a method to help determine whether interventions have affected the natural course of hiv epidemics
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A Method To Help Determine Whether Interventions Have Affected The Natural Course of HIV Epidemics. Timothy Hallett & Kelly Sutton Imperial College London. Aims. We often see reports of epidemiological changes. Are they just part of the natural epidemiological dynamics ? - PowerPoint PPT PresentationTRANSCRIPT
A Method To Help Determine Whether Interventions Have Affected The Natural Course of HIV Epidemics
Timothy Hallett & Kelly SuttonImperial College London
We often see reports of epidemiological changes.• Are they just part of the natural epidemiological
dynamics?• Or do they signify that interventions are having an
impact?– And if so, which interventions?
We need to retrospectively look at at epidemiological data and programmes holistically in order to decide.
Aims
Decline ...?(Due to AIDS deaths)
“Flat line”?
Increase?Antiretroviral therapy Prevalence higher
We often see reports of epidemiological changes.• Are they just part of the natural epidemiological
dynamics?• Or do they signify that interventions are having an
impact?– And if so, which interventions?
We need to retrospectively look at at epidemiological data and programmes holistically in order to decide.
Aims
Aims
We often see reports of epidemiological changes.• Are they just part of the natural epidemiological
dynamics?• Or do they signify that interventions are having an
impact?– And if so, which interventions?
We need to retrospectively look at at epidemiological data and programmes holistically in order to decide.
Approach
Data Synthesis
Program Outputs
DHS Surveys
Program Data
ANC Surveillance
Behaviour
Prevalence
CD4 at initiation
Political Events
Peoples’ Experience
Number on ART
Number circumcised
Mathematical Models
Approach
Compare observed trends to model representing the Natural Epidemiological Dynamics – but no intervention effect.
IF the model fits data well, then we conclude no evidence for interventions affecting the course of the epidemic. (What we see is just the natural course of the evolving epidemic).
IF model does not fit well, then conclude that intervention implementation must have affected the natural course of epidemic. Estimate timing of that change, nature of the change and its impact.
Compare those estimates with interventions that have been used (Historical Mapping).
Why A Model?
• Need a model to construct that can construct counterfactual projections.– Even without intervention effect, prevalence/ behaviour indicators may go up
or down.
• Allows us to be clear about what we believe about epidemiology and how we interpret data.
• Allows us to keep track of what we don’t know (uncertainty).
Coping with UncertaintyCreating the Counterfactual
• Some parameters – we have good prior information:– Mean rate of partner change and changes in partner numbers– Rate of transmission of HIV per partnership– Survival with HIV
• Some parameters – we have little information on:– Variance in sexual risk behaviour– Pattern of mixing– Replacement of high risk groups
Also have most effect on natural dynamics
Coping with Uncertainty
Big declines POSSIBLE but
UNLIKELY
Propagation of Uncertainty Through The Model
Comparison to Other Processes
• This is not the UNAIDS models, EPP, Spectrum, Goals or the ‘Modes of Transmission’ model.
• Aim is not to produce new estimates, intervention targets, recommendations for resource allocation.
• Aim is to test the data for evidence of interventions having had an impact.
Project FlowD
ata
on e
pide
mic
ModelModel
indication of effect/ no
effect
Consultation
Qualitative Data
Program Data
Agreement on impacting
factors on epidemic
Behaviour
change
Circumcis
ionART
After Simon Gregson
Overall Percentage HIV-Positive Among ANC Attendees Age 15-49 Years, Zimbabwe, 2000-2002 and 2002-2006
32.029.5 29.7
25.8
21.317.7
24.8
20.517.3
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
2000 2001 2002 2004 2006
Year
Per
cen
tag
e H
IV-P
osi
tive
Genscreen HIV Result Parallel HIV Result Median
ANC Report, Zimbabwe MOHCW, 2008 (Draft); Gregson et al.
Zimbabwe
Source: DHS; Gregson et al; Halperin et al
Zimbabwe
“B”: Partner numbers
Zimbabwe
Source: DHS; Gregson et al; Halperin et al
Zimbabwe
Percent that used a condom at last casual sex
Source: DHS; Gregson et al; Halperin et al
Urban and ‘other non-rural’ regions
Comparison of two model:
P(value) Likelihood ratio test<0.001
2ln(BF) >10
Compelling evidence for behaviour change
The shape is the key thing here.
Zimbabwe
Hallett et al, Epidemics, 2009
Zimbabwe
Hallett et al, Epidemics, 2009
HOW risk changed: Assessment
Zimbabwe case study: Each potential PROXIMATE and UNDERLYING factor was assessed against three criteria:
1: Extent to which changes in the factor concerned can reduce HIV transmission at the population level, as measured and modelled in scientific studies.
2: Extent to which changes in the given behavioural or biological determinant (by population sub-group) have occurred as observed in longitudinal surveys and/or programme data.
3: Extent to which the changes in risk behaviour etc. occurred during the period of most rapid reduction in risk as determined by the epidemiological modelling assessment (i.e. 1998-2003)
HOW risk changed: Assessment
Proximate Factors
Halperin et al. PLoS Med. 2011
HOW risk changed: Assessment
Underlying Factors
Halperin et al. PLoS Med. 2011
Issues of Interpretation
(1) The absence of evidence is not equal to the evidence of absence.
Not finding evidence does not mean there hasn’t been an effect – just
that we didn’t find one in this particular evaluation exercise.
(2) We are evaluating interventions impact on reducing HIV incidence
ART will have reduced mortality and morbidity and we don’t seek to test
the data for signals of that.
BotswanaRuralUrban
Prevalence Prevalence
Year-on-Year Change Year-on-Year Change
Botswana
Botswana
DRAFT RESULTS
Conclusions
• Methods– Understanding whether programs have affected the course of an epidemic
requires integrating a wide range of epidemiological, program and
qualitative data.
– A reasonable approach has been proposed and successfully applied and
this form of evaluation can usefully inform decision making.
• Botswana– An impact of ART on reducing incidence is credible, although – with a
highly pessimistic point of view – other competing explanations cannot be
excluded.