Mathematical modelling of HIV prevention intervention
Thiébaut, Rodolphea; May, Margaret T.b
aINSERM U897, ISPED, Bordeaux, France
bSchool of Social and Community Medicine, University of Bristol, Bristol, UK.
Correspondence to Rodolphe Thiébaut, INSERM U897, ISPED, 146 Rue Leo Saignat, 33076 Bordeaux Cedex, France. Tel: +33 5 57 57 45 21; fax: +33 5 56 24 00 81; e-mail: firstname.lastname@example.org
Received 18 October, 2012
Accepted 30 October, 2012
The aphorism ‘all models are wrong, but some are useful’ (attributed to George E.P. Box) is very well illustrated by the current topic of the prevention of HIV infection wherein clinical studies have demonstrated the efficacy of new interventions, but models are still needed to predict their impact on the population scale in the absence of evaluations using experimental designs. Recent randomized clinical trials have shown the efficacy of two new strategies based on antiretroviral drugs in reducing the risk of HIV transmission, namely early antiretroviral treatment (ART) of HIV-infected individuals and preexposure prophylaxis (PrEP) of uninfected individuals. These interventions come in addition to condom use and male circumcision. Having these tools in hand and accounting for potential limitations such as suboptimal adherence, the key question is how does their deployment translate to improvements in public health? This leads to many related questions on the implementation of these interventions and their impact on populations. We are talking about the translational research from ‘bedside to population side’. Here also, randomized clinical trials have a place, but these should be adapted for comparing interventions at the population level, for example, by using pragmatic cluster randomized clinical trials, along the lines of the STRETCH trial conducted to examine task shifting of ART from doctors to primary care nurses in South Africa .
Mathematical modelling is useful for bridging the gap between demonstrating the efficacy of the intervention and implementing it on a whole population. This approach helps in exploring complex scenarios of interventions in circumstances that may be difficult to implement (such as very long term effect). A modelling study  has clearly pushed the field ahead demonstrating that, under optimistic assumptions, the HIV epidemics could be eradicated by early diagnosis and treatment of newly HIV-infected patients. Many other modelling works have been published, some finding a more modest effect on HIV transmission . Models are also used to study the feasibility of large-scale interventions . For instance, it has been done for the cluster randomized ANRS 12249-TasP trial in South Africa using the CEPAC model . Key intermediate factors that led to the highest variation in HIV transmission (e.g. linkage to care immediately upon HIV diagnosis) have been isolated and used as intermediate endpoints in the current first phase study .
Another useful application of these models is for cost-effectiveness analyses as presented in the study published in the current AIDS issue . In this study, Cremin et al. compared the cost-effectiveness of the combination of several interventions. The impact of an intervention depends not only on its efficacy but also on its coverage. This latter factor is intrinsically limited by the costs. The model helps to quantify the potential benefit of PrEP interventions according to the current cost of the intervention and the amount of money that could be devoted to it. A first interesting result is the modest impact of PrEP at its current cost and the need to reduce the cost several fold in order for it to be useful at a population level. A key consideration is that, unlike in discordant partner studies wherein HIV prevalence is 50 : 50, in the population there are many more uninfected than infected individuals even in areas of relatively high HIV prevalence. Not surprisingly, treating the smaller infected population with ART is more cost-effective than treating the much larger uninfected population with PrEP in order to reduce incidence and stem the epidemic.
The model's cost frontier shows that it is most important to achieve higher coverage of ART started at a CD4 cell count of 350 cells/μl or less then to provide early ART to all who test positive. Nevertheless, early ART alone is insufficient to reduce HIV incidence to very low levels and the authors show that PrEP could play a role in addition to earlier ART in achieving greater overall reductions in incidence. Coverage of ART at any CD4 threshold could be increased by using point-of-care CD4 testing . This would enable clinics to stage patients rapidly onsite after enrolment, and would reduce opportunities for pretreatment loss to follow-up. As a result, more patients would be identified as eligible for and initiate ART earlier. Point-of-care testing might therefore be a critical intervention in achieving some of the scenarios described in this article.
These interesting results are not definitive, as many other approaches could challenge those studied here. Targeting high-risk populations may help to improve the cost-effectiveness but would make the intervention more complex. Model parameters would need to be updated whenever there are changes in costs or efficacy. Furthermore, other interventions under development such as an HIV vaccine may also contribute to the control of the HIV epidemics .
In any case, the scale-up of any single intervention or combination of interventions would need to be evaluated in practical settings, as unexpected or difficult-to-quantify barriers (e.g. adherence) may diminish the final impact.
Conflicts of interest
There are no conflicts of interest.
1. Fairall L, Bachmann MO, Lombard C, Timmerman V, Uebel K, Zwarenstein M, et al. Task shifting of antiretroviral treatment from doctors to primary-care nurses in South Africa (STRETCH): a pragmatic, parallel, cluster-randomised trial. Lancet 2012; 380:889–898.
2. Granich RM, Gilks CF, Dye C, De Cock KM, Williams BG. Universal voluntary HIV testing with immediate antiretroviral therapy as a strategy for elimination of HIV transmission: a mathematical model. Lancet 2009; 373:48–57.
3. Walensky RP, Paltiel AD, Losina E, Morris BL, Scott CA, Rhode ER, et al. Test and treat DC: forecasting the impact of a comprehensive HIV strategy in Washington, DC. Clin Infect Dis 2010; 51:392–400.
4. Boily MC, Masse B, Alsallaq R, Padian NS, Eaton JW, Vesga JF, et al. HIV treatment as prevention: considerations in the design, conduct, and analysis of cluster randomized controlled trials of combination HIV prevention. PLoS Med 2012; 9:e1001250.
6. Cremin I, Alsallaq R, Dybul M, Piot P, Garnett G, Hallett TB. The new role of antiretrovirals in combination HIV prevention: a mathematical modelling analysis. AIDS 2013; 27:447–458.
7. Jani IV, Sitoe NE, Alfai ER, Chongo PL, Quevedo JI, Rocha BM, et al. Effect of point-of-care CD4 cell count tests on retention of patients and rates of antiretroviral therapy initiation in primary health clinics: an observational cohort study. Lancet 2011; 378:1572–1579.
8. Barouch DH, Klasse PJ, Dufour J, Veazey RS, Moore JP. Macaque studies of vaccine and microbicide combinations for preventing HIV-1 sexual transmission. Proc Natl Acad Sci U S A 2012; 109:8694–8698.
antiretroviral therapy; cost-effectiveness; HIV; mathematical modelling; prevention
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