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

Transcription

1 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

2 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

3 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 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.

4 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 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)

5 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 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 Health spending projections are available from 2016 to 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 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.

6 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 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 ( )

7 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 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 Table 1.3.1: Descriptive statistics of variables included in the principal components analysis Variable Observations Mean Standard Minimum Maximum deviation Surgeries 172 4, , ,537 SBA Total health spending 172 1, , ,237 Physicians Hospital beds Tobacco control policy implementation 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 * 1 p-value 0 N/A observations THE Correlation * * 1 p-value 0 0 N/A observations Physicians Correlation * * * 1 p-value N/A observations Hospital beds Correlation * * * * 1 p-value N/A observations Tobacco control policy implementation Correlation * *

8 p-value N/A observations 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 Albania Algeria Andorra Angola Antigua and Barbuda Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bhutan

9 Location name Hospital beds Physicians Surgeries SBA THE Tobacco control policy implementation Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Colombia Comoros Congo Costa Rica Côte d Ivoire Croatia Cuba Cyprus

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

11 Location name Hospital beds Physicians Surgeries SBA THE Tobacco control policy implementation Grenada Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hungary Iceland India Indonesia Iran Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati Kuwait

12 Location name Hospital beds Physicians Surgeries SBA THE Tobacco control policy implementation Kyrgyzstan Laos Latvia Lebanon Lesotho Liberia Libya Lithuania Luxembourg Macedonia Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Mauritania Mauritius Mexico Moldova Mongolia

13 Location name Hospital beds Physicians Surgeries SBA THE Tobacco control policy implementation Montenegro Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Paraguay Peru Philippines Poland Portugal Qatar Romania

14 Location name Hospital beds Physicians Surgeries SBA THE Tobacco control policy implementation Russia Rwanda Samoa Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Solomon Islands South Africa South Korea Spain Sri Lanka Suriname Swaziland Sweden Switzerland Tajikistan Tanzania

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

16 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

17 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 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 Component Component Component Component Component Table 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 Skilled birth attendance Total health spending (per capita) Physicians Hospital beds Tobacco control policy implementation 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

18 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 displays the index and accompanying minimum and maximum ranking each country achieved, plotted against GDP per capita.

19 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 is a recalculated Figure 5 from the main text.

20 Figure 2.1.3: Change in NCD burden by health system rankings calculated for sensitivity analysis Table 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 Skilled birth attendance Total health spending (per person) Physicians Hospital beds Tobacco control policy implementation NCD strategy indicator

21 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 ) We apply the annualized rate of change calculated from the data to the death and DALY rates and iterate through years to reach ) 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 to display the four figures from the manuscript with these alternate set of forecasts. Figure 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 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.

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

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

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

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

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

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

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

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

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

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

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

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

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

35 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

36 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

37 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

38 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

39 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

40 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

41 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

42 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

43 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

44 Appendix Works Cited 1 GBD 2015 Mortality and Cause of Death Collaborators. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, : a systematic analysis for the Global Burden of Disease Study Lancet. 2016;388(10053): GBD 2015 DALYs and HALE Collaborators. Global, regional, and national disability-adjusted life-years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE), : a systematic analysis for the Global Burden of Disease Study Lancet. 2016;388(10053): The World Bank. World Development Indicators 2017 [Internet]. Washington, DC: World Bank; 2017 [cited 2017 May 11]. Available from: 4 Global Burden of Disease Health Financing Collaborator Network. Future and potential spending on health : development assistance for health, and government, prepaid private, and out-of-pocket health spending in 184 countries. Lancet. 2017;389(10083): Global Burden of Disease Study Global Burden of Disease Study 2015 (GBD 2015) Covariates Seattle, United States: Institute for Health Metrics and Evaluation (IHME), World Health Organization. Noncommunicable Diseases Progress Monitor 2015 [Internet]. Geneva: WHO; 2015 [cited 2017 Aug 23]. Available from: 7 United Nations. World population prospects: the 2015 revision, methodology of the United Nations population estimates and projections [Internet]. New York (NY): United Nations; 2015 [cited 2017 Sep 25]. (Working Paper No. ESA/P/WP.242). Available from: 8 World Health Organization. Monitoring the building blocks of health systems: a handbook of indicators and their measurement strategies [Internet]. Geneva: WHO; c 2010 [cited 2017 Sep 25]. Available from: