Mapping virtual platforms to estimate the population size of men who have sex with men (MSM) who use internet to find sexual partners: implications to enhance HIV prevention among MSM in Kenya

Introduction

Kenya has the joint third largest epidemic in the world, with over 1.6 million people living with HIV¹. Kenya is characterized as having a generalized epidemic among the adult population; however, key populations including female sex workers, men who have sex with men (MSM) and people who inject drugs are at a heightened risk of HIV acquisition and transmission due to their sexual and social behaviors^2,3. MSM carry a disproportionate burden of HIV in Kenya, with a HIV prevalence of 18.2%, compared to a HIV prevalence of 3.1% among adult Kenyan men (including MSM), and contribute to nearly 15% of new infections each year^3–5. In Kenya, “MSM” encompass a range of sexual identities including gay, bi-sexual, heterosexual and men who sell sex to other men⁶. To scale up HIV prevention, there is a need to estimate the size of key populations and also understand where they congregate and can be reached⁷. Programmatic mapping and size estimation of MSM conducted by NASCOP in 2018 estimated a total number of 32,580 MSM (ranging between 24,704 to 40,455) in 34 counties in Kenya⁸. Since discriminatory legislation, criminalization and stigma attached to same-sex relationships poses major barriers for MSM to seek other male partners at physical sites, estimates obtained from conventional size estimation approaches including geographic and programmatic mapping are considered to underestimate of the total number of MSM⁹.

Globally, with recent advances in information technology and improved mobile networks, an increased number of MSM have started using the internet and geosocial networking mobile phone applications (GSN apps) to seek and meet other male sexual partners^10–12. A meta-analysis of internet use among MSM concluded that approximately 35% to 45% of MSM use social media to exchange information, socialize and support each other¹³. A recent study conducted among MSM in Kenya found that 64% of the respondents used the internet and virtual platforms to seek male sexual partners¹⁴. These include GSN apps such as Grindr, Scruff, Adam4Adam, Gay Exchange etc., as well as social media applications such as Facebook and general communication apps like WhatsApp. GSN apps use geo-location features of smart phones to display potential contacts based on their physical proximity, making sexual partner seeking quick and convenient¹⁵. Through these apps, users create individual profiles with pictures and are able to view people online within their geographical proximity¹⁶. Other social media apps such as Facebook and messenger apps such as WhatsApp are messaging and calling platforms available for free, provided internet is available¹⁷. Through these networks one could connect with people they already know or could join a local/international network or group through an invitation or a link.

As same sex relationships are stigmatized, the internet provides a safe space for MSM to connect and find sexual partners in a covert manner without facing stigma, discrimination and negative reactions from the larger community¹⁸. These virtual platforms act like a safe community space for MSM and that has led to a decline in the number and significance of public locations where MSM meet other men, especially in countries where same sex relationships are criminalized⁹. Given the increasing popularity of the internet to find sexual partners, compounded by discriminatory laws and social stigma specifically affecting MSM populations, it is more challenging to enumerate their numbers and estimate population size and expand HIV prevention services to MSM engaged in these virtual sites^19,20. While there has been research conducted to understand the MSM using virtual sites, the studies have mostly looked at risk behaviors and operations of this dynamic sub-typology^12,13,15. Indirect estimation of the population size of MSM has also been attempted; however, such estimation provides more generalized estimates based on the number of MSM registered on various sites¹¹. In this paper we present an emerging new approach called virtual mapping to locate and estimate the population size of MSM who seek male sexual partners through the internet and various phone-based applications. The approach has been tested and utilized with reliable results in a few countries and was modified to the local context in Kenya.

Methods

Results

Table 1 presents information collected for various virtual platforms and internet sites used by MSM in the study counties. We identified a total number of 60 platforms classified within three broader types i.e., 24 GSN apps, 18 Facebook pages and 18 WhatsApp groups. Among the GSN sites, Badoo, Grindr, Tagged, Planet Romeo and Hornet were the five most used apps in all three counties. The majority (79%) of the MSM knew of a virtual platform other than the one they primarily used to find partners. Nearly one-fifth (19.3%) of MSM had only one registered profile, 62% had two to four user profiles on different virtual platforms and 15.4% had more than four user profiles. On average, each MSM had 3.7 registered profiles on multiple virtual platforms. Analyzed by type, those using WhatsApp had 5.6 profiles on multiple sites, followed by GSN app users and Facebook users who had 3.8 and 2.7 profiles, respectively. While a high number of profiles were reported, approximately two profiles were reported to be used by respondents in the last month. Nearly three-quarters (74%) of respondents reported that they also visited physical spots, with a higher percentage of those who visited being from WhatsApp groups (86%). However, 26% of the respondents reported exclusively using virtual platforms. Overall, we estimated a total number of 6,672 (95%CI = 6,174 to 7,259) MSM registered on all virtual platforms included in this study. Facebook had the highest number of registered MSM at 5,910 (95%CI = 5,076 to 7,072), followed by GSN sites (3,118; 95%CI = 2,937 to 3,324) and WhatsApp (746; 95%CI = 642 to 891).

Table 2 presents the estimated number of MSM who operate through virtual platforms by county. Results show that MSM in Mombasa used the most virtual platforms (43) followed by those in Kiambu (34) and Kisumu (29). Kiambu had the highest number of estimated MSM, calculated at 3,635 (95%CI = 3,335 to 3,990), followed by Kisumu at 1,567 (95%CI = 1,480 to 1,665) and Mombasa at 1,469 (95%CI = 1,335 to 1,604).

Socio-demographic characteristics and sexual profiles of MSM using various virtual platforms disaggregated by county as well as by the type of virtual platform used are shown in Table 3. Of the 435 respondents interviewed, 61% were below the age of 25 years. Kisumu had a high proportion (68%) of respondents who were under 25 years of age compared to Kiambu (61%) and Mombasa (51%). No age specific differences were noted between MSM using various platforms or sites. The majority of respondents had completed at least secondary education. In terms of sexual orientation, 69% of respondents self-identified as gay men, while 30% identified as bisexual. A large proportion of the respondents, 83% in Mombasa and 79% in Kiambu, identified themselves as gay men compared to 52% in Kisumu. A higher proportion of respondents (77%) using Facebook identified themselves as gay, followed by GSN apps (68%) and WhatsApp (60%). The majority of respondents (97%) identified themselves as male, with 3% identifying themselves as transgender. Almost half (48%) of respondents in Kisumu reported assuming the top sexual role compared to 40% in Mombasa and 34% in Kiamabu. Similarly, 63% of MSM on WhatsApp reported to assume the top sexual role as compared to 39% on GSN sites and 38% on Facebook. The study also found that 69% of respondents sold sex for money as sex workers; Mombasa reported a large proportion of sex workers (70%) compared to Kisumu (60%) and Kiambu (45%). More respondents using WhatsApp (68%) reported selling sex for money compared to Facebook (65%) and GSN apps (56%).

Respondents reported connecting with 16.8 male partners in the last week, the highest being in Kiambu (22.7) compared to Kisumu (19.8) and Mombasa (7.9) and on GSN apps (19) compared to WhatsApp (17.4) and Facebook (10.1). Respondents reported an average of 4.9 men with whom they had anal sex in the last week and sold sex to an average of 2.8 men. County-wide analysis showed respondents in Kisumu had a significantly higher number of partners (8.1 and 4.4 with whom they had anal sex and sold sex, respectively) compared to Kiambu (2.7 and 1.3) and Mombasa (2.5 and 1.9) while no differences were noted in site-based analysis of sexual encounters in the last week. Similar differences were seen in the platforms/sites, with respondents using WhatsApp reporting having anal sex with five men in the last week and selling sex to 1.7 men compared to GSN platforms (4.9 and 2.7) and Facebook (4.8 and 3.5).

Discussion

Although a virtual mapping approach has been used in a few countries^23–25 it was used for the first time in Kenya and has successfully identified all key virtual platforms and internet sites, along with the estimated number of MSM who use these platform/sites to find male sexual partners. Based on how each platform or site operates, we classified them into three broader types. The first is GSN apps (e.g., Badoo, Grindr, Hornet), which require GPS-enabled smart phones, and allow subscribers to register profiles with personal information, upload pictures, share their location and see other network members within a specific distance. The second variant is social media applications like Facebook and, finally, communication applications such as WhatsApp. The latter two are general purpose social networking platforms, have a larger generalized use and are not uniquely designed for seeking partners. Interestingly, we found several designated Facebook pages and WhatsApp groups specifically created for the purpose of finding MSM sexual partners even though these sites needed invitation or acquaintance with an existing member of these groups to join. Our finding that Facebook was the most used virtual platform by MSM to seek male sexual partners is also in agreement with previous research²⁶. The MSM estimates developed through this study may be much more realistic and closer to the actual number of MSM population in Kenya. The previous MSM estimates were derived using geo-spot based programmatic mapping techniques and estimated 1,664 MSM in Kiambu, 2,492 in Kisumu and 2,855 in Mombasa⁸. Since that methodology did not include MSM who use virtual platforms, there could have been an underestimation of the total number of MSM in Kenya. Although our study focused on MSM who use virtual platforms and the internet and estimated 3,635 MSM in Kiambu, 1,567 MSM in Kisumu and 1,469 MSM in Mombasa, we found a significant overlap between MSM who use virtual sites and those who go to physical spots to find new partners. Nearly three-quarters of the MSM mainly using virtual platforms also visited physical spots to find partners, which is in concurrence with another survey conducted among MSM in these three counties. The survey showed that 14.7% of the MSM sought male sexual partners only in physical sites, 64.0% in both physical and virtual sites and 21.2% in only virtual sites¹⁴. These findings warrant the importance of conducting mapping of both physical and virtual platforms, as developing size estimates from a single approach may underestimate the number of MSM. At the same time, this presents another important insight that the overall estimate of MSM isn’t a simple additive function of geo-spot based MSM and virtual mapping estimates, but would require an adjustment of the overlapping proportion of MSM who use both physical and virtual sites. When adjusted for these overlaps, the total number of MSM in Kenya were approximately 25% in excess of the estimated number of MSM derived through geo-mapping.

In addition to providing population size estimates, the current study has also enhanced our understanding of virtual platform and internet based MSM in terms of their profiles, networks and how they connect with each other. Our results show that the participants in this study are young males with high education levels, which has also been shown in previous studies^10,12. This may be reflective of the access and ease of use of the internet within a specific segment of the population in a resource constrained country. We have also seen that more than 80% of MSM use multiple sites, and also have multiple identities registered on a single site. This is also in concurrence with previous research^25–27. Estimating the size of this population based on total counts of registered MSM at various virtual sites, without adjusting for these duplications will lead to an over-representation of the population size manyfold more than the actual numbers¹¹.

The findings of this study have several implications for HIV programming for MSM in Kenya. A substantial proportion of MSM stay hidden and are unlikely to receive services regularly through the existing MSM programs. Missing this population from a HIV program would mean denying critical HIV prevention and treatment services to a very high risk and vulnerable sub-population of MSM. In Kenya, we previously found that MSM who operate through virtual sites alone had a much higher HIV prevalence (26.7%) compared to those who seek sexual partners in physical and virtual sites (15.4%) or only physical sites (8.5%)¹⁴. Identifying MSM who engage in these virtual sites provides an avenue to reach them with HIV prevention and care services. HIV prevention programs should include virtual mapping in their strategic design and engage outreach workers and peer educators to reach out to these MSM. Those who wish to stay invisible and do not feel comfortable coming to the MSM led clinic or services can be offered outreach services at a safe space of their choice.

The findings of this study should be considered in light of a few limitations. Firstly, the accuracy of results is dependent on the accuracy of the app itself i.e., geo-specificity allowed by the GSN apps. Thus, the design of the study will not work appropriately in contexts where geo-specification of the catchment area is not allowed by the GSN apps. Sampling of respondents was also subject to a level of selection bias. Although the sample was based on a random selection of multiple sites, the selection of MSM was based on who was available at that moment and also who was willing to participate. The estimation of the population size of MSM is also based on the total number of MSM registered on various GSN apps and internet sites, and some registrations could have been redundant as well. Finally, our approach to virtual-site sampling made it challenging to document a non-response rate, which further limits our ability to judge selection bias. Although the methodology has limitations, it still provides a simple approach to estimate the number of MSM connected to virtual platforms in addition to understanding the operational dynamics of this concealed sub-typology which can be utilized to improve their reach and coverage.

Conclusions

To conclude, as internet usage around the world increases, its use by MSM will continue to gain popularity to find sexual partners, especially in cultures where same sex relationships are stigmatized. There is an increasing need to understand this subgroup, its size and dynamics to plan, develop and implement evidence-based prevention programs. The research methodology presented in this paper was able to map various virtual platforms and internet sites used by MSM and provide a methodology to estimate their size. The approach is simple and pragmatic and could be utilized to immediately initiate interventions among MSM who operate through virtual networks and stay hidden from programs. Although the approach might have limitations, there is a clear indication that use of geo-spot based mapping alone underestimates the total number of MSM in a given context and therefore should be used in conjunction with this methodology to calculate population estimates, set programmatic targets and initiate interventions to reach hidden and hard to reach MSM.

Data availability