Methods for establishing the extent of HIV epidemics and trends in prevalence Geoff Garnett Imperial College London

UNAIDS methods for developing countries: Models Earlier model start time and current prevalence fitted to a gamma curve Epidemic Projection Package (EPP) – Generalised epidemic – fits prevalence trends to ANC data (later adjusted for national surveys) Workbooks – Concentrated & low level epidemics – numbers and prevalence in specific groups Spectrum – Calculates ages specific incidence and impact on the basis of overall prevalence trends. Short term incidence model

Risk behaviours HIV incidence HIV prevalence AIDS Deaths Primary prevention Secondary prevention Tertiary prevention Measuring epidemiological impact of prevention Behavioural surveys Surveillance with novel tests / modelling Sentinel surveillance / population based surveys Case reports Registration / census / surveys

HIV Prevalence data Generalised epidemics ANC data Household based surveys Fitted in EPP to generate trends Derived incidence Spectrum – age structured Mortality, orphanhood. Concentrated epidemics ANC data minus estimated SWs & IDUs IDUs, MSM, SWs, clients Size estimates Associated prevalence estimates Upper and lower bounds over time Workbooks spreadsheet

Sources of uncertainty in HIV/AIDS estimates for generalised epidemics Estimate Source Uncertainty Relationship of adult prevalence to prevalence among pregnant women Survival of infected adults Epidemic curve National coverage of sentinel surveillance Adult HIV prevalence, new infections and AIDS mortality New infections in children Probability of mother to child transmission Child AIDS deaths and HIV prevalence Child survival (AIDS and other causes

The natural course of incidence and prevalence of a local HIV epidemic over time Time (years) Percent Incidence /Prevalence Prevalence HIV Incidence HIV infection Incidence AIDS deaths R t =R 0 >1 R t <1 R t =1 Interested in current incidence – but even if a validated test available would require an order of magnitude increase in sample sizes.

Year HIV prevalence (%) Data – HIV prevalence in ANC clinics in urban and semi-urban Zimbabwe Year Year-on-year change (%) Median Change Change in specific clinic

Models fitted to urban ANC HIV prevalence trends in Zimbabwe Sample importance resampling (sample 4,000,000 resample 8,000) Year HIV prevalence (%) No behaviour change Behaviour change Bayes Factor 58 Likelihood ratio test p<

Incidence trends associated with model fits – urban Zimbabwe HIV incidence (per 100 per year) Year Mode 2.5% 97.5%

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HIV Prevalence data Generalised epidemics ANC data Household based surveys Fitted in EPP to generate trends Derived incidence Spectrum – age structured Mortality, orphanhood. Concentrated epidemics ANC data minus estimated SWs & IDUs IDUs, MSM, SWs, clients Size estimates Associated prevalence estimates Upper and lower bounds over time Workbooks spreadsheet

HIV prevalence among at-risk groups Population size of at-risk groups Survival of infected adults Adult new infections and AIDS mortality Age and sex distribution of HIV prevalence in at-risk groups Impact of HIV on fertility Female age-specific fertility rate in at-risk groups New infections in children Child AIDS deaths & HIV prevalence Child survival (AIDS-related & other causes) Rates of entering and leaving at-risk groups Adult HIV prevalence Coverage of sentinel surveillance system Probability of mother-to-child transmission Sources of uncertainty in concentrated HIV epidemics

Concentrated epidemics - Workbooks Estimates upper and lower bounds for risk group size and for prevalence in risk groups – local expert based. Calculates a non-overlapping number of infections at a point in time If multiple time points fits a simple curve (either a single or double logistic curve)

Adult population Entire population At risk Children of +ves MSMIDUs Sex Workers Partners of those with risk Immigrants from high prevalence states Populations at risk in Europe (need to avoid double counting)

Sources of information on the extent of HIV spread HIV prevalence surveys General populationHigh risk group Estimate of size of high risk group Case reports (HIV/AIDS/deaths) Risk behaviour data Prediction of future HIV trends Need consistent sources of information and sampling over time to explore trends

Estimates of size of high risk groups: Counting/ mapping Capture/recapture Multiplier Contact tracing/snowball sampling Population in HIV prevalence survey needs to match population for which size is estimated Prevalence in population based surveys MSM in capital city club men who frequently have sex with men men who occasionally have sex with men men who have ever had sex with men

Tipping point R 0 =1 Increasing contacts, transmission likelihood, duration Stable HIV Prevalence Increased heterogeneity What do we expect the long term prevalence of HIV to be? When can we expect new outbreaks?

Routes into healthcare Undiagnosed Diagnosed at VCT Attends ANC Never diagnosed Presents at clinic when develops severe symptoms Enters Health-care system Referred Diagnosed at VCT Referred

Number of HIV and AIDS diagnoses and deaths Year of HIV or AIDS diagnosis or death HIV diagnoses AIDS diagnoses Deaths New HIV and AIDS diagnoses in the UK, and deaths among HIV infected individuals: (HPA)

Estimated late diagnosis of HIV infection by prevention group, UK: 2007 (HPA) CD4 cell count <200 per mm3 within three months of diagnosis among adults 19% 42% 36% 30% 31% 0% 10% 20% 30% 40% 50% MSMHeterosexual men Heterosexual women Injecting drug users Overall Percentage diagnosed late Total n= 2,679 1,434 2, ,649

Figure 1 The BED response function relationship between probability of sample being classified as recent by BED test and time since HIV-infection. The first 2 years (shaded area) is informed by observational data (see text), and the pattern over the remaining time is uncertain and three hypothetical scenarios are constructed. Scenario III Scenario II Scenario I

Proportions of infections of at least one year that are miss-classified by the BED test for six African countries over time (ages 15-49), using BED response scenario B (increasing proportion false positive) Incidence based on spectrum model fits to EPP prevalence trends Kenya Lesotho Mozabique Uganda Zambia Nigeria

Median CD4 count at diagnosis by prevention group: UK ( ) Data on pregnancy status only from 2000

Modelled decline in CD4+ cells – 5 realisations from 1000

Sources of information on the extent of HIV spread HIV prevalence surveys General populationHigh risk group Estimate of size of high risk group Case reports (HIV/AIDS/deaths) Risk behaviour data Prediction of future HIV trends Variable and diverse data sources can be combined in a modelling framework with consistent relationships between behaviours, incidence, prevalence, CD4 counts, opportunistic infections and death

Sources of information on the extent of HIV spread HIV prevalence Case reports (HIV/AIDS/deaths) With CD4 counts Risk behaviour data Models linking epidemiological processes Outputs compared with observation where available Screening, testing and care patterns