Risky diagnoses

When you're cautious in taking sexual risks, you help both yourself and your partners. The former effect can be purely selfish optimisation. The latter could be due to other-regarding preferences in relationships where you care about the other person, or just a positive externality.

Why does this matter? Consider what happens if more HIV testing is funded. If you're behind the veil and don't know about your status or your partner's, prudence dictates some caution. If you're tested, you know your status but don't necessarily know your partner's. If you're tested and wind up being negative, then the returns to prudence are higher, as you know with certainty that you aren't already infected so you can make things worse, but you might also think that the risks you've taken so far are safer than you'd thought. And how much weight people put on the risk they impose on partners is hard to tell: if you find out you're positive, you either reduce caution if partners' utility doesn't weigh heavily, or increase caution if others' utility counts.

And so what people do on getting a test result is an empirical question.

Enter Erick Gong. He finds that ..., well, scratch that. I'll just quote from his introduction as it's rare to see this kind of clarity in academic writing. Bottom line: if you're going to fund free testing, couple it with funding for anti-retro virals so that when people find out they're positive, they do less harm to others.

I use data from the Voluntary Counselling and Testing (VCT) Efficacy study conducted in Kenya and Tanzania, which randomly assigned people into HIV testing and followed up with them six months later (The Voluntary HIV-1 Counselling and Testing Efficacy Study Group,2000). I construct a measure of people's beliefs about their HIV status before getting tested using questions on the baseline survey. To measure risky sexual behaviour, I use biological markers that are not susceptible to self-reporting bias. Data are collected on newly contracted infections of gonorrhoea and chlamydia (henceforward known as ‘sexually transmitted infection’ or ‘STI’) that occur during the study.5 An STI only results from unprotected sex with someone who has an STI and serves as an objective measure of risky sexual behaviour. The random assignment of testing enables me to identify the effect that HIV tests have on sexual behaviour conditioned on prior beliefs of HIV infection.

My findings suggest that HIV tests have the largest effects on risky sexual behaviour when test results provide unexpected information to an individual. I find that people surprised by an HIV-positive test (i.e. those who believed they were at low risk for HIV before testing and learn they are HIV-positive) have a 10.5 percentage point increase in their likelihood of contracting an STI compared to an HIV-positive control group who had similar beliefs of HIV risk but were untested at baseline.6 I interpret this increase in contracting an STI as an indication that those surprised by an HIV-positive test increased their risky sexual behaviour – an unintended consequence of testing. I estimate that these types on average increased their number of new partners by about 2.4 over a six-month time frame. People surprised by an HIV-negative test (i.e. those who believed they were at high risk for HIV before testing and learn they are HIV-negative) have a 5 percentage point decrease in the likelihood of contracting an STI compared to an HIV-negative control group with similar beliefs of HIV risk but were untested at baseline.7 This decrease in the likelihood of contracting an STI suggests that those surprised by HIV-negative tests decrease their risky sexual behaviour. Both of these results indicate that when people make decisions about risky sexual behaviour, self-interests dominate altruistic preferences. People who discover they are HIV-positive no longer have any incentive to practice safe sex (i.e. ‘nothing to lose’), while those who learn they are HIV-negative face greater incentives to avoid risky behaviour. Finally, when HIV test results agree with a person's beliefs of HIV status, the effects of testing on STI likelihood are not statistically different from zero. This is consistent with an economic model where the behavioural responses to HIV tests are greatest when they provide unexpected information.

I use the empirical results described above and combine them with a simple epidemiological model to simulate the short-run effect of rolling out HIV testing in an urban setting. While this exercise inherently requires a set of strong assumptions, and hence the results should be interpreted with caution, it does address an important policy question. I use the distribution of beliefs of HIV risk and actual HIV status from the Demographic Health Surveys in Kenya, Mozambique and Zambia – all three countries faced with a generalised HIV epidemic. I find that in the cases of Kenya and Zambia, testing leads to declines in new infections, while testing leads to an increase in infections in Mozambique. However, when ARVs are provided at an earlier stage in the infection, testing leads to large reductions in HIV infections in all three countries. Since ARVs greatly reduce the infectivity of HIV-positive individuals, the aggressive provision of ARVs can mitigate the risks posed by HIV-positive individuals who increase their risky sexual behaviour after testing.8

The other particularly interesting bit: surprise negatives yield reductions in risk-taking.

I'd missed this when it came out in 2015. I thank Ole Rogeberg for the pointer.