Risk Compensation and HIV Therapy: A Field Experiment in South Africa

Abstract

Risk compensation—the phenomenon positing that people adjust their risky behaviours in response to changes in perceived risks—could have the adverse effect of worsening health outcomes. Consequently, understanding potential behavioural responses is critical for designing effective public policies. This study examines the relationship between improved human immunodeficiency virus (HIV) therapy and subsequent risky sexual behaviour. Using a field experiment in South Africa, I estimate the causal effects of improved HIV therapy adherence on subsequent risky sexual behaviour among HIV-positive patients. I find that access to HIV therapy induces a substantial increase in the demand for unsafe sex.

Keywords:

• Field experiment
• risk compensation
• ex-post moral hazard
• health
• HIV
• Sub-Saharan Africa

JEL CLASSIFICATION CODES:

• D81
• D84
• H40
• I12
• I18
• J13
• K32
• O15

Acknowledgments

Lawrence Katz, Erica Field, Claudia Goldin, Amitabh Chandra, Roland Fryer, David Cutler, David Canning, Richard Zeckhauser, Joshua Angrist, Jessica Cohen, Ajay Mahal, Edward Glaeser, Emily Oster, Seema Jaychandran, Sendhil Mullainathan, Amanda Pallais, Andrei Schleifer, David Lam, Tristan Zajonc, Lorenzo Casaburi, William Dow, Gil Shapira, Willa Friedman, Pascaline Dupas, Harsha Thirumurthy, Gunther Fink, Margaret McConnell, Norman Daniels, Zoe McLaren, Rebecca Thornton, Martin Abel, José Luis Olea Montiel, Dana Rotz, Laszlo Sandor, Sam Asher, Melissa Dell, Danny Yagan, Eduardo Azevedo, Dmitry Taubinsky, James Mahon, Benjamin Schoefer, Mark Shepard, Livia Montana, Elif Yavuz, Oliver Hillenkamp, and Susan Watkins provided helpful comments to this project. David Titus, Matthew Bonci, William Lombardo, Sharon Itin, Steve Yeh, and Yee-Lynn Lee provided outstanding research assistance.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Generally, previous empirical studies on risk compensation (i.e. the so-called ‘Peltzman effect’) in high-income countries document moderate Peltzman effects (Peltzman, 1975; see also Evans & Graham, 1991; Keeler, 1994).

2 I use clinical data on CD4 count as a proxy of HIV therapy. Changes in individual CD4 count are a function of multiple patient factors, including improved adherence to the drug regimen.

3 South Africa’s HIV prevalence rate is among the highest in the world (UNAIDS, 2015, 2017, 2019).

4 Several countries, especially in Sub-Saharan Africa, subsidise HIV therapy take-up and adherence (Rosen, Fox, & Gill, 2007). South Africa’s public sector programme subsidises take-up and transport to clinics (Cluver et al., 2016; Govender, Fried, Birch, Chimbindi, & Cleary, 2015).

5 The study design is a natural field experiment based on the taxonomy in Harrison and List’s (2004) work.

6 Throughout the paper, I use ‘therapy’ to refer to the key independent variable, HIV therapy (T_i), in this analysis. Since I do not directly observe T_i and because T_i is endogenous, I instrument it with a binary indicator of whether a study unit belongs to Group B or Group C. I use CD4 blood count as a proxy for T_i. I use the term ‘treatment’ to refer to the experimental treatment groups. The experimental treatment groups in this study are patients receiving biweekly encouragement to adhere to an HIV regimen (Group B) and patients receiving biweekly encouragement to adhere to an HIV regimen plus a nutritional supplement (Group C); Group A, the reference group, received HIV therapy (antiretroviral treatment [ART]) only.

7 More generally, several studies examine general behavioural responses to various factors, such as information (Dupas, 2011; Gong, 2015; Paula, Shapira, & Todd, 2014), belief changes (Godlonton, Munthali, & Thornton, 2016), longevity (Fortson, 2011; Oster, 2012; Thirumurthy, Pop-Eleches, Habyarimana, Goldstein, & Zivin, 2012), risk (Robinson & Yeh, 2011), and testing (Delavande & Kohler, 2012) in the context of HIV in Sub-Saharan Africa. Friedman (2018) examines the effect of HIV therapy among HIV-negative individuals in Kenya. In the context of the US, previous studies also examine the effect of the availability of HIV therapy on risky behaviour (Chan, Hamilton, & Papageorge, 2016; Mechoulan, 2007).

8 See Peltzman (1975), Evans and Graham (1991), and Keeler (1994).

9 Friedman (2018), Godlonton and Thornton (2013), and Godlonton et al. (2016) examine compensatory behaviour in response to HIV-related factors in the context of a developing country and report contradictory findings. The studies that specifically examine risk compensation in response to information about the benefits of circumcision or in response to circumcision (Godlonton et al., 2016) find little evidence of risk compensation. In contrast, the studies that examine risk compensation in the context of HIV therapy or HIV testing rates (Friedman, 2018; Godlonton & Thornton, 2013) find some or substantial evidence of risk compensation, especially in response to HIV therapy provision. A potential reason for this discrepancy is that the protective benefit of HIV drugs (viral inoculation) far surpasses any benefits of the interventions studied by Godlonton et al. (2016) (i.e. information, circumcision, information about the HIV status of potential other partners within the community). Therefore, it is likely that compensatory behavioural responses are more substantial once affected individuals internalise the much more significant protective benefits of particular interventions (e.g. HIV drugs). A novel feature of my study is that I focus only on HIV-positive individuals, whereas Friedman (2018)’s sample includes individuals who are HIV-negative.

10 Using data from the US, Delavande, Goldman, and Sood (2010) and Sood, Wagner, and Wu (2015) examine how policy changes can influence HIV-related behavioural responses. Delavande et al. (2010) examine how the stringency of law enforcement for HIV-positive individuals who wilfully expose others to the infection influences subsequent risky sexual behaviour and find that prosecutions deter sexual activity, increase safe sex and increase sexual encounters with more promiscuous partners. Sood et al. (2015) find that an increase in insurance coverage increases HIV testing rates and disproportionately increases testing rates among high-risk groups.

11 Godlonton et al. (2016) examine behavioural responses to providing information on the efficacy of male circumcision or the effects of male circumcision.

12 Various observational studies, such as those by Thirumurthy et al. (2012) and Kennedy, O'Reilly, Medley, and Sweat (2007), explore the overall effect of therapy on unsafe sex but do not investigate the contribution of each sub-mechanism of behaviour change. All these studies rely on ordinary least squares (OLS) or propensity score matching, which do not fully account for selection into treatment based on unobservable individual characteristics.

13 Models III and IV of Appendix B (available in the Supplementary Materials) specifically focus on the relationship between improved HIV therapy and risky sexual behaviour among HIV-positive individuals.

14 The treatment regimens used in the public service were 1a (stavudine, lamivudine and efavirenz), 1b (stavudine, lamivudine and nevirapine) and 2 (azidothymidine, didanosine, lopinavir and ritonavir).

15 Inclusion criteria included being of a minimum age of 18 years, having commenced ART within the past five weeks and residing in the town or village in which the relevant health facility was located.

16 The trial adopted a Zelen-type double randomised consent design. This design is appropriate when blinding is not practicable or possible, the use of classical randomisation and informed consent procedures significantly threatens internal validity, the interventions are highly attractive, the control group receives standard care or the study focuses on a clinically relevant objective(s) and offers important new insights (Kaptchuk, 2001; MacLehose et al., 2000; Rains & Penzien, 2005). Within such a design, study participants are only offered the treatment to which they are randomised and can accept or reject treatment.

17 The actual therapy regimens in the intervention were 1a (d4T/3TC/efavirenz), 1b (d4T/3TC/NVP), and 2 (AZT/ddI/lopinavir/ritonavir).

18 ART readiness was assessed with various qualitative readiness indicators related to self-reported readiness to start antiretrovirals, motivation to start antiretrovirals, mindset about a positive outcome of the therapy and intention to start ART within 30 days.

19 A brief survey instrument was used to collect key information from the peer adherence supporters working in the project. The summary statistics for this group reveal that they were predominantly female (98%), their mean age was 35.8, the majority (56%) walked when they visited a study patient, the average time to a patient was approximately 39 minutes by foot and the majority (98%) met their clients at home.

20 To be considered eligible to become adherence supporters, individuals had to have been on ART for ≥ 12 months, have at least a grade-10 certificate and live within walking distance of the relevant clinic. Peer adherence reporters received basic training in ART and adherence support from staff at the School of Nursing at the University of the Free State. The training focused on seven main themes: facts about HIV/AIDS, ART, adherence support required by an ART client, nutrition, infection control at home and use of a health care team approach. On the fifth day of training, peer adherence supporters had their knowledge and practical skills assessed by the trainers via an oral test and a practical exercise.

21 The study team underscored the importance that the nutritional supplements were for study participants’ consumption. This was done at the time of obtaining written informed consent as well as at each visit by the peer adherence supporter. However, due to monitoring limitations, it is likely some participants shared the supplements with other household members.

22 Before the study, the research team had conducted a qualitative survey regarding the acceptability of the supplement to patients. All patients had indicated that, if provided with such services, they would eat meatballs and spaghetti in tomato sauce and would eat it on a weekly basis when it was provided as part of the research study.

23 While the accuracy is usually higher since the defined recall period is closer to the time of inquiry, this also creates a caveat for my results. This is based on numerous examples of recall difficulties in sex research, notwithstanding many other methodological factors affecting self-reported behaviour. In my setup, if E(ε_i,S_it) = 0 in (1), then OLS estimates unbiased by standard errors will be higher. Conversely, if E(ε_i,S_it) > 0, then OLS will be biased and inconsistent. I return to this point in the Results section when I compare the OLS with the IV estimates.

24 Self-reported outcomes are prone to potential misreporting (Mensch, Hewett, & Erulkar, 2003). Such misreporting occurs due to social norms and social networks (Mensch et al., 2003; Tavory & Swidler, 2009), urbanicity and gender (Fenton, Johnson, McManus, & Erens, 2001). To this end, and consistent with other studies, I report results based on self-reported outcomes as well as biological data, such as childbearing (e.g. Fishbein & Pequegnat, 2000). Importantly, even if misreporting or under-reporting of risky sexual behaviour occurred, the random assignment likely considerably mitigated any potential bias.

25 Written, informed consent was obtained from study participants by the nursing personnel at the respective clinics (for antiretroviral patients), as well as by the enumerator (for ART patients and ART patient/comparison households).

26 Because risky sexual behaviour is a sensitive subject, an obvious concern relates to misreporting and measurement error (Helleringer, Kohler, Kalilani-Phiri, Mkandawire, & Armbruster, 2011). Previous studies document that under-reporting numbers of sexual partners is a particular issue with regard to self-reported data. This phenomenon occurs due to social norms or potential stigma in most African cultures (Palen et al., 2008). To gauge the reliability of the self-reported outcomes, and consistent with previous studies (Fishbein & Pequegnat, 2000; Minnis et al., 2009), I report results based on the self-reported outcomes and outcomes based on biological data (childbearing). It is important to underscore the random assignment feature of this study in relation to possible reporting biases; even if under-reporting of risky sexual behaviour occurred, the only way the estimated effect size is biased is through a relationship between the reporting of sexual activity and the treatment group assignment. Since assignment into Group B and Group C was randomised, the measurement error or any potential reporting bias is likely to be unrelated to the instruments I employ in this study.

27 Relative to participants in Group B or C, participants in Group A attrited at a higher rate but the difference was not statistically significant.

28 Table A2 (in Appendix A available in the Supplementary Materials) reports the t-tests of the comparison of the attrition rates for each pair of treatment groups.

29 Angrist and Pischke (2008) formally prove this claim with Theorem 4.4.1. Given the features of Groups B, C and A, there is no simple way to disentangle the treatment effect due to the specific features of the adherence support provided.

30 I control for individual characteristics, such as education, gender, income, marital status, population group, household assets, household members and BMI.

31 The CD4 count is a proxy measure of the individual response to treatment.

32 I assume that the changes in the risky sexual behaviour of ‘untreated’ individuals (i.e. those who adhere less to the HIV therapy) are unrelated to the individuals who are treated more (i.e. those who adhere to HIV therapy more). Further, I assume that the nutritional supplement and the peer adherence support influence sexual activity only via changes in the individual CD4 count. Finally, I assume that the more one receives nutritional supplementation or adherence counselling support, the more one takes up his or her HIV therapy.

33 Though this test is supportive of the procedure used in the identification strategy, the method is not foolproof: such over-identification tests might not lead to a rejection even when not all instrumental variables are valid.

34 For both specifications, the F-statistics are well above 10.

35 As a robustness check (not reported), I also estimate specification (2) only using Treatment Groups A and C; the pattern of the results based on this robustness exercise is very similar to the results reported in Table 6.

36 For some of the patients for whom I lack CD4 data, I proxy treatment status when I observe a 10 per cent change in BMI between time points.

37 See Bunnell et al. (2006); Lakdawalla et al. (2006) analyse the impact of highly active antiretroviral therapy (HAART) on risky sexual behaviour among HIV-positive individuals in the US using arguably exogenous variations of the state portion of the Medicaid insurance generosity toward drug reimbursements as an instrument for treatment status. As in this study’s results, the authors show that a simple OLS method generates a spurious negative correlation between HAART and unsafe sexual activity (although I find this negative relationship only with the condom use variable). With the IV method, they find a positive relationship between access to treatment and risky behaviours.

38 Even if there is a random measurement error in T_i, the results will be biased downwards, not upwards.

39 First, a female who is in a long-term relationship with one partner is more likely to become pregnant than one who is having several short-term relationships. Second, pregnancy caused by younger males is unlikely to result in marriage or child support. Therefore, younger females who become pregnant might be more likely to abort if the father of the child is also young. Other biological proxy measures, such as sexually transmitted infections (STIs), are significantly more intrusive to collect than self-reported methods. Comprehensive data on STIs were not collected for this sample.