1. Diverse age patterns of HIV incidence rates in Africa Till Bärnighausen 5, Sam Biraro 1, JohnBaptist Bwanika 2, Simon Gregson 4, Tim Hallett 4, Victoria Hosegood 5, Raphael Isingo 3, Tom Lutalo 2, Milly Marston 6, Phyllis Mushati 4, Wambura Mwita 3, Anthony Ndyanabo 2, LeighAnne Shafer 1, Jim Todd 1, Makandwe Nyirenda 5, Alison Wringe 3 and Basia Żaba 6 for the Alpha Network. 1 MRC/UVRI, Masaka, Uganda 2 RHSP, Rakai, Uganda 3 TAZAMA, Kisesa, Tanzania 4 MHSPP, Manicaland, Zimbabwe 5 ACDIS, Hlabisa, South Africa 6 LSHTM, London, UK Presented at XVII International AIDS conference Mexico City 2008 session TUAC02 11.00 am Tuesday 5 th August

2. Founded 2005, funded by Wellcome Trust 6 independent HIV community-based cohort studies Uganda (x2), Tanzania, Malawi, Zimbabwe and South Africa Pool data for comparative studies and meta-analysis following on from capacity-building workshops Focus on demographic analyses (impact and risk) Aim: to maximise usefulness of data generated in community-based longitudinal HIV studies in sub-Saharan Africa for national and international agencies involved in designing or monitoring interventions and epidemiological forecasting. http://www.lshtm.ac.uk/cps/alpha/

3. Estimating HIV incidence: problems with indirect approaches ANC surveillance (data available throughout Africa) –individual and clinic selection biases –age detail usually unavailable –no individual follow-up –need models to generate national prevalence estimates and further models to generate incidence estimates National sample surveys (19 countries, 3 with 2 surveys) –no individual level follow-up between successive surveys –models must allow for participation rates and HIV-related mortality (incident cases = currently infected – infected earlier + deaths) –imprecision is worst when mortality is highest (older ages)

4. Changes in numbers infected between individually linked successive sero-surveys (1994, 1997, 2000 & 2003) in Kisesa cohort Survey interval

5. Changes in numbers infected between unlinked cross-sectional surveys net change Survey interval

6. 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

7. Estimating HIV incidence: is direct measurement the solution? Cohort studies (5 ALPHA countries) –not nationally representative –require long follow-up –expensive and intrusive re-testing –may be biased by in- and out- migration ALPHA network incidence analysis plan 1.examine participation biases, develop robust adjustment methods 2.descriptive analysis of age-specific patterns, inter-site comparisons 3.develop model representations for use in projection and estimation 4.investigate any surprising features

8. Incidence LEVEL measure: life time risk = cumulated risk to age 65 (cumulated incidence = 1 – cumulated proportion never infected) Kisesa, 1994-2004 Life time risk of HIV infection = 40% Kisesa, 1994-2004 Average HIV prevalence = 10.3%

9. Incidence PATTERN measures: modal age (location) and peak rate x 50 relative to level (dispersion) Mode 30 yrs Peak 1.5 % Mode 27 yrs Peak 1.2 % Kisesa, 1994-2004

10. 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 more dispersed * 40 x peak incidence for Hlabisa

11. Smoothed age-specific incidence hazard rates by sex and study site To compare incidence patterns in the South African cohort with the others demands some re-scaling

12. Normalised rates allow shape comparison Each age-specific point was divided by the peak value, so all patterns peak at one.

13. Age scales re-calibrated so origin is at peak Slide the curves left and right on the age axis in order to make all the peak values coincide. The age scale is now marked in years before or after the peak. Patterns from different sites are fairly similar before the peak, and up to 10 years after. Distributions are right skewed for both sexes, male distributions are more dispersed. Several sites show evidence of secondary peaks at older ages. When analysis is done by time period the secondary peaks emerge later.

14. Model representation of age-specific HIV incidence using cubic splines, ignoring secondary peaks

15. Cubic spline model: Measured directly T = peak rate M = modal age Fitted using function minimisation methods (or assume default values) α = start ageM - 15 ω = end ageM + 45 Parameters

16. What is the expected trend in age dispersion of incidence rates? HIV “kills off” people with risky sexual behaviour If behavioural traits persist over a person’s lifetime, at the beginning of the epidemic there will be a mix of people with “risky” and “safe” behaviour at all ages As the epidemic matures we expect older age groups to contain a lower proportion of people with risky behaviour than at the start So incidence patterns would be expected to fall more at older ages compared to younger ages even if there was no behaviour change

17. Why does incidence age dispersion increase in mature epidemics? The increasing dispersion (and secondary peaks) may be due to increased instability of marriages (widowhood and divorce) bringing older people back into the “marriage market” where they face an increased risk of forming an HIV discordant partnership. ALPHA sites have started further investigation: reconstructing marital histories – particularly in sites that have long time series of data: Kisesa, Masaka and Rakai.

18. all of these are non-marital partners, though some may. become spouses later not quite monogamous? Relatively large numbers of over 40s are in marriages that ended or became less stable

19. proportionately more non-marital partners than in “monogamous” first marriages Relatively large numbers of polygamous men report 3+ partners (but only 2 wives) Older men don’t stay single long

21. Conclusion People over 40 lead interesting lives Risk of infection continues to older ages There is widespread evidence of high risk behaviour within marriage and amongst those who are no longer married HIV prevention campaigns should not focus exclusively on young people