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.