1. An Estimation and Projection Package for Multiple Groups and Epidemics The UNAIDS/WHO EPP Tim Brown East-West Center/Thai Red Cross Society Collaboration on HIV Modeling, Analysis & Policy April 2003

2. The ultimate objective To develop a simple model that –Allows countries to estimate current HIV burden –Permits short term projections (5-year) –Is epidemiologically plausible –Can reproduce real world trends in HIV –Can be applied in-country Ideally a simple single curve that fits all situations, but….

3. To paraphrase Willy Fowler: One of the great tragedies of modern epidemiology is the murder of elegant models by cold, ugly data We try to fit simple models, but it never quite fits……

4. Nasty lessons from the real world Dynamics of real world HIV epidemics is complex Never a single HIV epidemic Each consists of multiple sub-epidemics –Affecting different sub-populations –In different geographic areas –Evolving at different rates

5. Nasty lessons from the real world Modeling large countries requires geographic decomposition –Unclear picture of the largest countries, e.g., China, India and Indonesia Generalized epidemics often vary greatly between urban and rural settings –Vary in intensity –Vary in timing

6. Nasty lessons from the real world Concentrated epidemics differ radically from country to country –Varying contributions from sub-populations –Differences in timing of epidemic take-off –Variable rates of sub-epidemic evolution

7. So we need a tool that…. Can deal with geographic diversity Can incorporate sub-population epidemics Can obtain different fits for each observed geographic and sub-population HIV trend Simplifies the process of combining sub- epidemics into the national epidemic

8. The approach Start with existing HIV trend data Fit a model through the data –Test possible epidemiological parameters –Choose a set minimizing least squares Project future course based on the fitted parameters

9. Fitting an epidemic

10. Why not use the gamma function? Epimodel is based on a gamma function modified for HIV mortality, but…. Incidence always goes to zero, so the gamma function cannot reproduce endemic epidemics –Short term fits will generally underestimate long term prevalence trends and always show declining trends –With more data will shallow out, but still cannot settle into endemic state

11. Gamma function fits to Congo data

12. What we fit – the Reference Group Model Uses a plausible epidemiological model Incorporates population change over time Fits 4 parameters –r – controlling the rate of growth –f 0 – the proportion of new risk pop entrants –t 0 – the start year of the epidemic – – behavior change parameter

13. Reference group fit to Congo data

14. Reference Group model parameters t0t0 f0f0 r

15. Effect of varying r – rate of growth r 2r r/2

16. Effect of varying f 0 – new entrants at-risk f0f0 2f 0 f 0 /2

17. Effect of varying t 0 – start time of epidemic t 0 = 2000 t 0 = 1990 t 0 = 1980

18. Effect of varying phi – recruitment =100 = -100 = 0

19. The Projection Page in EPP

20. Building a national epidemic in EPP The curvefit –Basic unit of computation –Represents a specific sub-population of people vulnerable to HIV –EPP collects demographic data and HIV trends for that sub-population –Then fits a Reference Group model to the HIV trends in that sub-population C

21. Building a national epidemic in EPP The sub-epidemic –Is composed of one or more curvefit –Optionally includes other sub-epidemics –Total HIV in a sub-epidemic is formed by summing HIV in its curvefits and sub- epidemics SE1 CC SE2 C

22. Building an epidemic in EPP The workset (the national epidemic) –Includes all curvefits and sub-epidemics used to build the national epidemic –Sub-epidemics may optionally be used to model different geographic areas –Total HIV is the sum of HIV in all curvefits contained in the workset

23. The workset tree SE1 CC SE2 C Workset CC

24. Examples of worksets - Botswana Botswana RuralUrban

25. Examples of worksets - Thailand North FSW Thailand Northeast Central South BKK ClientIDURemain FSWClientIDURemain

26. Templates – predefined epidemics Default templates –Concentrated –Urban-Rural User can create & name own templates –Geographic breakdowns –Specific sub-populations

27. Demo I Worksets page Creating a workset Creating a workset from a template Define Epidemic page Adding and deleting curvefits Adding and deleting sub-epidemics Adding a template

28. The Worksets Page in EPP Workset panel Template panel Epidemic structure Name & country selection

29. The Define Epidemic Page in EPP Epidemic structure User controls to add & delete curvefits & sub- epidemics

30. Defining your populations in EPP Specify base year and give total population in that year –Defaults: UN Pop for 2003 For base year –Specify number in each sub-population –Reduce unassigned population to zero

31. Defining your populations in EPP Choose special pop characteristics –MSM, IDU, FSW, Clients, STI, or lo-risk Set demographic parameters –proportion male –b – birth rate –mu – mortality –l15 – survival to age 15 –gr – 15+ pop growth rate

32. Demo II Define Pops page Assigning population and dividing it among the curvefits in the workset

33. The Define Pops Page in EPP National and unassigned population Special characteristics Demographics

34. The Data Entry Page in EPP User defined site names Automatic means and medians Prevalence by site & year

35. Data adjustments within EPP Prevalence adjustments –Annual increases or reductions for a changing mix of high and low prevalence sentinel sites –0.8 adjustment for rural sites by default - they overestimate actual prevalence in most places Weights –Applied on a per-site basis Selective inclusion of sites –Double-click box to include/exclude specific sites

36. Prevalence adjustments on the Data Entry Page Reduce or increase the prevalence values before using them for fitting –Adjust for lack of representativeness of available surveillance sites –If sites underestimate prevalence, use adjustment > 1.0 –If overestimate, use adjustment < 1.0 –Reference Group recommendation for rural projections is to use 0.8

37. Weights and checkboxes on the Data Entry Page Weights used in the calculation of means, medians and least squares Checkboxes completely exclude sites

38. Demo III Data Entry page Effect of prevalence adjustments, weights, and checkboxes

39. The Projection Page in EPP What & how to fit Initial guess

40. EPP Projection Page - Features Can fit different things –All data –Medians –Means All fits are made with adjustments, site selection and weighting applied as chosen by user on Data Entry Page

41. EPP Projection Page - Features Can fit different ways –Fix t0, vary r, f0 and phi (default) –Fit all variable (t0, r, f0 and phi) –Fix r, vary rest –Fix f0, vary rest If click Set to fix phi, no phi fitting done User can change initial guesses

42. The Projection Page in EPP Best fit & user changes

43. EPP Projection Page - Features Can change parameters manually after fitting and save results Can reset to the best fit if you really mess things up

44. EPP Results Page Allows you to examine any combination of curvefits & sub-epidemics Can plot original data Can see trends in prevalence, number HIV+, and sub-population size Allows numerical results to be viewed Can generate Spectrum file

45. EPP Results Page Which curvefits and sub-epidemics to show Get the numbers, export to Spectrum Graph of results What to display

46. Audit Check Page Need to check your concentrated epidemics against: –Plausible sizes for sub-populations –Maximum prevalences observed –Lo-risk to high-risk infection ratio

47. Audit Check Page Sub-pop size checks Lo-risk/hi-risk check Prevalence checks

48. Demo IV Projections page Fitting the epidemic Results Page Looking at the results Audit Check Validating your concentrated epidemic

49. And if you have a question on any page….. Just hit the Help button! –Page specific help –More detailed explanations

50. When do we use EPP? Reference Group recommendation: –When we have 5 years of trend data for at- risk populations

51. How should we use EPP? For 5 year projection into future –By default end year is 2008 User can change this on Worksets page, but not recommended Examine influence of sub-epidemic components and timing –Look at impact of different sub-populations –Explore different fits for sub-populations Timing of peak, height of peak, endemic level

52. Technical issues in applying EPP Concentrated epidemics –Size of at-risk populations –Inclusion of low-risk partner populations –Use of remaining population Consider validity of generalizing from limited studies of at-risk populations

53. Technical issues in applying EPP Always –Review impact of data outliers on fits –Run Audit Check to validate against international experience

54. Issues to consider When to use EPP and when to use spreadsheets in concentrated epidemics –Data availability Trends needed for EPP –Certainty of key sub-population size estimates

55. Closing remarks The tools cannot substitute for the absence of data The tools cannot improve bad data –GIGO (garbage in, garbage out) Thus, the tools must be seen as part of a process of both improving surveillance systems and preparing more accurate estimates The process will play out over years

56. Formal Model Description Z = at-risk population X = not at-risk population Y = infected N = X + Y + Z For those with strong stomachs (do not show after lunch):