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Sexual networks and HIV infection Partnership concurrency and (some of) its possible implications Stéphane Helleringer PopFam.

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Presentation on theme: "Sexual networks and HIV infection Partnership concurrency and (some of) its possible implications Stéphane Helleringer PopFam."— Presentation transcript:

1 Sexual networks and HIV infection Partnership concurrency and (some of) its possible implications Stéphane Helleringer PopFam

2 HIV Prevention in eastern/southern Africa Generalized HIV epidemics: >1% of adults – Prevalence of HIV over 12% of adults in 9 southern African countries, “hyperendemic settings” Common approaches to HIV prevention in eastern and southern African countries: – ABC (Abstain, Be faithful, Use condoms) Incidence of HIV remains high in Southern and Eastern Africa

3 “New” Ideas in HIV Prevention Reducing infectivity of HIV in relationships: – Vaccines – Male circumcision – Microbicides ( compounds that can be applied inside the vagina to protected against HIV) – Increased access to Anti-retroviral treatment: “Treatment is prevention” Reducing exposure to HIV in populations: Behavior change interventions to address key features of sexual networks – Multiple and Concurrent Partnerships (“MCP”)

4 MCP and HIV epidemics Concurrent partnerships: Two or more sexual partnerships that overlap in time Opposed to “serial monogamy”: partnerships that follow each other sequentially Individual 1 (Concurrent) t  Individual 2 (Serial) A B A B C C

5 MCP and HIV epidemics Does not present specific risk for indiv. 1 vs 2. Higher risk of HIV infection for partners of an index case. Possible interaction with trends in viral loads: – Acute infection may amplify effects of partnership concurrency Individual 1 (Concurrent) Individual 2 (Serial) AB ABC C

6 Morris and Kretzschmar (1997) The more concurrency in a population: – The faster the spread of HIV – The larger the final epidemic size

7 MCP and HIV spread in Africa Heated debate (recently in Lancet): are concurrent partnerships the “key driver” of generalized HIV epidemics? – Does concurrency explain uneven spread of HIV between countries/regions? Yes, because: Models predict it; More concurrency in Uganda than in Thailand and the US No, because: More concurrency in west Africa than in east Africa, but less HIV (WHO 4 cities)

8 Debate largely focused on population-level effects of concurrency: – Polygyny and differences in HIV prevalence across the continent Difficult empirical question: concurrency is one of many properties of sexual networks Separate question: is concurrency an independent individual-level risk factor for HIV in sub-Saharan populations? MCP and HIV spread in Africa

9 Measures of partnership concurrency Different ways to measure concurrency: – Direct vs. indirect. Direct method: ask respondent about X more recent partners; then ask whether had other partners had the time. – “At any time during your relationship with _____, did you have sex with someone else?” – Differentiates well between serial and concurrent multiple partnerships

10 Measures of partnership concurrency Indirect method: ask respondent about X more recent partners; – Date of first sex with _____? – Date of last sex with _______? – Expect to have sex again with _______? Concurrency reconstructed from overlap between dates of consecutive partnerships Different measures available in LNS data

11 Data on MCP Ego-centric network data (e.g., DHS): only index case interviewed and tested Network data including partner tracing: partners elicited and contacted ABC ABC

12 Interventions addressing concurrency currently rolled-out in several countries (e.g., OneLove campaign in SA) Focus of the debate among prevention community has been on behavioral change exclusively: are BCC campaigns warranted given the evidence? Other implications? – Concurrency and biomedical prevention? – Concurrency and HIV treatment? MCP and HIV prevention in Africa

13 Objectives 1.Describe a network study conducted on Likoma Island, Malawi 2.Assess association of partnership concurrency and HIV infection at the individual-level on Likoma 3.Draw implications of the impact of sexual networks on HIV spread for HIV testing services


15 Likoma Network Study Longitudinal study investigating population- level sexual networks and their impact on HIV epidemics First data collection in 2005/06 Follow-up in 2007/08 Likoma

16 Likoma Island From Mozambique From Malawi

17 Likoma Island 7,000 inhabitants over 11 sq. miles Fishing community Importance of remittances from mainland migrants Limited market activity Isolated but frequent movement in/out of the island

18 Likoma Island: HIV Services Testing and counseling services available. ARV treatment available on the island since December 2005. Very limited prevention activities – Occasional ABY programs in schools


20 ACASI SEXUAL NETWORK SURVEY (Retrospective info On partnerships) LISTS OF 5 MOST RECENT SEXUAL PARTNERS (Names, nicknames Age, occupation Residence) HOUSEHOLD CENSUS (Detailed info on Names, nicknames, Age, socioeconomic Characteristics) Likoma Network Study Process Enroll all 18-49 years old Name generator (1) (2) (3) Phonetic and attribute-based matching


22 LNS: HIV testing Home-based HIV testing with all eligible individuals Team of 20 health workers, resident of the mainland Pre- and post test counseling Two parallel rapid test kits used for HIV diagnosis: – Concordant results: communicated to the respondent – Discordant results: referred to hospital


24 LNS: Participation 11 villages of the island included in the study population: – 2,433 eligible respondents aged 18-49, 2,176 conducted sexual network survey – 95 made no reports of relationships 1,684 tested for HIV infection: – 199 with known HIV infection (detected either in 2005/06 or in 2007/08)

25 Partnership concurrency among women

26 Partnership concurrency among men

27 Reliability of concurrency data 90 respondents (43 men, 37 women) re- interviewed on average 10 days after initial interview 81% of men made similar/consistent reports of concurrency during a given relationship 73% of women made similar /consistent reports of concurrency during a given relationship

28 Concurrency and HIV risk Index case engaged in… N(%)aOR (95% ci) Monogamous relationship 776 (11.3%)1 Serial multiple partnerships 339 (15.3%)2.01 (1.35, 2.99) Concurrent multiple partnerships 342 (12.3%)1.75 (1.14, 2.69)


30 Concurrency and HIV risk among partners Index case engaged in… N(%)aOR (95% ci) Monogamous relationship 37 (51.3%)1 Serial multiple partnerships 53 (58.5%)1.33 (0.50, 3.54) Concurrent multiple partnerships 34 (76.5%) 3.76 (1.12, 12.6)

31 Limitations Small sample sizes – Does association vary with relation type, index gender… Retrospective data: assessing association between concurrency and HIV among partners of HIV cases. – Not causation, need data on incidence among partners Sample selection bias due to: – Selective partner tracing / Selective participation in HIV testing Does not allow calculating duration of overlap between concurrent partnerships

32 Summary Concurrency widespread among both men and women on Likoma Island HIV transmission occurring in sexual networks: – Concurrent relationships may have an additional effect on transmission

33 Sexual networks and HIV testing Key problem for scaling-up of HIV services: – Limited case finding because low uptake of HIV testing and counseling Patients present late for treatment (compromises treatment outcomes) Limited population-level effect of secondary prevention (“prevention for positives”)

34 Sexual networks and HIV testing Large costs associated with increasing case finding: – Screening mechanisms Opt-out testing and counseling in hospitals and other clinical settings (e.g., Antenatal care) Door-to-door VCT: – Yearly visit to all households – Identifies co-residing partners of HIV index cases

35 Sexual networks: contact tracing Limited partner notification: – responsibility of the patient Contact tracing: elicit partners from HIV index case and make plans to inform partners of possible exposure: – Client-initiated/Provider-initiated Effective if: – High prevalence of HIV among partners (high yield) – Low costs of tracing partners

36 High prevalence of HIV among partners No significant differences in HIV prevalence between spouses and other (possibly) concurrent partners


38 Contact tracing: possible benefits High prevalence of HIV among partners of HIV index cases: – Potential for detection of infection clusters; particularly through tracing of non-marital partners – Potential to prevent further transmission in sexual networks Cost-effective if:

39 Contact tracing: possible benefits Save on case finding  increase (or maintain) spending on treatment Earlier initiation of treatment: – Improved clinical outcomes – Reduced transmission in sexual networks

40 Contact tracing: possible benefits Possible externalities on the strengthening of health systems Tracing capacity useful for: – Tracing of patients lost to follow-up in ARV treatment programs and other chronic care programs – Tracing of outbreaks of other infectious diseases/surveillance Development of health information systems: – Data confidentiality

41 Conclusion Concurrency may play a key role in HIV transmission on Likoma More studies based on longitudinal designs are needed Structures of Sexual networks (serial or concurrent) are a key input in planning delivery of health services

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