A Method To Help Determine Whether Interventions Have Affected The Natural Course of HIV Epidemics Timothy Hallett & Kelly Sutton Imperial 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

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 Uncertainty Creating 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 Flow Data on epidemic Model Model indication of effect/ no effect Consultation Qualitative Data Program Data Agreement on impacting factors on epidemic Behaviour change Circumcision ART After Simon Gregson

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< ln(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 )

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.

Botswana Rural Urban Prevalence Year-on-Year Change

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.