1. HIV research in the era of ART: changing priorities in Tanzania Basia Zaba SOAS 3 rd March 2011

2. Overview Introduction – data sources & measurement strategies – Monitoring the epidemic at a national level – Evaluating prevention and treatment responses – What difference does ART availability make? Results from observational studies of HIV in Tanzania – Ante-natal Clinic Surveillance: historical trends – Nationally representative X-section sample surveys – Clinical cohorts for studying ART patients – Community-based cohort studies – Combining data from different sources Tanzania compared to other African countries

3. Monitoring the epidemic at a national level Need for a representative population sample – men and women, all ages – rural and urban – not just sick people Convenience and cost – accessibility of health facilities – build on routine record keeping – locate in interested institution Nationally representative household survey with HIV testing (e.g. DHS) Ante-Natal clinic surveillance

4. Evaluating prevention and treatment response Prevention: need individual follow-up data to measure – rate of new infections – prior behavioural risks – influence of campaigns Treatment questions: – do people know they are infected? – what proportion accesses treatment? – how do those on treatment fare? Community cohort studies (e.g. Kisesa) Clinical cohort studies Referral studies Household surveys e.g. DHS

5. HIV observational study designs Cross-sectionalLongitudinal Facility basedANC surveillanceClinic cohorts Community basedDHS surveysCommunity cohorts Cross-sectional studies may be repeated several times to get overall trends, they are only called longitudinal if individuals are linked from round to round simple & cheap complex & costly

6. What difference does ART availability make? Greater willingness to learn HIV status – HIV is no longer a death sentence – stigma is still a big issue Ethical obligation of researchers to encourage people to learn status, and if necessary access treatment – study design allows for diagnosis as well as measurement – protocols must include “realistic” referral procedures Facility data analysis has to account for possible biases due to treatment seeking or test avoidance Need to link individual’s clinic and community records to study certain impacts – e.g. treatment drop out, partner infections

7. Ante-natal clinic surveillance Testing of pregnant women coming to ANC is still main source of national estimates of HIV trends world wide – Before ~2005 based on unlinked anonymous tests of residue of blood sample used for syphilis testing (no feedback) – Since ~2005, usually based on results of PMTCT testing (mothers get test feedback) Representative samples of Tanzanian clinics began to be selected after 2000, prior trend estimates must take account of changing clinic selection Clinic samples may be biased if women who think they are infected seek out clinics that do PMTCT testing

8. Tanzania: HIV prevalence data from ANC surveillance sites: 1988-1998

9. Deriving prevalence trends when reporting clinics vary over time Method developed by UNAIDS Only use data from clinics that report more than once Do a separate trend analysis for urban and rural clinics, then weight results by size of urban and rural populations Use median clinic prevalence rather than mean to give less weight to extremes

10. Fitting UNAIDS model to median prevalence in ANC clinics Peak in early 90’s Projected to level out at under 10%

11. Demographic & Health Surveys DHS: nationally representative sample surveys with an international standard questionnaire May include additional modules on special topics such as malaria prevention Recent studies have added collection of bio-markers, including anonymous HIV tests Tanzania has done more DHS surveys than any other country, including two with HIV testing (2004, 2007)

12. Tanzania HIV prevalence: DHS 2004

13. Adjusting UNAIDS model to observed DHS prevalence Projected to level out at under 9%

14. Putting together results of two DHS surveys Most regions experienced a significant prevalence decline between 2004-07

15. The UNAIDS prevalence trend model needs re-adjusting 2010 Decline has been much steeper than UNAIDS prediction

16. Treatment and care: interpreting data from different sources Community-based data on HIV diagnostic testing (but no direct questions about results or treatment) Referral data: what do HIV+ people do after learning they are infected Care and Treatment Clinic (CTC) follow-up data: what happens to people referred to clinics for care (monitoring) and treatment

17. Access to ART Access to ART HIV negative HIV positive – no ART need HIV positive - needs ART whole community Attend VCT Referred ART Attend ART eligible ART start ART clinic data only tell us this part of the story

18. Trend in knowledge of HIV status, % Know their HIV status

19. Estimated % of HIV infected in care (ART or pre-treatment monitoring), by region, 2010. Varies by region from 14% to 55% of HIV infected in care Overall estimate: 255,000 to 367,000 HIV +ve in care in Tanzania = 21%-30% of those infected (aim is 100%)

20. Access to HIV preventative treatment for mothers and newborn children in Magu district, 2009 −110 (66%) did not receive any PMTCT drug treatment at all −2 (1%) reported obtaining drugs only for the child; −15 (9%) only received drugs for herself; −41 (24%) received full PMTCT drug treatment for self and her child Of the 168 HIV-positive women who had a live birth:

21. HIV Care & Treatment Clinic (CTC) record follow-up Studies done as part of monitoring and evaluation of national Anti Retroviral Treatment (ART) programme In Tanzania, 666 out of 909 CTC facilities reported current and/or new numbers of patients receiving care 101 facilities have computerised patient record databases, can even trace patients moving between facilities (unique patient IDs) Can use the computerised data to construct a clinic cohort to study patient welfare and clinic attendance

22. Tracking enrolment, attendance & drop-out: Kisesa 2005-07

23. Death rates following ART initiation high death rates at start of treatment due to late initiation and drug toxicity

24. Median CD4 count following ART threshold for treatment initiation most people initiate treatment too late CD4 counts improve due to drugs and because of deaths of those with very low initial counts

25. HIV community cohort studies Whole communities are followed over long periods of time, with frequent (at least yearly) household censuses (demographic surveillance) Adults in the communities have HIV status measured at regular intervals (at least once every 3 years) and HIV status is individually linked to demographic data Also do periodic surveys of known HIV risk factors (e.g. sexual partnerships, condom use, blood transfusions) and possible consequences (e.g. infant mortality) and people’s knowledge and attitudes

26. Kisesa cohort study components

27. HIV status life-histories collected in cohort study new infection at risk of infection at risk of death HIV+ death

28. Describing incidence (rate of new infections) Crude measures (and trends): Specific patterns – incidence classified by: – age and sex – place of residence – marital status Comparing different populations: life time risk

29. Incidence trends by age, sex and residence

30. Incidence age pattern, Kisesa 1994-2004 Mode 30 yrs Peak 1.5 % Mode 27 yrs Peak 1.2 %

31. Incidence LEVEL measure: life time risk = cumulated risk to age 65 Kisesa, 1994-2004 Life time risk of HIV infection = 40% Kisesa, 1994-2004 Average HIV prevalence = 9.3%

32. Mortality and survival after HIV infection Most common way of comparing severity of HIV mortality across sites is to look at how long infected people survive without treatment Not ethical to try to measure this in the era of ART treatment, but community cohort studies like Kisesa have survival data collected over many years before treatment was available In Kisesa as in other sites we found that people infected at older ages have much worse survival patterns – this is not just because older people have higher mortality We can also study age-specific mortality patterns and compare infected and uninfected, and mortality among HIV infected persons before and after ART became available

33. Proportion surviving following HIV infection, Kisesa 1994-2005

36. HIV mortality and ART need In CTC clinics, individual ART need is assessed using CD4 count and clinical staging of HIV disease For the population as a whole, we can define the need for ART in an age group as the proportion who would die within the next 3 years if they didn’t get treatment Cohort data on age-specific HIV mortality in the pre-treatment era allow us to estimate proportions of HIV infected persons by age who would be expected to die within 3 years – this is the base-year treatment need for an ART program at start-up We can also determine the build up of treatment need in a successful program, with suitable assumptions about mortality of those on treatment

37. Theoretical build up of treatment need by program year

38. Initial ART need in 2005, Kisesa Total need, both sexes: 123 Total on treatment: 27

39. Cumulated ART need by 2008, Kisesa Total need, both sexes: 193 Total on treatment: 207

40. Tanzania compared to other African countries (data from other cohort studies in the ALPHA network)

41. Results: Incidence level & pattern comparison across sites Males have a higher life time risk of HIV infection... … an older age distribution of risk … … peak rates are broadly similar … … pattern is slightly less concentrated * 40 x peak incidence for Hlabisa

42. Graphical results: smoothed age-specific incidence rates by sex and study site To compare incidence patterns in the South African cohort with the others demands some re-scaling

43. Non-African studies