Sex, risk and incentives: The effect of new HIV prevention products on the market for commercial sex in South Africa

Transcription

1 Sex, risk and incentives: The effect of new HIV prevention products on the market for commercial sex in South Africa Matthew Quaife 1, Fern Terris-Prestholt 1, Peter Vickerman 2 1 Department of Global Health and Development, LSHTM 2 School of Social and Community Medicine, University of Bristol Health Economics Seminar 28/9/2016

2 Outline Introduction and rationale Economic perspectives of risk in sex work Methods: Repeated DCE with framing Results Conclusions & next steps

3 Introduction and Rationale South Africa has one of the largest and high profile generalised HIV epidemics in the world Very high HIV prevalence among female sex workers (FSW) 72% in Johannesburg 20% of annual infections (~350,000) in South Africa are connected with sex work

4 Introduction and Rationale South Africa has one of the largest and high profile generalised HIV epidemics in the world Very high HIV prevalence among female sex workers (FSW) 72% in Johannesburg 20% of annual infections (~350,000) in South Africa are connected with sex work For years, condoms were the only effective means of preventing HIV transmission Many reasons why condoms have not been effective at preventing a large epidemic

5 Introduction and Rationale New antiretroviral (ARV)-based HIV prevention methods on the brink of roll-out. Five products in development different ways of delivering ARV drugs Potential to increase agency of vulnerable groups no partner participation required

6 Introduction and Rationale New antiretroviral (ARV)-based HIV prevention methods on the brink of roll-out. But: Five products in development different ways of delivering ARV drugs Potential to increase agency of vulnerable groups no partner participation required Only oral PrEP has been proven efficacious Single purpose only protect against HIV (for the moment) Efficacy effectiveness => adherence issues Concerns of substitution from condom use

7 Economic incentives for risk in sex work

8 Economic views of risk in sex work Unprotected sex requires a sex worker to bear more risk, which is compensated by an increase in act price Theory of equalising differences - Rosen (1986) Evidence of a price premium for unprotected sex condom differential Rao (2003) JDE; Gertler, Shah and Bertozzi (2013) AEA; Arunachalam and Shah (2013) JHR; de la Torre (2010) JAE

9 Economic views of risk in sex work Unprotected sex requires a sex worker to bear more risk, which is compensated by an increase in act price Theory of equalising differences - Rosen (1986) Evidence of a price premium for unprotected sex condom differential Rao (2003) JDE; Gertler, Shah and Bertozzi (2013) AEA; Arunachalam and Shah (2013) JHR; de la Torre (2010) JAE HIV, other STIs, and unwanted pregnancy risk all matter Quast (2016) HE; Manda (2015); Robinson and Yeh (2011) AEA:AE; Levitt and Venkatesh (2007) Superfreakonomics Some other work on the extensive margin of sex work Della Giusta (2009) AEA:AE; Edlund and Korn (2002) JPE; Cameron (2003) EJLE

10 Hypothesised impact of prevention products Price premium of unprotected sex Supply1: Baseline Supply2: After Intervention Premium Decrease A B Demand: remains constant Quantity Increase Quantity of Unprotected Sex

11 Research Questions 1. What are sex worker preferences for client and act characteristics? 2. Does HIV status affect preferences? 3. Will incentives for unprotected sex change after the introduction of new HIV prevention products?

12 Methods: Development and sampling DCE development: Four focus group discussions, economic and epidemiological literature review Close working with sex workers, spokeswomen, and community advisory board Pre-pilot and pilot work Efficient design from piloting priors (D-error)

13 Methods: Development and sampling DCE development: Four focus group discussions, economic and epidemiological literature review Close working with sex workers, spokeswomen, and community advisory board Pre-pilot and pilot work Efficient design from piloting priors (D-error)

14 Methods: Development and sampling DCE development: Four focus group discussions, economic and epidemiological literature review Close working with sex workers, spokeswomen, and community advisory board Pre-pilot and pilot work Efficient design from piloting priors (D-error) Sample of 203 practicing FSWs via respondent-driven sampling 203 recruited 81 self-reported HIV positive (40%)

15 Methods: Repeated DCE with framing

16 Methods: Repeated DCE with framing

17 Methods: Attributes and analysis Price 50 Rand 100 Rand 200 Rand 400 Rand 800 Rand Condom No Condom Male or Female Condom HIV Risk You think this client has HIV You don't think this client has HIV STI Risk You think this client has an STI You don't think this client has an STI Sex Vaginal sex Anal sex

18 Methods: Repeated DCE with framing Introduction and survey questions Socio-economic status Sexual history Exposure to intimate partner violence Bought/sold transactional sex Risk aversion Subjective wellbeing and quality of life HIV knowledge

19 Methods: Repeated DCE with framing Introduction and survey questions First DCE task (no framing) You have the choice between providing services to one of two clients. Which would you prefer?

20 Methods: Repeated DCE with framing Introduction and survey questions First DCE task (no framing) Introduction to new prevention products

21 Methods: Repeated DCE with framing Introduction and survey questions First DCE task (no framing) Introduction to new prevention products Second DCE task (with framing) Now I would like you to choose between 10 more sets of clients, but this time I would like you to make your choices imagining you were using a product which prevented you from getting infected with HIV. This means that there would be no risk of getting HIV from any client, whether or not you use a condom.

22 Methods: Discrete choice models Utility, U, can be decomposed into a part we can observe, V, and a part we cannot, ε

23 Methods: Discrete choice models Utility, U, can be decomposed into a part we can observe, V, and a part we cannot, ε We can estimate how utility is affected by observable factors

24 Estimation: Multinomial logit P ijc = exp(x jcβ) σ J exp( X jc β) Estimates the probability that alternative j is chosen from choice set c

25 Estimation: Multinomial logit P ijc = exp(x jcβ) σ J exp( X jc β) Estimates the probability that alternative j is chosen from choice set c

26 Estimation: Multinomial logit P ijc = exp(x jcβ) σ J exp( X jc β) Estimates the probability that alternative j is chosen from choice set c Assumptions: All errors independent and follow type-1 extreme distribution Error terms and choice probabilities subject to IID (and therefore IIA) restriction Does not account for variation in preferences across individuals Computationally quick and easy to run, good to scope out model structure MNL unlikely to be acceptable final model

27 Estimation: Random parameter logit U ij = V ij + [η ij + ε ij ] β ik = β k + δ k Z i + η k v ik Allows for preference (taste) heterogeneity across individuals Allows for some correlation across error terms => relaxes IID (or IIA) assumption Does not allow for scale heterogeneity

28 Results Descriptive statistics Research questions: 1. What are sex worker preferences for client and act characteristics? 2. Does HIV status affect preferences? 3. Will incentives change after the introduction of new HIV prevention products?

29 Results Descriptive statistics Research questions: 1. What are sex worker preferences for client and act characteristics? 2. Does HIV status affect preferences? 3. Will incentives change after the introduction of new HIV prevention products?

30 Results: Descriptive Statistics HIV Negative HIV Positive Difference N (%) 122 (60%) 81 (40%) Age *** Education: Secondary or below 79% 73% Married 2% 2% Report consistent condom use with clients 98% 95% Experienced violence in previous year 34% 36% Drank alcohol at last sex 20% 22% Amount received for last sex act (Rand) In debt 35% 43% Amount earned through sex work in last week (Rand) 1,763 1,371 ** Receive all income from sex work 89% 81% *** * p<0.1, **p<0.05, ***p<0.01

31 Results: Descriptive Statistics HIV Negative HIV Positive Difference N (%) 122 (60%) 81 (40%) Age *** Education: Secondary or below 79% 73% Married 2% 2% Report consistent condom use with clients 98% 95% Experienced violence in previous year 34% 36% Drank alcohol at last sex 20% 22% Amount received for last sex act (Rand) In debt 35% 43% Amount earned through sex work in last week (Rand) 1,763 1,371 ** Receive all income from sex work 89% 81% *** * p<0.1, **p<0.05, ***p<0.01

32 Results: Descriptive Statistics HIV Negative HIV Positive Difference N (%) 122 (60%) 81 (40%) Age *** Education: Secondary or below 79% 73% Married 2% 2% Report consistent condom use with clients 98% 95% Experienced violence in previous year 34% 36% Drank alcohol at last sex 20% 22% Amount received for last sex act (Rand) In debt 35% 43% Amount earned through sex work in last week (Rand) 1,763 1,371 ** Receive all income from sex work 89% 81% *** * p<0.1, **p<0.05, ***p<0.01

33 Results Descriptive statistics Research questions: 1. What are sex worker preferences for client and act characteristics? 2. Does HIV status affect preferences? 3. Will incentives change after the introduction of new HIV prevention products?

34 Results: HIV Status MNL Attribute (1) SE (2) Interacted SE Price (*100 Rand) 0.10 * Condom used 3.90 *** *** 0.11 Vaginal sex 1.24 *** *** 0.08 Client looks HIV +ve *** *** 0.07 Client looks STI +ve *** *** 0.08 HIV Positive*Price 0.18 *** 0.05 HIV Positive*Condom used *** 0.16 HIV Positive*Vaginal sex * 0.11 HIV Positive*Client looks HIV +ve ** 0.10 HIV Positive*Client looks STI +ve Constant (opt-out) 2.91 *** *** 0.10 Log likelihood AIC No. obs * p<0.1, **p<0.05, ***p<0.01

35 Results: HIV Status MNL Attribute (1) SE (2) Interacted SE Price (*100 Rand) 0.10 * Condom used 3.90 *** *** 0.11 Vaginal sex 1.24 *** *** 0.08 Client looks HIV +ve *** *** 0.07 Client looks STI +ve *** *** 0.08 HIV Positive*Price 0.18 *** 0.05 HIV Positive*Condom used *** 0.16 HIV Positive*Vaginal sex * 0.11 HIV Positive*Client looks HIV +ve ** 0.10 HIV Positive*Client looks STI +ve Constant (opt-out) 2.91 *** *** 0.10 Log likelihood AIC No. obs * p<0.1, **p<0.05, ***p<0.01

36 Results: HIV Status RPL Attribute (1) SE (2) Interacted SE Price (*100 Rand) 0.10 * Condom used 3.90 *** *** 0.12 Vaginal sex 1.24 *** *** 0.09 Client looks HIV +ve *** *** 0.07 Client looks STI +ve *** *** 0.08 HIV Positive*Price HIV Positive*Condom used HIV Positive*Vaginal sex HIV Positive*Client looks HIV +ve *** 0.15 HIV Positive*Client looks STI +ve Constant (opt-out) 2.91 *** *** 0.13 Log likelihood AIC No. obs * p<0.1, **p<0.05, ***p<0.01

37 Results Descriptive statistics Research questions: 1. What are sex worker preferences for client and act characteristics? 2. Does HIV status affect preferences? 3. Will incentives change after the introduction of new HIV prevention products?

38 Results: Effect of PrEP (1) DCE 2: Attribute DCE 1: No Frame SE Protected Frame SE Price (*100 Rand) *** 0.03 Condom used 2.26 *** *** 0.17 Vaginal sex 1.45 *** *** 0.12 Client looks HIV +ve *** * 0.10 Client looks STI +ve *** *** 0.11 Constant (opt-out) 1.29 *** *** 0.11 Log likelihood AIC No. obs

39 Results: Effect of PrEP (2) Attribute Interacted RPL SE Price (*100 Rand) 0.07 ** 0.03 Condom used 4.18 *** 0.24 Vaginal sex 1.54 *** 0.17 Client looks HIV +ve *** 0.07 Client looks STI +ve *** 0.06 DCE2*Price 0.06 *** 0.02 DCE2*Condom used ** 0.08 DCE2*Vaginal sex DCE2*Client looks HIV +ve DCE2*Client looks STI +ve Constant (opt-out) 3.33 *** 0.27 Log likelihood AIC No. obs * p<0.1, **p<0.05, ***p<0.01

40 Hypothesised impact of prevention products Price premium of unprotected sex Supply1: Baseline Supply2: After Intervention Premium Decrease A B Demand: remains constant Quantity Increase Quantity of Unprotected Sex

41 Summary HIV positive FSWs have significantly different preferences than HIV negative FSWs Introducing new HIV prevention products could substantively change the market for commercial sex Strong evidence that price will be more influential to choices Price premium for unprotected sex reduces => increase in demand? Framing understood by participants Non-significant HIV coefficient for framed DCE DCEs a good way to avoid acceptability bias in reporting/predicting sexual behaviour

42 Limitations and next steps Does choice task really reflect those FSWs face in the real world? Perhaps not, but may reduce hypothetical bias compared to survey responses Integrate findings with: Economic model of commercial sex Mathematical transmission model => any change on product impact or costeffectiveness?

43 Acknowledgements All study participants Interviewers and staff of Progressus Research and Development Economic and Social Research Council Bill and Melinda Gates Foundation Wits RHI, Johannesburg

44 Thank you for