The epidemic in Côte d'Ivoire initially spread quickly1 with HIV prevalence in Abidjan, the country's largest city, reaching 38% of female sex workers (FSWs) in 1986 and 3%–10% of pregnant women in 1989.1–3 During those years, AIDS incidence in Abidjan was among the highest observed worldwide.4 This prompted the creation of the national anti-AIDS program in 1987.5,6 Subsidies for condoms were introduced in 1991 and retailed under the Prudence trademark.5 Multiple social marketing programs were initiated and Prudence condoms became widely available in more than 10,000 selling points nationally. Sales of this brand increased from 2 million in 1991, to 49 million in 1996, and to 120 million in 2003.6 Given the high prevalence observed among FSW and their clients, core group interventions were also implemented in 1991, mostly in urban areas.7
During the 1990s, the national public health response in Côte d'Ivoire primarily focused on the general population with some interventions targeting FSW and their clients. Despite its politico-military troubles, the country's response became more organized in the 2000s, with the establishment of a dedicated ministry to fight HIV and drafting of the first National Strategic Plan.8 The first 2 National Strategic Plans (2002–2004 and 2006–2010) recommended focusing activities on a number of key populations (KP). However, in practice, these KP included vast segments of the population, with different degrees of vulnerability to HIV acquisition and transmission.8,9 Men who have sex with men (MSM) were included as a KP starting in 2006. The last action plan (2011–2015) recentered prevention activities to the most vulnerable KP (ie, FSW, MSM, and young people) and prioritized treatment scale-up and prevention of mother-to-child transmission (PMTCT).10
In the early 2000s, antiretroviral therapy (ART) treatment scale-up was slow and access was limited for most people living with HIV, despite Côte d'Ivoire being part of UNAIDS initiative pilot phase for treatment access.11 Under this national initiative, only 3190 individuals received highly active ART between 1998 and 2002.12 A number of initiatives, including provision of free ART from 2008 onward, increased the number of people receiving ART to 77,531 by 2010.13 Similarly, only 636 HIV-positive pregnant women benefited from PMTCT in 200214 compared with 11,588 in 2010.13
Despite a relatively well-documented HIV epidemic and rapid national response, the impact achieved by existing HIV interventions in Côte d'Ivoire and the long-term contribution of KP such as FSW, MSM, and young women to HIV acquisition and transmission has never been estimated. With a national HIV prevalence of 3.7% in 2012,15 the lessons learned from an in-depth review and comprehensive analysis of the past national response in Côte d'Ivoire would improve the evidence base for existing and future health policies. Furthermore, recent studies have also shown the importance of understanding which subgroups of susceptible individuals are more likely to acquire new infections (ie, the source of HIV acquisitions) and which groups of HIV-positive individuals are more likely to transmit infections (ie, the source of HIV transmissions) in the long term using transmission dynamic models.16–18 The source of HIV transmission, also called the transmission Population Attributable Fraction (PAF),19 incorporates not only information on infections from index cases to their partners but also from such secondary infected partners to their partners (ie, chains of transmission). The transmission PAF metric is more useful than the source of acquisitions to identify epidemic drivers—defined as the group with a risk factor that accounts for most of HIV transmission and for which transmission could be most efficiently prevented if it was blocked. As such, dynamic models of HIV transmission, such has the one used in this study, provide more useful information to inform HIV prevention programs than static model such as the modes of transmission metric.16,17,20,21
Therefore, our study aimed (1) to estimate the population-level impact of past condom, ART, and PMTCT interventions on HIV transmission in Côte d'Ivoire and (2) to estimate the long-term contribution of subgroups of individuals with selected risk factors (eg, age, sex work, sex between men, and primary phase of infection) on HIV acquisition and transmission between 1976 and 2015.
Mathematical Model Structure
The dynamic compartmental model represents the open population (15–59 years old) of Côte d'Ivoire, growing at a constant rate,22 stratified into 8 sexual risk categories: low-risk and high-risk women (>1 partner per year), low-risk and high-risk men (>2 partners per year), FSW, their clients of female sex workers (CFSWs), MSM who report bisexual practices, and exclusive MSM (Supplemental Digital Content, Figure S1, https://links.lww.com/QAI/B17). People who inject drugs were omitted because they represent a small minority of the population (∼120 in Abidjan).23 The size of the different risk groups was held constant through time but FSW could leave sex work and integrate the high-risk women group (and vice versa). For ease of description, we will use the term general population to refer to the heterosexual population, excluding FSW, in the remaining of the text.
The population is further stratified in 4 age groups: 15–19, 20–24, 25–49, and 50–59 years old. A fraction of susceptible individuals enter the sexually active population at either ages 15 or 20 years. This fraction of 15- to 19-year-old virgins increases with time.15,24–26 All individuals aged 20–59 are assumed to be sexually active. Côte d'Ivoire has experienced high rates of immigration, which was accounted for in the model.27,28 Age mixing and mixing between low- and high-risk women and men was informed by the 2011–2012 Demographic and Health Survey.15
Sexually active susceptible individuals can get infected at a per capita force of infection that depends on the annual number of new sexual partners, type of sexual partner, HIV prevalence among these sexual partners, numbers of sexual acts per partner per year, sexual mixing pattern between age and sexual risk groups, type (vaginal/anal) and fraction of sex acts protected by condoms, partner's infectiousness (eg, varying by disease stage, ART treatment status, and viral suppression status), and the individual's susceptibility (eg, young women have higher risk of infection29,30). The reduction in HIV susceptibility of male circumcision was not explicitly parameterized but captured in per act transmission probabilities because more than 95% of men in Côte d'Ivoire are circumcised.15,26
Recently HIV infected and untreated individuals are assumed to progress from an initial short highly infectious primary infection through 4 disease stages of various durations (Supplemental Digital Content S2, https://links.lww.com/QAI/B17), proxied by CD4 cell counts: >500, 350–500, 200–349, and <200 CD4 cells/μL. Individuals not in the primary phase of infection can get tested at a rate that varies with calendar time, risk and age groups, and symptomatic status. Once tested, diagnosed HIV-positive individuals initiate treatment at a time-dependent rate that is a function of disease stage. Treated individuals become virally suppressed after a short time lag. Individuals on ART no longer progress through the different disease stages and have lower rates of AIDS-related mortality than untreated individuals.31,32 Treatment failures and ART discontinuation occur at fixed rates. In such cases, the disease will follow its natural progression unless individuals reinitiate ART. Individuals can exit the population through aging, natural mortality, or AIDS-related mortality.
A linked decision tree model was used to estimate vertical transmission of HIV from infected mother to their newborn using information from the dynamic compartmental model on the number of HIV-positive females and their ART status (Supplemental Digital Content, Figure S3, https://links.lww.com/QAI/B17). The probability of an HIV mother transmitting to her child depends on her CD4 count and PMTCT coverage over time: no intervention (up to 1999), single-dose nevirapine (from 2000 to 2005), dual prophylaxis (from 2006 to 2010), and the World Health Organization (WHO) option A/B (from 2010). Peripartum and postnatal transmission probabilities were abstracted from Rollins.33
Detailed information on sexual mixing, the model's ordinary differential equations, the equations for the force of infection, and those for PMTCT are presented as Supplemental Digital Content, Text S1–S4, https://links.lww.com/QAI/B17.
Data Sources for Model Parameterization
The complete list of all model parameters is presented as supplementary materials. Briefly, demographical parameters were abstracted from the World Population Prospect22 (Supplemental Digital Content, Table S1, https://links.lww.com/QAI/B17). Sexual behaviors parameters for the general heterosexual population were informed primarily by the 1994, 1998–1999, 2005, and 2011–2012 Demographic and Health Survey or AIDS Indicator Survey (Supplemental Digital Content, Table S2, https://links.lww.com/QAI/B17). The scientific literature informed biological, PMTCT, and sexual behavior parameters for MSM and FSW (Supplemental Digital Content, Tables S3 and S4, https://links.lww.com/QAI/B17). Additional analyses of secondary survey data were also performed to inform MSM and FSW parameters. Data on past intervention trends, such as condom use, treatment, and PMTCT, were abstracted from government reports and the scientific literature (Supplemental Digital Content, Tables S5 and S8, https://links.lww.com/QAI/B17). Information on historical trends for these interventions is presented as supplementary material (Supplemental Digital Content, Text S5, https://links.lww.com/QAI/B17). Access to programmatic data was facilitated through on-site visits and face-to-face meetings with program managers.
Model Calibration and Data Sources
Uncertainty in the model's parameter estimates was explicitly taken into account by eliciting previous distributions. Incremental Mixture Importance Sampling was used to better approximate the posterior distributions of interests.34 A Latin Hypercube sampling35 algorithm was used at the initial stage to sample 5 million parameter sets from their previous distributions and to select those that satisfied the prespecified constraints36,37 for the following model outputs: national HIV prevalence by sex and age groups in the general population,15,26,38 HIV prevalence among FSWs,1,7,39–41 HIV prevalence among CFSW,42 HIV prevalence among MSM by age groups,43 and ART coverage13,44–49 (Supplemental Digital Content, Table S9, https://links.lww.com/QAI/B17). Some surveys conducted before 1991 were adjusted for their imperfect diagnostic assays (Supplemental Digital Content, Text S6, https://links.lww.com/QAI/B17). The model's likelihood was calculated by summing the binomial log-likelihoods of the following outcomes: HIV prevalence by sex and age groups (general population), FSWs, CFSW, and MSM (Supplemental Digital Content, Table S9, https://links.lww.com/QAI/B17). The importance sampling stage was then performed to obtain the posterior distributions of interests by sampling 1000 parameter sets of the 41,146 identified (these are summarized using their median, 2.5th, and 97.5th percentiles).
The dynamic system was initialized in 1970 and, in 1975, HIV was seeded in FSW, clients of FSW, and MSM. The model was coded in MATLAB and solved numerically using an Euler algorithm with a time step of 0.05 year.
Estimating Intervention Impact
The impact of interventions was estimated using averted fractions (AF) derived as the relative difference between the cumulative number of adult incident HIV infections (for PMTCT, impact was assessed among infants) under the observed levels of interventions and a counterfactual scenario with lower coverage. The counterfactual scenarios for ART and PMTCT assumed the absence of these interventions over the relevant period of assessment. However, as condom use may have been promoted for contraception and/or prevention of other sexually transmitted infections, using a counterfactual that assumed no condom use was deemed unrealistic. Instead, 3 counterfactual scenarios were explored by setting condom use levels constant either to (1) those observed in 1987 when the national response to HIV was first organized, (2) those observed in 1990 before subsidies for condoms were introduced, or (3) 1993 levels, 2 years after subsidies, but before achieving full coverage. AF for condom use were computed for the 1987–2015 period for all 3 counterfactuals. The AF of observed increased condom use among specific risk groups (ie, general population, FSW, and MSM) were also assessed while maintaining other risk groups at their counterfactual condom use levels. Finally, we investigated the plausibility that the trend in HIV prevalence was in fact the result of the self-reported increases in condom use using the approach described by Williams et al50 and Pickles et al.51 This was achieved by examining if the model could be calibrated to the observed HIV prevalence data (see Supplemental Digital Content, Table S9, https://links.lww.com/QAI/B17) if we assumed that levels of condom use had not increased beyond their 1987 levels (using the same previous distributions as for the original model calibration).
Estimating Sources of Acquisition and Transmission
We characterized the HIV epidemic by the source of HIV acquisitions and the source of HIV transmissions in the long term. The first is measured by the distribution of cumulative incident infections (ie, the fraction of all new HIV infections that are acquired by a specific risk group over a defined period of time). The second is measured by the transmission PAF of a specific risk factor (ie, the fraction of all new infections directly transmitted by individuals with certain risk factors and the infections transmitted by those who have acquired infections from them: secondary transmission). Both measures were estimated for different risk and age groups for successive 10-year periods over 1976–2015.
Specifically, the for a specific risk factor (the incidence with the risk factor present is noted IRisk) over a given period (t−t0) is estimated as follows:
INo Risk is derived by setting the probability of HIV transmission from individuals with the selected risk factor to all their partners to zero. This was independently performed for each sex and age group and selected behavioral (for CFSW, only transmission from clients to women not involved in sex work was set to zero) and biological risk factors (primary infections, undiagnosed infections, untreated diagnosed infections, and treated infections). Because individuals may share more than 1 risk factor and susceptible individuals can get infected by different partners, it is known that the transmission PAF can sum to more than one.52
Model outputs fitted well HIV prevalence data (Fig. 1) and other epidemiological and intervention outcomes (Supplemental Digital Content, Figures S4–S6, https://links.lww.com/QAI/B17).
Impact of Past Interventions
Available data suggest that the rise in condom use in the 1990s was more pronounced among FSW than among the general population and MSM (Fig. 2). Model results suggest that condom use averted a median of 61% [95% credible interval (CrI): 56%–66%], 50% (44%–58%), and 23% (20%–26%) of all new HIV infections during 1987–2015 that would have occurred if condom use had remained at their 1987, 1991, and 1993 levels, respectively (Fig. 2). If condom use had only increased in the general heterosexual population, and remained constant at the 1987 baseline level in other risk groups, only 19% of new infections would have been averted between 1987 and 2015. This contrasts with the 46% of new HIV infections that would have been averted if condom had been scaled-up only among FSW (while condoms remained at the 1987 baseline levels in other risk groups). Estimates using the 1990 and 1993 counterfactuals yielded slightly more conservative impact estimates (Fig. 2). Overall, the AF for condom use increases among MSM alone were small (≤1%) across counterfactual scenarios. Condoms nevertheless were effective among this group and averted a minimum of 28% of MSM HIV infections (results not shown). The hypothesis that increases in condom use can explain the course of the HIV epidemic in Côte d'Ivoire is highly plausible. In fact, assuming no increase in condom use beyond their 1987 levels was highly inconsistent with the HIV prevalence data: none of the 5 million Latin hypercube samples fitted our outcomes (see Supplemental Digital Content, Table S9, https://links.lww.com/QAI/B17).
Data suggest that coverage of ART and PMTCT activities (Supplemental Digital Content, Tables S7 and S9, https://links.lww.com/QAI/B17) was low before 2010 (Fig. 3) and remains suboptimal, reflecting in part ART eligibility criteria. These were expanded in 2012 to offer treatment to all patients with HIV with CD4≤350 cells/μL or with WHO stage 4 clinical disease.53 However, the country has yet to adopt the new WHO guidelines for ART.54 Approximately 8% of all HIV infections were estimated to have been averted by ART overall during 2000–2015 (Fig. 3). The largest impact occurred during 2010–2015, where ART prevented an estimated 15% of all HIV infections owing to an estimated increase in ART coverage from 16% (10%–19%) in 2010 to 38% (28%–42%) of HIV-positive individuals in 2015. Similarly, the impact of PMTCT among infants was very modest during 2000–2005 because of suboptimal coverage of HIV-positive mothers receiving PMTCT in 2005 (8% coverage; 95% CrI: 6%–11%). From 2010 to 2015, however, PMTCT prevented an estimated 23% of vertically transmitted infections. Yet, an estimated 17% (14%–20%) of infants born to HIV-positive mothers were still acquiring HIV in 2015.
Source of HIV Acquisition
Young women aged 15–24 years have been disproportionally affected by HIV over the entire 1976–2015 period, representing 26%–32% of new acquired infections compared with young men (<21%) (Table 1). The estimated fraction of new infections acquired by FSW, composing only 1.6% (1.2%–2.2%) of the modeled female population, declined from 13% in 1976–1985 to 5% in 2005–2015 (Table 2). The fraction of HIV infections acquired by CFSW also decreased over time from 41% to 18%. Only ≤4% of new infections are estimated to have occurred among MSM, which nevertheless represents a high burden of infection for MSM given that they represent 1.2% (0.9%–1.6%) of the male population (see Supplemental Digital Content, Table S1, https://links.lww.com/QAI/B17 for the prior distribution regarding the size of this risk group). During 1976–1985, 36% of new infections were acquired by women not engaging in sex work, which increased to 49% over 2005–2015, as a result of HIV spreading from FSW to CFSW and further.
Source of HIV Transmission
At the beginning of the epidemic (1976–1985), sex work accounted for most new HIV transmission (PAF = 95%; 95% CrI: 91%–97%; Table 2). In comparison, sexual mixing between CFSW and their non-FSW partners alone contributed 51% of new transmissions; consistently more than high-risk men or high-risk women (Table 2). The contribution of FSW and CFSW declined over time, as HIV prevalence increased in other risk groups and condom use in commercial sex acts rose. Early on, 15- to 24-year-old women and men, which also include young FSW and CFSW, respectively transmitted 86% and 69% of new infections declining to 30% and 15% in 2005–2015 (Table 1). If we exclude FSW, young women consistently acquired more infections than they transmitted, except over 2005–2015, where they acquired the same proportion of infections as they transmitted (Table 2). As HIV prevalence increased over time, the relative contribution of the different age groups to HIV transmission shifted to older ones, particularly for older men who transmitted 3 times more than young men in the latest period. Despite the high HIV prevalence predicted among MSM (18% in 2015; 95% CrI: 12%–22%), model estimates suggest that the contribution of MSM to overall HIV transmission has remained marginal over the course of the epidemic (<4%).
Comparatively to the behavioral risk factors, the contribution to transmission of individuals in the primary phase of infection decreased from 86% in 1976–1985 to 25% during 2005–2015 (Table 2), reflecting that proportionally more people are in a later and less infectious HIV stage in mature HIV epidemics. Over 2005–2015, 87% of new HIV infections were attributed to undiagnosed infections, 24% to untreated but diagnosed infections, and 6% to treated infections.
With renewed worldwide impetus to sustainably curb the HIV epidemic,55 understanding changes in transmission dynamics is an important step toward informing future health policies. Our results show how the contribution of different risk groups to HIV acquisition and transmission has evolved over time in Côte d'Ivoire, mirroring the maturation of the HIV epidemic and past interventions. As for other epidemics driven by unprotected sex work,56,57 commercial sex contributed markedly to HIV transmission early in the epidemic. This contribution diminished with time, however, partly because of rising condom use by FSW, reflecting the importance of sustaining FSW interventions.50,58,59
Importantly, bridging of CFSW with their non-FSW partners steadily transmitted around 44% of new HIV over 2005–2015; more than the contribution of acute infections (>25%) and similar to older adult males. Thus, interventions targeting CFSW—promoting condom use with their partners not involved in sex work and/or increasing ART coverage in this group—could be important components of the future HIV response.60 Focusing activities on this risk group, however, has its own challenges.61 Studies of venue-based interventions have nevertheless shown that clients can, at least partly, be reached,42,62–64 and structural interventions to increase safety of sex work environment have the potential to limit HIV acquisition and transmission by CFSW.65 The contribution of high-risk men and women to the overall epidemic, which was initially modest, has increased slightly over time, never exceeding 18%. This reflects that members of these groups have relatively high number of partners and lower condom use than FSW. Interventions targeting high-risk men and women could also help reduce their contribution to transmission; perhaps less cost-effectively than for FSWs who represent a much smaller fraction of the population and who are easier to identify and reach.
Importantly, young women have been, and remain, a vulnerable population, as they consistently acquired more infections than other age groups and young men. However, their contribution to transmission, especially early on, was mainly mediated through sex work. After excluding young FSW, the contribution of young women to transmission never exceeded their contribution to HIV acquisition at any epidemic stages, highlighting their vulnerability and lesser role as epidemic driver. This contribution also needs to be interpreted in light that young women represent a sizeable fraction of the female population (∼38%) compared with FSW or CFSW.
In line with available empirical estimates, our model projected an HIV prevalence of 18% among MSM in 2015, much higher than that of the general population, and slightly above that of FSW during 2005–2015. Nevertheless, MSM contributed little over time to new HIV infections acquired and transmitted. This reflects in part the limited data available, suggesting a smaller size of the MSM population, lower number of partners, and more limited sexual mixing with the general population, as compared to FSW. As sex between men remains a sensitive topic in Côte d'Ivoire,66 obtaining accurate estimates of the size of this group is a challenge. Yet, given the high prevalence and modeled incidence, both MSM and FSW remain disproportionally vulnerable to HIV, despite uncertainties in their respective population size estimates.67
In general, our estimates of the proportion of infections acquired by the different KP over 2005–2010 are quite different from the 2010 Modes of Transmission (MOT) assessment for Côte d'Ivoire.68 For example, we estimated that FSW and MSM acquired between 3%–7% and 2%–6%, respectively, of all new HIV infections over 2005–2010. MOT estimates for 2010 of the proportion of HIV infections acquired was much lower for FSW (1%–2%) and higher for MSM (6%–19%).68 These differences could be explained by the fact that the MOT model used slightly different estimates for the size of those KP, only examined HIV acquisition over 1 year, did not include the changing infectivity of HIV by disease stage or the impact of ART, did not account for differential sexual mixing by age, and has not been calibrated to epidemiological data. For these reasons, concerns have been raised regarding the reliability of MOT outputs for sub-Saharan Africa.21
Our model suggests that condom interventions have been very effective in averting infections so far. This corroborates findings that behavior changes, through increased condom use, can curb transmission in diverse settings of sub-Saharan Africa.69,70 In Côte d'Ivoire, this impact is predominantly because of the documented increases in condom use by FSW,7 which may have prevented up to 46% of all infections. This is comparable with results of condom interventions for FSW implemented in Benin50 and Burkina Faso.58 Condom use among MSM had a more limited impact on overall transmission because of limited sexual mixing of MSM with the general population. However, increasing condom use by MSM, currently estimated at 73% of sex acts protected, could avert more infections in this group. Other interventions, not currently implemented in Côte d'Ivoire, such as pre-exposure prophylaxis could be considered if current demonstration projects found this to be cost effective.71,72 Altogether, our results highlight the importance of targeted HIV prevention for high-risk groups. Indeed, such targeted preventions have been shown to be highly cost effective.51,73
As for the more recent ART and PMTCT interventions, they have yet to achieve their full population-level effectiveness because of suboptimal coverage, especially during the early 2000s. Over the last 5 years (2010–2015), however, ART is estimated to have prevented 15% of all infections in adults. A slightly higher relative impact was observed over the same period for PMTCT interventions, which averted 23% of vertically transmitted infections. Elimination of MTCT would nevertheless still require improving interventions' access to all HIV-positive pregnant women.
Limitations of this study are chiefly due to data issues. First, behavioral and epidemiological data on MSM populations are scarce in Côte d'Ivoire such that assumptions for the whole country needed to be extrapolated from MSM surveys conducted in few urban areas. Similarly, studies concerning FSW, albeit more numerous, were mostly conducted in Abidjan. Most parameters extracted from triangulations of the various FSW were, however, consistent. Second, and not unique to our study, self-reported data on sexual behaviors could be biased. Finally, the PMTCT sub-model was not calibrated because of the absence of reliable national data on vertical HIV transmission. However, results from this decision tree on the number of HIV-positive infants were cross-validated and consistent with UNAIDS estimates.
Our study has many strengths, as it is the first transmission modeling exercise that characterized in detail the HIV epidemic in Côte d'Ivoire, identifying long-term drivers of HIV acquisition and transmission over serial periods.16 This exercise is based on a comprehensive review of all available epidemiological and programmatic data, which was complemented by on-site interviews with program managers.74 Second, our impact measures explicitly incorporate the dynamical nature of HIV and are estimated from reliable simulations of the epidemic against well-defined counterfactual scenarios. Third, the Bayesian framework used enables us to explicitly integrate parameters uncertainty in our inferences.
This study modeled the HIV epidemic in Côte d'Ivoire at the national level. Using a similar approach, important additional insights could be gained by examining how geographical heterogeneities in demography, sexual behaviors, and intervention coverage, influence transmission dynamics and effectiveness of interventions. Furthermore, a model-based epidemic appraisal that distinguishes between infections acquired and transmitted over the short and longer terms, at different stages of the epidemic, can be used to develop a framework informing the choice of risk groups to target and interventions to adopt.
This article suggested that the national response in Côte d'Ivoire, especially early condom interventions targeting FSW, was effective at preventing an important number of HIV infections. This is particularly important because the greater allocation of financial resources for ART over the last few years resulted in funding declines for condoms and prevention activities for KP.74 Our results support the strengthening of KP condom activities for the national response to be sustainable. Notwithstanding challenges in reaching these men, enhanced prevention among clients, often a neglected risk group, could further reduce transmission. Concomitantly, further efforts are required to develop intervention packages to increase access and uptake of ART, as well as to increase PMTCT coverage. The greatest contributors to new infections in the treatment cascade were undiagnosed infections, highlighting the importance of increasing testing and linkage to care.75 Detailed implementation research on how best to scale-up this intervention would be valuable. Continuous evaluation of these interventions is recommended if they are to achieve their maximal population-level effectiveness.
The authors thank the UNAIDS Regional Office for West and Central Africa and the HPTN Modelling Centre (HPTN). The authors also thank Josephine Aho, Avi Hakim, and Sheree Schwartz for providing additional information on surveys of FSWs and MSM conducted in Côte d'Ivoire.
1. Koffi K, Gershy-Damet GM, Peeters M, et al. Rapid spread of HIV infections in Abidjan, Ivory Coast, 1987–1990. Eur J Clin Microbiol Infect Dis. 1992;11:271–273.
2. Odehouri K, De Cock KM, Krebs JW, et al. HIV-1 and HIV-2 infection associated with AIDS in Abidjan, Côte d'Ivoire. AIDS. 1989;3:509–512.
3. Denis F, Barin F, Gershy-Damet G, et al. Prevalence of human T-lymphotropic retroviruses type III (HIV) and type IV in Ivory Coast. Lancet. 1987;1:408–411.
4. De Cock KM, Porter A, Odehouri K, et al. Rapid emergence of AIDS in Abidjan, Ivory Coast. Lancet. 1989;2:408–411.
5. Amat-Roze J. Sexually transmitted diseases and HIV/AIDS in Côte d'Ivoire. In: Setel P, Lewis M, Lyons M, eds. Histories of Sexually Transmitted Diseases and HIV/AIDS in Sub-Saharan Africa. Westport, CT: Greenwood Publishing Group; 1999:43–64.
6. MLS. Communication pour le changement de comportement dans le domaine du VIH/SIDA en Côte d'Ivoire: Analyse des stratégies et de la réponse de 1985 à 2004. Abidjan, Côte d'Ivoire: Ministère de la lutte contre le SIDA, Projet Retro-CI, Centre Africain de Recherche et d'Intervention en Dévleoppement, Johns Hoplkins Bloomberg School of Public Health/Center for Communication Programs; 2005.
7. Ghys PD, Diallo MO, Ettiègne-Traoré V, et al. Increase in condom use and decline in HIV and sexually transmitted diseases among female sex workers in Abidjan, Côte d'Ivoire, 1991–1998. AIDS. 2002;16:251–258.
8. MLS. Plan stratégique national de lutte contre le VIH/SIDA 2002–2004. Abidjan, Côte d'Ivoire: Ministère Délégué auprès du Premier Ministre, chargé de la lutte contre le SIDA; 2002.
9. CNLS. Plan stratégique national de lutte contre le VIH/SIDA 2006–2010. Abidjan, Côte d'Ivoire: Secrétariat technique du Conseil National de Lutte contre le Sida; 2006.
10. CNLS. Plan stratégique national de lutte contre l'infection a VIH, le SIDA et les IST 2011–2015. Abidjan, Côte d'Ivoire: Conseil National de Lutte contre le SIDA; 2011.
11. Msellati P, Vidal L, Moatti J. L'accès aux traitements du VIH/Sida en Côte d'Ivoire—Évaluation de l'initiative Onusida/Ministère Ivoirien de la Santé Publique: aspects économiques, sociaux et comportementaux. Paris, France: Agence nationale de recherches sur le sida; 2001.
12. MSLS. Suivi de la déclaration d'engagement sur le VIH/SIDA (UNGASS). Période Janvier—Décembre 2002. Abidjan, Côte d'Ivoire: Ministère de la Santé et de la Lutte contre le Sida; 2003.
13. DIPE. Rapport annuel des indicateurs VIH du secteur Santé en Côte d'Ivoire 2010. Abidjan, Côte d'Ivoire: Direction de l'Information, de la Planification et de l'Évaluation, Ministère de la Santé et de l'Hygiène Publique; 2011.
14. MLS. Stratégie nationale de communication pour le changement de comportement face au VIH/SIDA 2005–2008. Abidjan, Côte d'Ivoire: Ministère de la Lutte contre le SIDA; 2005.
15. INS, ICF International. Enquête Démographique et de Santé et à Indicateurs Multiples de Côte d'Ivoire 2011–2012. Calverton, MD: Institut National de la Statistique et ICF International; 2012.
16. Mishra S, Pickles M, Blanchard JF, et al. Distinguishing sources of HIV transmission from the distribution of newly acquired HIV infections: why is it important for HIV prevention planning? Sex Transm Infect. 2014;90:19–25.
17. Shubber Z, Mishra S, Vesga JF, et al. The HIV modes of transmission model: a systematic review of its findings and adherence to guidelines. J Int AIDS Soc. 2014;17:18928.
18. Mishra S, Sgaier SK, Thompson LH, et al. HIV epidemic appraisals for assisting in the design of effective prevention programmes: shifting the paradigm back to basics. PLoS One. 2012;7:e32324.
19. Mishra S, Steen R, Gerbase A, et al. Impact of high-risk sex and focused interventions in heterosexual HIV epidemics: a systematic review of mathematical models. PLoS One. 2012;7:e50691.
20. Mishra S, Boily MC, Schwartz S, et al. Data and methods to characterize the role of sex work and to inform sex work programs in generalized HIV epidemics: evidence to challenge assumptions. Ann Epidemiol. 2016;26:557–569.
21. Prudden HJ, Watts CH, Vickerman P, et al. Can the UNAIDS modes of transmission model be improved? A comparison of the original and revised model projections using data from a setting in west Africa
. AIDS. 2013;27:2623–2635.
22. UNDP. World Population Prospects: The 2015 Revision—Key Findings and Advance Tables. New York, NY: Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat; 2015.
23. Bouscaillou J, Evanno J, Prouté M, et al. Santé des personnes usagères de drogue à Abidjan en Côte d'Ivoire—Prévalence et pratiques à risque d'infection par le VIH, les hépatites virales, et autres infections: Médecins du Monde et le Fonds Mondial—Alliance Côte d'Ivoire; 2014.
24. N'Cho S, Kouassi L, Koffi A, et al. Enquête Démographique et de Santé, Côte d'Ivoire 1994. Calverton, MD: Institut National de la Statistique et Macro International Inc.; 1995.
25. INS, ORC Macro. Enquête Démographique et de Santé, Côte d'Ivoire 1998–1999. Calverton, MD: Institute National de la Statistique et ORC Macro; 2001.
26. INS, MLS, ORC Macro. Enquête sur les Indicateurs du Sida, Côte d'Ivoire 2005. Calverton, MD: Institut National de la Statistique (INS), Ministère de la Lutte contre le Sida (Côte d'Ivoire) et ORC Macro; 2006.
27. RGPH. Recensement général de la population et de l'habitat 2014-Principaux résultats préliminaires. Abidjan, Côte d'Ivoire: Secrétariat Technique Permanent du Comité Technique du RGPH; 2014.
28. Zanou B. Rapport d'analyse du recensement général de la population et de l'habitation—1998. Thème 2: Migrations. Abidjan, Côte d'Ivoire: Institut national de la statistique; 2001.
29. Mackelprang RD, Baeten JM, Donnell D, et al. Quantifying ongoing HIV-1 exposure in HIV-1-serodiscordant couples to identify individuals with potential host resistance to HIV-1. J Infect Dis. 2012;206:1299–1308.
30. Naicker N, Kharsany AB, Werner L, et al. Risk factors for HIV acquisition in high risk women in a generalised epidemic setting. AIDS Behav. 2015;19:1305–1316.
31. Granich RM, Gilks CF, Dye C, et al. Universal voluntary HIV testing with immediate antiretroviral therapy as a strategy for elimination of HIV transmission: a mathematical model. Lancet. 2009;373:48–57.
32. Ray M, Logan R, Sterne JA, et al. The effect of combined antiretroviral therapy on the overall mortality of HIV-infected individuals. AIDS. 2010;24:123–137.
33. Rollins N, Mahy M, Becquet R, et al. Estimates of peripartum and postnatal mother-to-child transmission probabilities of HIV for use in spectrum and other population-based models. Sex Transm Infect. 2012;88(suppl 2):i44–i51.
34. Raftery AE, Bao L. Estimating and projecting trends in HIV/AIDS generalized epidemics using incremental mixture importance sampling. Biometrics. 2010;66:1162–1173.
35. Blower S, Dowlatabadi H. Sensitivity and uncertainty analysis of complex models of disease transmission: an HIV model, as an example. Int Stat Rev. 1994;62:229–243.
36. Alkema L, Raftery AE, Brown T. Bayesian melding for estimating uncertainty in national HIV prevalence estimates. Sex Transm Infect. 2008;84(suppl 1):i11–i16.
37. Poole D, Raftery AE. Inference for deterministic simulation models: the bayesian melding approach. J Am Stat Assoc. 2000;95:1244–1255.
38. Benoit SN, Gershy-Damet GM, Coulibaly A, et al. Seroprevalence of HIV infection in the general population of the Côte d'Ivoire, West Africa
. J Acquir Immune Defic Syndr. 1990;3:1193–1196.
39. Vuylsteke B, Semdé G, Sika L, et al. HIV and STI prevalence among female sex workers in Côte d'Ivoire: why targeted prevention programs should be continued and strengthened. PLoS One. 2012;7:e32627.
40. Bamba A, Grover E, Ezouatchi R, et al. Étude biologique et comportementale des IST/VIH/SIDA chez les professionnelles du sexe du district d'Abidjan et examen des interventions en direction des populations clefs en Côte d'Ivoire: Ministère de la Santé et de la Lutte contre le SIDA, ENDA Santé, Johns Hopkins University; 2014.
41. Schwartz S, Papworth E, Thiam-Niangoin M, et al. An urgent need for integration of family planning services into HIV care: the high burden of unplanned pregnancy, termination of pregnancy, and limited contraception use among female sex workers in Côte d'Ivoire. J Acquir Immune Defic Syndr. 2015;68(suppl 2):S91–S98.
42. Vuylsteke BL, Ghys PD, Traoré M, et al. HIV prevalence and risk behavior among clients of female sex workers in Abidjan, Côte d'Ivoire. AIDS. 2003;17:1691–1694.
43. Hakim AJ, Aho J, Semde G, et al. The epidemiology of HIV and prevention needs of men who have sex with men in Abidjan, Cote d'Ivoire. PLoS One. 2015;10:e0125218.
44. DIPE. Rapport annuel VIH/Sida du secteur santé en Côte d'Ivoire 2007–2008. Abidjan, Côte d'Ivoire: Direction de l'Information, de la Planification et de l'Évaluation, Ministère de la Santé et de l'Hygiène Publique; 2009.
45. DIPE. Rapport annuel des indicateurs VIH du secteur santé en Côte d'Ivoire 2009. Abidjan, Côte d'Ivoire: Direction de l'Information, de la Planification et de l'Évaluation, Ministère de la Santé et de l'Hygiène Publique; 2010.
46. DIPE. Rapport annuel des indicateurs VIH du secteur santé en Côte d'Ivoire 2011. Abidjan, Côte d'Ivoire: Direction de l'Information, de la Planification, et de l'Évaluation, Ministère de la Santé et de la Lutte contre le Sida; 2012.
47. DIPE. Rapport annuel des indicateurs VIH du secteur santé en Côte d'Ivoire 2012. Abidjan, Côte d'Ivoire: Direction de l'Information, de la Planification et de l'Évaluation, Ministère de la Santé et de la Lutte contre le SIDA; 2013.
48. DIPE. Rapport annuel des indicateurs VIH du secteur santé en Côte d'Ivoire 2013. Abidjan, Côte d'Ivoire: Direction de l'Information, de la Planification et de l'Évaluation; 2014.
49. DIPE. Rapport annuel des indicateurs VIH du secteur santé en Côte d'Ivoire 2014-Non consolidé. Abidjan, Côte d'Ivoire: Direction de l'Information, de la Planification et de l'Évaluation, Ministère de la santé et de la lutte contre le sida; 2015.
50. Williams JR, Alary M, Lowndes CM, et al. Positive impact of increases in condom use among female sex workers and clients in a medium HIV prevalence epidemic: modelling results from Project SIDA1/2/3 in Cotonou, Benin. PLoS One. 2014;9:e102643.
51. Pickles M, Boily MC, Vickerman P, et al. Assessment of the population-level effectiveness of the Avahan HIV-prevention programme in South India: a preplanned, causal-pathway-based modelling analysis. Lancet Glob Health. 2013;1:e289–e299.
52. Rowe AK, Powell KE, Flanders WD. Why population attributable fractions can sum to more than one. Am J Prev Med. 2004;26:243–249.
53. MSLS. Recueuil des protocoles thérapeutiques nationaux de pathologies. Abidjan, Côte d'Ivoire: Ministère de la Santé et de la Lutte contre le SIDA; 2013.
54. WHO. Global Update on the Health Sector Response to HIV, 2014. Geneva, Switzerland: World Health Organization; 2014.
55. Piot P, Abdool Karim SS, Hecht R, et al. Defeating AIDS–advancing global health. Lancet. 2015;386:171–218.
56. Djomand G, Quaye S, Sullivan PS. HIV epidemic among key populations in west Africa
. Curr Opin HIV AIDS. 2014;9:506–513.
57. Papworth E, Ceesay N, An L, et al. Epidemiology of HIV among female sex workers, their clients, men who have sex with men and people who inject drugs in West and Central Africa. J Int AIDS Soc. 2013;16(suppl 3):18751.
58. Low A, Nagot N, Konate I, et al. Potential impact of existing interventions and of antiretroviral use in female sex workers on transmission of HIV in Burkina Faso: a modeling study. J Acquir Immune Defic Syndr. 2015;68(suppl 2):S180–S188.
59. Adjorlolo-Johnson G, De Cock KM, Ekpini E, et al. Prospective comparison of mother-to-child transmission of HIV-1 and HIV-2 in Abidjan, Ivory Coast. JAMA. 1994;272:462–466.
60. Lowndes CM, Alary M, Labbé AC, et al. Interventions among male clients of female sex workers in Benin, West Africa
: an essential component of targeted HIV preventive interventions. Sex Transm Infect. 2007;83:577–581.
61. Faugier J, Cranfield S. Reaching male clients of female prostitutes: the challenge for HIV prevention. AIDS Care. 1995;7(suppl 1):S21–S32.
62. Espirito Santo ME, Etheredge GD. How to reach clients of female sex workers: a survey by surprise in brothels in Dakar, Senegal. Bull World Health Organ. 2002;80:709–713.
63. Leonard L, Ndiaye I, Kapadia A, et al. HIV prevention among male clients of female sex workers in Kaolack, Senegal: results of a peer education program. AIDS Educ Prev. 2000;12:21–37.
64. Pitpitan EV, Strathdee SA, Semple SJ, et al. Buffering syndemic effects in a sexual risk-reduction intervention for male clients of female sex workers: results from a randomized controlled trial. Am J Public Health. 2015;105:1866–1871.
65. Shannon K, Strathdee SA, Goldenberg SM, et al. Global epidemiology of HIV among female sex workers: influence of structural determinants. Lancet. 2015;385:55–71.
66. Aho J, Hakim A, Vuylsteke B, et al. Exploring risk behaviors and vulnerability for HIV among men who have sex with men in Abidjan, Cote d'Ivoire: poor knowledge, homophobia and sexual violence. PLoS One. 2014;9:e99591.
67. Tanser F, de Oliveira T, Maheu-Giroux M, et al. Concentrated HIV subepidemics in generalized epidemic settings. Curr Opin HIV AIDS. 2014;9:115–125.
68. UNAIDS. New HIV Infections by Mode of Transmission in West Africa
: A Multi-country Analysis. Dakar, Sénégal: UNAIDS—Regional Support Team for West and Central Africa; 2010.
69. Johnson LF, Hallett TB, Rehle TM, et al. The effect of changes in condom usage and antiretroviral treatment coverage on human immunodeficiency virus incidence in South Africa: a model-based analysis. J R Soc Interface. 2012;9:1544–1554.
70. Hallett TB, Aberle-Grasse J, Bello G, et al. Declines in HIV prevalence can be associated with changing sexual behaviour in Uganda, urban Kenya, Zimbabwe, and urban Haiti. Sex Transm Infect. 2006;82(suppl 1):i1–i8.
71. Verguet S, Stalcup M, Walsh JA. Where to deploy pre-exposure prophylaxis (PrEP) in sub-Saharan Africa? Sex Transm Infect. 2013;89:628–634.
72. Venter WD, Cowan F, Black V, et al. Pre-exposure prophylaxis in Southern Africa: feasible or not? J Int AIDS Soc. 2015;18(4 suppl 3):19979.
73. Vassall A, Pickles M, Chandrashekar S, et al. Cost-effectiveness of HIV prevention for high-risk groups at scale: an economic evaluation of the Avahan programme in south India. Lancet Glob Health. 2014;2:e531–e540.
74. Diabaté S, Maheu-Giroux M, Vesga J, et al. Plan d'accélération de la réponse nationale au VIH en Côte d'Ivoire. Rapport présenté à ONUSIDA—Région d'Afrique de l'ouest et du centre; 2015.
75. Maheu-Giroux M, Tanser F, Boily MC, et al. Determinants of time from HIV infection to linkage-to-care in rural KwaZulu-Natal, South Africa. AIDS. 2017;31:1017–1024.