Gay, bisexual, and other men who have sex with men (collectively referred to as MSM) continue to be disproportionately affected by HIV, accounting for 67.0% of new diagnoses in 2021 [1]. Black or African American (hereafter referred to as Black) MSM accounted for 37.0% of diagnoses, and young Black MSM (YBMSM) aged 13–24 years were particularly affected, accounting for 53.0% of new HIV diagnoses among MSM in 2021 [1]. Despite the effectiveness of HIV prevention strategies such as routine HIV testing and pre-exposure prophylaxis (PrEP), uptake of and adherence to these strategies remains challenging [2, 3], especially among Black and young MSM [4–6], which tends to exacerbate racial and ethnic HIV disparities among MSM.

In the recent decade, online geosocial networking applications (GSN apps) have become increasingly popular among MSM, especially among young MSM, with over two-thirds of MSM reporting having ever used a GSN app (e.g., Grindr, Jack’d, Scruff) [7]. GSN apps are frequently used to meet sexual partners and engage in sexual behavior [8–12]. Further, recent studies have found that users of GSN apps, compared with non-users, had significantly higher sexually transmitted infection (STI) rates, number of sexual partners [13, 14], and more frequent substance use [15], increasing the chance for STI transmission and acquisition among users who may meet in-person and engage in unprotected sex. Studies among MSM also suggested that the use of GSN apps for public health interventions is both feasible and acceptable [16, 17]. We found that using GSN apps to promote the use ofHIV/STI treatment and prevention services among YBMSM [18] was acceptable and feasible. Due to their popularity, feasibility and acceptability among MSM users [19], GSN apps continue to be promising tools to reach MSM, specifically young MSM, with effective recruitment and prevention methods [16, 17].

Best practices and strategies for the use of online spaces for public health prevention and control efforts, however, are needed. One challenge for the routine use of online spaces for public health efforts is identifying users in online spaces with behaviors/environment associated with HIV/STI risk, particularly when using GSN apps that only allow targeted messaging via geography rather than other user characteristics. Another challenge for public health efforts is the linkage of online spaces to physical or geographic spaces for in-person interventions, such as where to optimally place an HIV/STI mobile testing van which coordinates with an online space offering the testing. These strategies and methods are necessary to ensure linkage to services and provide the efficient use of public health resources. This study explored strategies and methods to use online spaces for public health efforts in Baltimore City, MD, where the majority of new HIV diagnoses were found among MSM (61.4%), non-Hispanic Black persons (81.6%), and persons aged 20–39 years (72.8%) [20].

The objectives of this study were to describe one strategy to link GSN users to geographic HIV/STI hotspot transmission areas [21, 22], and to determine the GSN user profile characteristics associated with app utilization in hotspot areas. The authors are not aware of other studies that have considered a similar strategy.

Using routinely collected public health surveillance data and profile characteristics from GSN app users in Baltimore City, we identified profile characteristics associated with app utilization in high and medium CVL census tracts. These characteristics included self-reported Black race and user profiles reporting versatile or top sex position preferences. As prevention messaging is increasingly directed to online spaces, GSN app users in high and medium CVL areas can potentially be used to identify subgroups of MSM who may benefit from enhanced HIV prevention messages or intervention.

The association between Black race and GSN app use in high CVL census tracts found in our study is consistent with prior work demonstrating that Black MSM have greater exposure to sexual networks with high HIV transmission [33–36]. Our finding also extends this prior work by identifying a specific subgroup of Black MSM on GSN apps who may be meeting sex partners in a geographic hotspot with high HIV transmission (i.e., census tracts with high CVL) and how GSN apps may be a potential access point for reaching men disproportionately affected by HIV.

In addition to prioritizing Black MSM users, our results suggest that online prevention could also focus on users who self-report preference for certain sex positions. In our study, GSN app user profiles with a versatile or top sex position preference were associated with app use in high/medium CVL census tracts. This association may reflect the practice of seropositioning among app users to reduce their acquisition of HIV in areas they consider high risk. This rationale presumes users are aware these areas are HIV hotspots, which may or may not be accurate. Alternatively, this association may reflect higher contribution to HIV transmission by users with a top or versatile position preference driven by two critical aspects of HIV transmission dynamics in MSM: (1) during anal sex, HIV is most efficiently transmitted from the insertive partner to the receptive partner [37–38] and (2) HIV is more efficiently transmitted in MSM networks where a significant proportion of men engage in both receptive and insertive anal sex (e.g., versatile position preference) [39]. This study is cross-sectional, so directionality or causality cannot be accurately assessed. Whether either explanation is accurate, our findings suggest users with these position preferences could benefit from messaging focused on HIV prevention tools to reduce both HIV/STI transmission and acquisition including PrEP, doxycycline post-exposure prophylaxis, anti-retroviral therapy for viral suppression, and condom use. Based on the biology of HIV transmission dynamics in MSM populations, prevention messaging that reduces the risk of HIV transmission among MSM living with HIV with a top position preference may be an especially important implications of our findings. Thus, the association between app user profile characteristics (e.g., sex position preference) and app utilization in high CVL census tracts, which persisted even after adjusting for race, may provide additional context beyond racial identity for tailoring prevention messaging and developing interventions that are inclusive of GSN app users with these preferences.

Notably, most users did not list several profile characteristics, including their HIV status (34.8%), with the majority of these users utilizing the app in high or medium CVL census tracts. This finding could be related to users evading disclosure of living with HIV due to stigma or not being aware of their HIV status. It may also reflect less disclosure of personal characteristics or preferences among GSN app users in high or medium CVL census tracts as compared with those in low CVL census tracts. Given app user profiles without demographic characteristics (age, race), sexual position preference, seeking preference, or HIV status were more frequently used in high and/or medium CVL census tracts, it may be that these types of GSN app users are most susceptible to HIV acquisition or transmission. The information gleaned from GSN app users and their behaviors/environments associated with HIV/STI risk is useful, but is limited when users create profiles to quickly “hook up” with others, thus leaving many profile characteristics blank when not required by the app. For this reason, strategies for tailoring and delivering HIV prevention and treatment messages and interventions to a risk environment (e.g., high and medium CVL census tracts, areas with higher poverty levels and characteristics correlated with higher HIV rates) may be more effective compared to focusing solely on MSM with certain demographic or sexual behavior characteristics. To account for the missing data and better understand the profile characteristics of the GSN app users in our analysis, we used imputation methods. Our study is the first, to our knowledge, to use these techniques with GSN app data in the context of HIV hotspots. Using imputation methods more frequently with dating app data to understand users’ behaviors/environment associated with HIV/STI risk and how to reach this population should be considered when possible. However, imputation methods are just one step to characterizing this population when missing data is abundant, and additional research is needed to further understand the characteristics of the users.

Overall, GSN apps are acceptable tools for outreach to MSM, especially young MSM [18], and they may provide an ideal venue to reach MSM because of the significant amount of time spent on these apps by users [10]. Disseminating effective HIV messaging with images that resonate with GSN app users in high CVL census tracts, especially during peak use hours, can potentially increase the impact and return on funding for geotargeted advertisement and other online outreach [40]. Additional novel uses to reach MSM populations through GSN apps could include sending real-time notifications about mobile vans offering testing, treatment, and/or post-exposure prophylaxis of infections such as HIV, STIs, and mpox to users in the vicinity based on app geolocation data [41–48]. GSN apps could also notify users in geographic hotspots about where to access free HIV self-testing kits in-person or through the mail and facilitate the delivery of other sexual health tools [47–48]. While many of the proposed strategies are not currently accessible services in GSN apps, prior studies have indicated the acceptability of similar strategies among MSM who use GSN apps [49–50]. Furthermore, public health advocates have successfully partnered with app companies to develop other structural interventions to help users manage their behaviors/environment associated with HIV/STI risk [7]. These findings may warrant additional efforts to increase the services public health programs can use to engage MSM on GSN apps.

This analysis has limitations. First, the results are limited by self-reported data collected from user profiles. Secondly, many users did not list their profile characteristics. Missing data could be related to certain fields not being required as determined by the app or users evading disclosure of certain personal characteristics. We adjusted for missing data using statistical imputation methods; however, our results may be biased, and estimates and standard errors may be over or under-estimated. Third, the utilization of backward elimination, which relies on significant p-values, may have led to the exclusion of crucial confounding factors. This automated method for selecting confounders can potentially introduce bias into effect estimates and underestimate their variance [42]. To address this limitation, variables linked to sexual risk behavior in other GSN app studies were included, regardless of their bivariate significance. HIV Status and Active Time of Week were excluded, while Active Time of Day was kept due to its prior significance [32]. Fourth, an individual may have multiple user profiles across platforms, and deduplication is not feasible (identifiable information was not collected from user profiles). Fifth, it is possible that GSN app users may have used a GPS spoofing app to change the location of their mobile device to the geographic location of the selected census tracts and may not reside in that census tract. Sixth, preferences for condom use, experience with PrEP, or antiretroviral therapy (ART) which are now commonly available and completed by users [7], were not profile characteristic measures at the time of this study; thus, limiting our ability to understand the users’ behaviors/environment associated with HIV/STI risk. Lastly, it is possible that incomplete VL data and a lack of timely information on in-care VLs could have led to misclassification of census tracts by CVL category. While CVL use among researchers is not uncommon [21, 43–45], methods on how to measure CVL vary [46], and use of alternate measures of population-level VL burden (e.g., such as geometric mean VL per census tract) could lead to different inferences in the classification of CVL categories.

Despite these caveats, findings from this study describe the profile characteristics from GSN app users that were online in high CVL census tracts that can be used to prioritize and engage with MSM through GSN apps, at optimal times, with effective HIV prevention messaging, intervention, and notification of sexual health tools in their vicinity. User profiles with self-reported Black race, and versatile or top sexual position preference may represent preferred profiles for focused recruitment into HIV prevention and interventions in priority communities. Similarly, user profiles with limited personal characteristics or preferences may also be important to prioritize for HIV prevention and other interventions. Findings from this study lay a groundwork for researchers, health departments, and community-based organizations, and other public health practitioners to identify and reach diverse MSM populations with vital HIV/STI screening, prevention, treatment and care messaging and intervention.