease due to hypertension Chronic kidney disease due to glomerulonephritis Chronic kidney disease due to other causes Urinary diseases and male infertility Gynecological diseases Interstitial nephritis and urinary tract infections Urolithiasis Benign prostatic hyperplasia Male infertility due to other causes Other urinary diseases

Musculoskeletal disorders Uterine fibroids Polycystic ovarian syndrome Female infertility due to other causes Endometriosis Genital prolapse Premenstrual syndrome Other gynecological diseases Hemoglobinopathies and hemolytic anemias Thalassemias Thalassemia trait Sickle cell disorders Sickle cell trait G6PD deficiency G6PD trait Other hemoglobinopathies and hemolytic anemias Endocrine, metabolic, blood, and immune disorders Rheumatoid arthritis Osteoarthritis Low back and neck pain Gout Low back pain Neck pain Other musculoskeletal disorders Other non-communicable diseases Congenital anomalies Neural tube defects Congenital heart anomalies Orofacial clefts Down syndrome Turner syndrome Klinefelter syndrome Skin and subcutaneous diseases Chromosomal unbalanced rearrangements Other chromosomal abnormalities Dermatitis Psoriasis Cellulitis

Pyoderma Scabies Fungal skin diseases Viral skin diseases Acne vulgaris Alopecia areata Pruritus Urticaria Decubitus ulcer Other skin and subcutaneous diseases Sense organ diseases Glaucoma Cataract Macular degeneration Uncorrected refractive error Other hearing loss Other vision loss Other sense organ diseases Oral disorders Deciduous caries Permanent caries Periodontal diseases Edentulism and severe tooth loss Other oral disorders Sudden infant death syndrome

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