Introduction

Migrants are among the latest sub-groups to be classified as an HIV ‘key population’, being acknowledged as epidemiologically carrying increased risk, vulnerability and burden of infection due to a combination of biological, socio-economic and structural factors [1]. While HIV prevalence is 18.9% among the general population in South Africa, its concentration is characteristically higher in specific locations and sub-populations that are reported to practice high-risk behaviours [2].Some rural communities, such as in KwaZulu-Natal (KZN) Province, are important socio-geographic settings for HIV outcomes, where adult migration rates are as high as 26% and HIV prevalence ~ 40% [3]. The exceptionally high prevalence of internal migration in South Africa (i.e. up to 42%) is rooted in apartheid era policies that were aimed at ensuring the supply of rural African male laborers in urban and industrial centers, such as Johannesburg [4, 5].

Existing literature on the link between migration and HIV discuss the importance of the social and political-economic context of risk, with migration and mobility playing a central role in the rapid spread of the epidemic in South Africa [6]. Historically, migration was a male domain that entailed the positive selection of prime-aged men (i.e. unmarried, or if married, often migrated without their wives and families), who left their rural communities for work [7,8,9]. Under these arrangements, migration fueled the epidemic [10], and facilitated temporal disengagement from the home region’s sexual networks, thereby creating a demand for new sexual networks in the urban destination, where there is greater occurrence of risky sexual behavior that has been linked to HIV transmission [11,12,13]. In a Gauteng Province study, migrant men were more likely to engage in risky sexual behaviours, such as having unprotected sex (83%) and casual sex partners (93%), than non-migrant men [14]. More recently after 2000, it has been demonstrated that a large increase in internal female migration [15, 16], often associated with more frequent trips over shorter distances than the male migration [9, 10], may contribute to the persistent HIV epidemic. Women tend to migrate for purposes of employment and often access seasonal and low-paying informal jobs (e.g. domestic work, informal selling and trade) [17], being thus at risk of turning to transactional sex, either as a primary/secondary livelihood strategy [18, 19].

Despite the evidence indicated above, little is known about the pathways linking migration and HIV transmission in sub-Saharan Africa [20]. The link between labour migration and HIV is illustrated by Weine and Kashuba [21], who showed that, across diverse global locations (i.e. Africa, the Americas, Europe, South East Asia and Western Pacific), increased HIV risk was associated with sexual practice determinants, such as low condom use, multiple partnering and lack of HIV knowledge. However, they have not reported on female migrants engaging in commercial sex work (i.e. with a large number of partners) and other mobile populations groups, such as refugees and/or asylum seekers. Moreover, their review featured results from only seven of the 97 South African studies that address HIV risk among labour migrants despite evidence that the country contains more than 19% of the world’s HIV-positive population [1]. This potentially underrepresented data from the largest epidemic in the world and region with higher than average prevalence of migration.

This paper therefore summarized the South African literature since there is no evidence of systematic review reports on migration and HIV risk. It attempts to address this issue through a collation of the demographic and behavioral health-science literature, and examines the relationship between measures of migration, HIV risk behaviours and infection. Given that HIV prevention strategies are tailored to maximize condom use, and that voluntary counselling and testing services for migrant populations are unavailable [22], the objective of this review is to more fully understand the complex relationship between mobility, risky sexual practices and HIV acquisition. This necessitated examining the various definitions of migration, and summarizing the key findings, predictors of HIV infection, risky sexual behavior and biological outcomes of HIV in the studies included. Our undertaking here may provide important evidence to support the development of effective prevention programs for this vulnerable population.

Methods

Study Eligibility

The review spans work published from 2000 to 2017, with the selection following the Population, Intervention, Comparative group, Outcomes, Study design (PICOS) criteria for systematic reviews [23], the inclusion criteria being empirical studies conducted in South Africa examining HIV and migration. Specifically, those reporting data on HIV risk factors, prevalent and incident HIV infection linked to migration.

Literature Searches

Systematic literature searches were performed on five academic databases, namely: PubMed Central, Sage Publications, Google Scholar, Web of Science and J-STOR, which identified over 4800 articles on South Africa. Title and abstract searches were conducted on relevant English studies between 2000 and 2017. This 17 year span marks an important period of HIV viral evolution [24] due to the improved ART coverage, and was associated with increased life expectancy, and a likely lowering of HIV risk perception in key populations [25].

Based on an initial scoping of the mobility and HIV-risk literature, the following query terms and phrases were included: HIV, AIDS, migrant, migrants, labour migrants, truck drivers, mobility and migration. Studies indexing the concept of mobility were identified by using the truncation ‘migrat*’ (exclusive to Web of Science and PubMed Central), returning results from multiple keyword variations such as; ‘migration’, ‘migrancy’, ‘migrate’ and ‘migrant’. Building on this, the subject heading ‘HIV risk behaviour’ was added to the search, including terms synonymous with sexual behaviour, with implications for HIV transmission “sexual risk behaviour”, “transmission risk”, “multiple sexual partnerships”, “AIDS risk behaviour” etc. We also explicitly examined incident infections by including common keywords, such as; “HIV infection”, “HIV incidence”, “new infections” “risk of HIV acquisition” etc. as detailed in Table 1. This demonstrated a composite filter process for our first round attempt at identifying the literature linking how aspects of migration intersect with HIV risk factors and outcomes.

Table 1 Keywords for article search on Web of Science
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Other study retrieval methods, while remaining secondary to the main searches above, were conducted to obtain additional literature, including hand-searches. This technique is a purposive page-by-page examination of relevant journal issues, specific studies and conference proceedings [23]. Moreover, the ‘cited by’ reference-searching feature compliant with Web of Science (i.e. linking journal articles to other studies in which they have been cited) was used.

Table 1 details the final search query terms pertaining to the independent/explanatory variable ‘migration’ and the outcome of interest/dependent variable ‘HIV risk’, which, when combined with Boolean operators AND/OR within each concept (labour migration and HIV risk), identified an initial range of relevant study records. The database searches began in January 2017, with electronic, hand searching (i.e. searching through content of journal articles to identify relevant studies) and examining the results generated from the initial search, and concluded on 30 August, 2018. Notably, the database search (PubMed Central) returned 4887 hits, while 11 studies were obtained through hand searching. A total of 4898 records were retrieved, and after multiple iterations and search query refinement, title and abstract screening was performed on 71 full-text records that were extracted to Mendeley (v1.16.2. reference manager), based on their relevance to the scope of the review. However, a further 42 articles were discarded after failing to meet the inclusion criteria, which resulted in 29 studies being included, as presented in Fig. 1.

Fig. 1
figure 1

Flow diagram of primary studies selected for systematic review of migration and effects on risky sexual behavior and HIV infection

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Data Extraction

The data from the 29 empirical studies (published between 2000 and 2017) selected for inclusion, after successive rounds of filtering, were retrieved and entered onto a Microsoft Excel workbook. These studies comprised four longitudinal, 16 cross-sectional, five qualitative and five mixed-methods studies. Two independent reviewers, [i.e. a content and a method (systematic review) expert] conducted title-screening to verify which reports should be included in the final review. Queries regarding inclusion were resolved through consultation with the review team members. The Mixed Method Appraisal Tool (MMAT) was used to generate a score to measure the methodological quality of the studies and evidence reported to establish the risk of bias. Once consensus over the validity of study selection was reached, the following were explored: defining migration, identifying key conceptual frameworks and predictors of HIV infection, risky sexual behaviour and biological outcomes of HIV.

Meta-analysis

Data were analyzed using STATA version 15 (StataCorp, College Station, Texas, USA), with estimates of HIV acquisition risk and 95% confidence intervals (CI) of exposure-outcome pairs being presented on forest plots. These data were obtained from the studies that reported hazard ratio and 95% CI. I2 statistics were used to measure the percentage of observed total variation across the studies due to heterogeneity rather than by sampling error [26, 27]. We made a prior decision to use a random-effects model for combined effects (given our assumption about anticipated variation among the studies), and presumed that the studies were conducted independently. In terms of potential publication bias arising from small-study effects, we did not construct funnel plots or test them for asymmetry, given inadequate number of studies [28] (it is recommended to have a minimum of 10 studies). Due to the large variation in their sample sizes i.e. large population-based research versus small studies, we conducted a sensitivity analysis to examine the influence of each study on the overall meta-analysis summary estimate by omitting each study in turn.

Results

Defining Migration

Table 2 shows that while all of the studies offered definitions of migration, there were important differences among them. These included dimensions of mobility, including selectivity to migration (i.e. whether individuals had distinct characteristics suiting them to migrate), and migration stream (that migrants had a common origin and destination). Such studies used conventional demographic definitions of migration, such as ‘internal migration’, ‘temporary labour migration’ and ‘circular migration’, referring to migration within South Africa, where persons keep their contact with a rural homestead. Definitions further included immigration, in that migrant were referred to as: ‘foreign migrants’ or ‘external migrants’, ‘cross-border migrants’ and ‘transnational migration’ [29,30,31,32,33].

Table 2 Definitions and of migration and HIV
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Nine of the 29 studies were conducted in demographic surveillance sites (DSS), either the Africa Centre Demographic Information System (ACDIS) or the Agincourt Demographic and Health Surveillance Site (ADHSS), respectively located in rural communities in KZN and Limpopo Provinces. They examine forms of mobility involving a change of residency, denoting migration events [34, 35]. Using residence history data, the DSS studies define in- and out-migration on the basis of residence change that consists of crossing the boundaries of the designated surveillance area [10], thusly classifying internal or external migrants. Conversely, other studies used the census definition that internal migrants cross geo-political boundaries, usually magisterial districts or provinces [14]. Migration for the purposes of labour or ‘occupational mobility’ featured prominently in the studies sampling mine workers [14, 36,37,38,39,40], truck drivers [41, 42] and migrant women who engage in sex-work in the destination and/or are mobile sex workers [29, 31, 32, 43].

Study Hypotheses and Key Messages

The studies reviewed diverse aspects of the migration-HIV link, which we summarized accordingly the various study objectives and key findings that indicated the mechanisms affecting this relationship (Table 2). Cohort prospective studies capturing individual sexual histories highlighted the impact of time-variant factors, such as income Adjusted Hazard Ratio (AHR) 0.93 (s.e. 0.0279) p = 0.022 wealthier compared to poor and sex AHR 1.83 (s.e. 0.3616) p = 0.002, on risk of HIV acquisition [44]. Other studies highlighted the socio-economic differences between the male migrants and non-migrants that were associated with risky sexual practices, such as having more than two lifetime sex partners [4, 39]. The main reasons why foreign migrant women who engage in sex work were implicated in HIV infection and risky behaviour relates to their very high exposure to structural and sexual violence, including poor access to local medical care and support service inequalities [31, 33]. In three studies, non-migrating spouses of male labour migrants were included, with the hypothesis of a bi-directional stream of HIV transmission being emphasized [42, 45, 46].

Risky Sexual Behaviour

An array of self-reported sexual behaviour practices in both men and women in migration situations have implications for HIV infection (Table 3). Disaggregated by gender, migrant men, mainly those residing in single-sex settings, were more likely to engage in risky sexual behaviours, such as unprotected and anal sex, multiple partnering and visiting commercial sex workers. One study found that migrant men were 1.5 times more likely to have multiple sex partners compared to non-migrant men (95% CI 1.03–2.20) [47]. Other factors (i.e. attitudes) for increased HIV vulnerability included limited knowledge of HIV transmission and low self-assessment for HIV risk prevention [30].

Table 3 Study characteristics and qualitative results
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Reported Condom Use

Migrants were less likely to report condom use compared to non-migrants in the included studies. This lower usage was more prominent when results were disaggregated by migrant origin, and whether before or after a behaviour intervention. Non-migrant women compared to migrant women were more likely to report condom use (OR 1.88, 95% CI 1.02–3.45) [39]. One study shows that male migrants compared to non-migrants were less likely to report condom use with regular sexual partner (10.9% vs. 23.7%, p = 0.04). Foreign migrant women who engage in sex work in South Africa were less likely to use condoms compared to local migrant women for the same activities (90.6% vs. 94.5%, p = 0.08) (see Table 4). Similarly, among South African women, migrants in sex work compared to non-migrants were less likely to report condom use with clients (94.5% vs. 89.1%, p = 0.02) in the same study [43]. In 2000, a study comparing the prevalence of risky sexual practices among mineworkers between a pre- and post-HIV prevention campaign noted that condom use with spouse was 18% before and 26% after, and with other partners, 60% before and 67% after [36].

Table 4 Quantitative studies with sexual behaviour outcomes
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Multiple Partnering

Many studies reported higher rates of multiple partnering among migrants compared to non-migrants. Older, married, migrant men, sexually active young men under the age of 35 years, younger women, and resident women married to migrant men reported having additional sexual partners other than their spouse or main sexual partner [10, 33, 37, 45, 46, 48, 49]. On the other hand, being a non-migrant, shorter distance migration and returning home regularly was not associated with multiple partnering. Migrant men returning home frequently had their odds for having multiple sexual partners reduced by 53% compared to those who did not (OR 0.47, p = 0.024). Migrants working close to home were less likely to have multiple partnerships compared to those working far away (OR 0.66, p = 0.071) [46]. One study showed that female migrants had additional partners ranging between two and 11 in the past 6 months [31]. In a mining township in Carletonville, migrant women were four times more likely to have two or more partners compared to non-migrant women (OR 4.18, 95% CI 2.25–7.76, p < 0.001). Additional partners, other than the regular ones, were more likely to be reported by migrant women compared to non-migrants (OR 2.38, 95% CI 1.60–3.54) [39]. A study enrolling migrant men from KZN found that those who migrate had a mean lifetime number of sexual partners of 18.2 compared to 13.4, p = 0.0001 among those who do not migrate [38]. Employed migrant men compared to those unemployed were more likely to have multiple sexual partners (50% Vs 38%). Gender specific comparisons by migration status and prevalence of multiple sexual partnerships showed increased odds for men (AOR 1.51, 95% CI 1.03–2.20) compared to women (AOR 1.05, 95% CI 0.53–2.07) [47].

Visiting Sex Workers/Engaging in Sex Work

Most studies in Table 4 reported that male migrants compared to non-migrants had a heightened likelihood of being clients of sex workers. In addition, female migration was frequently identified as a predictor and/or outcome of sex-work, this being an important risk factor for mobile women [33, 50]. In two studies conducted over a decade apart, i.e. 2002 and 2014, a high proportion of long-distance truck drivers reported frequently using the services of sex workers (37% and 30%) [41, 42]. One qualitative study reported that mineworkers often visited commercial sex workers in single-sex hostels in Mpumalanga and Northwest Provinces mining towns to cope with long detachment from spouses or main partners. Recent migrants were significantly more likely to be sex workers in South Africa shortly before the 2010 Soccer World Cup compared to recent migrants after the event (2.4% vs. 0.4%, p = 0.011) [43]. Foreign female migrants were 2.3 times more likely to be sex workers in South Africa compared to non-mobile local women (95% CI 1.5–3.7). Internal migrants were however 1.5 times more likely to be sex workers compared to non-migrating women [42]. Other qualitative studies on women in migration situations highlighted that unemployed migrant young women were at risk of engaging in transactional and commercial sex-work as a survival strategy [48]. Compared to non-migrants, the majority of migrants were more likely to have had received cash for casual sex in the past year (53% vs. 30%) [14].

Casual Sex

Several studies in Table 4 reported increased casual sex in both migrant men and women in comparison to those who do not migrate. Casual contact was defined as any sex with a partner not considered to be a steady partner, such as a casual associate, friend etc. Migrant mineworkers in the Carletonville and Khutsong male hostels were more likely to report having casual encounters in the past compared to non-migrant men (53% vs. 32%). In the same study, migrant women compared to non-migrant women were more likely to reporting having casual sex in the past 12 months (37% vs. 17%) [14]. In another study, significant differences in the proportions and odds for casual sex by gender and migration status were observed, with migrant men compared to non-migrant men being more likely to report having at least one casual partner in the past year (20% vs. 6%, p = 0.02). Similarly, migrant women compared to non-migrant women were 1.6 times more likely to have had casual sex in the past year (95% CI 1.03–2.53) p < 0.05 [35].

Men Who Have Sex with Men

Three studies in Table 4 included men who have sex with men (MSM). While data in these studies do not have comparative endpoints and measures quantifying the extent of the risk of being a labor migrant, they descriptively highlight the prevalence of unprotected sex with other men in masculine same-sex workplaces and hostels (e.g. mines and factories), whether consensual or ‘forced’. A high number of long distance truck drivers in KZN (42%, n = 92 of N = 220) were reported to have sex with other men [41]. Based on recollections of former migrant miners, one qualitative study found that men working in mine in Impalahoek, Mpumalanga Province, regularly had unprotected sex with younger colleagues in exchange for money, protection and preferential treatment at work [37].

HIV Infection Rates

This section presents the odds ratio and hazard rate effect sizes of HIV prevalence and risk acquisition by migration status. We further present the meta-analysis results based on reported adjusted hazard ratios in Figs. 2 and 3.

Fig. 2
figure 2

Effects of migration on HIV acquisition. Note The mid-point of the dark box represents the point effect estimate for each study. The area of the box represents the weight given to the study. Horizontal line shows 95% CIs of the effect estimate for each study. Diamond represents the combined-effect estimate and its width shows 95% confidence interval

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Fig. 3
figure 3

HIV incidence of migrants compared to non-migrants and confidence intervals. Note Combined effect size parameters at 95% CIs are represented by the vertical lines

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HIV Prevalence

The data in Table 5 are prevalent HIV estimates for both migrants and non-migrants. In a study in rural KZN, female migrants had more than double the odds of infection (AOR = 2.55; 95% CI 2.07–3.13) compared to male non-migrants. Moreover, female migrants also had a 48% higher odds of being HIV-positive than female non-migrants (AOR 1.48, 95% CI 1.23–1.77) [51]. An earlier study found that migrant women were more likely to be HIV positive than their non-migrant counterparts (51% vs. 39%; p = 0.002) [14]. Another study showed that migrant women were 1.5 times more likely to be infected by HIV than non-migrant women (OR 1.52, 95% CI 1.01–2.28, p = 0.046) [39], while 25.9% of migrant men compared 12.7% of non-migrant men were infected with HIV (OR 2.4, 95% CI 1.1–5.3; p = 0.029) [38]. Migrant men compared to partners of migrant men were more likely to be infected by STI’s, including HIV (OR 1.54, 95% CI 1.00–2.37; p = 0.049) [39]. HIV infection among non-residents compared to residents was AOR 1.8 (95% CI 1.3–2.4) for men and AOR 1.5 (95% CI 1.2–2.0) for women in a rural area in KZN [52]. More recently, in the same DSS, non-residents compared to residents in the study area had higher odds of infection: AOR 1.19 (95% CI 1.07–1.33) for men and AOR 1.8 (1.10–1.26) for women [53]. Other results by duration of mobility associated with higher HIV prevalence i.e. spending more time travelling per-month, increased the odds of HIV infection by 1.5 times compared to spending less time travelling per-month (95% CI 0.9–2.3) [42]. Truck drivers were four times more likely to be HIV positive compared to the men of the same age in the general population AOR (4.26 (95% CI 24–28) [42].

Table 5 Quantitative studies with HIV prevalence measures
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Hazard of Acquiring HIV

Four of the studies with multivariate statistics for incident infections are presented in Table 6. For instance, male migrants compared to non-migrants were more likely to acquire HIV (AHR 2.54, 95% CI 1.67–3.85; p = 0.0001) and female migrants compared to non-migrants were more likely to acquire HIV (AHR 1.57, 95% CI 1.14–2.17; p = 0.006) [3]. In KZN Province, non-migration reduced the risk of HIV acquisition by over 50% (AOR 0.4988, 95% CI 1.23–3.24; p = 0.005) [44].

Table 6 Quantitative studies with hazard of acquiring HIV measures
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Meta-analysis Results

To examine the overall estimate of the effect of migration on increasing the risk of acquiring HIV, we statistically summarized the six estimates from four studies, as shown in Table 6. According to our meta-analysis (Fig. 3), exposure to migration was associated with a 69% increase in the HIV acquisition risk (95% CI 1.33–2.14). We found low to moderate heterogeneity in the combined effect size, (I2 = 35.0%, p = 0.17). Our sensitivity analysis, which was conducted by omitting each study in turn, did not alter the significant relationship between migration and risk of HIV acquisition. Our study also suggests that the removal of small studies, such as Hargreaves et al. [47], did not alter the significance of our overall findings.

Discussion

The risk of acquiring HIV increased by 69% (95% CI 1.33–2.14) in migrants compared to non-migrants, according to our meta-analysis results of four studies. These studies were conducted over eight years (2007–2015), a period covering two HIV treatment eras; one in which it barely existed, with an associated increase in prevalence and incidence, and the latter in which it was widely available and HIV incidence decreased in the general population. Despite universal ART coverage being achieved after 2010, the meta-analysis result suggests that HIV incidence due to migration remained high, as demonstrated in a cohort study in Uganda [20]. HIV prevalence was high in migrants compared to non-migrants, the largest difference being across genders, with female migrants having more than double the odds of infection (aOR = 2.55; 95% CI 2.07–3.13) compared to male non-migrants [17]. Another key finding was that sexual practices (i.e. multiple partnering, limited condom use, client to sex workers/engaging in sex work, casual sex and transactional sex) are important mechanisms shaping the risk of HIV infection among those who migrate, confirming an earlier observation [21]. We highlight the intersectionality of bio-demographic and structural factors in modifying the continuum of HIV risk in migrants at an ecological level in South Africa [48]. For instance, we found that unemployed, migrant young women had a higher risk of having additional sex partners and being involved in sex work or transactional sex compared to non-migrants.

Multiple partnering was frequently identified as high among migrants compared to non-migrants, with having additional sexual partners when separated from the main partner during a migration episode being significantly increased across gender and age group. In two studies, female migrants (34.4 years, mean) were 4.2 times more likely to have two or more lifetime sexual partners compared to non-migrants [39]. Male migrants (37.4 years, mean) had an average of 18.2 compared to 13.4 lifetime sexual partners in non-migrants compared to non-migrants [38], highlighting large differences by migration status. This age and gender clustering challenges the traditional male-infector model, which presumes that only prime-aged migrant men are vulnerable to HIV risk through their interaction with external partners and thereby infect their wives or regular partners. As suggested in Ferrand [53], easy generalizations, such as viewing separate and static characteristics of individuals as independent risk factors of HIV, are no longer plausible, instead, the interaction of social and biological influences (age, gender, nature of sexual relationships) conjointly increases the risk of infection. This study showed that both migrant men and women reported having a greater number of additional sexual partners outside their community of origin, but gender differences in the experience of risk emerged. Typically, men with multiple partners had better paying jobs, and were therefore more likely to afford additional partners and further expand their sexual networks while maintaining multi-local partnerships in comparison to those who do not migrate. In contrast, women, in poorly paying jobs, as is often the case with female migrant labour [17], send most of their earnings home i.e. two-thirds higher than men’s [54], and thus may rely on multiple sexual partners or engage in transactional sex as an additional source of income, as noted in some qualitative studies [18, 19]. Here we demonstrated that the finding for women did not necessarily conflict with that for men. This supports our initial hypothesis that employment is not protective for women, as livelihoods associated with migration often include commercial sex work and transactional sex.

It follows that further studies exploring the migrant employment continuum (i.e. looking for employment to already working) are needed to confirm the findings about the intersections of various correlates of migration and sexual behaviour outcomes. A synthesis of the sexual behaviour data reveals that women who migrate were associated with increased prevalence of HIV risk behaviours. Young (or older), employed (or unemployed) and migrating men and women reported having more multiple sex partners and transactional sex than non-migrants. Moreover, that some women who engaged in risky sexual contact were employed [48] reflects that economic insecurity (as conventionally interpreted), may not completely explain why they are disproportionately at greater risk of HIV infection. The literature likely explaining this highlights that material deprivation and consumerism instigates this sex-gift exchange, and influences young women’s high sexual risk taking [55, 56].

In three of the studies, adult men who did not migrate with their spouses/partners, returned home less frequently or migrated to the furthest destinations were associated with high additional sexual partnering [10, 42, 46]. In light of this result, it is possible to infer that increased risky sexual behaviour in married men is driven by extraordinarily long episodes of absence from home, i.e. mineworkers and long-distance truck drivers were frequently reported to have casual sexual partners.

There are two important observations emerging from this finding. Firstly, it indirectly affirms the evidence from other studies in rural South Africa showing that migrant men who work close to their home communities were less likely to report multiple sexual partners than those travelling to destinations further afield (returning once or twice a year), suggesting that short distance migration is a protective factor to sexual risk [17]. Secondly it gives credence to our examination of migration definitions in this study and forthcoming studies that may use mobile application technology to collect real-time data (i.e. on distance travelled and pattern of migration) to test more thoroughly the proposition that detailed measurement of migration (i.e. length of time away and destination correlates) is a key HIV independent risk factor.

However, these results may have limited explanatory value, due to a number of methodological challenges. For instance, many of the existing studies examining multiple partnering obtained no data on the characteristics of the sexual partnerships, such as the length of overlaps between and the type of sexual partners, information that is particularly important in determining transmission during concurrent partnerships. If characteristics of the partner or partnership are not collected, it is difficult to determine their epidemiological interpretation, given that some partnerships may be once-off sexual encounters. For instance, in one study reporting multiple sexual partnering in women, up to 11 sexual partners were reported, although it may not be known with certainty whether additional partners were encountered during on-going specific partnerships. It is therefore important that definitions of multiple partnering are standardized, particularly in quantitative studies, to avoid misreporting, given its implications for HIV risk. Similarly, it is difficult to accurately interpret results from studies measuring migration differently. Systematic attention to definitions would simplify cross-study comparison of multiple partnering results, and may render effective the need for clear messages aimed at reducing multiple partnerships, irrespective of whether those overlap in time [57].

A few studies (four) measured HIV incidence and 13 reported on prevalence. Unfortunately, with the shortage of data on new HIV diagnoses as opposed to prevalence, it is impossible to ascertain any causal relationship, as migrants may have been infected in the past when they had different sexual behaviours. This finding implies the need for more cohort studies on migrant HIV risk to rigorously estimate the effect of mobility on new infections. Accordingly, this undertaking is consistent with the UNAIDS current funding model and package of prevention strategies, which involves identifying main modes of HIV transmission, key affected populations, and core epidemiological trends to ensure a greater impact on reducing new infections [1].

Conclusion

Mobility is highly associated with increased prevalence of HIV risk behaviours, and confers up to 69% increase in the risk of HIV acquisition. Studies included in this review documented increased multiple sexual partnering, unprotected sexual intercourse, visiting sex workers and engaging in sex work in migrants compared to non-migrants. Escalation of this sexual behaviour and risk of HIV acquisition among migrants in comparison to non-migrants calls for increased reliance on the targeted and best-combination HIV prevention strategies. Our review found only four studies on migration and HIV risk acquisition, emphasizing the need for incidence data to establish if the timing of new infections corresponds with the migrant sexual behaviours above, and can predict future risk patterns. The implications of this study include monitoring and tracking key trends of the epidemic in migrants to evaluate country level success towards the UNAIDS’s focus on optimizing the reduction of new HIV infections. Effective combination HIV prevention strategies that target migrant populations are urgently required.