Outline of the Global Burden of Diseases, Injuries, and Risk Factors Study

1. The Global Burden of Diseases, Injuries, and Risk Factors Study

2. Presentation Outline Goal and key attributes Project structure and partners Mortality Causes of Death Systematic reviews Analysis of disease-specific data for calculating YLD Disability Weights measurement Future work 2

3. GBD Goal To produce new, robust, and reliable estimates of burden for all major diseases, injuries, and risks that are widely disseminated, understood, and easily used by policymakers, researchers, funders, and practitioners. 3

4. Key Attributes Producing specific DALY, YLL, and YLD estimates for over 300+ diseases/injuries and 40+ risk factors by age and sex for 21 regions for the years 1990, 2005, and 2010. Providing a consistent time trend (methods for current ‘00, ’02, ‘04 estimates are not comparable to ‘90). Providing first comprehensive revision of Disability weights since 1996. Many Burden estimates done after the original study had used ad hoc DW based on Dutch study. Providing improved analytical tools to facilitate Burden estimates and policy use. 4

5. Presentation Outline Goal and key attributes Project structure and partners Mortality Causes of Death Systematic reviews Analysis of disease-specific data for calculating YLD Disability Weights measurement Future work 5

6. Organizational Structure Core Team External Advisory Board YLD Sub-Team Rafael Lozano and Colin Mathers CRA Sub-Team Majid Ezzati COD Sub-Team Rafael Lozano Mortality Sub-Team Chris Murray and Alan Lopez DW Sub-Team Josh Salomon Cluster E Noncommunicable Diseases Catherine Michaud Harvard University Cluster D Communicable Diseases Neff Walker Johns Hopkins University Cluster B Child/Maternal Bob Black Johns Hopkins University Cluster A CVD, COPD, Cancer Majid Ezzati Harvard University Cluster C Injuries and Mental Health Theo Vos University of Queensland

7. Organizational structure Vision, decision-making, and leadership are handled by the core team, a group of 12 key individuals from the collaborating institutions. Specific analytical tasks are grouped into (1) Causes of Death, (2) Comparative Risk Assessment, (3) Disability Weights, and (4) Mortality Estimation. Each of these “subteams” are led by 1 or 2 members of the core team to guide each category’s specific scientific progress and analysis. Management of diseases, injuries and risks are organized into clusters and are led by 1 member of the core team who oversees the cluster’s expert groups. Expert groups are comprised of knowledgeable specialists of a disease, injury, or risk.

8. 8 Collaborating Partners

9. Expert Groups 44 Cluster A CVD, COPD, Cancer Majid Ezzati Harvard University Cluster B Child/Maternal Bob Black Johns Hopkins University Cancers Cardiovascular Diseases Chronic Respiratory Diseases Climate Changes Indoor Air Pollution Metabolic Risks Nutritional Risks Outdoor Air Pollution Physical Inactivity Socioeconomic Factors Tobacco ARI Meningitis Sepsis Child Nutrition Congenital and Neonatal Diarrhea Malaria Maternal Conditions Selected Vaccine Preventable Diseases Water, Sanitation, Hygiene Hepatitis HIV/AIDS Parasitic &Vector Diseases STIs Tuberculosis Unsafe Sex Cluster D Communicable Diseases Neff Walker Johns Hopkins University Dental Diabetes Gastrointestinal Genitourinary Diseases Hearing Loss Hemoglobinopathies Skin Diseases Vision Loss Alcohol Use Collective Violence Illicit Drug Use Intimate Partner and Sexual Violence Lead Exposure Mental Disorders Musculoskeletal Neurological Disorders Occupational Risks Other Injuries Road Traffic Accidents Cluster E Noncommunicable Diseases Catherine Michaud Harvard University Cluster C Injuries and Mental Health Theo Vos University of Queensland

10. 10

11. 11

13. Carcinogens

14. Airborne particulates

15. Ergonomic stressors

16. Noise

17. Pesticides

19. High cholesterol

20. High blood glucose

21. Dietary fats

22. High BMI

23. Low intake of fruit and vegetable

24. Physical inactivity

26. Age Groups for Results <1 month 1 – 11 months 1 – 4 years 5 – 9 years 10 – 14 years 15 – 19 years 20 – 24 years 25 – 34 years 35 – 44 years 45 – 54 years 55 – 64 years 65 – 74 years 75 – 84 years 85+ years 14

27. Presentation Outline Goal and key attributes Project structure and partners Mortality Causes of Death Systematic reviews Analysis of disease-specific data for calculating YLD Disability weights measurement Future work 15

28. Generating regional estimates for age and sex All cause mortality based on demographic sources recording the event of death. The sum of all cause specific deaths for any age-sex group must equal, and not exceed, the overall mortality envelope for that age-sex group. 16 Mortality

29. Mortality Estimation 17

30. Mortality estimation: Synthesis Gaussian Process Regression: Synthesizes discordant time series of mortality estimates into a best estimate of smooth trend Assigns probabilities to different functions according to how likely they are to be the true function Uses prior beliefs, the data and the uncertainty in the data to inform those probabilities 18

31. Child Mortality in Nicaragua: Example of Using VR and Survey and Census Data 19

32. 20 Child Death Numbers: East Sub-Saharan Africa

33. 21 Child Death Numbers: Global

34. GPR for Adult mortality: Nicaragua 22

35. GPR for Adults: Zimbabwe 23

36. Using 5q0 and 45q15 to a Complete Lifetable WHO uses modified logitlifetable system (Murray et al 2003) to generate complete lifetables. HIV mortality is modeled separately from demographic sources and added on after demographic estimation. We wanted to improve the performance of model life table systems and avoid modeling strategies that are not empirically based. Over 18 months, developed a semi-parametric approach to the modified logitlifetable system that improves performance and captures the empirical impact of HIV. 24

37. Key Attributes of Mortmatch Based on 5q0, 45q15 and HIV sero-prevalence searches database of (nearly 8000 lifetables) for nearest matches using Mahalanobis distance. Matched lifetables used to establish standard life table. Modified logit transformation including bend factors used to estimate full survivorship curve based on matched standard. 25

38. Validation Results 26

39. Presentation Outline Goal and key attributes Project structure and partners Mortality Causes of Death Systematic reviews Analysis of disease-specific data for calculating YLD Disability Weights measurement Future work 27

41. PAHO, EMRO, WPRO mortality databases

42. National Ministries of Health

43. Networks: INDEPTH, Matlab, India, etc.

44. Researchers

47. Almost 1 billion deaths from 1950 to 2008, only 34% of total expected 31 % Developed Countries 55 Eastern & Central Europe 18 Latin-America and Carb 16 Asia & Oceania 7.5 Middle Eats & North Afr 1.9 S.S.A. 0.7

48. VA Literature Search Process 32

49. VA Literature Screening Criteria 33 Four criteria: Population based study Using verbal autopsy method Open to any age group Open to any set of causes

50. 34

51. 1.3 million deaths from VA studies by GBD region and source 35

52. 1.3 million deaths from VA studies 36

53. 37 Cleaning: Preparing VR Data for Analysis

55. ICD 9 BTL, ICD 10 Tab A

56. China (ICD 9 and 10)

57. Russia (ICD 9 and 10)24 CodMod II 39 CodModB 317 (290)* GBD 2005 Cause List (ICD 10 4 digit) *Causes of death

58. 39 CODMOD level B(39) CODMOD level 2 (24) Mapping GBD Cause List with ICD Revisions and Other Tabulated List GBD 2005 Cause List (317 ) GBD 1990 Cause List (100) BTL 10 Tab 1 2 3 4 5 Tab B 6,7 Tab A 8 9 VA 10 9 tab ICD and other formats 2000 1900

59. CODMOD II Tuberculosis A1 HIV/AIDS A2 STDs excluding HIV A3 Intestinal infectious diseases A4 CODMOD I Selected Vaccine Preventable Childhood Diseases A5 3 Malaria A6 Parasitic and vector diseases A7 24 Meningitis and encephalitis and Hepatitis and Other infectious diseases A8 CODMOD II Respiratory infections A9 Maternal conditions A10 Perinatal and infant causes A11 Nutritional deficiencies A12 Small pox A13 Malignant neoplasm and B. Other neoplasm B14 Diabetes mellitus B15 This level can be presented with 24 causes and subgroups and 3 big groups Endocrine, nutritional, blood and immune disorders B16 Mental and behavioral disorders--- Neurological conditions-- Sense organ diseases B17 Cardiovascular and circulatory diseases B18 Respiratory diseases B19 Digestive diseases---Oral conditions B20 Genitourinary diseases---Skin diseases----Musculoskeletal diseases B21 Congenital anomalies B22 Unintentional injuries C23 Intentional injuries C24

60. 8.1 Meningitis and encephalitis 8.3 Other infectious diseases 14.1 Esophagus cancer 14.2 Stomach cancer 14.4 Larynx , Trachea, bronchus and lung cancers 14.5 Breast cancer 14.6 Cervix and Corpus uteri cancer 14.9 Other malignant and benign neoplasm 18.2 Ischaemic heart disease 18.4 Cerebrovascular disease 18.5 Other circulatory diseases 20.1 Cirrhosis of the liver 20.2 Other digestive diseases 23.1Transport Injures 23.3 Falls 23.5 Accidental drowning and submersion 23.6 Exposure to smoke, fire and flaes, contact with heat and hot substances 23.7 Accidental poisoning by and exposure to noxious substances (acute or chronic) 23.8 Accidental exposure to other and unspecified factors 24.1 Self-inflicted injuries 24.2 Interpersonal violence 24.3War and civil conflict and Legally sanctioned deaths CODMOD B 3 CODMOD I 24 CODMOD II 39 CODMOD B From the 24 causes we are dividing Malignant Neoplasm, CVD and Injuries

61. 42 Evolution of Garbage Codes in GBD Studies 1990: ill defined; heart failure and atherosclerosis; cancer without defined site and injuries ill defined 2000: same codes of 1990 with better methods of redistribution 2005: Completely different approach, based on new concepts and methods More garbage codes and more targets Sequences for redistribution Methods of redistribution

63. 10 causes accumulate 75%

64. Intermediate causes are the most important Garbage Codes 35.0 30.0 Specials Immediate Sequelae 25.0 Intermediate I&D UNS Cancer 20.0 % of GC Ill Def 15.0 10.0 5.0 0.0 SSA Asia LA Europe C&E ALL Europe W Caribbean N.America Australasia

65. Percent of deathswithgarbagecodesSelectCountries of theAmericas, circa 2005

66. Causes of Death Modeling Strategy Challenges Dependent variable: age-specific rates or age-specific cause-specific mortality fractions. Model each cause as a function of critical covariates available for most countries/sites: GDP, education, tobacco consumption, HIV sero-prevalence, TFR, DTP coverage, SBA, water and sanitation, war, disasters …. Covariates only explain 30-40% of the variance depending on cause. Sparse data for some developing regions Compositional bias, data in each time period reflects a changing set of countries/sites Small numbers – VA studies and small countries have huge sampling and non-sampling variation 45

67. Causes of Death Modeling Strategy – 3 Steps Step 1 – run outlier resistant models using basic covariates including negative binomial regression, quantile (L1) regression. Evaluate residuals – drop outliers using Box-plot methods, assess correlations over space and time in residuals using heatmaps. Use local regression methods (two-dimensional Loess) to model residuals. Space dimension relatedness is based on the observed correlation structure in the heatmaps. 46

68. 47

69. 48 Using VR data for 2005

70. 49

71. 50

72. Validation of Models Many variants possible at each stage. How to choose most valid predictive models and how to pool results across a range of models. Three tests of predictive validity: Exclude 20% of country-years at random and predict for them out of sample Exclude last 10 years of sequence for all countries and predict them out of sample Exclude 20% of countries and predict them entirely out of sample. 51

73. Presentation Outline Goal and key attributes Project structure and partners Mortality Causes of Death Systematic reviews Analysis of disease-specific data for calculating YLD Disability weights measurement Future work 52

74. Systematic Reviews Objective: To evaluate and interpret allavailable research evidence relevant to a particular condition To date we have: Recruited over 800 experts worldwide Worked with experts and Core team to revise the cause list Begun processing epidemiological reviews from experts Next steps: Upcoming expert group meeting May 2010 Complete systematic epidemiological reviews Peer review 53

75. CardiomyopathyEpi Review Process 54 Inclusion criteria : diagnostic methods ICD coding epidemiological factors, population-based demographics

76. CardiomyopathyEpi Review Data 55

77. Presentation Outline Goal and key attributes Project structure and partners Mortality Causes of Death Systematic reviews Analysis of disease-specific data for calculating YLD Disability weights measurement Future work 56

78. Analysis of Disease-Specific Data for YLD YLD = Disability Weight x Incidence x Duration The GBD links losses of health to disease and injury causes through the concepts of cases and sequelae. For incident cases of a given disease or injury in the population, there will be a distribution of current and future health states in the population, and the GBD maps this distribution of health states to a small set of discrete entities for which epidemiological estimates and YLD calculations are made. Case definitions are based upon expert group guidance 57

79. 58 Cardiomyopathy Disease Model

80. DisMod III uses a compartmental model of disease progression to infer consistent epidemiological parameters from sparse and noisy data. Generic Model of Disease 59 DisMod III States S: healthy (susceptible) C: diseased (condition of interest) D: dead from the disease M: dead from all other causes Transition rates i: incidence r: remission ƒ: case fatality m: all other mortality

81. 60 DisMod III Analysis: Cardiomyopathy Cardiomyopathy for males in Asia Pacific High Income region in 2005

82. DisMod III Analysis: Cardiomyopathy 61 Cardiomyopathy for males in Asia Pacific High Income region in 2005

83. Cardiomyopathy Prevalence 62

84. Presentation Outline Goal and key attributes Project structure and partners Mortality Causes of Death Systematic reviews Analysis of disease-specific data for calculating YLD Disability Weights measurement Future work 63

85. Objectives: Derive disability weights for ~250 sequelae, which capture the major health consequences of all of the causes in the GBD Study Address criticisms of previous approaches: Focus on valuations from community respondents in a “Disability Weights Measurement Survey” Use of techniques that are well-matched to the intended measurement construct Provide transparent, standardized and replicable approach that will easily accommodate additions or amendments 64 Disability Weights Measurement

86. Disability Weights Measurement Disability weights provide the bridge between mortality and non-fatal outcomes in disability adjusted life years (DALYs) Disability weights quantify overall health levels associated with different states, on a continuum between perfect health (which has a value of 0) and death (which has a value of 1) Construct reflects decrements from perfect health, distinct from broader notions of well-being or social value Must be measured on meaningful cardinal scale 65

87. Disability Weights Measurement Survey components Community surveys in 6 sites (Tanzania, Indonesia, Bangladesh, Peru, South Africa, United States), focusing on random paired comparison and time trade-off questions for 108 sequelae Open access Web-based surveys including all sequelae, and paired comparison, time trade-off and population equivalence questions Community surveys are using computer-assisted personal interview approach with laptops 66 Household interview in Pemba, TZ 10/23/2009

88. Presentation Outline Goal and key attributes Project structure and partners Mortality Causes of Death Systematic reviews Analysis of disease-specific data for calculating YLD Disability weights measurement Future work 67