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EPP Introduction

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EPP Introduction Powered By Docstoc
					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
           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….
   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……
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
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
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
     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
            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
                     % HIV+
19




       0
           10
                20
                     30
                          40
                               50
                                    60
  80                                     70

19
  85

19
  90

19
  95

20
  00

20
  05

20
  10
                                              Fitting an epidemic




20
  15

20
  20
  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
    Gamma function fits to Congo data

           4
% HIV+




           2



           0
           80



                  85



                         90



                                95



                                       00



                                              05
         19



                19



                       19



                              19



                                     20



                                            20
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
    – f0 – the proportion of new risk pop entrants
    – t0 – the start year of the epidemic
    –  – behavior change parameter
    Reference group fit to Congo data


           4
% HIV+




           2



           0
           80



                  85



                         90



                                95



                                       00



                                              05
         19



                19



                       19



                              19



                                     20



                                            20
Reference Group model parameters
          50

          40
                                                                     
% HIV+




          30

          20                                     f0
                 t0
          10                         r
           0
           80


                  85


                         90


                                95


                                           00


                                                  05


                                                         10


                                                                15


                                                                       20
         19


                19


                       19


                              19


                                         20


                                                20


                                                       20


                                                              20


                                                                     20
         Effect of varying r – rate of growth
           8


           6
                  2r
% HIV+




           4
                              r

           2                                r/2

           0
           80



                  85



                         90



                                95



                                       00



                                              05
         19



                19



                       19



                              19



                                     20



                                            20
 Effect of varying f0 – new entrants at-risk
          15



          10           2f0
% HIV+




                                     f0
           5
                                                          f0/2

           0
           80


                  85


                         90


                                95


                                       00


                                              05


                                                     10


                                                            15


                                                                   20
         19


                19


                       19


                              19


                                     20


                                            20


                                                   20


                                                          20


                                                                 20
Effect of varying t0 – start time of epidemic
          10


                t0 = 1980
% HIV+




           5
                                     t0 = 1990             t0 = 2000


           0
           80


                  85


                         90


                                95


                                        00


                                               05


                                                      10


                                                              15


                                                                     20
         19


                19


                       19


                              19


                                      20


                                             20


                                                    20


                                                            20


                                                                   20
     Effect of varying phi – recruitment
           8


           6                                 =100
% HIV+




           4
                                                 =0
           2
                                  = -100
           0
           80



                  85



                         90



                                95



                                       00



                                                05
         19



                19



                       19



                              19



                                     20



                                              20
The Projection Page in EPP
 Building a national epidemic in EPP
• The curvefit                     C
  – 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
  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

                       SE2         C           C

                       C
   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
      The workset tree

             Workset

       SE1             C   C

SE2    C         C

C
Examples of worksets - Botswana

                 Botswana



         Urban              Rural
 Examples of worksets - Thailand

                        Thailand

North      Northeast         Central      South    BKK



FSW     Client     IDU        Remain


                       FSW       Client      IDU   Remain
 Templates – predefined epidemics

• Default templates
  – Concentrated
  – Urban-Rural
• User can create & name own templates
  – Geographic breakdowns
  – Specific sub-populations
           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
         The Worksets Page in EPP

                        Name & country selection
Workset panel



                               Epidemic structure


Template panel
The Define Epidemic Page in EPP


                        User controls to
Epidemic structure       add & delete
                        curvefits & sub-
                          epidemics
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
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
              Demo II

         Define Pops page
Assigning population and dividing it among
        the curvefits in the workset
       The Define Pops Page in EPP
                  National and unassigned population



   Special
characteristics



                                          Demographics
       The Data Entry Page in EPP

                          Automatic means and medians




User defined site names
                               Prevalence by site & year
   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
      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
     Weights and checkboxes
              on the Data Entry Page

• Weights used in the calculation of
  means, medians and least squares

    w x     i i
                   LSQ   w ( xi  xi )
                                2
                                    ˆ      2
 x  i
                                i
    w   i
              i             i


• Checkboxes completely exclude sites
             Demo III

          Data Entry page
Effect of prevalence adjustments, weights,
              and checkboxes
 The Projection Page in EPP

What & how to fit



   Initial guess
 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
 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
The Projection Page in EPP




                         Best fit &
                       user changes
 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
         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
             EPP Results Page
 Which curvefits and
sub-epidemics to show

                           Graph of results




  What to
  display



                 Get the numbers, export to Spectrum
          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
Audit Check Page


                  Sub-pop size
                    checks



                          Prevalence
                            checks

         Lo-risk/hi-risk check
           Demo IV

       Projections page
         Fitting the epidemic
         Results Page
        Looking at the results
          Audit Check
Validating your concentrated epidemic
  And if you have a question on
           any page…..
• Just hit the “Help” button!
  – Page specific help
  – More detailed explanations
      When do we use EPP?

• Reference Group recommendation:
  – When we have 5 years of trend data for at-
    risk populations
     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
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
Technical issues in applying EPP

• Always
  – Review impact of data outliers on fits
  – Run Audit Check to validate against
    international experience
          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
             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
  For Model Description
Formalthose with strong stomachs (do not show after lunch):

Z = at-risk population       dZ
X = not at-risk population       f ( X / N ) Et  (   rY / N   ) Z
Y = infected                 dt
N=X+Y+Z
                             dX
                                 (1  f ( X / N ))Et  X
                             dt
                                           t
                      (rY / N   ) Z   rYx / N x   x Z x g (t  x)dx
                  dY
                  dt                     0


                                             X              
                                        exp (  (1  f 0 ))
                         f (X / N)          N              
                                         X                1
                                     exp (  (1  f 0 ))   1
                                         N                f0

				
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