1. UNIVERSITY OF WASHINGTON DisMod III Abraham D. Flaxman JSM Vancouver, 2010 Integrated systems modeling for disease burden’s long tail

2. Introduction For Global Burden of Disease Study (GBD) : 250 Must estimate incidence and duration for more than 250 diseases (by Nov 2010) Estimates based on review of all available data, developed by 44 expert groups Need estimates for 21 world regions, for males and females, for 1990 and 2005 (and 2010) How? 2

3. Introduction For Global Burden of Disease Study (GBD) : 250 Must estimate incidence and duration for more than 250 diseases (by Nov 2010) Estimates based on review of all available data, developed by 44 expert groups (these data are inconsistent) Need estimates for 21 world regions, for males and females, for 1990 and 2005 (and 2010) How? 3

4. DisMod III Methods Outline Consistency of epidemiological parameters Bayesian priors Borrowing strength between regions Web 2.0 interface Example Application - Guillain-Barré syndrome 4

5. Some Example Data - Dementia

6. Some Example Data - Anxiety

7. Compartmental Model for Consistency 7

8. Bayesian Statistical Model 8

9. 9

10. Bayesian Inference via MCMC Computationally intensive, but possible Allows expert priors 10

11. DisMod Expert Priors Smoothing Heterogeneity Level bounds / values Increasing, decreasing, unimodal 11

12. Expert Priors: Smoothing 12

13. Expert Priors: Smoothing 13

14. Expert Priors: Smoothing 14

15. Expert Priors: Smoothing 15

16. Expert Priors: Monotonicity 16

17. DisMod generates consistent estimates 17

18. DisMod generates consistent estimates 18

19. Sparsity – Regions with little anxiety data Regionprevalenceincidenceremissionmortalitytotal Europe, Western 2231450242 Australasia 69000 Europe, Central 65000 North America, High Income 6001061 Latin America, Southern 80008 Sub-Saharan Africa, East 60006 Caribbean 10001 Asia, Southeast 10001 Sub-Saharan Africa, Central 00000 Oceania 00000 Latin America, Andean 00000 Asia, Central 00000 19

20. Statistical Model 20

21. DisMod Empirical Priors 21

22. DisMod Empirical Priors 22

23. DisMod Empirical Priors 23

24. DisMod Empirical Priors 24

25. Burden of Disease Workflow Clean Data Check format Check definitions with expert group Check definitions in original data source Clean as necessary Load Data Explore in STATA or other general programs Explore in DisMod III Incorporate additional data if necessary Analyze Data Run Data Adjust Expert Priors, adjust covariates Discuss with Expert Groups Repeat as necessary Output Data Graphs, tables, STATA Validation of results with other sources Share results with expert groups 25

26. DisMod III Web-based User Interface 26

27. DisMod III 27

28. DisMod III 28

29. DisMod III 29

30. DisMod Disease View

31. DisMod Expert Priors Smoothing Heterogeneity Level bounds / values Increasing, decreasing, unimodal 31

32. DisMod Covariates 32

33. DisMod Status Panel 33

34. Validation by Simulation Study Generate gold-standard data, 8400 rates with consistent incidence, prevalence, remission, excess-mortality Sample small portion of data, with noisy data generation model Run DisMod III on the sample 34

35. DisMod Example: Guillain-Barré syndrome (GBS) Autoimmune disorder affecting the peripheral nervous system following an infectious disease Characterised by an ascending paralysis, spreading from legs to upper limbs and face

36. GBS data inputs Incidence Remission Mortality set to 0 after adjusting incidence by pooled case- fatality assuming that disease specific mortality risk is early in disease with no further excess mortality thereafter

37. 2005 GBS model posteriors

38. GBS Incidence in females, 1990

39. GBS Incidence in females, 2005

40. Conclusion and Lessons Learned Systematic literature review quality are crucial o Precious raw material that DisMod runs on… o …or GIGO? Expert knowledge from Doctors and Epidemiologists is crucial o Bayesian Priors will affect output, especially for parameters without much data o Covariate selection will affect output, especially in regions without much data 40

41. Acknowledgements DisMod Visionaries o Chris Murray o Moshen Naghavi o Theo Vos o Rafael Lozano o Steve Lim o Colin Mathers o Majid Ezzati o Jan Barendregt o Rebecca Cooley DisMod Software Engineer o Jiaji Du DisMod Early Adopters o Jed Balore o Allyne Dellosantos o Samath Dharmaratne o Merhdad Forouzan o Maya Mascarenhas o Nate Nair o Rosanna Norman o Farshad Purmalek o Saied Shahraz o Gretchen Stevens 41