Investigating Social Network Peer Effects on HIV Care Engagement Using a Fuzzy-Like Matching Approach:...

Social network data are essential and informative for public health research and implementation as they provide details on individuals and their social context. For example, health information and behaviors, such as HIV-related prevention and care, may disseminate within a network or across society. By harmonizing egocentric and digital networks, researchers may construct a sociocentric-like "fuzzy" network based on a subgroup of the population.

We aimed to generate a more complete sociocentric-like "fuzzy" network by harmonizing alternative sources of egocentric and digital network data to examine relationships between participants in the Neighborhoods and Networks (N2) cohort study. Further, we examined network peer effects of the status-neutral HIV care continuum cascade.

Data were collected from January 2018 to December 2019 in Chicago, Illinois, United States, from a community health center and via peer referral sampling as part of the N2 cohort study, comprised of Black sexually minoritized men and gender expansive populations. Participants provided sociodemographics, social networks, sexual networks, mobile phone contacts, and Facebook friends list data. Lab-based information about the HIV care continuum cascade was also collected. We used an experimental approach to develop and test a fuzzy matching algorithm to construct a more complete network across social, sexual, phone, and Facebook networks using R and Excel. We calculated social network centrality measures for each of these networks and then described the HIV care continuum within the context of each network. We then used Spearman correlation and a network autocorrelation model to examine social network peer effects with HIV status and care engagement.

A total of 412 participants resulted in 2054 network connections (ties) across the confidant and sexual partner social networks (participants=387; ties=445), peer referral network (participants=412; ties=362), phone contacts (participants=273; ties=362), and Facebook network (participants=144; ties=1383), reaching the entire study sample in one fully connected "fuzzy" network. Results from the individual networks' autocorrelation model suggest there are no peer effects on status-neutral HIV care engagement. Results from the final fuzzy-like sociocentric network autocorrelation model, adjusted for HIV serostatus, suggest that participants who were proximate to network members engaged in HIV care were significantly more likely to be engaged in care (ρ=0.128, SE 0.064; P=.045).

Using alternative sources of network data allowed us to fuzzy match a more complete network: fuzzy matching may identify hidden ties among participants that were missed by examining alternative sources of network data separately. Although sociocentric studies require significant resources to implement, more complete sociocentric-like networks may be generated using a fuzzy match approach that leverages egocentric, peer referral, and digital networks. Enriching offline networks with digital network data may provide insights into characteristics and norms that egocentric approaches may not be able to capture.