1. Survival following HIV infection in developing countries in the pre-ART era Jim Todd 1, Judith Glynn 2, Milly Marston 2,3, Tom Lutalo 4, Sam Biraro 1, Wambura Mwita 3, Vinai Suriyanon 5, Ram Rangsin 6, Kenrad Nelson 7, Pam Sonnenberg 2,8, Dan Fitzgerald 9, Etienne Karita 10, Basia Zaba 2,3. for the Alpha Network. 1. MRC/UVRI, Masaka, Uganda 2. LSHTM, London UK 3. TAZAMA, Kisesa, Tanzania 4. RHSP, Rakai, Uganda 5. RIHS, Chiang Mai, Thailand 6. CoM, Phramongkutlao, Thailand 7. JHUSPH, Baltimore, USA 8. UCL, London, UK 9. GHESKIO, Port au Prince, Haiti 10.PSF, Kigali, Rwanda Presented at XVII International AIDS conference Mexico City 2008 satelite TH SAT 01 6.30 pm Thursday 8 th August

2. Alpha - a network of African community-based HIV studies Aims: Help HIV community cohort studies to analyse their longitudinal demographic data Compare and pool data from different sites to strengthen analytical conclusions Present the analyses in ways that are useful to national and international health planners Build the capacity of study sites to do the data analysis locally

3. Third workshop, Entebbe, Nov 2006 topic: survival post infection 6 member sites Community-based Wellcome Trust funded All from Africa –Masaka, Uganda –Rakai, Uganda –Kisesa, Tanzania –Karonga, Malawi –Manicaland, Zimbabwe –Hlabisa, South Africa 5 other studies Mixture of cohorts UNAIDS funded Gone global –South African miners –Thai military recruits –Thai blood donors & ptnrs –Haiti STD clinic –Rwanda ante-natal clinic –Umkhanyakude, South Africa

4. Why are survival and/or mortality important in estimating numbers infected? We measure prevalence, but need incidence for estimates and projections of epidemic Approximate relationships: incidence rate = prevalence / duration of survival post infection Incident cases = change in Prevalent cases + HIV Deaths If we under-estimate survival (= over-estimate mortality) we will: –over-estimate incidence, and therefore –over-estimate rate at which prevalence will grow in future –over-estimate survival gains due to ART

5. StudyCohort type Approx size: Adults / SCs Study start Sero- survey Demographic follow up Karonga, MalawiCommunity15,000 / ….20011continuous Hlabisa, South AfricaCommunity12,862 / 170200023 x year Kisesa, TanzaniaCommunity16,771 / 432199452 x year Masaka, UgandaCommunity21,609 / 422198917yearly, at sero Rakai, UgandaCommunity34,617 / 837199211at sero ~2 yrs Manicaland, ZimbabweCommunity22,815 / 32319983at sero ~3 yrs S. African minersOccupational2,565 / 1,9501991-VR+ active f/up Thai military recruitsOccupational… / 2331991-VR+ active f/up Thai blood donorsClinic837 / 1501988-VR+ active f/up Haiti STI patientsClinic… / 421992-active follow up Rwanda, ANC clientsClinic… / 1471986-active follow up Data come from 11 participating studies:

6. Shared data for this study Date of birth Sex Dates of first & last negative HIV tests Dates of first & last positive HIV tests (Estimated date of sero-conversion) Dates of entry into and exit from observation Exit type: death, loss to follow-up, (and for community cohorts only: out migration) Entry type: recruitment, (and for community cohorts only: birth, or in-migration)

7. Study Eligible sero- convrtrs % maleDeaths Total pers/yrs Max f/up time (yrs) Kisesa36850448909.9 Masaka31748991,37713.4 Rakai4544584164410.7 Manicaland1234581983.8 SA miners1,95010063611,93411.6 Thai donors17167739229.7 Thai military233100771,2537.9 Haiti42291624212.5 Rwanda147072 (+17)1,25316.1 Sero-converters and person-years follow-up

8. Non-African studies

9. Comparison of observed survival patterns 75% survival time Median survival time % alive at 2 yrs % alive at 5 yrs % alive at 7 yrs Kisesa 6.1 (4.8-9.1)-94%82%77% Masaka 5.8 (4.5-6.4)10.0 (8.3-11.7)96%77%64% Rakai 5.5 (5.0-6.3)-97%80%68% SA miners 6.6 (6.3-6.9)10.5 (10.0-10.8)97%86%72% Thai donors 5.6 (5.1-6.0)7.7 (6.7-9.0)99%85%56% Thai military 5.6 (5.3-6.3)7.9 (7.3-undef)97%82%60% Haiti STD 6.0 (4.7-7.3)7.4 (6.1-undef)98%89%59% Rwanda ANC 7.3 (6.0-8.3)12.2 (9.3-14.1)99%87%77%

10. Survival post infection – Kisesa, 1994 - 2005 Confidence intervals get wider as person-years observed shrink. Kisesa median survival could be as low as 9.2 years

11. Age at infection is very important in all East African studies

12. Also important in South Africa, but less differentiated in Thailand small numbers over age 25healthy worker effect?

13. Non-African studies Africa / non Africa differences are not explained by age at infection

14. Sex does not influence survival, whether or not we control for age

15. How much of the differences might be due to background mortality? “net” probability of surviving x years after infection, S N (x) can be estimated from observed “gross” survival probability, S G (x) and probability of surviving x years among the uninfected, S U (x) … same formula that is used for constructing “cause deleted” life tables to answer the question “what would be the effect on the overall life table of removing all deaths from a particular cause?”

16. Differences are relatively small, and only one none-African study has negative “controls”

17. Net survival adjusted to age 25-29

18. Before fitting models checked observed shape of hazard distributions (Kisesa example) Compared to exponential fit Compared to Weibull fit Apparent decline at longer survival times – but small numbers lead to wide CI – might be frailty effect? Hazard = instantaneous death rate

19. Regression analysis (Weibull model) 1.adjusted for age only

21. Age specific mortality rates among HIV infected persons in each study site Highest mortality rates and widest divergence at older ages → largest uncertainty introduced in models

22. Conclusion: implications for the numbers Previous UNAIDS estimates assumed median survival post infection of 9.5 years for all developing countries, based on only available earlier evidence: Masaka study, prior to 2001 Now using median of 11.5 years for Africa, and 7.0 years for some Asian epidemics – so new projections imply slower growth and lower current level in Africa, marginally faster growth in some Asian countries Speculation about reasons for differences are focusing on viral subtype

23. All the details are in … Ghys P, Żaba B, Prins M. (Editors) Survival and mortality of people infected with HIV in low- and middle- income countries: results from the extended ALPHA network. 2007. AIDS vol 21 supplement 6: S1-S4 Site-specific and joint studies, including papers on mortality patterns in sites that did not have long enough data series to estimate survival post infection. Also methodological papers on net mortality methods and censoring issues.