an estimation and projection package for multiple groups and epidemics the unaids/who epp tim brown...
TRANSCRIPT
![Page 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](https://reader035.vdocument.in/reader035/viewer/2022070305/551505ac550346c77d8b4592/html5/thumbnails/1.jpg)
An Estimation and Projection Package for Multiple Groups and Epidemics
The UNAIDS/WHO EPP
Tim BrownEast-West Center/Thai Red Cross Society
Collaboration on HIV Modeling, Analysis & Policy
April 2003
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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….
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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……
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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
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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
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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
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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
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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
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Fitting an epidemic
0
10
20
30
40
50
60
70
% H
IV+
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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
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Gamma function fits to Congo data
0
2
4
% H
IV+
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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
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Reference group fit to Congo data
0
2
4
% H
IV+
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Reference Group model parameters
0
10
20
30
40
50
% H
IV+
t0f0
r
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Effect of varying r – rate of growth
0
2
4
6
8
% H
IV+ r
2r
r/2
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Effect of varying f0 – new entrants at-risk
0
5
10
15%
HIV
+
f0
2f0
f0/2
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Effect of varying t0 – start time of epidemic
0
5
10%
HIV
+
t0 = 2000t0 = 1990
t0 = 1980
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Effect of varying phi – recruitment
0
2
4
6
8
% H
IV+
=100
= -100
= 0
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The Projection Page in EPP
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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
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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
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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
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The workset tree
SE1
CC SE2
C
Workset
CC
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Examples of worksets - Botswana
Botswana
RuralUrban
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Examples of worksets - Thailand
North
FSW
Thailand
Northeast Central South BKK
Client IDU Remain
FSW Client IDU Remain
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Templates – predefined epidemics
• Default templates– Concentrated– Urban-Rural
• User can create & name own templates– Geographic breakdowns– Specific sub-populations
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Demo I
Worksets pageCreating a workset
Creating a workset from a template
Define Epidemic pageAdding and deleting curvefits
Adding and deleting sub-epidemics
Adding a template
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The Worksets Page in EPP
Workset panel
Template panel
Epidemic structure
Name & country selection
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The Define Epidemic Page in EPP
Epidemic structureUser controls to
add & delete curvefits & sub-
epidemics
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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
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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
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Demo II
Define Pops pageAssigning population and dividing it among
the curvefits in the workset
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The Define Pops Page in EPP
National and unassigned population
Special characteristics
Demographics
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The Data Entry Page in EPP
User defined site names
Automatic means and medians
Prevalence by site & year
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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
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Prevalence adjustmentson 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
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Weights and checkboxeson the Data Entry Page
• Weights used in the calculation of means, medians and least squares
• Checkboxes completely exclude sites
ii
iii
w
xwx
22 )ˆ( iii
i xxwLSQ
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Demo III
Data Entry pageEffect of prevalence adjustments, weights,
and checkboxes
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The Projection Page in EPP
What & how to fit
Initial guess
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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
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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
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The Projection Page in EPP
Best fit &user changes
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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
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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
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EPP Results PageWhich curvefits and
sub-epidemics to show
Get the numbers, export to Spectrum
Graph of results
What todisplay
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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
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Audit Check Page
Sub-pop sizechecks
Lo-risk/hi-risk check
Prevalencechecks
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Demo IV
Projections pageFitting the epidemic
Results PageLooking at the results
Audit CheckValidating your concentrated epidemic
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And if you have a question on any page…..
• Just hit the “Help” button!– Page specific help– More detailed explanations
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When do we use EPP?
• Reference Group recommendation:– When we have 5 years of trend data for at-
risk populations
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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
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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
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Technical issues in applying EPP
• Always– Review impact of data outliers on fits– Run Audit Check to validate against
international experience
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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
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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
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Formal Model Description
Z = at-risk populationX = not at-risk populationY = infectedN = X + Y + Z
ZNrYENXfdt
dZt )/()/(
XENXfdt
dXt ))/(1(
t
xxxx xtgZNrYZNrYdt
dY
0
dx)(/)/(
11
))1((exp
))1((exp)/(
00
0
ff
NX
fNX
NXf
For those with strong stomachs (do not show after lunch):