EPP 2007 EPP 2007 Working with concentrated epidemics new features and approaches UNAIDS/WHO Working Group on Global HIV/AIDS & STI Surveillance
UNAIDS Estimation & Projection UNAIDS Estimation & Projection Package 2007 (EPP 2007) Package 2007 (EPP 2007) • Objectives – Allow national counterparts to build models of their national epidemics composed of • Separate sub-epidemics in different at-risk populations • Geographically diverse regional sub-epidemics – Giv short-term projections of HIV prevalence (<5 yrs) – Serve as input to Spectrum for assessing incidence, impacts, ART, etc. 2007 en 2
What basically does EPP do? What basically does EPP do? • Fits plausible epidemiological model to existing data • Modified Reference Group model – 4 fitting parameters – r – controlling the rate of growth – f 0 – the proportion of new risk pop entrants – t 0 – the start year of the epidemic – φ – behavior change parameter • …for concentrated epidemics – d – average time in group (duration) 2007 en 3
UNAIDS Reference Group model UNAIDS Reference Group model 50 φ 40 % HIV+ 30 f 0 20 t 0 d r 10 0 2007 en 4
EPP’ ’s s job: fit the model to the data job: fit the model to the data EPP 70 60 50 % HIV+ 40 30 20 10 0 0 5 0 5 0 5 0 5 0 8 8 9 9 0 0 1 1 2 9 9 9 9 0 0 0 0 0 1 1 1 1 2 2 2 2 2 2007 en 5
Steps in constructing a national epidemic Steps in constructing a national epidemic • Choose your country and name this attempt at national projections (the workset in EPP) • Decide the key groups in the epidemic and its geographic breakdown • Define population characteristics – Demographics 2007 en 6
Steps in constructing a national epidemic Steps in constructing a national epidemic • Enter HIV prevalence data and sample sizes for each sub-population or regional sub-epidemic • Fit the Reference Group model to each of them • Adjust your prevalence up or down to match any large scale survey data that may be available • Display and review the results of your work 2007 en 7
EPP interface takes you through the steps EPP interface takes you through the steps The tabs at the top take you through these steps 2007 en 8
A quick demo of creating A quick demo of creating a national projection a national projection
Features relevant to concentrated Features relevant to concentrated epidemics with turnover epidemics with turnover Important in countries with long- -standing standing Important in countries with long epidemics in at- -risk populations risk populations epidemics in at
EPP 2007 includes turnover in populations EPP 2007 includes turnover in populations Clients of sex workers 200 in (1000 men with 200 out 5 yr duration) Death General pop males 2007 en 11
The Define Pops page - - Concentrated Concentrated The Define Pops page You determine if turnover is on or off & enter duration in the group 2007 en 12
The Calibrate page - - Concentrated Concentrated The Calibrate page You adjust curves up or down (calibration) You determine what happens to people who leave the group if there is turnover 2007 en 13
Why is assign prevalence here? Why is assign prevalence here? • The model in EPP 2007 includes population turnover – Many HIV+ ex-members of at-risk populations, e.g., HIV+ ex- sex workers or HIV+ ex-IDUs • These HIV+s are sometimes captured in other surveillance populations – e.g., ex-sex workers showing up in antenatal clinic data • But other times, they’re missed – e.g., ex-IDUs may be missed because of limited male surveillance 2007 en 14
Fits to Thai Central Region IDU Data Fits to Thai Central Region IDU Data Changes to the fit Changes to the fit 45 40 35 30 No turnover 25 Dur 10 yrs 20 Data 15 10 5 0 0 3 6 9 2 5 8 1 4 7 8 8 8 8 9 9 9 0 0 0 9 9 9 9 9 9 9 0 0 0 1 1 1 1 1 1 1 2 2 2 2007 en 15
Living ex- -IDUs IDUs with 10 year duration with 10 year duration Living ex Thailand IDUs IDUs Thailand 30000 25000 20000 15000 10 yr duration 10000 At peak this is 5.4% of adult 5000 male prevalence 0 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2007 en 16
How is assignment of HIV+ ex’ ’s done? s done? How is assignment of HIV+ ex • One selects the population from which the HIV+s are coming – Only populations with turnover show up here • One selects where they are to go after they leave the group – Only populations without turnover (closed pops) here • One decides to add or replace prevalence 2007 en 17
What do “ “add add” ” & & “ “replace replace” ” prevalence mean? prevalence mean? What do • Add prevalence – The HIV+ former at-risk group members are added to the HIV+ members of the target population – This means they have NOT been captured in surveillance there • Replace prevalence – Some of the HIV+’s in the target population are assumed to come from the former at-risk group members – The remaining infections that occurred “within group” are calculated 2007 en 18
Where do you see the effects? Where do you see the effects? • In the graphs on the Results page • By pushing the “Reassigns” button on the Results page • Example – Sex workers and general population women in Mumbai 2007 en 19
The Results page – – Concentrated form Concentrated form The Results page 2007 en 20
The Reassignment table The Reassignment table 2007 en 21
New features in EPP 2007 New features in EPP 2007
New features in EPP 2007 New features in EPP 2007 • Uncertainty for generalized epidemics – Bayesian melding • Initial guesses for concentrated epidemics • Review mode • Changes to the fitting and calculations (under the hood) – Fuller exploration of possible solutions for r, f 0 , phi and t 0 – Speed improvements • Improved calibration for generalized epidemics – Including adjustments for multiple national surveys • A larger interface 2007 en 23
EPP 2007 – – review mode review mode EPP 2007 • Can open a projection w/o changing it • Disables saves • Indicated two ways: – Title bar says “Review mode” – “Save & continue” becomes “Continue” • Two ways to exit – On Workset Page, click “Edit” mode – On any page, hit “Save a copy” 2007 en 24
Review mode – – the interface the interface Review mode 2007 en 25
Changes to “ “Save a Copy Save a Copy” ” Changes to • When you “Save a copy” – Makes a copy of the current workset with the name you specify – Loads that copy – Restores the page you were on 2007 en 26
Slider sensitivity adjustments Slider sensitivity adjustments New button Adjust sliders 2007 en 27
Slider sensitivity panel Slider sensitivity panel 2007 en 28
Major changes – Major changes – Uncertainty in generalized epidemics Uncertainty in generalized epidemics
What have we learned about the What have we learned about the Reference Group model? Reference Group model? • Sometimes the EPP fitter selects strange curves • This is a “feature” of the Reference Group model • Some data sets are not constraining 2007 en 30
Many curves can fit the same data – – Many curves can fit the same data some we know are not realistic some we know are not realistic Source: Adrian Raftery 2007 en 31
So we’re going to do the process we mentioned before…. Try many different combinations of r, f 0 , phi and t 0 …. 32
33 And even if we eliminate the unreasonable curves….
Some countries with a lot of data have only Some countries with a lot of data have only a few curves that fit – – data constrains it data constrains it a few curves that fit 8 curves Botswana through 2003 – 50,000 curves tried 2007 en 34
Other countries with more limited data have a lot Other countries with more limited data have a lot of curves that fit – – data does not constrain data does not constrain of curves that fit 240 curves Senegal urban through 2003 – 50,000 curves tried 2007 en 35
In this variation, lies a way of In this variation, lies a way of assessing uncertainty assessing uncertainty Countries where the data limits us to only a few curves have less uncertainty about the epidemic 36
This has now been built into EPP This has now been built into EPP for generalized epidemics for generalized epidemics 37
However, for concentrated epidemics However, for concentrated epidemics we don’ ’t yet know how to estimate t yet know how to estimate we don uncertainty uncertainty 38
Uncertainty in concentrated epidemics Uncertainty in concentrated epidemics • Size of populations is one of the largest unknowns – We don’t know how to estimate uncertainty here yet • Samples are much more restricted – Geographically – Access to populations is more limited (FSW, IDU, MSM, etc.) – Data often not representative – convenience samples 2007 en 39
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