An Estimation and Projection Package for Multiple Groups and Epidemics The UNAIDS/WHO EPP Tim Brown...
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Transcript of An Estimation and Projection Package for Multiple Groups and Epidemics The UNAIDS/WHO EPP Tim Brown...
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
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
Fitting an epidemic
0
10
20
30
40
50
60
70
% H
IV+
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
0
2
4
% H
IV+
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
0
2
4
% H
IV+
Reference Group model parameters
0
10
20
30
40
50
% H
IV+
t0f0
r
Effect of varying r – rate of growth
0
2
4
6
8
% H
IV+ r
2r
r/2
Effect of varying f0 – new entrants at-risk
0
5
10
15%
HIV
+
f0
2f0
f0/2
Effect of varying t0 – start time of epidemic
0
5
10%
HIV
+
t0 = 2000t0 = 1990
t0 = 1980
Effect of varying phi – recruitment
0
2
4
6
8
% H
IV+
=100
= -100
= 0
The Projection Page in EPP
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
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
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
SE1
CC SE2
C
Workset
CC
Examples of worksets - Botswana
Botswana
RuralUrban
Examples of worksets - Thailand
North
FSW
Thailand
Northeast Central South BKK
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 pageCreating a workset
Creating a workset from a template
Define Epidemic pageAdding and deleting curvefits
Adding and deleting sub-epidemics
Adding a template
The Worksets Page in EPP
Workset panel
Template panel
Epidemic structure
Name & country selection
The Define Epidemic Page in EPP
Epidemic structureUser controls to
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 pageAssigning 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
User defined site names
Automatic means and medians
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 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
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
Demo III
Data Entry pageEffect 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 PageWhich curvefits and
sub-epidemics to show
Get the numbers, export to Spectrum
Graph of results
What todisplay
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 sizechecks
Lo-risk/hi-risk check
Prevalencechecks
Demo IV
Projections pageFitting the epidemic
Results PageLooking at the results
Audit CheckValidating 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
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):