Scientific Approaches to Program Components: Opportunities Challenges and Impact

1. Scientific Approaches to Program Components: Opportunities Challenges and Impact Sevgi Aral, PhD Istanbul, Turkey March 21, 2011 National Center for HIV/AIDS, Viral Hepatitis, STD , and TB Prevention

2. Why now? • So many efficacious STI/HIV interventions – so little impact

3. Goal: To prevent individual acquisition To prevent individual transmission To reduce population incidence

4. Prevention of individual acquisition and transmission Reduction of population incidence

5. Efficacious Individual Intervention distribution of technology dissemination of knowledge cost affordability side-effects Access/ acceptability correct use consistent use correct timing correct dose long-term maintenance of above Adherence changes in risk perception ↑ other risk behaviors Disinhibition (risk compensation) Successful Prevention Of Individual Acquisition/Transmission

6. Successful Preventionof Individual Acquisition/Transmission Identification affected subpopulations sexual structures social practices; demographic patterns; migration & turnover organizational structures epidemic phase/epidemic trajectory identified need organizational capacity/financial resources synergies and antagonisms among interventions context XX intervention interactions Intervention Mix • sexual mixing patterns → who are exposed to pathogen • who are transmitting pathogen • sexual networks → which are central positions and roles Prevention targets numbers and social location of persons to prevent acquisition/ transmission pathways for introduction and scale-up of interventions Coverage & scale-up subpopulations sexual structures sexual behaviors intervention implementation POPULATION (SUBPOPULATION) INCIDENCE M & E Reduction of Population Incidence

7. B • B • B C D E • A • B • A • B • B • B Not only at individual level bridge populations men

8. Often it seems Prevention of acquisition and transmission (in individuals) framework is used when thinking about reducing population incidence ?? Is the hidden individualistic biomedical (psychological) model the culprit???

9. Opportunities Huge we are not doing an adequate job of: identification; determining intervention mix; determining prevention targets; coverage and scale-up issues; monitoring and evaluation (of population impact) assessing the relationships among these assessing the interactions between context and intervention schemes

10. How well do we assess the context? Epidemiology distribution and concentration of infection emergent clusters geographic distribution sexual structures → where the epidemic is going? epidemic trajectory epidemic phase Socio-demographic context behavior patterns sexual mixing patterns migration turnover in key populations key populations – powerful men, police, military

11. Perhaps we do not examine epidemiology sufficiently (or we do not respect what we observe sufficiently)

12. Paper # 137Identification of Localized Clusters of High HIV Incidence in a Widely Disseminated Rural South African Epidemic: A Case for Targeted Intervention Strategies Frank Tanser*1, T Bärnighausen1,2, and M-L Newell1,3 1Africa Ctr for Hlth and Population Studies, Univ of KwaZulu-Natal, Durban, South Africa; 2Harvard Sch of PublHlth, Boston, MA, US; and 3Inst of Child Hlth, UnivColl London, UK Session 38-Oral Abstracts http://www.retroconference.org/2011/Abstracts/41395.htm

13. Paper # 137Identification of Localized Clusters of High HIV Incidence in a Widely Disseminated Rural South African Epidemic: A Case for Targeted Intervention Strategies Conclusions:  Targeting efforts at settings where HIV transmission is most intense is crucial. Our study provides clear empirical evidence for the localized clustering of new HIV infections. The results show that even in a severely affected rural African community, interventions that specifically target, geographically defined, high-risk communities could be highly effective in reducing the overall rate of new infections. Session 38-Oral Abstracts http://www.retroconference.org/2011/Abstracts/41395.htm

14. HIV incidence across the study area with high-incidence clusters superimposed Session 38-Oral Abstracts http://www.retroconference.org/2011/Abstracts/41395.htm

15. Distribution of sero-conversions

16. ….and it is not only infections that cluster geographically…. In the Bagalkot district of Karnataka in South India 15 % of the villages accounted for 54% of all rural FSW Blanchard JF et al. Sex Transm Infect 2007; 83:i30-i36 d oi:10.1136/sti.2006.023572

17. In the UK…Project SIGMA found “….Most individuals (60%) who engage in AI do so only once or twice a month, but there is a long tail of those who do it much more. In terms of the amount of AI acts, one-tenth of the individuals are performing half of the acts of AI. The Gini coefficient of concentration is high (0.55).” Coxon PM and McManus TS. The Journal of Sex Research 2000

18. In the U.S. 20% of women account for 60% of vaginal sex acts in past 4 weeks and 24% of men account for 61% of vaginal sex acts in past 4 weeks Leichliter JS et al. Sex Transm Infect December 2010; 86(Suppl 3):

19. In the U.S. 20% of women account for 47% of opposite sex partners in past year and 20% of men account for 57% of opposite sex partners in the past year Leichliter JS et al. Sex Transm Infect December2010; 86(Suppl 3)

20. In the U.S. (county level analysis) 20% of the population accounts for 39% of Chlamydia 52% of Gonorrhea 64% of Primary and Secondary Syphilis Chesson HW et al. Sex Transm Infect December 2010; 86(Suppl 3)

21. Perhaps we do not assess “context” sufficiently (or we do not respect the context we observe adequately)

22. The concurrency debate

23. Aral SO.  Partner concurrency and the STD/HIV Epidemic.  Curr Infect Dis Rep 2010; 12(2):134-139.

24. Concurrency is more complex than it seemsMirjam Kretzschmar,1,2 Richard G. White,3 and Michel Caraël4 1Centre for Infectious Disease Control, RIVM, Bilthoven, The Netherlands 2Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, The Netherlands 3Infectious Disease Epidemiology Unit, Department of Epidemiology and Population Health and Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK 4Department of Social Sciences, Free University of Brussels, Belgium Corresponding Author: Dr MirjamKretzschmar, Corresponding Author’s Institution: University of Bielefeld Keywords: Polygyny, concurrency, HIV transmission Published in final edited form as: AIDS. 2010 January 16; 24(2): 313–315. doi: 10.1097/QAD.0b013e328333eb9d.

25. Concurrency is more complex than it seemsMirjam Kretzschmar,1,2 Richard G. White,3 and Michel Caraël4 However, the empirical basis proving that concurrency actually is the driving force behind the continuing high prevalence of HIV in sub Saharan Africa has been lacking. While some studies investigated the impact of concurrent partnerships on the prevalence of HIV in various sub-Saharan Africa populations [7-9], they were not able to identify concurrency as a strong explanatory factor. Also, epidemiological observations like the decrease of HIV prevalence in Uganda following the advocacy of the “zero grazing” strategy for HIV prevention [10, 11] is not conclusive evidence for the impact of concurrent partnerships on HIV transmission, because of the possibility of ecological inference fallacy. Now Reniers and Watkins demonstrate in an ecological study of HIV prevalence in 34 sub-Saharan Africa countries that concurrency in the traditional form of polygyny can even be negatively correlated with HIV prevalence [12]. Published in final edited form as: AIDS. 2010 January 16; 24(2): 313–315. doi: 10.1097/QAD.0b013e328333eb9d.

26. Concurrency is more complex than it seemsMirjam Kretzschmar,1,2 Richard G. White,3 and Michel Caraël4 It clearly shows the need to view concurrent partnerships in their social and cultural context. It matters what the motivation is for establishing concurrent partnerships, how they are distributed among men and women, and in how far they are anchored in the culture of a society. Published in final edited form as: AIDS. 2010 January 16; 24(2): 313–315. doi: 10.1097/QAD.0b013e328333eb9d.

27. It turns out that it is “mutual non-monogamy” or “symmetric concurrency” that drives STI/HIV spread…. Polygyny and symmetric concurrency: comparing long-duration sexually transmitted infection prevalence using simulated sexual networks ShaliniSanthakumaran, Katie O'Brien, Roel Bakker, Toby Ealden, Leigh Anne Shafer, Rhian M Daniel, Ruth Chapman, Richard J Hayes, Richard G White Sex Transm Infect 2010;86:553-558 doi:10.1136/sti.2009.041780 Non-monogamy: risk factor for STI transmission and acquisition and determinant of STI spread in populations Sevgi O. Aral, Jami S. Leichliter Sex Transm Infect 2010;86:iii29-iii36 Published Online First: 5 October 2010 doi:10.1136/sti.2010.044149

28. ….. “The role of STI in HIV spread” debate Differences among Mwanza Rakai Masaka ….

29. Perhaps we do not respect sexual structures adequately (or we do not integrate what we know about sexual structures into program design sufficiently)

30. Infections reflect where the epidemic is Sexual structures and mixing patterns reflect where the epidemic is going St. Petersburg; Saratov; ?? Tallin

31. Take home message: Sexual structures can be assessed through systematic rigorous rapid assessments

32. migration and population movements of all kinds where the infection is going need to be considered in program planning and design

33. Movement networks and disease transmission Networks of movements as explanation for spatio temporal spread of infections Matt Keeling et al. PNAS, May 2010

34. A study of geographic profile of partnerships • Proportionately more long distance partnerships among gc infected compared to those with chlamydia • Proportionately more long distance partnerships among co-infected compared to those infected with gc or chlamydia • Proportionately more long distance partnerships among chlamydia repeaters compared to non-repeaters. • ? Implications for effectiveness of PN Hippe and Jolly – In preparation Jolly – personal conveersation

40. How well do we plan and design prevention programs?

41. Do we determine prevention targets based on a good understanding of sexual structures?

42. Do we determine the intervention mix based on a good understanding of: preventiontargets affordability sustainability interactions with context synergies and antagonisms among interventions cost-effectiveness?

43. In program planning and design – do we consider the required coverage (for population impact)? Do we plan adequately for scale-up? Do our scale-up plans consider populations and health systems to be CAS’s?

44. Do we have the correct monitoring and evaluation plans in place? • Do we know what needs monitoring? • Do we know what can be effectively evaluated?

45. Questions • Effect of an intervention? • or • Effect of the interaction between context and intervention?

46. Questions (con’t) • Is it possible to tease out • the effects of a particular intervention on population incidence?

47. Questions (con’t) • Do Community Randomized Trials provide the best evidence for population impact of community interventions?

48. Choices as we delve into our knowledge base

49. Choices ….. (con’t)

50. Population health as complex adaptive system • Location • Life course perspective/ path dependence (chains of consequences) • Mutual determination feedback loops (feedback – feed forward) • Dynamic aspects • Spatial aspects • Multilevel aspects • Interactions between levels