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HEALTH SYSTEMS RESEARCH UNIT

HEALTH SYSTEMS RESEARCH UNIT

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HEALTH SYSTEMS RESEARCH UNIT

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  1. HEALTH SYSTEMS RESEARCH UNIT Using Respondent Driven Sampling for HIV surveillance: Case study amongst sugar daddies in Cape Town Dr Mickey Chopra

  2. Using epidemiology to improve prevention • HIV epidemic is multiple epidemics, taking place in multiple sup-populations • To understand epidemic dynamics, need to know what is going on in these networks of individuals at enhanced risk because of their behavior and their environment • Men in peri-urban settings who have concurrent partners and age disparity are a particularly critical group • To develop effective interventions for specific populations

  3. How do we find out about these populations? • Qualitative and quantitative research: • Qualitative research (e.g. rapid assessment) • Case studies • Ethnography • Surveillance • Routine program data • Survey research

  4. For survey research and behavioral surveillance • Probability sampling • Time-location sampling • Respondent-driven sampling • Snowball sampling

  5. Limitations • Ideally probability sampling methodologies are best. But, do not efficiently reach hidden populations • No sampling frame • Would need huge sample sizes • Too costly • Time-location studies, snowballing • Open to biases • Can be difficult to conduct • Samples not self-weighted

  6. Respondent driven sampling • Is a form of snowball sampling which allows us to obtain a “probability sample” How does it work? • Start with initial contact or “seed” who meets eligibility criteria • After “seed” participates in survey, she becomes a “recruiter” • Each “recruiter” is asked to invite a maximum of 3 persons from her network of friends and acquaintances who meet the eligibility criteria

  7. The theory behind RDS • Given sufficiently long referral chains (i.e. 5-6 waves), the sample composition becomes stable regardless of the person (s) with whom you started • This means that, with respect to key characteristics and behaviors, the composition of the final sample will be independent of those selected as “seeds” • It also means that the final sample will be similar to the population-at-large from which you are recruiting

  8. RDS is a probability sampling method because… • If you follow the method and keep track of the linkages between recruiters and recruits • You can calculate selection probabilities and adjust for them in the analysis (similar to weighted analysis) • You can estimate the sampling error, and put confidence intervals around the estimates

  9. Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Seed

  10. Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Seed

  11. Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Markov Process

  12. Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Markov Process

  13. Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Markov Process

  14. Recruitment chain of Sex Workers starting with a street seed, HCMC, Vietnam, 2004 Seed

  15. Using Respondent-Driven Sampling to Study Spatial Networks (Using Zip Code Level Data) Network structure is revealed by successive waves of peer recruitment. The beginning point for one recruitment network, Seed #4, a black female bass player, is marked by the red pin near Times Square.

  16. Wave 1, Seed 4 Douglas Heckathorn, Cornell University, 2003

  17. Wave 2, Seed 4 Douglas Heckathorn, Cornell University, 2003

  18. Wave 3, Seed 4 Douglas Heckathorn, Cornell University, 2003

  19. Wave 4, Seed 4 Douglas Heckathorn, Cornell University, 2003

  20. All Waves, All Seeds Douglas Heckathorn, Cornell University, 2003

  21. Panning Out…. Douglas Heckathorn, Cornell University, 2003

  22. Wave 1, Seed 4 Douglas Heckathorn, Cornell University, 2003

  23. Wave 2, Seed 4 Douglas Heckathorn, Cornell University, 2003

  24. Wave 3, Seed 4 Douglas Heckathorn, Cornell University, 2003

  25. Wave 4, Seed 4 Douglas Heckathorn, Cornell University, 2003

  26. All Waves, All Seeds Douglas Heckathorn, Cornell University, 2003

  27. Surveillance amongst players in Khayelitsha • Eligibility Criteria • males were 18 years and older, who had more than one female sexual partner in the previous six months who was younger than 24 years old or more than 5 years younger than the participant 8 seeds non-randomly selected Each seed given 3 coupons and incentive in the form of cell phone vouchers

  28. Results • A total of 13 waves were achieved and 421 men recruited into the study. Most (79.9%; CI 74.0-86.0) were single and not residing with a sexual partner. Key SES differences

  29. Results • The men reported a range of between 2 and 39 partners in last 3 months with a mean of 6 and median of 5. The vast majority of these partners (5/6) were outside of, but concurrent with, their steady relationship • Just over a third of men (36%; CI 28.5-42.8) reported using a condom during their last sexual intercourse with their steady girlfriend. A higher proportion reported the use of a condom with their casual and once-off sexual partners (64.1%; CI55.8-70.2 and 57.6%%; CI 50.0-66.1% respectively).

  30. Results • The HIV prevalence was 12.3% (CI: 8.3, 16.9). • 2X higher than the HSRC Household Survey for Western Cape for similar aged men • Multi-variate analysis found that being older than 24 years and not using a condom during the last sexual intercourse with a once-off sexual partner were significantly associated with HIV infection

  31. Conclusion • Really important group of men who have multiple, concurrent partners • High levels of risk behavior • High levels of HIV prevalence that will probably increase substantially unless there is a profound change in behavior • No prevention intervention at scale presently focused upon this group • RDS effective methodology in establishing surveillance and perhaps interventions

  32. Acknowledgements • Loraine Townsend • Cathy Matthews • Lisa Johnstone • Heidi O Bra • Carl Kendal • CDC Pretoria