1.
The Global Burden of Diseases, Injuries, and Risk Factors Study<br />

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

3.
GBD Goal<br />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.<br />3<br />

4.
Key Attributes<br />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.<br />Providing a consistent time trend (methods for current ‘00, ’02, ‘04 estimates are not comparable to ‘90).<br />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.<br />Providing improved analytical tools to facilitate Burden estimates and policy use.<br />4<br />

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

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

7.
Organizational structure<br />Vision, decision-making, and leadership are handled by the core team, a group of 12 key individuals from the collaborating institutions.<br />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.<br />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. <br />Expert groups are comprised of knowledgeable specialists of a disease, injury, or risk. <br />

8.
8<br />Collaborating Partners<br />

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

10.
10<br />

11.
11<br />

12.
Addictive substances <br />Tobacco use<br />Alcohol use<br />Illicit drug use <br />Environmental <br />Unsafe water, sanitation, and hygiene <br />Urban ambient air pollution <br />Household air pollution from solid fuel use <br />Lead exposure <br />Passive smoking / Environmental tobacco smoke<br />Food contamination<br />Road and vehicle safety <br />Violence related<br />Sexual violence<br />Intimate partner violence<br />Collective violence<br />Possession of firearms<br />Undernutrition (child and maternal)<br />Folic acid deficiency<br />Anaemia and/or iron deficiency<br />Small-for-gestational age<br />Growth retardation<br />Suboptimal breasfeeding<br />Vitamin A deficiency<br />Zinc deficiency<br />Reproductive and sexual <br />Unsafe sex <br />Unwanted pregnancies<br />Risks related to medical practice<br />Genetic <br />Systemic<br />Global climate change<br />Socioeconomic factors<br />Other selected risks to health <br />Osteoporosis<br />12<br />Risk Factors<br />Occupational<br /><ul><li>Risks for injuries

13.
Carcinogens

14.
Airborne particulates

15.
Ergonomic stressors

16.
Noise

17.
Pesticides

18.
Other</li></ul>Metabolic, nutritional and lifestyle<br /><ul><li>High blood pressure

19.
High cholesterol

20.
High blood glucose

21.
Dietary fats

22.
High BMI

23.
Low intake of fruit and vegetable

24.
Physical inactivity

25.
Other nutritional </li></li></ul><li>21 GBD Regions<br />13<br />

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

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

28.
Generating regional estimates for age and sex<br />All cause mortality based on demographic sources recording the event of death.<br />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. <br />16<br />Mortality<br />

29.
Mortality Estimation<br />17<br />

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

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

32.
20<br />Child Death Numbers: East Sub-Saharan Africa<br />

33.
21<br />Child Death Numbers: Global<br />

34.
GPR for Adult mortality: Nicaragua<br />22<br />

35.
GPR for Adults: Zimbabwe<br />23<br />

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

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

38.
Validation Results<br />26<br />

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

40.
Sources: Gathering COD Data<br />Almost all information related with causes of death is useful.<br />Types of sources<br />Verbal Autopsies<br />Household Surveys<br />Hospital Records<br />Sentinel Registration<br />Demographic Surveillance Systems<br />Sample Registration Systems<br />Vital Registration with Certification of Cause of Death<br />Data providers<br /><ul><li>WHO mortality database (Geneva)

41.
PAHO, EMRO, WPRO mortality databases

42.
National Ministries of Health

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

44.
Researchers

45.
Literature Review</li></li></ul><li>> 5,000 country-years observed<br />29<br /><ul><li>VR data don’t get more than 100 countries per year

46.
VA and maternal deaths added important value</li></li></ul><li>Data for more of 180 countries and territories <br />

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

48.
VA Literature Search Process<br />32<br />

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

50.
34<br />

51.
1.3 million deaths from VA studies by GBD region and source<br />35<br />

52.
1.3 million deaths from VA studies <br />36<br />

53.
37<br />Cleaning: Preparing VR Data for Analysis<br />

54.
38<br />Name of List and Number of Causes<br />3<br />CodMod I<br /><ul><li> ICD 1,2,3,4,5,6,7,8,9,10

55.
ICD 9 BTL, ICD 10 Tab A

56.
China (ICD 9 and 10)

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

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

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

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

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

62.
Distribution of Garbage Codes by Type and Region<br /><ul><li> ~20% total deaths from VR are GCs

63.
10 causes accumulate 75%

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

65.
Percent of deathswithgarbagecodesSelectCountries of theAmericas, circa 2005<br />

66.
Causes of Death Modeling Strategy Challenges<br />Dependent variable: age-specific rates or age-specific cause-specific mortality fractions.<br />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 ….<br />Covariates only explain 30-40% of the variance depending on cause.<br />Sparse data for some developing regions<br />Compositional bias, data in each time period reflects a changing set of countries/sites<br />Small numbers – VA studies and small countries have huge sampling and non-sampling variation<br />45<br />

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

68.
47<br />

69.
48<br />Using VR data for 2005<br />

70.
49<br />

71.
50<br />

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

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

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

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

76.
CardiomyopathyEpi Review Data<br />55<br />

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

78.
Analysis of Disease-Specific Data for YLD<br />YLD = Disability Weight x Incidence x Duration<br />The GBD links losses of health to disease and injury causes through the concepts of cases and sequelae.<br />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.<br />Case definitions are based upon expert group guidance<br />57<br />

79.
58<br />Cardiomyopathy Disease Model<br />

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

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

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

83.
Cardiomyopathy Prevalence<br />62<br />

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

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

86.
Disability Weights Measurement<br />Disability weights provide the bridge between mortality and non-fatal outcomes in disability adjusted life years (DALYs)<br />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)<br />Construct reflects decrements from perfect health, distinct from broader notions of well-being or social value<br />Must be measured on meaningful cardinal scale<br />65<br />

87.
Disability Weights Measurement<br />Survey components<br />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<br />Open access Web-based surveys including all sequelae, and paired comparison, time trade-off and population equivalence questions <br />Community surveys are using computer-assisted personal interview approach with laptops<br />66<br />Household interview in Pemba, TZ 10/23/2009<br />

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