Randomized Evaluation of HIV Testing

1. Randomized Evaluation of HIV Testing Example of “Random Promotion” or “Encouragement Design” Rebecca Thornton University of Michigan

2. Recall – Promotion Design • Promotion was random: those who get the promotion are just like those who didn’t • Promotion worked: those who get the promotion actually act on it • Promotion doesn’t affect outcome behavior except through the behavior itself

3. HIV Testing • “Taking an HIV test is the single most influential driver for behavior change” (Global Business Coalition; Richard Holbrooke) • “Making people aware of their HIV status is the only way to make them change their sexual behavior” (Bill Clinton) • 55% of the total HIV/AIDS expenditure spent on testing (Mozambique) • Can we justify these financial resources being spent on HIV testing?

4. Conventional View #1 “Once people learn their HIV status, they will engage in safer sexual behavior” • Why might this be ambiguous for HIV positives? • Altruistic towards partner • …but safe sex is costly and there is no longer a return to use protection • Why might this be ambiguous for HIV negatives? • Increased return to safe sex • …but no longer a need to act altruistically • Individuals may also believe that they face lower risk

5. Conventional View #2 “It is difficult to get people to learn their HIV status” “I cannot accept to be tested. It is better to just live without knowing my status and see if I get sick than to know that I have AIDS in advance because then I cannot be living happily.” “The cost of stigma is quite high, more than the bus fare to town.”

6. Research Questions • How does the demand for learning HIV results respond to small changes in costs and benefits? • What is the impact of learning HIV results on sexual behavior?

7. Research Design • Limitations with existing studies and challenges • Self-reported behavior • How might this bias the results? • Selection • Who chooses to test or learn results • Where testing centers are located • How might self-selection bias the results?

8. Research Design • We want to measure the effect of learning HIV results • Think through your exact question and plan for analysis • Among HIV positives: compare those who know their results with those who don’t • Among HIV negatives: compare those who know their results with those who don’t • Potential for selection bias? • What would we randomize?

9. Research Design • How can we randomize getting HIV results? • Incentives to learn results (subsidies of price) • Distance to results centers (subsidies of time/transport)

10. Malawi Panel Study • Panel study (1998, 2001, 2004) • 125 rural villages in three districts – why important? • Randomly selected households (one in four) • Women and their husbands, adolescents • 2004: Offered free HIV and STI tests • 93% accepted (2,859) • 6.7% HIV Positive

11. Experimental Design • Randomly assigned voucher to learn HIV results • Between $0-$3.00 (average=$1.04) • 20% received no incentive • How much were the incentives? • Average daily wage: ~$1.00 • Smallest amount: $0.10 • Why randomize by individual?

12. Experimental Design • Results centers • Randomly placed based on household GPS coordinates and clustered by village • Average distance = 2 km

13. Experimental Design

14. Experimental Design

15. Experimental Design

16. Percent Learning HIV Results 80% 60% 40% 20% 0 Received No Incentive Received Some Incentive

17. Percent Learning HIV Results 80% 60% 40% 20% 0 0 0.1 0.3 0.6 1.1 1.6 2.1 2.6 Amount of Incentive (Dollars) -0.2 -0.5 -1.0 -1.5 -2.0 -2.5 -3.0

18. Percent Learning HIV Results Distance from the results center 90% 80% 70% 60% 0km 1 km 2 km 3 km 4km

19. Experimental Design • How do we measure the effect of learning HIV results (going to the tent)? • Need a follow-up with outcome variables – like what? • Follow-up survey (2 districts) • Gave $0.30 – why do this? • Sold condoms at half retail price • Only allowed individuals to purchase condoms from the money they were given • Design issues • Why have respondents purchase condoms? • Why give them any money? • Why cap the number of condoms allowed?

20. Impact of Learning HIV Status • Two months after HIV results were available • 62% reported having sex • 8% reported purchasing condoms • Sold condoms • 24% purchased condoms • 3.7 condoms on average • We want to measure the demand for safe sex. Why might using condom sales be problematic for this? • One idea: Distinguish between those that were sexually active and those that were not

21. Percent Purchasing Condoms • HIV Negatives who had sex at Baseline 50% 40% 30% 20% 10% 0 Did Not Get Results Got Results

22. Percent Purchasing Condoms • HIV Positives who had sex at Baseline 50% 40% 30% 20% 10% 0 Did Not Get Results Got Results

23. Effects after Learning HIV Results • HIV positives with a sexual partner: • Significant increase in the likelihood of purchasing subsidized condom • But the number of condoms purchased was small – only 2 additional condoms • No effects among HIV negatives • No effects among those not having sex • No effect on likelihood of sexual activity

24. How could this apply to MC? • Prices of MC • Prizes • Location of mobile clinics • Information – counselors, media • Other ways?

26. Distribution of Incentives – Day 1

27. Distribution of Incentives - overall