Global Burden of Disease: Methods and Implications for Indonesia

1. Global Burden of Disease:Methods and Implications for Indonesia 26 February 2014 Sarah Wulf, MPH, PhD candidate Research Associate

2. Is Mary healthier than Rosa?

3. Outline: Burden of Disease Motivation • Why do we care about it? Methods • How do we measure it? Implications • What do we do with the results?

4. Motivation: summarize population health Primary goal: to have an accurate, comprehensive, and comparable summary of a population’s health Historically, heavy dependence on • Mortality rates • Life expectancy for key decision making and planning Do these metrics achieve the goal? Then why this dependence?

5. Motivation: better health burden evidence 1 2 3

6. Motivation: Global Burden of Disease • GBD is a systematic, scientific effort to quantify the comparative magnitude of health loss to diseases, injuries, and risk factors by age, sex, and geography over time • GBD approach • Analyze all available sources of information and correct problems with the data • Measure health loss using a common metric for a comprehensive set of diseases, injuries, and risk factors • Decouple epidemiological assessment and advocacy • Inject non-fatal health outcomes into health policy debate

7. Teaser: Global Burden of Disease results http://viz.healthmetricsandevaluation.org http://viz.healthmetricsandevaluation.org/gbd-compare/

8. Teaser: Top 10 causes of Death in Indonesia

9. Teaser: Top 10 causes of DALYs in Indonesia

10. Outline: Burden of Disease Motivation • Why do we care about it? Methods • How do we measure it? Implications • What do we do with the results?

11. DALYs = YLLs + YLDs Overall health loss Health loss due to premature mortality Health loss due to living with disability

12. Methods: Overview • GBD approach to measurement is different than many single disease, injury or risk factor studies. • Differences are philosophical and technical. • Comprehensive Comparisons • Estimating and Communicating Uncertainty • Internal Consistency • Iterative Approach to Estimation

13. Three broad groups of causes of health loss Communicable, maternal, neonatal, and nutritional conditions Non-communicable causes including cancers, diabetes, cardiovascular disorders and chronic respiratory diseases Injuries, both unintentional and intentional

14. Cause List • List of causes used to produce estimates of mortality, morbidity and burden • Hierarchicalstructure of diseases and injuries • 5 levels of aggregation • Mutually exclusive categories in each level: add up to 100% of burden • GBD 2010 • 291 diseases and injuries and 1,160 sequelae • Cause of death for 235 diseases and injuries • Non-fatal estimates for 290 diseases and injuries and 1160 sequelae

15. Methods • Demographics  Population and all-cause mortality • Covariates • Cause of death burden  Years of Life Lost due to premature mortality (YLLs) • Non-fatal health burden by cause  Years Lived with Disability (YLDs) • Total burden  Disability-Adjusted Life Years (DALYs) • Risk factor attribution

16. 1 Methods: Demographics • Mortality rate = (deaths in an age group in a year) (population in an age group at the midpoint of the year) • Commonly reported probabilities of death • 1q0 – infant mortality ‘rate’, the probability of death between birth and exact age 1. • 5q0 – child mortality, the probability of death between birth and exact age 5. • 45q15 – adult mortality, the probability of death between age 15 and exact age 60 conditional on being alive at age 15.

17. 1 250 Years of Child and Adult Mortality

18. 1 Alternative Mortality Measurement Methods Complete birth histories Summary birth histories Sibling survival Household deaths in the last 12 months Demographic surveillance systems

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22. Methods • Demographics  Population and all-cause mortality • Covariates • Cause of death burden  YLLs • Non-fatal health burden by cause  YLDs • Total burden  DALYs • Risk factor attribution

23. 2 Methods: Covariates • A major component of GBD is making estimates if data are sparse or conflicting data from multiple sources • Covariates help in modeling • Database of 84 covariate topic areas and 179 variants of the covariates* • Missing data addressed using spatial-temporal regression and Gaussian process regression * The full list of covariates can be found in the supplementary appendix to the Lancet comment "GBD 2010: design, definition, and metrics" (http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(12)61899-6/fulltext)

24. 2 Covariate: Lag-Distributed Income (LDI) • A composite of 7 different GDP series • IMF ID (2005 base year) • Penn ID (2005 base year) • WB ID (2005 base year) • Maddison ID (1990 base year) • WB USD (2005 base year) • IMF USD (2005 base year) • UN USD (2005 base year) • Composite GDP smoothed over preceding 10 years to produce LDI • Ref: James et al (http://www.pophealthmetrics.com/content/10/1/12) IMF= International Monetary Fund Penn=University of Pennsylvania Meddison: Angus Maddison’s research homepage at the University of Groningen Department of Economics

25. 2 Covariate:Alcohol (liters per capita) FAO Food Balance Sheets, World Drink Trends

26. Methods • Demographics  Population and all-cause mortality • Covariates • Cause of death burden  YLLs • Non-fatal health burden by cause  YLDs • Total burden  DALYs • Risk factor attribution

27. 3 Methods: Cause of death burden • Identify and obtain all published and unpublished sources of data on causes of death. • Assess and enhance data quality and comparability • Develop and apply models for 235 individual causes of death • CoDCorrect – develop final estimates for each age-sex-country-year where the sum of the 235 individual causes of death equals the age-sex specific all-cause mortality rate

28. 3 Cause of Death Ensemble modeling • “CODEm” -- used for most causes • Develop a large range of plausible models for each cause – all combination of selected covariates tested. Models retained that are significant with coefficients in the expected direction. All permutations tested for four families of models: mixed effects log rates, mixed effects logit cause fractions, ST-GPR log rates, ST-GPR logit cause fractions. • Create combinations ‘ensembles’ of the best performing models • Statistical tests of out-of-sample predictive validity all models • Select the best performing model or ensemble of models

29. 3 CoDCorrect Algorithm • Estimates for each age-sex-country-year for the 235 causes are constrained to equal the demographic estimate of all cause mortality for that age-sex-country-year. • This rescaling is repeated1000 times to propagate the uncertainty in the estimates for each cause into the final results

30. 3 YLL calculation YLLs X = deathsX * eX Years of life lost due to premature mortality Standard life expectancy at age x Number of deaths at age x

31. Based on lowest mortality rates at each age observed in any population of 5M or more. Most estimates for Japanese women Same standard for men and women 3 Standard life expectancy

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34. Methods • Demographics  Population and all-cause mortality • Covariates • Cause of death burden  YLLs • Non-fatal health burden by cause  YLDs • Total burden  DALYs • Risk factor attribution

35. 4 Methods: Non-fatal burden by cause YLDs = Prev * DW Years lived with disability Prevalence of condition Disability weight

36. 4 Methods: Non-fatal burden by cause • Incidence rate = (number of new cases of a disease) (person-time of observation) • Prevalence rate = (number of individuals with a disease) (population) • Prevalence ≈ Incidence * Duration • assuming incidence/remission/mortality rates are relatively stable over time and/or duration is short

37. 4 Challenges of YLD estimation Data sources • No single source of data for YLDs from all conditions • Inconsistency and gaps in information Process specifications • Complex disease epidemiology • Severity distributions of health states • Comorbidity Uncertainty • Uncertainty from data itself, lack of data, disability weights

38. 4 YLD calculation Prevalence: • Estimates of country-/year-/age-/sex-specific disease sequela prevalence • Identify and pool all usable data sources Disability weights (DWs): • Estimates of the disability associated with each health state • GBD Disability Survey, 2012

39. 4 Data sources • Systematic literature reviews • Population surveys • Cancer registries • Renal replacement therapy registries • Hospital data • Outpatient data • Cohort follow-up studies • Disease surveillance systems

40. 4 Data adjustments Crosswalk Adjust upwards Correct for at-risk population Downweight

41. 4 Methods • DisMod-MR • Natural history models • Geospatial models • Back-calculation models • Registration completeness models

42. 4 DisMod • Bayesian Disease Modeling statistical tool • Performs crosswalks to adjust for methodological variation • Incorporates assumptions to inform the model • Borrows strength using covariates and super-region, region, and country random effects to inform regions/countries with little or no data • Forces consistency among disease parameters

43. 4 Three estimation strategies with DisMod Direct estimation of disease sequelae Maternal sepsis • Hearing loss Disability envelopes for etiological attribution • Otitis media • Congenital • Meningitis • Other causes • Diabetes mellitus Disability envelopes for disease sequelae • Diabetic retinopathy • Diabetic neuropathy • Diabetic foot ulcer • Diabetic amputation • Uncomplicated diabetes

44. 4 DisMod output • Epidemiological parameters estimated by: • Country • Year • Age • Sex • Estimates repeated 1,000 times to define uncertainty  Need to build in reality of comorbidity

45. 4 Comorbidity adjustment 1 • Simulate comorbidity distribution • Use prevalence and disability weights across hypothetical 20,000 people in each demographic group 2 Calculate combined disability weights (CDW) where n = number of health states observed for individual i 3 • Reaggregate by disease sequela • Apportion CDWs to each of the contributing sequelae in proportion to the DW of a sequela on its own 4 • Quantify uncertainty • Repeat 1,000 times to estimate uncertainty Comorbidity-adjusted YLDs with uncertainty

46. Methods • Demographics  Population and all-cause mortality • Covariates • Cause of death burden  YLLs • Non-fatal health burden by cause  YLDs • Total burden  DALYs • Risk factor attribution

47. 5 Methods: Total burden DALYs = YLLs + YLDs Overall health loss Health loss due to premature mortality Health loss due to living with disability

48. Methods • Demographics  Population and all-cause mortality • Covariates • Cause of death burden  YLLs • Non-fatal health burden by cause  YLDs • Total burden  DALYs • Risk factor attribution

49. 6 Methods: Risk factor attribution

50. 6 GBD 2010 – risks quantified Unimproved water and sanitation Unimproved water Unimproved sanitation Air pollution Ambient particulate matter pollution Household air pollution from solid fuels Ambient ozone pollution Other environmental risks Residential radon Lead exposure Child and maternal undernutrition Suboptimal breastfeeding Non-exclusive breastfeeding Discontinued breastfeeding Childhood underweight Iron deficiency Vitamin A deficiency Zinc deficiency Tobacco smoking and secondhand smoke Tobacco smoking Second-hand smoke Alcohol and other drugs Alcohol use Drug use (opioids, cannabis, amphetamines) Physiological risks for chronic diseases High fasting plasma glucose High total cholesterol High systolic blood pressure High body mass index Low bone mineral density Sexual abuse and violence Childhood sexual abuse Intimate partner violence