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Estimation of the number of people with undiagnosed HIV infection in a country

This presentation discusses different approaches to estimate the number of undiagnosed HIV infections in a country, including prevalence surveys and reported HIV diagnoses. It also explores the challenges and assumptions involved in these estimation methods.

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Estimation of the number of people with undiagnosed HIV infection in a country

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  1. HIV in Europe Meeting 2 November 2009, Stockholm. Estimation of the number of people with undiagnosed HIV infection in a country Andrew Phillips, UCL, London

  2. Approaches to estimation of the number of people with undiagnosed HIV infection in a country • based on prevalence surveys • based on reported numbers of HIV diagnoses • based on reported simultaneous HIV/AIDS cases

  3. Approaches to estimation of the number of people with undiagnosed HIV infection in a country • based on prevalence surveys • based on reported numbers of HIV diagnoses • based on reported simultaneous HIV/AIDS cases

  4. Divide population into categories according to risk MSM MSM Africans IDU

  5. Assess HIV Prevalence in the risk category MSM MSM Africans HIV prevalence IDU

  6. Estimate number of people in the risk category (size) MSM MSM Africans HIV prevalence x Size IDU

  7. Multiply to give estimated number with HIV MSM MSM Africans HIV prevalence x Size = Number with HIV IDU

  8. Subtract the number with diagnosed HIV MSM Africans Number with HIV – Number with diagnosed HIV IDU

  9. ….to give the number with undiagnosed HIV MSM Africans Number with HIV – Number with diagnosed HIV = Number with undiagnosed HIV IDU

  10. Add estimates across risk categories MSM Africans Number with HIV – Number with diagnosed HIV = Number with undiagnosed HIV + + IDU +

  11. Alternative Approach MSM MSM Africans HIV prevalence x Size = Number with HIV IDU

  12. Alternative Approach MSM MSM Africans Undiagnosed HIV prevalence x Size = Number with undiagnosed HIV IDU

  13. Approach based on prevalence surveys Issues to consider What risk categories to divide population into ? How to estimate the size of each category ? What prevalence to assume for those not falling into any of the selected ‘risk’ categories ? Are the prevalence surveys based on representative samples of the risk category ?

  14. Is prevalence survey based on representative sample of the risk group of the size estimated ? Sexual risk activity in MSM HIV prevalence Low High Level of risk activity

  15. Is prevalence survey based on representative sample of the risk group of the size estimated ? • Prevalence survey • performed in this • group • If applied to all • MSM will result • in over-estimation • of HIV prevalence Sexual risk activity in MSM HIV prevalence Low High Level of risk activity

  16. Is prevalence survey based on representative sample of the risk group of the size estimated ? Sexual risk activity in MSM Divide MSM into two categories: high and low risk – one prevalence survey in each HIV prevalence Low High Level of risk activity

  17. Approach based on prevalence surveys • Advantages • Assumptions are explicit and effect of changing them can • be investigated • Can provide up-to-date estimates • Avoids assumptions involved in other methods

  18. Approaches to estimation of the number of people with undiagnosed HIV infection in a country • based on prevalence surveys • based on reported numbers of HIV diagnoses • based on reported simultaneous HIV/AIDS cases

  19. Original “back-calculation” approach, before availability of treatment Number of AIDS cases diagnosed Calendar year

  20. Original “back-calculation” approach, before availability of treatment What can this tell us about how many people were infected and when they were infected ? Observed number of AIDS cases diagnosed Calendar year

  21. Curve linking infection to AIDS, without treatment Expected number of new AIDS cases per year after 1000 people infected - illustration Number of new AIDS cases per year 2 3 10 25 40 65 80 90 100 100 100 90 80 70 55 30 25 15 10 5 5 Curve known from seroconverter cohorts 0 5 10 15 20 Years from infection

  22. Numbers of AIDS cases expected over time if 1000 people infected at t0, t1 and t2 2 3 12 28 50 92 123 165 205 230 265 270 270 260 235 200 2 3 10 25 40 65 80 90 100 100 100 2 3 10 25 40 65 80 90 100 100 100 90 80 70 2 3 10 25 40 65 80 90 100 100 100 90 80 70 55 30 t0 t0 t1 t2 t0 Assume a certain number of people infected in each year, and calculate the expected number of AIDS cases by year - how close is this to the observed number ? Adjust the assumed number infected in each year to give the best fit to the observed number of AIDS cases

  23. Original “back-calculation” approach, before availability of treatment Estimated number of people infected (incidence curve) Observed number of AIDS cases diagnosed Calendar year From the incidence curve it was possible to work out the number estimated to be living with HIV by subtracting the number of deaths

  24. Revised back-calculation approach Question changes… How many people must be infected, and when must they have been infected, in order to produce the numbers of new AIDS we have observed ? How many people must be infected, and when must they have been infected, and what must the probability of getting diagnosed have been, in order to produce the numbers of new HIV diagnoses we have observed ? from: infection AIDS to: infection HIV diagnosis

  25. Curve linking infection to HIV diagnosis Expected number of HIV diagnoses per year after 1000 people infected Number of HIV diagnoses 145 145 145 125 100 85 70 50 40 30 15 10 8 7 6 5 4 3 2 2 2 1 Curve unknown 0 5 10 15 20 Years from infection

  26. Curve linking infection to HIV diagnosis Curve will differ by calendar year – more testing in more recent years Number of HIV diagnoses Infected in 2000 Infected in 1995 0 5 10 15 20 Years from infection

  27. Inferring incidence of new infections and the diagnosis rate from the number of new diagnoses Diagnosis rate No. of people New diagnoses

  28. Inferring incidence of new infections and the diagnosis rate from the number of new diagnoses Diagnosis rate No. of people New infections Diagnosis rate New diagnoses

  29. Inferring incidence of new infections and the diagnosis rate from the number of new diagnoses Diagnosis rate No. of people Diagnosis rate New infections New diagnoses

  30. Approaches based on reported numbers of HIV diagnoses and AIDS cases • Advantages • - Based on routine case reporting data only – does not • require prevalence studies • Can tell us about the predicted time from infection of those • undiagnosed

  31. Approaches to estimation of the number of people with undiagnosed HIV infection in a country • based on prevalence surveys • based on reported numbers of HIV diagnoses • based on reported simultaneous HIV/AIDS cases

  32. Table. Example calculation. n = total number undiagnosed. Approach based on reported simultaneous HIV/AIDS cases n = number of people with undiagnosed HIV Observed number of simultaneous HIV/AIDS diagnoses = n x 0.080

  33. Approach based on reported simultaneous HIV/AIDS cases Issues to consider - Distribution of CD4 count in undiagnosed - Under-diagnosis and under-reporting of AIDS

  34. Approach based on reported simultaneous HIV/AIDS cases • Advantages • Uses information on CD4 count at diagnosis • Particularly well suited to estimating number of • undiagnosed people with low CD4 count • See poster: Lodwick et al PE 18.1/5

  35. Summary and Conclusions Countries need to know the number of people living with HIV in various risk groups as a starting point for planning prevention measures and clinical care needs. This requires estimation of the number with undiagnosed HIV. At least three different types of approach exist. Each has advantages and disadvantages. Since they use different data they should provide independent estimates. If it is possible to use all approaches this will provide the greatest insight. Simple guidance is needed for countries on how to use the various approaches.

  36. How to implement ? - Dynamic iterative approach • produce document on guidance for countries on methods • for estimating prevalence of undiagnosed infection, given • current state of the field • through ECDC, try to enourage countries to implement • estimation • - this should help to stimulate more complete collection • of surveillance data • this process will be part of an ongoing process of evaluating • the relative value of alternative approaches • the guidance document on methods will evolve to include • more extensive data modelling approaches

  37. Acknowledgements Useful discussions with Daniela de Angelis Paul Birrell Valerie Delpech Matthew Law Caroline Sabin Jens Lundgren Colette Smith Alison Rodger Rebecca Lodwick Geoff Garnett Lodwick et al - poster PE 18.1/5, EACS, Cologne

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