Definitions and Measures of Multiple and Concurrent Partnerships

1. The World Bank Global HIV/AIDS Program (GHAP) Definitions and Measures of Multiple and Concurrent Partnerships Masauso Nzima UNAIDS RST-ESA Johannesburg May 2009 UNAIDS RST-ESA Johannesburg

2. Presentation Outline • Changes that we want to see with MCP efforts • Indicators to measure the changes • Measurement methods • Where to next? Future developments UNAIDS RST-ESA Johannesburg

3. Some Background • Multiple and concurrent sexual partnerships facilitate rapid HIV spread (particularly in Southern Africa); • Discussions and research on specific role in the spread of HIV impeded by lack of clear definition and indicators of concurrency; • Hence focus of the 20-21 April meeting in Nairobi UNAIDS RST-ESA Johannesburg

4. Concurrency – What is it? Serially monogamous partnerships Concurrent partnerships time Same contact rate (5 partners/yr), but sequence of start and end dates is different UNAIDS RST-ESA Johannesburg

5. Definition of concurrency Defined as: “overlapping sexual partnerships where sexual intercourse with one partner occurs between two acts of intercourse with another partner” REFER TO ILLUSTRATION UNAIDS RST-ESA Johannesburg

6. Changes we want to measure • We need more MCP programmes and so we should see changes in the HIV community’s priorities and strategies. • Changing sexual practices in the context of MCP means that the sexual networks need to be broken, therefore we should see changes inindividual behaviours towards fewer multiple and concurrent partnerships (i.e. reduction in prevalence of concurrency), or increased harm reduction practices if concurrency is practiced. • Decreasing the acceptability of MCP in communities and families – changes in social norms. • Changes in how societies, communities, families and couples act towards each other UNAIDS RST-ESA Johannesburg

7. More MCP programmes, skills to deliver and funding for them INPUTS Improved relationships Changed social norms and society ‘rules and regulations’ about relationships More people reached with MCP programmes OUTPUTS OUTCOMES Fewer MCPs – lower prevalence of multiple partners, and lower prevalence of concurrent parnters OUTCOMES More harm reduction during MCP OUTCOMES Fewer new HIV infections IMPACTS UNAIDS RST-ESA Johannesburg

8. Indicators with which to measure the changes: INPUTS • percent of HIV funding spent on prevention (all prevention efforts, as opposed to treatment, care, impact mitigation and response management) [NASA] • percent of HIV prevention funding spent on behaviour and social change communication programmes [NASA] • number of policies and strategies relating to HIV that specifically address MCP as a key focus area for the HIV prevention response in the country [National composite policy index section of the UNGASS reporting processes] • percent of HIV implementers trained to implement programmes addressing MCP [Programme monitoring data] • percent of existing prevention intervention guidelines (VCT, PMTCT, STI, life skills, workplace programmes) that have incorporated MCP [Programme monitoring data] • number of research activities that address MCP programmes [Programme monitoring data] UNAIDS RST-ESA Johannesburg

9. Indicators with which to measure the changes: OUTPUTS • number of mass media spots that address MCP • percent of members of cabinet that have addressed MCP in public speeches of national interest • number of people reached with mass media activities focusing on MCP • percent of community-based organisations with programmes that address MCP behaviour for couples in relationships • number and percent of community leaders trained in MCP • number and percent of communities in which inter-personal communication (IPC) programmes relating to MCP (including family unit strengthening programmes) have been implemented • number and percent of people who have been reached with behaviour change communication programmes relating to MCP through interpersonal communications (disaggregated by sex and age group) UNAIDS RST-ESA Johannesburg

10. Indicators with which to measure the changes: OUTCOMES AT INDIVIDUAL & COUPLE LEVEL Changes in individual knowledge: • percent of men and women who can correctly identify the increased risks associated with MCP • percent of men and women who can correctly identify the behavioral changes that would minimise risk associated with MCP Changes in individual attitudes • percentage of men and women who think that MCP is acceptable • percentage of men and women who think that men/women who are not married and are having sex should have sex with only one partner • percentage of men and women who think that men/women should only have sex with their wives/husbands • percentage of men and women who intend to have multiple partners • percentage of men and women who discuss MCP with others UNAIDS RST-ESA Johannesburg

11. Indicators with which to measure the changes: OUTCOMES AT INDIVIDUAL & COUPLE LEVEL Changes in individual behaviour: • percent of men and women having multiple partnerships - PREVALENCE OF MULTIPLE PARTNERSHIPS • percent of men and women whose partners have multiple partnerships • percent of men and women having concurrent partnerships – PREVALENCE OF CONCURRENT PARTNERSHIPS • percent of men and women whose partners have concurrent partnerships • percent of young women 15 – 19 who had a first sexual partner who was 5 years or more older, and 10 years or more older • percent of men and women with multiple partners who have reported consistently using a condom during last sex with non-marital, non-cohabiting partner UNAIDS RST-ESA Johannesburg

12. Indicators with which to measure the changes: OUTCOMES AT INDIVIDUAL & COUPLE LEVEL Changes in how couples relate to each other and in relationships • percent of men and women in steady relationships who report that they have discussed their relationship values and agreed on safe sexual practices such as mutual monogamy or consistent condom use • percent of men and women who have a spouse/partner who works away from home and is not co-resident for more than 6 months of the year • percent of young men and women who are married UNAIDS RST-ESA Johannesburg

13. Indicators with which to measure the changes: OUTCOMES AT COMMUNITY & SOCIETY LEVEL • percent of men and women who believe that a woman is justified in insisting on condom use if she knows her partner has another sexual partner • percentage of men and women who believe a woman is justified in refusing to have sex with her husband/partner when she knows that he has had sex with another woman (or woman other than his wives, in the case of polygamy) • percentage of men and women who believe it is acceptable in the community for a man to have multiple sexual partners • percentage of men and women who believe it is acceptable in the community for a woman to have multiple sexual partners • percentage of men and women who believe that MCP is common in the community UNAIDS RST-ESA Johannesburg

14. Indicators with which to measure the changes: IMPACTS • Reduced HIV incidence UNAIDS RST-ESA Johannesburg

15. Main uncertainty about indicators for concurrency, is how to define the prevalence of concurrencyTHEREFORE, the rest of the presentation will focus on this aspect of MCP measurement UNAIDS RST-ESA Johannesburg

16. Measurement of the Prevalence of Concurrency Two main indicators agreed on at meeting in Nairobi: • Primary indicator – Point prevalence of concurrency “The proportion of adults aged 15 to 49 reporting more than one on-going sexual partnerships at an instant in time” • This is to be used as the main indicator of concurrent partnerships in a population; • It also best distinguishes between concurrency and rapid serial monogamy UNAIDS RST-ESA Johannesburg

17. Measurement of the Prevalence of Concurrency 2. Secondary indicator - cumulative prevalence of concurrency: “The proportion of adults aged 15 to 49 reporting overlapping partnerships over time (typically 12 months)” • It measures the intensity of exposure • Focuses on and can determine the duration of overlap • Provide data about the frequency of sex with each partner • Does not measure the rate of partner change (partner change rates that resemble ABABABABAB is epidemiologically more risky than partner change that resemble AAAABBBBBBBB (maybe due to migration and temporary separation)) UNAIDS RST-ESA Johannesburg

18. To measure these two indicators, need to ask 3 questions (about each partner) • When was the first time you had sex with this person? (cumulative prevalence and intensity) • When was the last time you had sex with this person? (cumulative prevalence and intensity) • Are you still having sex with this person?(momentary degree distribution, which is the point prevalence) UNAIDS RST-ESA Johannesburg

19. Measurement Methods UNAIDS RST-ESA Johannesburg

20. Measurement Methods Now that we know what to measure, the next question is, how do we measure it? • Proxy measure • Direct question method • Date method • Coital diaries • Partner’s concurrency UNAIDS RST-ESA Johannesburg

21. Using the Date Method - ‘Typical’ Sexual Behaviour Questionnaire UNAIDS RST-ESA Johannesburg Have you ever had sex? General behaviour questions: • Age at first sex • Marriage Counts of partners • Lifetime, past year, past month • New partners ‘Name Generator’ (Morris 2004) • Last X partners in Y duration • Stratified by partner type? ‘Name Interpreters’ (Questions about each partner) • Dates/duration of first and last sex • Partnership type (Marriage, steady, casual, one-time etc.) • Condom usage & coital frequency • Partner characteristics: age, location, sexual behaviour (other partners) • Partnership characteristics: type (spouse, casual, one-time, etc.), transactional, coerced, alcohol/drug use 21

22. Identifying Concurrent Partnerships (1) UNAIDS RST-ESA Johannesburg • Proportion of individuals where start and end dates of previous X partners overlap [in last Y duration] • Name generator + Date (MM/YYYY) of first and last sex with each partner • Name generator + duration since last sex (in days/weeks/months) + duration since partnership began (weeks/months/years) • Gives detailed network data • Sensitive to recall bias for dates/durations 22

23. Identifying Concurrent Partnerships (2) UNAIDS RST-ESA Johannesburg Number of current ongoing partnerships > 1 • Single question: How many sexual partnerships do you consider yourself to currently be in? • Name generator + “Is this partnership ongoing?” or “Do you plan to have sex with this person again” • Measures presence of stable partnerships • Misses once-off & short partnerships (is this “concurrency”?) • Identifies momentary degree distribution, less indicative of individual risk behaviour? • Reduces gender differences in # of partners? 23

24. Identifying Concurrent Partnerships (3) UNAIDS RST-ESA Johannesburg Proportion ever had another sex partner during current/most recent partnership [in the last Y duration] • Are you currently in a relationship? + Have you had sex with any other person since you began this relationship? • Name generator + “During this relationship did you have sex with any other person” or “During the past year within this relationship did you have sex with any other person” • More exposure to “concurrency” in longer relationships • Does not distinguish once-off encounters from long-term overlapping partnerships 24

25. Identifying Concurrent Partnerships (4) UNAIDS RST-ESA Johannesburg • Proportion reporting > 1 partner in last month • How many people have you had sex with in the past month? • Name generator + “How many times have you had sex with this person in past month?” • Does not guarantee strict concurrency • Alternate estimate of degree distribution • Compared to (2) more likely to include short or once-off partnerships 25

26. Novel interview methods ↑Privacy & confidentiality • Self-administered questionnaire (SAQ) • Feels more confidential • Issues: Literacy, skip patterns, patience! • Assisted self-completion questionnaire (ASCQ) • Interviewer only reads questions/answers but don’t record them • Computerised • Audio/Computer assisted self interviewing (A/CASI) • Palm top assisted self interviewing (PASI, PDA) • Confidentiality, reduce entry errors • Issues: new technologies, • Non computerised methods • Tape recorder • ICVI • PBS No gold standard UNAIDS RST-ESA Johannesburg

27. Illustration ICVI voting box PBS polling box Clipboard with enclosed PDA Self reported coital diaries

28. Importance of Interviewing Technique: Swaziland Percent reporting 1, 2, 3 partners in the last 3 months – 2006 (Ngudzeni ADP) • 2006-07 Swaziland DHS (nationally) • Females reporting more than one partner in the last 12 months: 2.3% Sources: James, V. and R. Matikanya (2006). Protective Factors: A Case Study for Ngudzeni ADP (Swaziland), World Vision Australia/Swaziland.; Central Statistical Office [Swaziland] and ORC Macro (Unpublished). Swaziland Demographic and Health Survey, 2006-2007. Calverton, Maryland, Central Statistical Office and ORC Macro. UNAIDS RST-ESA Johannesburg

29. Some breakthroughs at Nairobi Meeting • Concurrency reporting, in some cases, increases threefold, when using different data collection methods (voting boxes vs FTFIs); • HIV is higher among migrant populations, as well as those that report having concurrent partners (or whose partners report having concurrent partners); • A study in northern Tanzania (Kisesa) is showing some behaviour change; UNAIDS RST-ESA Johannesburg

30. Where to next? • A community randomised trial (‘break the network’ BCC interventions vs. standard HIV messaging) planned for 2010 • PSI and others are currently planning evaluations on MCP (Botswana & Mozambique); Namibia too UNAIDS RST-ESA Johannesburg

31. Additional areas of research • Further research into methods of measuring concurrency and sexual behavior, relationship between concurrency and HIV transmission and social norms and concurrent partnerships; • The need to continue measuring these behavioral aspects through population-based surveys* • These are also very useful to collect self-reported behavior on a large enough scale; • Need for more in-depth data gathering and that these should be triangulated with each other and with survey data UNAIDS RST-ESA Johannesburg

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