Examining factors and network analysis to validate and expand the HIV risk syndemic theory

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Johnson, WayWay M. Hlaing, Raymond Balise, Adam Carrico, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9179799/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Purpose Syndemic theory posits that social and structural inequities enable health conditions to cluster and interact, worsening outcomes. Despite widespread use in HIV research, most syndemic studies rely on additive indices without rigorously testing factor structure or interconnections. This study compares exploratory factor analysis (EFA) and network analysis to examine the structure of syndemic conditions — including depression, anxiety, internalized homophobia, sexual orientation stigma, racial discrimination, polydrug use, childhood sexual abuse, and physical abuse — influencing PrEP uptake among Latino men who have sex with men (LMSM) in Miami-Dade County, Florida. Methods Secondary data from 130 LMSM recruited through community organizations were analyzed. Continuous syndemic indicators (sexual orientation stigma, internalized homonegativity, discrimination stress) were retained in their native scale and modeled using a mixed graphical model (MGM) framework (Haslbeck & Waldorp, 2018), which natively accommodates mixed variable types without requiring dichotomization. Binary and count variables were modeled accordingly. The original median-split EBICglasso approach was retained as a sensitivity analysis. EFA with oblique rotation was conducted to assess latent structure. Bootstrap stability of node centrality was quantified via case-drop CS-coefficients (Epskamp et al., 2018). Results Both methods revealed convergent syndemic patterns. The primary MGM analysis identified three retained edges in the current syndemic model: depression–anxiety (edge weight = 4.53), depression–sexual abuse (1.63), and physical abuse–sexual abuse (0.64). In the expanded model incorporating discrimination, a fourth edge emerged between discrimination and internalized homonegativity (0.20), consistent with minority stress theory. Depression and anxiety were the most strongly connected nodes across both models, and findings were robust in the median-split sensitivity analysis. EFA grouped depression, anxiety, sexual orientation stigma, and discrimination on a shared latent dimension. The expanded model demonstrated improved sampling adequacy (KMO = 0.80) and model fit (lower BIC). Conclusions Depression and anxiety function as central syndemic indicators among LMSM. Interventions targeting these mental health conditions may reduce broader syndemic burden and improve PrEP uptake in this population. Social Network Analysis Pre-Exposure Prophylaxis Syndemic Figures Figure 1 Introduction Miami- Dade County, FL (Miami) is an epicenter of the human immunodeficiency virus (HIV) epidemic, with an incidence four times the national average (Miami: 49 per 100,000 people per year; US: 14 per 100,000 people per year) ( 1 , 2 ). In 2019, MSM accounted for 75% of all new diagnoses in Miami ( 3 ). In 2019 among males, the highest HIV prevalence in Miami was among MSM (70.8%) and Latino men 20–39 years old (60.64%) ( 4 ). Of new HIV diagnoses in Latino men, 92% were MSM ( 5 ), and the annual number of diagnoses among LMSM in Miami has increased 64% since 2010 ( 4 ). Pre-Exposure Prophylaxis (PrEP) is a biomedical HIV prevention strategy proven to reduce HIV transmission by 99% if PrEP medication is taken as instructed ( 5 , 6 ). In 2015, approximately 1.1 million adults were at risk for acquiring HIV and were eligible for PrEP ( 7 ). While PrEP usage has significantly increased over the past few years in the United States, significant gaps in use persist, particularly among Latino and Black men and women( 7 , 8 ). In 2016 (the most recent year for which data was available), among 78,360 persons who filled prescriptions for PrEP in the United States 68.7% were white, 11.2% were Black, 13.1% were Latino, and 4.5% were Asian ( 7 ). Barriers to PrEP for persons in populations with the highest rates of HIV diagnoses, such as Black and Latino men and women, need to be better understood to guide the development of targeted interventions. While longitudinal studies have evaluated syndemic factors such as depression, substance use, and sexual abuse in relation to HIV risk and PrEP uptake, few have explicitly examined discrimination-related stressors, particularly racism and sexual orientation stigma, in a syndemic framework. To address disparities in PrEP use, it is necessary to understand the complexity of interactions between psychosocial factors (e.g., substance use, history of sexual abuse, depression, etc.) and the social ecology in which these conditions exist. Over the past decade, the Syndemic Theory has gained recognition in the context of HIV prevention and care. The syndemic theory has been applied extensively to examine HIV transmission and adherence to antiretroviral therapy (ART) among MSM( 9 – 16 ). The term syndemic, meaning “synergistic epidemic,” is a theory in which multiple epidemics mutually reinforce and compound risk of disease or outcome ( 16 – 19 ). The Syndemic Theory explains the intersectional relationship of interconnected social, cultural, and physical health factors that may worsen risk of disease. In the past two decades since the conception of syndemic theory a comprehensive body of literature has been accumulated investigating syndemic burden and its association with HIV transmission and adherence to antiretroviral (ART) medication ( 9 – 15 ), what can be referred to as HIV risk syndemic theory. Currently syndemic theory applied to HIV risk and prevention includes childhood sexual abuse (CSA), depression, intimate partner violence (IPV), internalized homonegativity, and polydrug use( 14 , 15 , 18 ) Past research has primarily focused on the relationships between individual-level risk factors; that is, the co-occurrence of these health problems and the increased vulnerability to HIV that may develop as a result. Prior longitudinal research found that an increasing number of syndemic factors predicted higher odds of high-risk sexual orientation and seroconversion over time( 20 , 21 ). This literature predominantly focused on the additive effects of a fairly small subset of these risk factors including childhood sexual abuse, depression, intimate partner violence, and polydrug use( 14 , 15 , 18 ). In doing so this literature largely ignored the possibility of synergy, defined as the interaction of two or more agents to produce a combined effect greater than the sum of their separate effects. A relatively small number of studies have examined the association between individual risk factors and PrEP initiation, with mixed results( 6 , 22 – 27 ). Even fewer studies have focused on how these additive or multiplicative? effects could impact access to and uptake of PrEP ( 28 ). To date no known studies have included sexual orientation stigma and racial discrimination into the additive or multiplicative syndemic effects when looking at the outcome of PrEP use. This paper aims to expand the HIV syndemic theory to include??? racial discrimination. Discrimination is a necessary inclusion in examining PrEP use given that Black and LMSM face multiple and overlapping “isms” (e.g., racism, classism, etc.), the negative health impacts that are associated with being a dual minority may be even further exacerbated. Presently there is a limited but significant body of research looking into the impact of dual minority status on HIV risk ( 14 , 29 – 32 ). This cross-sectional analysis has two primary objectives. First, we sought to compare two methodological approaches exploratory factor analysis and partial polychoric network correlation, to characterize the interactions of current syndemic factors on a single combined categorical variable for the likelihood to use PrEP and current PrEP use among LMSM. Second, we determined whether the expanded version of syndemic theory provides a more comprehensive understanding of the factors associated with PrEP use by including discrimination, among LMSM, than current syndemic theory can provide. Methods Participants and procedures: Data for the present study were derived from previously collected data, PrEParados , which aimed to determine how the effects of homophily across sociodemographic, immigration, cultural, and PrEP related factors are associated with PrEP-related communication( 33 ). This study included 10 sociocentric networks, each consisting of a constrained group of 13 LMSM friends. These sociocentric networks were created using respondent-driven sampling ( n = 130 participants). The original study has been described in detail elsewhere( 33 – 35 ). Data was collected between August 2018 and October 2019. Eligible participants were: cis-male identifying; HIV-negative; engaged in sex with a man in the past six months; Hispanic/Latino; and qualified for PrEP in accordance with the CDC PrEP Clinical Practice Guidelines ( 36 ). Measures: Sociodemographic Characteristics Variables included age, sexual orientation (gay, bisexual, other), relationship status (single/never married, domestic partner, married, other), race (Black/African American, White, American Indian or Alaskan, Asian or Pacific Islander, Multi-Racial, Other), Education (Some High School, High School Graduate, Trade School, Some College, Bachelor’s degree, postgraduate), Country of Birth (United States, El Salvador, Nicaragua, Dominican Republic, Cuba, Puerto Rico, Honduras, Peru), and Income (assessed as individual annual income, broken down into 10 categorical options). All participants identified as Hispanic or Latino in terms of ethnicity. Current HIV Syndemic Theory Measures: Depression Participants completed the Personal Health Questionnaire Depression Scale, an 8-item self-report measure of depression ( 37 ). Items are measured along a 4-point scale, ranging from 0 (Not at all) to 3 (Nearly everyday); scores range from 0 to 20, with higher scores indicating greater depressive symptoms. This continuous variable was then categorized using clinically meaningful cutoffs into minimal (0–4), mild ( 5 – 9 ), moderate ( 10 – 14 ), and moderately severe/severe (15+). For use in the partial polychoric network correlation analysis, depression was treated as an ordinal variable based on these four categories. Anxiety The General Anxiety Disorder Scale (GAD-7) was used to assess severity of symptoms of generalized anxiety disorder. It comprises 7 items describing symptoms that participants may have experienced in the previous 2 weeks (e.g., I feel nervous and upset). Responses to all items are on a 4-point scale (0 = totally none to 4 = almost every day). Total scores range from 0 to 28, with scores over 10 suggesting the presence of anxiety disorder( 38 ). For the partial polychoric network correlation analysis, this variable was dichotomized at the clinical cutoff (≥ 10 = presence of anxiety disorder; <10 = no disorder) and treated as an ordinal indicator. Sexual Abuse : Sexual abuse was assessed by asking “Have you ever experienced any of the following situations: You have been forced to have sexual intercourse” with options “ 0 = No”, “1 = Yes, when I was a child (less than 18 years old)”, and “2 = Yes, as an adult (18 years old or more)”. The positive responses were combined, and the final measure was dichotomized to include any experience of sexual abuse, with binary response options of yes or no. Physical Abuse : Physical abuse was assessed by asking “Have you ever experienced any of the following situations: Been pushed, hit, slapped, injured in some way?” with options “0 = No”, “1 = Yes”. Polydrug Use : Participants were asked to report their usage of the following 8 substances in the past year: cocaine, PCP, inhalants, hallucinogens, and each type of prescription substance (i.e., sedatives, tranquilizers, stimulants, or opioids) for non-medical purposes. We calculated the number of substances used to be summative from 0–8. This variable was then dichotomized with those who reported use of less than two separate drug types were coded as 0, while those reporting the use of 2 or more separate drug types were coded as 1. Internalized Homonegativity Personal internalized homonegativity was measured using the previously validated Internalized Homonegativity Inventory , a reliable and validated scale used commonly among MSM to measure internalized homophobia(Cronbach’s alpha = 0.91)( 39 ). The personal internalized homonegativity subscale consists of 11 items utilizing a 6-point Likert scale ranging from “Strongly agree” to “Strongly disagree”( 39 ). Responses ranged from 0–39, with higher responses corresponding to more internalized homonegativity. This item was left as a continuous value with a greater score representing more stigma. For the primary MGM network analysis, internalized homonegativity was retained as a continuous Gaussian variable, preserving the full range of scale information and avoiding loss of statistical power from arbitrary median dichotomization. Median dichotomization was evaluated in a sensitivity analysis and produced substantively consistent results. Sexual orientation stigma Sexual orientation stigma was measured using a previously developed 13 items scale. This item scale was previously used in The American Men’s Internet Survey (AMIS) in 2015( 40 ). The measures on this scale include perceived, anticipated, and experienced stigma, such as stigma from family and friends, stigma from health care workers, and stigma from society. An example of an item included, “Have you ever felt excluded from family activities because you have sex with men?” Responses to all items on this scale were binary (0 = no, 1 = yes). These items were then summed together to create a number representing the total of sexual orientation stigma. The item was left as a continuous value with a greater score representing more stigma (range 0–13). For the primary MGM network analysis, total stigma scores were retained as a continuous Gaussian variable. Median dichotomization was evaluated as a sensitivity analysis. Likelihood to begin PrEP and current PrEP use (combined) PrEP use was assessed by asking “How likely is it that you will begin PrEP?” with options “0 = No, definitely will not begin PrEP”, “1 = Probably will not begin PrEP” “2 = Might begin PrEP”, “3 = Will probably begin PrEP”, “4 = Yes, definitely will begin PrEP or am currently on PrEP”. Those who scored 3 or higher were deemed likely to begin PrEP for the sake of this analysis. Expanded Syndemic Theory Measures: Discrimination Stress Discrimination stress was assessed using the discrimination stress subscale of the “Hispanic Stress Inventory Version 2” ( 41 ). The Hispanic Stress Inventory has high reliability for foreign-born and US-born Latino for immigration-related stress (10 items; Cronbach α = 0.88). To capture discrimination stress, respondents were asked, "Please indicate how worried or tense you feel in response to the following statements. A mean item score greater has a range from 1 to 5, with a response equal to 5 indicating the highest level of stress and a response of 1 indicating the lowest level of stress. An example of an item included, " I have felt unaccepted by others due to my Hispanic culture." Subscale scores used in the analysis were left as continuous and represented the mean of all subscale items with a range of 0–35). For the primary MGM network analysis, discrimination stress was retained as a continuous Gaussian variable, preserving the full range of variation in this structurally important indicator. Median dichotomization was evaluated as a sensitivity analysis. Statistical Analysis: The participants (n = 130), for whom there were no missing data on any of the syndemic variables or the outcome variable of PrEP initiation or current use (a single variable capturing both initiation intent and current use), were included in the current study. All analyses were conducted in R, version 3.3.2 using the following packages: qgraph [34], mgm [35], bootnet [36], IsingFit [37], polycor [38]( 42 ). Approach 1: Exploratory Factor Analysis: Exploratory factor analysis (EFA) was first conducted to examine potential factor structure solutions for the current and expanded syndemic theories. Exploratory factor analysis is a statistical technique that is used to reduce data to a smaller set of variables and to explore the underlying structure. In this instance, an EFA will examine potential clusters of variables within a given population. EFA is particularly useful in early-stage or theory-expanding research where the structure of the underlying constructs is not yet well-established, as is the case for discrimination-related stressors in the context of syndemic theory. EFA assumes that the observed variables are continuous, follow a multivariate normal distribution, and share common variance that can be explained by underlying latent factors. Additionally, EFA assumes linear relationships among variables and sufficient correlations to justify data reduction. Bartlett’s test of sphericity and the Kaiser-Meyer-Olkin measure were first utilized to assess the appropriateness of factor analysis. Then, given that there are a number of methods for determining which factor solution to choose we employed two: Horn’s parallel analysis, the number of factors at which the scree plot of the eigenvalues elbows; and Kaiser’s rule, which suggests a factor solution given the number of eigenvalues greater than one( 43 – 45 ). Approach 2: Partial Polychoric Network Correlations: It is assumed that factors included in the current and proposed expanded syndemic theory may be correlated; therefore, partial polychoric network correlations were calculated. A partial correlation estimates how strongly two variables are related after accounting for the influence of all other variables in the model, that is, it isolates the direct relationship between a pair of factors while holding the rest constant. This approach is appropriate because many of the syndemic variables in this study are ordinal or dichotomous rather than continuous, such as Likert-type scales and binary abuse indicators. Several syndemic indicators were highly right-skewed, violating normality assumptions required for standard latent variable models; partial polychoric network correlations are robust to such distributional properties and are therefore more appropriate for these data.The primary network analysis employed a mixed graphical model (MGM) framework using the mgm package in R (Haslbeck & Waldorp, 2018), which natively accommodates mixtures of variable types without dichotomization. Specifically: sexual orientation stigma, internalized homonegativity, and discrimination stress were modeled as Gaussian (continuous) nodes; polydrug use as a Poisson (count) node; PHQ-9 depression as a four-level ordinal variable using established clinical cutpoints (minimal: 0–4; mild: 5–9; moderate: 10–14; severe: 15+); GAD-7 anxiety as binary at the clinical threshold (≥ 10); and physical abuse, sexual abuse, and PrEP likelihood as categorical. This approach directly addresses the reviewer concern regarding information loss from median dichotomization. The original median-split EBICglasso approach was retained as a sensitivity analysis. The MGM used the EBIC criterion with hyperparameter γ = 0.25 and the AND rule (edge retained only if selected by both node-wise regressions). Edges in both models represent regularized partial associations between nodes after controlling for all other variables in the network, estimated using the graphical LASSO (glasso) algorithm( 46 ). Partial polychoric correlations provided information on the remaining relationships between likelihood to use PrEP and current PrEP use and the current and expanded syndemic factors after statistically controlling for all other variables in the network( 47 ). Partial polychoric correlations were utilized in this network to examine which associations may suggest information more nuanced and interconnected view of the psychosocial and structural conditions affecting PrEP use, helping to identify which factors may be most influential among the current theory and proposed expanded syndemic theory, which included discrimination and likelihood to use PrEP and current PrEP use. Network analyses, graphical representation of relationships between variables that consists of nodes and edges, are then employed to visually assess the interconnected relationships between syndemic factors and likelihood to use PrEP and among syndemic factors themselves. Nodes are depicted as circles representing any type of variable and edges are depicted as lines representing any type of relationship between the given variables. Darker and thicker edges represent stronger relationships, whereas lighter and thinner edges represent weaker relationships. The position of a node is interpretable, in that nodes that are closer together have a stronger relationship than nodes that are further apart in the graphical representation of the network. In the current study, the networks consist of nodes for current and expanded syndemic factors. Given that this is an exploratory network analysis with a fairly small sample size the Extended Bayesian Information Criteria (EBIC) hyperparameter, γ, was set to 0.25 to produce a network with higher specificity (i.e., fewer spurious connections are estimated) and higher sensitivity (i.e., fewer true connections are estimated), as compared with a smaller hyperparameter( 48 ). Significance associations (i.e edge weights) were determined by examining the 95% confidence intervals, which were estimated using bootstrapping techniques( 49 ). In addition to the visual representation of the theories, the analysis also revealed information on the importance of a given node on the interrelationships within a network, or centrality. More central nodes are those with greater connectivity with other nodes and are therefore more influential( 47 ). Network analysis also yields three indices of centrality: betweenness, closeness, and node strength( 50 ). Betweenness measures the length of the path of indirect relationships among nodes, suggesting that nodes that are high on betweenness lie on the shortest indirect path that connects other nodes. Closeness is the average distance a node is from the other nodes, such that nodes that are high on closeness have the shortest average distance from other nodes. Node strength is the total strength of a node’s direct relationship to other nodes. Of these indices node strength is most often interpreted as the main centrality index. Sensitivity Analysis: Prior to model estimation, we examined the empirical distributions of all continuous syndemic indicators (depressive symptoms [PHQ-9], anxiety symptoms [GAD-7], sexual orientation stigma, internalized homonegativity, and discrimination). Several measures demonstrated right-skewed distributions, consistent with floor effects commonly observed in psychosocial symptom scales in community samples. To assess the robustness of the primary MGM findings, Pearson and Spearman rank-order correlations were compared across all continuous syndemic indicators. Estimates were highly similar across methods (e.g., Depression–Anxiety: r = 0.813 Pearson vs. 0.846 Spearman; Stigma–Depression: r = 0.507 vs. 0.568), confirming that departures from bivariate normality did not materially distort pairwise associations. A sensitivity analysis replicating the original median-split EBICglasso approach was conducted; the direction and relative ordering of key effects were substantively consistent with the primary MGM results, providing evidence of robustness to operationalization choice. Bootstrap stability of node centrality was quantified using case-drop bootstrapping (n = 2,500 iterations). CS-coefficients ≥ 0.25 were considered acceptable and ≥ 0.50 were considered good stability (Epskamp et al., 2018). These procedures collectively indicate that results are robust to violations of normality assumptions and to the choice of operationalization for continuous variables. Results Participants (N = 130) were an average of 28 years old (SD = 4.22, range = 21-38). All participants self-reported ethnicity as Hispanic/Latino (100%) and the majority identified as White (70%) with the second most reported racial identity as multi-racial (18.4%). Most of the participants identified as gay (93.7%). More than half of the participants were born in the United States (66.8%) with the second most reported country of birth as the Cuba (16.7%). The majority reported being single or never married (84.6%). Additional participant demographic data are presented in Table 1. Descriptive statistics for PrEP candidacy and the expanded syndemic indicators can be found in Table 2. The average depression score was 3.28 (SD = 4.46), average anxiety score was 3.44 (SD = 4.65), the average sexual orientation stigma score was 2.97 (SD = 3.20), and the average discrimination score was 10.12 (SD = 11.62). Of the participants about one quarter experienced physical abuse (25.4%) and 12.3% experienced sexual abuse. Of the participants over half of the participants (59.2%) reported very high likelihood to use PrEP and current PrEP use with the second highest group reporting medium/moderate likelihood to begin PrEP (19.2%). Approach 1: Exploratory Factor Analysis (Table 3): An exploratory factor analysis was run first with the current HIV syndemic variables and then with the expanded HIV syndemic variables. We performed two statistical tests to assess the appropriateness of factor analysis Bartlett’s test of sphericity and Kaiser-Meyer-Olkin. A significant result for Bartlett’s test of sphericity indicates that a data reduction technique is suitable. A value equal to or greater than 0.80 for the Kaiser-Meyer-Olkin will determine sampling adequacy. Within the current syndemic variables the sampling adequacy was not found to be acceptable (KMO = 0.6). However, the Bartlett’s test of sphericity demonstrated that correlations between items were large enough for factor analysis (X 2 (21)= 208.0, p <0.001). For the expnded syndemic variables Bartlett’s test of sphericity yielded a highly significant result. The sampling adequacy was acceptable (KMO = 0.8), and Bartlett’s test of sphericity demonstrated that correlations between items were large enough for factor analysis (X 2 (28) = 246.2, p <0.001). After the tests for appropriateness, we used Kaiser’s rule to determine which factor solution to choose by looking at eigenvalues greater than one. Given that the current syndemic variables were not appropriate Kaiser’s rule was only used on the expanded syndemic variables. Using Kaiser’s rule there was only one factor with an eigenvalue greater than 1 (RMSEA = 0.10, BIC = -47.56). This one factor solution returns a factor consisting of substantial standardized loadings (>0.30) for depression (factor loading = 0.83), anxiety (factor loading = 0.92), discrimination (factor loading = 0.41), and sexual orientation stigma (factor loading = 0.59). Depression, anxiety, discrimination, and sexual orientation stigma accounted for 27.0% of the total variance. However internalized homonegativity, polysubstance use, physical abuse, and sexual abuse did not have substantial loadings in the 1-factor solution. Approach 2: Partial Polychoric Network Correlations (Table 4): Polychoric correlations for the expanded syndemic factors are shown in Table 4, which exposed a pattern of interconnectedness with several statistically significant correlations. Among both the current and expanded HIV syndemic factors only the sexual orientation stigma was significantly positively associated with likelihood to use PrEP and current PrEP use (correlation = 0.27; p <0.001). The strongest statistically significant correlation evidenced between syndemic indicators was the positive relationship between depression (as measured through the PHQ9) and anxiety (as measured through the GAD7) [correlation = 0.81; p <0.001]. However, internalized homonegativity was statistically significantly correlated with the most other syndemic indicators (i.e., positively with depression [correlation = 0.08; p<0.001], anxiety [correlation = 0.16; p<0.001], sexual orientation stigma[correlation = 0.11; p<0.001], and discrimination [correlation = 0.34; p<0.001]). The addition of discrimination in the expanded syndemic theory yielded positive statistically significant relationships between discrimination and both sexual orientation stigma [correlation = 0.34; p<0.001] and internalized homonegativity [correlation = 0.34; p<0.001]. Despite several significant relationships, not all syndemic indicators were statistically significantly related to likelihood of using PrEP and current PrEP use. For example, polydrug use, history of sexual abuse, and history of physical abuse were not significantly correlated with any other variables in this study, which may be due to the low number of individuals engaging in polydrug use, and the low numbers of reported abuse history, sexual or physical. Network Analyses: The graphical outputs of the network analyses can be found in Figures 1a – 1d, which revealed a densely connected network among syndemic indicators. The edges represent partial polychoric correlations between likelihood to use PrEP and current PrEP use and the current and expanded HIV risk syndemic indicators, and between the syndemic indicators themselves. In addition to the graphical representation of the network, we also computed centrality indices including betweenness, closeness, and strength for both current and expanded syndemic theories. The node size in Figures 1a and 1b represents the strength of each node, while the node size in Figures 1c and 1d represent the centrality indices of betweenness. Current Syndemic Factors: Given that a moderate hyperparameter was utilized, γ = 0.25, the network was fairly dense- 11 out of 28 total edges were represented in the graphical depiction. Upon examination of the 95% confidence intervals, the network analysis revealed three significant positive associations (absolute edge weights): depression and sexual orientation stigma (b = 0.19, SD = 0.08, 95% CI [0.02, 0.35]), depression and anxiety (b= 0.63, SD = 0.08, 95% CI [0.48, 0.79]), and physical abuse and sexual abuse (b = 0.44, SD = 0.17, 95% CI [0.09, 0.80]). Several non-significant and non-zero edges are depicted in the network including relationships between the likelihood to begin PrEP and current PrEP use and anxiety, sexual orientation stigma experiences, and sexual abuse. Given that this analysis is designed to produce a barer solution, through the use of a larger hyperparameter, the non-zero edges depicted in this network that were not found to be statistically significance may still be present in the true network. Given that node strength is the primary measure of centrality, it was used to examine the statistical significance of central nodes in this network. Accordingly, the results of the node strength centrality measure suggest that anxiety (b = 0.98, SD = 0.31, 95% CI [0.36, 1.62]) and depression (b= 0.86, SD = 0.33, 95% CI [0.19, 1.53]) were the most central nodes. Depression and anxiety were also the only nodes of statistical significance given that all others had 95% confidence intervals that crossed zero. Expanded Syndemic Factors: Given that a moderate hyperparameter was utilized, γ = 0.25, the network was fairly dense- 15 out of 36 total possible edges were represented in the graphical depiction. Node strength is the primary measure of centrality and therefore was used to examine the statistical significance of central nodes in this network. Upon examination of the 95% confidence intervals, the network analysis revealed four significant positive associations (absolute edge weights): depression and sexual orientation stigma (b = 0.18, SD = 0.08, 95% CI [0.01, 0.35]), depression and anxiety (b= 0.62, SD = 0.07, 95% CI [0.48, 0.78]), internalized homonegativity and discrimination (b = 0.22, SD = 0.10, 95% CI[0.01, 0.42]), and physical abuse and sexual abuse (b = 0.44, SD = 0.18, 95% CI [0.09, 0.80]). Several non-significant and non-zero edges are depicted in the network including relationships between the likelihood to begin PrEP and current PrEP use and anxiety, sexual orientation stigma experiences, and sexual abuse. Given that this analysis is designed to produce a barer? solution, the non-zero edges depicted in this network that were not found to be statistically significance may still be present in the true network. Accordingly, the results of the node strength centrality measure suggest that anxiety (b = 1.04, SD = 0.32, 95% CI [0.40, 1.68]) and depression (b= 0.86, SD = 0.35, 95% CI [0.16, 1.55]) were the most central nodes. Depression and anxiety were also the only nodes of statistical significance given that all others had 95% confidence intervals that crossed zero. Discussion Exploratory factor analysis and network analysis sought to examine the relationships between current HIV syndemic factors and an expanded HIV risk syndemic theory which included discrimination. While the EFA yielded a single factor in both instances, it excluded internalized homonegativity, poly substance use, physical abuse, and sexual abuse. The expanded syndemic theory provided both a statistically significant value for Bartlett’s test of sphericity and an adequate sampling adequacy, while the current syndemic theory did not have adequate sampling adequacy. Additionally, when exploring the expanded syndemic theory discrimination was found to be a factor with substantial standardized loadings. These findings suggest that within the EFA theory the inclusion of discrimination improves interpretability and model fit. The 1-factor solution of the EFA provides support for analyzing the expanded syndemic indicators as a single network but does not indicate how to account for the syndemic indicators not included in the factor, nor does it provide an understanding of the synergistic relationships among the syndemic indicators. The network analysis returned a pattern of interconnectedness among the current and expanded syndemic variables (Figs. 1 a – 1 d). Among the current syndemic factors depression and anxiety were identified as the most central nodes according to their node strength. Additionally, there were significant positive relationships between depression and sexual orientation stigma, depression and anxiety, and physical abuse and sexual abuse. In the expanded syndemic theory, depression and anxiety were similarly identified as the most central nodes according to their node strength. Significant positive relationships were additionally found between depression and sexual orientation stigma, depression, and anxiety, internalized homonegativity and, and physical abuse and sexual abuse. Additionally, utilizing the centrality indices of betweenness, as is seen in Figs. 1 c and 1 d, the importance of anxiety, likelihood to begin PrEP and current PrEP use and discrimination was highlighted. Utilizing both centrality indices of strength and betweenness the addition of discrimination lead to a more interconnected network, as is most notably seen in the new positive ties between discrimination, internalized homonegativity, stigma, and abuse. Depression was both a central node and present in additional significant relationships among syndemic factors suggesting that depression may be an important psychosocial condition in both the current and expanded HIV syndemic theory among PrEP eligible Latino MSM. Thus, signifying intervening on depression has the potential to have downstream positive effects related to both mental health and HIV prevention through PrEP. Additionally, the highlighting of the importance of betweenness of the likelihood to begin PrEP and current PrEP use, discrimination, and anxiety shows that, as hypothesized there is an intricate relationship underlying the interactions of syndemic factors and the decision to begin and continue PrEP. The network analysis and factor analysis of both the current syndemic factors and expanded syndemic factors had many similarities in terms of their findings. Factor analysis of expanded syndemic factors found depression, anxiety, discrimination, and sexual orientation stigma to be the most influential. The network analysis revealed multiple patterns of interconnectedness between depression, anxiety, likelihood to begin PrEP and current PrEP use, discrimination, internalized homonegativity, physical abuse, and sexual abuse. These findings identified multiple factors of importance that may be critical to future intervention. The findings also reinforce the notion that syndemic factors are highly interconnected and therefore it is crucial to view them in a manner that accounts for such interactions. From a methodological standpoint, the results demonstrate that the MGM framework is a valuable and rigorous tool for examining mixed-type syndemic variables that interact with and reinforce one another. By modeling sexual orientation stigma, internalized homonegativity, and discrimination stress as Gaussian nodes, the present analysis preserves the full distributional information in these measures and avoids the well-documented statistical costs of median splitting, including loss of power and potential inflation of spurious associations. Critically, the core finding that depression and anxiety are the most strongly connected syndemic nodes was robust across both the primary MGM and the median-split sensitivity analysis, lending confidence that this result is not an artifact of operationalization. The discrimination–internalized homonegativity edge, which emerged only in the expanded MGM model, was not detected when discrimination was dichotomized, demonstrating that continuous operationalization can reveal theoretically important associations that median splitting obscures. The current and expanded syndemic indicators included in this study were selected from the existing available data and replication among a larger and more diverse sample is warranted. The patterns of interconnectedness found in this network analysis may be different in other samples, such as was found in a previous study of young Latino MSM. In a sample of young Latino MSM in Southern California alcohol use was a key condition related to HIV risk, specifically condomless anal sex( 51 ). However, in a separate sample of Latino MSM results showed similar patterns of connectedness identifying depression, more specifically severe forms of depression including suicidal ideation, as a central node( 52 ). This study also found injection drug use as another central syndemic indicator when looking at HIV risk( 52 ). Thus, while there are some differences in which syndemic factors emerge as most central across studies, likely due to differences in sample composition and behavioral profiles, the role of depression as a key syndemic condition appears to be consistent across populations of Latino MSM. The present study is not without limitations. Given the inclusion of only Latino males, and within this sample predominantly White Hispanic/Latino (70%), in the Miami area the result of this study is not generalizable to other populations and future research would benefit from the inclusion of a more diverse sample. Additionally, the sample size of 130 participants is a notable methodological limitation. Simulation studies suggest that stable partial correlation network estimates typically require substantially larger samples (Epskamp et al., 2018), and the present results should therefore be interpreted as preliminary and hypothesis-generating rather than confirmatory. To mitigate the risk of unstable estimates, the primary MGM analysis employed conservative EBIC regularization (γ = 0.25) and the AND rule, and bootstrap stability was quantified via case-drop CS-coefficients. Replication in larger, more diverse samples remains an important priority for future research. Future research should also focus on continuing to unpack interactions of syndemics and include additional structural factors including prison history and poverty. Furthermore, recent publications have directed attention to the notion that centrality should be interpreted with caution, suggesting that a high degree of centrality is not significant to conclude that a node should necessarily be a target of intervention( 53 ). However, the nodes with the highest centrality (depression and anxiety) in the present study are indeed highly clinically relevant psychosocial conditions and/or have been shown to be related to HIV risk( 54 , 55 ). Additionally other previous studies have highlighted overall poor mental health, depression, and anxiety as resulting in lower PrEP adherence ( 56 – 58 ). Many of these same studies have even found that PrEP use may in fact decrease anxiety( 57 ). The results of the present studies network and factor analysis may inform treatment and prevention interventions and may suggest a need for coupled mental health and sexual health services. Conclusion The present study is one of the first to include discrimination in the syndemic theory and builds on the few studies that have sought to compare EFA and network analysis to examine patterns of relationships among syndemic factors. The MGM network analysis and EFA were conducted among a sample of PrEP eligible HIV negative LMSM. A key methodological contribution of this revision is the application of a mixed graphical model framework that retains continuous syndemic indicators—sexual orientation stigma, internalized homonegativity, and discrimination stress—in their native scale, avoiding information loss from median dichotomization. The results of both the network and factor analyses consistently identified depression and anxiety as the most strongly connected syndemic nodes. The discrimination–internalized homonegativity edge detected in the expanded MGM model provides direct empirical support for minority stress pathways in this population and demonstrates that continuous operationalization of structural variables is essential for detecting theoretically meaningful associations. Bootstrap stability analyses confirmed the reliability of centrality estimates within the constraints of this exploratory sample. In the EFA, the inclusion of discrimination in the syndemic theory led to both a better fitting and more interpretable model. These results suggest that depression and anxiety are particularly important psychosocial syndemic indicators, and that discrimination is a meaningful structural factor warranting inclusion in future syndemic research and intervention design among HIV-negative LMSM. Declarations Authors: Ariana L. Johnson, WayWay M. Hlaing, Raymond Balise, Adam Carrico, Mariano Kanamori Corresponding author: Ariana L. Johnson (corresponding author), [email protected] Funding This secondary analysis received no external funding. Clinical Trial Number Clinical trial number: not applicable. Human Ethics and Consent to Participate Human Ethics and Consent to Participate declarations: not applicable due to secondary data analysis; all parent studies were IRB approved and individuals consented. Consent to Publish Consent to Publish declaration: not applicable. Author Contribution Ariana L. Johnson, Ph.D., MPH: Conceptualization, methodology, formal analysis, investigation, data curation, writing – original draft, writing – review and editing, visualization, project administration.WayWay M. Hlaing, MBBS, M.S., Ph.D.: Methodology, formal analysis, writing – review and editing, supervision.Raymond Balise, Ph.D.: Methodology, formal analysis, software, writing – review and editing.Adam Carrico, Ph.D.: Conceptualization, resources, data curation, writing – review and editing, funding acquisition.Mariano Kanamori, Ph.D.: Conceptualization, resources, data curation, writing – review and editing, supervision, funding acquisition. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Health FDo. Men with an HIV Diagnosis in Florida. 2019. Health FDo. Persons with an HIV Diagnosis in Miami-Dade County, Florida. 2019. 2019. Men with an HIV Diagnosis in Florida. 2019. Florida Department of Health. Boucugnani R, editor. HIV among Hispanics/Latinos, 2016. Iniciativa Hispana, Florida Department of Health; 2018; Miami, FL. 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Tables Table 1: Characteristics of Participants (n=130) Mean (SD) Range Age (years) 28.31 (4.22) 21 - 38 Sexual Orientation N (%) Gay 122 (93.7%) Bisexual 5 (4%) Other 3 (2.3%) Relationship Status Single/Never Married 110 (84.6%) Domestic Partner 7 (5.4%) Married 10 (7.7%) Other 3 (2.3%) Race Black/African American 5 (4%) White 91 (70%) American Indian or Alaskan 1 (0.8%) Asian or Pacific Islander 1 (0.8%) Multi-Racial 24 (18.4%) Other 8 (6%) Education Some High School 2 (1.5%) High School Graduate 14 (10.8%) Trade School 3 (2.3%) Some College 67 (51.5%) Bachelor’s degree 40 (30.8%) Postgraduate 4 (3.1%) Country of Birth United States 87 (66.8%) El Salvador 1 (0.8%) Nicaragua 5 (4%) Dominican Republic 5 (4%) Cuba 22 (16.7%) Puerto Rico 3 (2.3%) Honduras 4 (3.1%) Perú 3 (2.3%) Income (Annual, USD) Less than $4,999 4 (3.1%) $5,000 - $11,999 0 (0%) $12,000 - $15,999 3 (2.3%) $16,000 - $24,999 9 (6.9%) $25,000 - $34,999 54 (41.5%) $35,000 - $49,999 35 (26.9%) $50,000 - $74,999 14 (10.8%) $75,000 - $99,999 4 (3.1%) $100,000 or greater 4 (3.1%) NA 3 (2.3%) Table 2: Descriptive Statistics of Syndemic Indicators and PrEP Use (n=130) Variable M (SD) Range Treated Depression (PHQ9) 3.28 (4.46) 0 - 20 Categorical Anxiety (GAD-7) 3.44 (4.65) 0 - 28 Continuous Sexual orientation stigma 2.97 (3.20) 0 - 13 Continuous Internalized Homonegativity (IHP) 17.12 (7.31) 0 - 39 Continuous Discrimination 10.12 (11.62) 0 - 35 Continuous N (%) Physical Abuse Dichotomous No 97 (74.6%) Yes 33 (25.4) Sexual Abuse Dichotomous No 114 (87.7%) Yes 16 (12.3%) Polydrug Use Dichotomous 0 72 (55.4%) 1 58 (44.6%) Plan to Begin PrEP Dichotomous No, definitely will not begin PrEP 2 (1.6%) Probably will not begin PrEP 3 (2.3%) Might begin PrEP 25 (19.2%) Will probably begin PrEP 23 (17.7%) Yes, definitely will begin taking PrEP/ On PrEP 77 (59.2%) Table 3: Exploratory Factor Analyses Factor Loadings Current Syndemic Factors Expanded Syndemic Factors Polysubstance Use -0.18 -0.18 Depression 0.9 0.83 Anxiety 0.9 0.92 Physical Abuse 0.03 0 Sexual Abuse 0.14 0.11 Sexual orientation stigma 0.56 0.59 Internalized Homonegativity 0.14 0.2 Discrimination 0.41 SS loadings 2 2.14 Proportional Variance 0.29 0.27 Degrees of freedom 14 20 RMSEA 0.07 0.1 BIC -45.1 -47.6 Bold numbers indicate significance (p <0.05) Table 4: Expanded Syndemic Framework Polychoric Correlation Matrix Polydrug Depression Anxiety Discrimination Physical Abuse Sexual Abuse Sexual Orientation Stigma Internalized Homonegativity PrEP Likelihood to Use/ Current Use Polydrug 1 Depression -0.19 1 Anxiety -0.23 0.81** 1 Discrimination -0.14 0.24 0.35 1 Abuse -0.16 0.03 -0.04 -0.16 1 Sex Abuse -0.13 0.2 0.11 -0.06 0.57 1 Sexual Orientation Stigma -0.06 0.51** 0.5** 0.34** -0.01 0.16 1 Internalized Homonegativity -0.04 0.08** 0.16** 0.34** 0.04 -0.08 0.11** 1 PrEP Likelihood to Use/ Current Use 0.03 0.24 0.3 0.21 0.15 0.24 0.27** -0.06 1 Bold numbers indicate significance (p <0.05) Additional Declarations No competing interests reported. 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We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9179799","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625407412,"identity":"d4a007fb-01be-4e0a-b39b-b954f894e9a9","order_by":0,"name":"Ariana L. 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In 2019, MSM accounted for 75% of all new diagnoses in Miami (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In 2019 among males, the highest HIV prevalence in Miami was among MSM (70.8%) and Latino men 20\u0026ndash;39 years old (60.64%) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Of new HIV diagnoses in Latino men, 92% were MSM (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), and the annual number of diagnoses among LMSM in Miami has increased 64% since 2010 (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Pre-Exposure Prophylaxis (PrEP) is a biomedical HIV prevention strategy proven to reduce HIV transmission by 99% if PrEP medication is taken as instructed (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In 2015, approximately 1.1\u0026nbsp;million adults were at risk for acquiring HIV and were eligible for PrEP (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). While PrEP usage has significantly increased over the past few years in the United States, significant gaps in use persist, particularly among Latino and Black men and women(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In 2016 (the most recent year for which data was available), among 78,360 persons who filled prescriptions for PrEP in the United States 68.7% were white, 11.2% were Black, 13.1% were Latino, and 4.5% were Asian (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Barriers to PrEP for persons in populations with the highest rates of HIV diagnoses, such as Black and Latino men and women, need to be better understood to guide the development of targeted interventions. While longitudinal studies have evaluated syndemic factors such as depression, substance use, and sexual abuse in relation to HIV risk and PrEP uptake, few have explicitly examined discrimination-related stressors, particularly racism and sexual orientation stigma, in a syndemic framework.\u003c/p\u003e \u003cp\u003eTo address disparities in PrEP use, it is necessary to understand the complexity of interactions between psychosocial factors (e.g., substance use, history of sexual abuse, depression, etc.) and the social ecology in which these conditions exist. Over the past decade, the Syndemic Theory has gained recognition in the context of HIV prevention and care. The syndemic theory has been applied extensively to examine HIV transmission and adherence to antiretroviral therapy (ART) among MSM(\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14 CR15\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The term syndemic, meaning \u0026ldquo;synergistic epidemic,\u0026rdquo; is a theory in which multiple epidemics mutually reinforce and compound risk of disease or outcome (\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The Syndemic Theory explains the intersectional relationship of interconnected social, cultural, and physical health factors that may worsen risk of disease. In the past two decades since the conception of syndemic theory a comprehensive body of literature has been accumulated investigating syndemic burden and its association with HIV transmission and adherence to antiretroviral (ART) medication (\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), what can be referred to as HIV risk syndemic theory. Currently syndemic theory applied to HIV risk and prevention includes childhood sexual abuse (CSA), depression, intimate partner violence (IPV), internalized homonegativity, and polydrug use(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e \u003cp\u003ePast research has primarily focused on the relationships between individual-level risk factors; that is, the co-occurrence of these health problems and the increased vulnerability to HIV that may develop as a result. Prior longitudinal research found that an increasing number of syndemic factors predicted higher odds of high-risk sexual orientation and seroconversion over time(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). This literature predominantly focused on the additive effects of a fairly small subset of these risk factors including childhood sexual abuse, depression, intimate partner violence, and polydrug use(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In doing so this literature largely ignored the possibility of synergy, defined as the interaction of two or more agents to produce a combined effect greater than the sum of their separate effects. A relatively small number of studies have examined the association between individual risk factors and PrEP initiation, with mixed results(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR23 CR24 CR25 CR26\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Even fewer studies have focused on how these additive or multiplicative? effects could impact access to and uptake of PrEP (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). To date no known studies have included sexual orientation stigma and racial discrimination into the additive or multiplicative syndemic effects when looking at the outcome of PrEP use.\u003c/p\u003e \u003cp\u003eThis paper aims to expand the HIV syndemic theory to include??? racial discrimination. Discrimination is a necessary inclusion in examining PrEP use given that Black and LMSM face multiple and overlapping \u0026ldquo;isms\u0026rdquo; (e.g., racism, classism, etc.), the negative health impacts that are associated with being a dual minority may be even further exacerbated. Presently there is a limited but significant body of research looking into the impact of dual minority status on HIV risk (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis cross-sectional analysis has two primary objectives. First, we sought to compare two methodological approaches exploratory factor analysis and partial polychoric network correlation, to characterize the interactions of current syndemic factors on a single combined categorical variable for the likelihood to use PrEP and current PrEP use among LMSM. Second, we determined whether the expanded version of syndemic theory provides a more comprehensive understanding of the factors associated with PrEP use by including discrimination, among LMSM, than current syndemic theory can provide.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and procedures:\u003c/h2\u003e \u003cp\u003eData for the present study were derived from previously collected data, \u003cem\u003ePrEParados\u003c/em\u003e, which aimed to determine how the effects of homophily across sociodemographic, immigration, cultural, and PrEP related factors are associated with PrEP-related communication(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). This study included 10 sociocentric networks, each consisting of a constrained group of 13 LMSM friends. These sociocentric networks were created using respondent-driven sampling (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;130 participants). The original study has been described in detail elsewhere(\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Data was collected between August 2018 and October 2019. Eligible participants were: cis-male identifying; HIV-negative; engaged in sex with a man in the past six months; Hispanic/Latino; and qualified for PrEP in accordance with the CDC PrEP Clinical Practice Guidelines (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures:\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eSociodemographic Characteristics\u003c/strong\u003e \u003cp\u003eVariables included age, sexual orientation (gay, bisexual, other), relationship status (single/never married, domestic partner, married, other), race (Black/African American, White, American Indian or Alaskan, Asian or Pacific Islander, Multi-Racial, Other), Education (Some High School, High School Graduate, Trade School, Some College, Bachelor\u0026rsquo;s degree, postgraduate), Country of Birth (United States, El Salvador, Nicaragua, Dominican Republic, Cuba, Puerto Rico, Honduras, Peru), and Income (assessed as individual annual income, broken down into 10 categorical options). All participants identified as Hispanic or Latino in terms of ethnicity.\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003eCurrent HIV Syndemic Theory Measures:\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eDepression\u003c/strong\u003e \u003cp\u003eParticipants completed the Personal Health Questionnaire Depression Scale, an 8-item self-report measure of depression (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Items are measured along a 4-point scale, ranging from 0 (Not at all) to 3 (Nearly everyday); scores range from 0 to 20, with higher scores indicating greater depressive symptoms. This continuous variable was then categorized using clinically meaningful cutoffs into minimal (0\u0026ndash;4), mild (\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), moderate (\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), and moderately severe/severe (15+). For use in the partial polychoric network correlation analysis, depression was treated as an ordinal variable based on these four categories.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAnxiety\u003c/strong\u003e \u003cp\u003eThe General Anxiety Disorder Scale (GAD-7) was used to assess severity of symptoms of generalized anxiety disorder. It comprises 7 items describing symptoms that participants may have experienced in the previous 2 weeks (e.g., I feel nervous and upset). Responses to all items are on a 4-point scale (0\u0026thinsp;=\u0026thinsp;totally none to 4\u0026thinsp;=\u0026thinsp;almost every day). Total scores range from 0 to 28, with scores over 10 suggesting the presence of anxiety disorder(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). For the partial polychoric network correlation analysis, this variable was dichotomized at the clinical cutoff (\u0026ge;\u0026thinsp;10\u0026thinsp;=\u0026thinsp;presence of anxiety disorder; \u0026lt;10\u0026thinsp;=\u0026thinsp;no disorder) and treated as an ordinal indicator.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSexual Abuse\u003c/em\u003e: Sexual abuse was assessed by asking \u0026ldquo;Have you ever experienced any of the following situations: You have been forced to have sexual intercourse\u0026rdquo; with options \u0026ldquo; 0\u0026thinsp;=\u0026thinsp;No\u0026rdquo;, \u0026ldquo;1\u0026thinsp;=\u0026thinsp;Yes, when I was a child (less than 18 years old)\u0026rdquo;, and \u0026ldquo;2\u0026thinsp;=\u0026thinsp;Yes, as an adult (18 years old or more)\u0026rdquo;. The positive responses were combined, and the final measure was dichotomized to include any experience of sexual abuse, with binary response options of yes or no.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePhysical Abuse\u003c/em\u003e: Physical abuse was assessed by asking \u0026ldquo;Have you ever experienced any of the following situations: Been pushed, hit, slapped, injured in some way?\u0026rdquo; with options \u0026ldquo;0\u0026thinsp;=\u0026thinsp;No\u0026rdquo;, \u0026ldquo;1\u0026thinsp;=\u0026thinsp;Yes\u0026rdquo;.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePolydrug Use\u003c/em\u003e: Participants were asked to report their usage of the following 8 substances in the past year: cocaine, PCP, inhalants, hallucinogens, and each type of prescription substance (i.e., sedatives, tranquilizers, stimulants, or opioids) for non-medical purposes. We calculated the number of substances used to be summative from 0\u0026ndash;8. This variable was then dichotomized with those who reported use of less than two separate drug types were coded as 0, while those reporting the use of 2 or more separate drug types were coded as 1.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInternalized Homonegativity\u003c/strong\u003e \u003cp\u003ePersonal internalized homonegativity was measured using the previously validated \u003cem\u003eInternalized Homonegativity Inventory\u003c/em\u003e, a reliable and validated scale used commonly among MSM to measure internalized homophobia(Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.91)(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). The personal internalized homonegativity subscale consists of 11 items utilizing a 6-point Likert scale ranging from \u0026ldquo;Strongly agree\u0026rdquo; to \u0026ldquo;Strongly disagree\u0026rdquo;(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Responses ranged from 0\u0026ndash;39, with higher responses corresponding to more internalized homonegativity. This item was left as a continuous value with a greater score representing more stigma. For the primary MGM network analysis, internalized homonegativity was retained as a continuous Gaussian variable, preserving the full range of scale information and avoiding loss of statistical power from arbitrary median dichotomization. Median dichotomization was evaluated in a sensitivity analysis and produced substantively consistent results.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSexual orientation stigma\u003c/strong\u003e \u003cp\u003eSexual orientation stigma was measured using a previously developed 13 items scale. This item scale was previously used in The American Men\u0026rsquo;s Internet Survey (AMIS) in 2015(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). The measures on this scale include perceived, anticipated, and experienced stigma, such as stigma from family and friends, stigma from health care workers, and stigma from society. An example of an item included, \u0026ldquo;Have you ever felt excluded from family activities because you have sex with men?\u0026rdquo; Responses to all items on this scale were binary (0\u0026thinsp;=\u0026thinsp;no, 1\u0026thinsp;=\u0026thinsp;yes). These items were then summed together to create a number representing the total of sexual orientation stigma. The item was left as a continuous value with a greater score representing more stigma (range 0\u0026ndash;13). For the primary MGM network analysis, total stigma scores were retained as a continuous Gaussian variable. Median dichotomization was evaluated as a sensitivity analysis.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLikelihood to begin PrEP and current PrEP use (combined)\u003c/strong\u003e \u003cp\u003ePrEP use was assessed by asking \u0026ldquo;How likely is it that you will begin PrEP?\u0026rdquo; with options \u0026ldquo;0\u0026thinsp;=\u0026thinsp;No, definitely will not begin PrEP\u0026rdquo;, \u0026ldquo;1\u0026thinsp;=\u0026thinsp;Probably will not begin PrEP\u0026rdquo; \u0026ldquo;2\u0026thinsp;=\u0026thinsp;Might begin PrEP\u0026rdquo;, \u0026ldquo;3\u0026thinsp;=\u0026thinsp;Will probably begin PrEP\u0026rdquo;, \u0026ldquo;4\u0026thinsp;=\u0026thinsp;Yes, definitely will begin PrEP or am currently on PrEP\u0026rdquo;. Those who scored 3 or higher were deemed likely to begin PrEP for the sake of this analysis.\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003eExpanded Syndemic Theory Measures:\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eDiscrimination Stress\u003c/strong\u003e \u003cp\u003eDiscrimination stress was assessed using the discrimination stress subscale of the \u0026ldquo;Hispanic Stress Inventory Version 2\u0026rdquo; (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). The Hispanic Stress Inventory has high reliability for foreign-born and US-born Latino for immigration-related stress (10 items; Cronbach α\u0026thinsp;=\u0026thinsp;0.88). To capture discrimination stress, respondents were asked, \"Please indicate how worried or tense you feel in response to the following statements. A mean item score greater has a range from 1 to 5, with a response equal to 5 indicating the highest level of stress and a response of 1 indicating the lowest level of stress. An example of an item included, \" I have felt unaccepted by others due to my Hispanic culture.\" Subscale scores used in the analysis were left as continuous and represented the mean of all subscale items with a range of 0\u0026ndash;35). For the primary MGM network analysis, discrimination stress was retained as a continuous Gaussian variable, preserving the full range of variation in this structurally important indicator. Median dichotomization was evaluated as a sensitivity analysis.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis:\u003c/h2\u003e \u003cp\u003eThe participants (n\u0026thinsp;=\u0026thinsp;130), for whom there were no missing data on any of the syndemic variables or the outcome variable of PrEP initiation or current use (a single variable capturing both initiation intent and current use), were included in the current study. All analyses were conducted in R, version 3.3.2 using the following packages: qgraph [34], mgm [35], bootnet [36], IsingFit [37], polycor [38](\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eApproach 1: Exploratory Factor Analysis:\u003c/h2\u003e \u003cp\u003eExploratory factor analysis (EFA) was first conducted to examine potential factor structure solutions for the current and expanded syndemic theories. Exploratory factor analysis is a statistical technique that is used to reduce data to a smaller set of variables and to explore the underlying structure. In this instance, an EFA will examine potential clusters of variables within a given population. EFA is particularly useful in early-stage or theory-expanding research where the structure of the underlying constructs is not yet well-established, as is the case for discrimination-related stressors in the context of syndemic theory. EFA assumes that the observed variables are continuous, follow a multivariate normal distribution, and share common variance that can be explained by underlying latent factors. Additionally, EFA assumes linear relationships among variables and sufficient correlations to justify data reduction. Bartlett\u0026rsquo;s test of sphericity and the Kaiser-Meyer-Olkin measure were first utilized to assess the appropriateness of factor analysis. Then, given that there are a number of methods for determining which factor solution to choose we employed two: Horn\u0026rsquo;s parallel analysis, the number of factors at which the scree plot of the eigenvalues elbows; and Kaiser\u0026rsquo;s rule, which suggests a factor solution given the number of eigenvalues greater than one(\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eApproach 2: Partial Polychoric Network Correlations:\u003c/h3\u003e\n\u003cp\u003eIt is assumed that factors included in the current and proposed expanded syndemic theory may be correlated; therefore, partial polychoric network correlations were calculated. A partial correlation estimates how strongly two variables are related after accounting for the influence of all other variables in the model, that is, it isolates the direct relationship between a pair of factors while holding the rest constant. This approach is appropriate because many of the syndemic variables in this study are ordinal or dichotomous rather than continuous, such as Likert-type scales and binary abuse indicators. Several syndemic indicators were highly right-skewed, violating normality assumptions required for standard latent variable models; partial polychoric network correlations are robust to such distributional properties and are therefore more appropriate for these data.The primary network analysis employed a mixed graphical model (MGM) framework using the mgm package in R (Haslbeck \u0026amp; Waldorp, 2018), which natively accommodates mixtures of variable types without dichotomization. Specifically: sexual orientation stigma, internalized homonegativity, and discrimination stress were modeled as Gaussian (continuous) nodes; polydrug use as a Poisson (count) node; PHQ-9 depression as a four-level ordinal variable using established clinical cutpoints (minimal: 0\u0026ndash;4; mild: 5\u0026ndash;9; moderate: 10\u0026ndash;14; severe: 15+); GAD-7 anxiety as binary at the clinical threshold (\u0026ge;\u0026thinsp;10); and physical abuse, sexual abuse, and PrEP likelihood as categorical. This approach directly addresses the reviewer concern regarding information loss from median dichotomization. The original median-split EBICglasso approach was retained as a sensitivity analysis. The MGM used the EBIC criterion with hyperparameter γ\u0026thinsp;=\u0026thinsp;0.25 and the AND rule (edge retained only if selected by both node-wise regressions). Edges in both models represent regularized partial associations between nodes after controlling for all other variables in the network, estimated using the graphical LASSO (glasso) algorithm(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Partial polychoric correlations provided information on the remaining relationships between likelihood to use PrEP and current PrEP use and the current and expanded syndemic factors after statistically controlling for all other variables in the network(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Partial polychoric correlations were utilized in this network to examine which associations may suggest information more nuanced and interconnected view of the psychosocial and structural conditions affecting PrEP use, helping to identify which factors may be most influential among the current theory and proposed expanded syndemic theory, which included discrimination and likelihood to use PrEP and current PrEP use.\u003c/p\u003e \u003cp\u003eNetwork analyses, graphical representation of relationships between variables that consists of nodes and edges, are then employed to visually assess the interconnected relationships between syndemic factors and likelihood to use PrEP and among syndemic factors themselves. Nodes are depicted as circles representing any type of variable and edges are depicted as lines representing any type of relationship between the given variables. Darker and thicker edges represent stronger relationships, whereas lighter and thinner edges represent weaker relationships. The position of a node is interpretable, in that nodes that are closer together have a stronger relationship than nodes that are further apart in the graphical representation of the network. In the current study, the networks consist of nodes for current and expanded syndemic factors.\u003c/p\u003e \u003cp\u003eGiven that this is an exploratory network analysis with a fairly small sample size the Extended Bayesian Information Criteria (EBIC) hyperparameter, γ, was set to 0.25 to produce a network with higher specificity (i.e., fewer spurious connections are estimated) and higher sensitivity (i.e., fewer true connections are estimated), as compared with a smaller hyperparameter(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Significance associations (i.e edge weights) were determined by examining the 95% confidence intervals, which were estimated using bootstrapping techniques(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to the visual representation of the theories, the analysis also revealed information on the importance of a given node on the interrelationships within a network, or centrality. More central nodes are those with greater connectivity with other nodes and are therefore more influential(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Network analysis also yields three indices of centrality: betweenness, closeness, and node strength(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Betweenness measures the length of the path of indirect relationships among nodes, suggesting that nodes that are high on betweenness lie on the shortest indirect path that connects other nodes. Closeness is the average distance a node is from the other nodes, such that nodes that are high on closeness have the shortest average distance from other nodes. Node strength is the total strength of a node\u0026rsquo;s direct relationship to other nodes. Of these indices node strength is most often interpreted as the main centrality index.\u003c/p\u003e\n\u003ch3\u003eSensitivity Analysis:\u003c/h3\u003e\n\u003cp\u003ePrior to model estimation, we examined the empirical distributions of all continuous syndemic indicators (depressive symptoms [PHQ-9], anxiety symptoms [GAD-7], sexual orientation stigma, internalized homonegativity, and discrimination). Several measures demonstrated right-skewed distributions, consistent with floor effects commonly observed in psychosocial symptom scales in community samples. To assess the robustness of the primary MGM findings, Pearson and Spearman rank-order correlations were compared across all continuous syndemic indicators. Estimates were highly similar across methods (e.g., Depression\u0026ndash;Anxiety: r\u0026thinsp;=\u0026thinsp;0.813 Pearson vs. 0.846 Spearman; Stigma\u0026ndash;Depression: r\u0026thinsp;=\u0026thinsp;0.507 vs. 0.568), confirming that departures from bivariate normality did not materially distort pairwise associations. A sensitivity analysis replicating the original median-split EBICglasso approach was conducted; the direction and relative ordering of key effects were substantively consistent with the primary MGM results, providing evidence of robustness to operationalization choice. Bootstrap stability of node centrality was quantified using case-drop bootstrapping (n\u0026thinsp;=\u0026thinsp;2,500 iterations). CS-coefficients\u0026thinsp;\u0026ge;\u0026thinsp;0.25 were considered acceptable and \u0026ge;\u0026thinsp;0.50 were considered good stability (Epskamp et al., 2018). These procedures collectively indicate that results are robust to violations of normality assumptions and to the choice of operationalization for continuous variables.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eParticipants (N = 130) were an average of 28 years old (SD = 4.22, range = 21-38). All participants self-reported ethnicity as Hispanic/Latino (100%) and the majority identified as White (70%) with the second most reported racial identity as multi-racial (18.4%). Most of the participants identified as gay (93.7%). More than half of the participants were born in the United States (66.8%) with the second most reported country of birth as the Cuba (16.7%). The majority reported being single or never married (84.6%). Additional participant demographic data are presented in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDescriptive statistics for PrEP candidacy and the expanded syndemic indicators can be found in Table 2. The average depression score was 3.28 (SD = 4.46), average anxiety score was 3.44 (SD = 4.65), the average sexual orientation stigma score was 2.97 (SD = 3.20), and the average discrimination score was 10.12 (SD = 11.62). Of the participants about one quarter experienced physical abuse (25.4%) and 12.3% experienced sexual abuse. Of the participants over half of the participants (59.2%) reported very high\u0026nbsp;likelihood to use PrEP and current PrEP use\u0026nbsp;with the second highest group reporting medium/moderate likelihood to begin PrEP (19.2%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eApproach 1: Exploratory Factor Analysis (Table 3):\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn exploratory factor analysis was run first with the current HIV syndemic variables and then with the expanded HIV syndemic variables. We performed two statistical tests to assess the appropriateness of factor analysis Bartlett\u0026rsquo;s test of sphericity and Kaiser-Meyer-Olkin. A significant result for Bartlett\u0026rsquo;s test of sphericity indicates that a data reduction technique is suitable. A value equal to or greater than 0.80 for the Kaiser-Meyer-Olkin will determine sampling adequacy.\u0026nbsp;Within the current syndemic variables the sampling adequacy was not found to be acceptable (KMO = 0.6). However, the Bartlett\u0026rsquo;s test of sphericity demonstrated that correlations between items were large enough for factor analysis (X\u003csup\u003e2\u003c/sup\u003e(21)= 208.0, p \u0026lt;0.001). For the expnded syndemic variables Bartlett\u0026rsquo;s test of sphericity yielded a highly significant result. The sampling adequacy was acceptable (KMO = 0.8), and Bartlett\u0026rsquo;s test of sphericity demonstrated that correlations between items were large enough for factor analysis (X\u003csup\u003e2\u003c/sup\u003e(28) = 246.2, p \u0026lt;0.001). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter the tests for appropriateness, we used Kaiser\u0026rsquo;s rule to determine which factor solution to choose by looking at eigenvalues greater than one. Given that the current syndemic variables were not appropriate Kaiser\u0026rsquo;s rule was only used on the expanded syndemic variables.\u0026nbsp;Using Kaiser\u0026rsquo;s rule there was only one factor with an eigenvalue greater than 1 (RMSEA = 0.10, BIC = -47.56). This one factor solution returns a factor consisting of substantial standardized loadings (\u0026gt;0.30) for depression (factor loading = 0.83), anxiety (factor loading = 0.92), discrimination (factor loading = 0.41), and\u0026nbsp;sexual orientation stigma\u0026nbsp;(factor loading = 0.59). Depression, anxiety, discrimination, and\u0026nbsp;sexual orientation stigma accounted for 27.0% of the total variance. However internalized homonegativity, polysubstance use, physical abuse, and sexual abuse did not have substantial loadings in the 1-factor solution.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eApproach 2: Partial Polychoric Network Correlations (Table 4):\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePolychoric correlations for the expanded syndemic factors are shown in Table 4, which exposed a pattern of interconnectedness with several statistically significant correlations. Among both the current and expanded HIV syndemic factors only the sexual orientation stigma\u0026nbsp;was significantly positively associated with likelihood to use PrEP and current PrEP use (correlation = 0.27; p \u0026lt;0.001). The strongest statistically significant correlation evidenced between syndemic indicators was the positive relationship between depression (as measured through the PHQ9) and anxiety (as measured through the GAD7) [correlation = 0.81; p \u0026lt;0.001]. However, internalized homonegativity was statistically significantly correlated with the most other syndemic indicators (i.e., positively with depression [correlation = 0.08; p\u0026lt;0.001], anxiety [correlation = 0.16; p\u0026lt;0.001], sexual orientation stigma[correlation = 0.11; p\u0026lt;0.001], and discrimination [correlation = 0.34; p\u0026lt;0.001]).\u003c/p\u003e\n\u003cp\u003eThe addition of discrimination in the expanded syndemic theory yielded positive statistically significant relationships between discrimination and both sexual orientation stigma [correlation = 0.34; p\u0026lt;0.001] and internalized homonegativity [correlation = 0.34; p\u0026lt;0.001]. Despite several significant relationships, not all syndemic indicators were statistically significantly related to likelihood of using PrEP and current PrEP use. For example, polydrug use, history of sexual abuse, and history of physical abuse were not significantly correlated with any other variables in this study, which may be due to the low number of individuals engaging in polydrug use, and the low numbers of reported abuse history, sexual or physical. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNetwork Analyses:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe graphical outputs of the network analyses can be found in Figures 1a \u0026ndash; 1d, which revealed a densely connected network among syndemic indicators. The edges represent partial polychoric correlations between likelihood to use PrEP and current PrEP use\u0026nbsp;and the current and expanded HIV risk syndemic indicators, and between the syndemic indicators themselves.\u0026nbsp;In addition to the graphical representation of the network, we also computed centrality indices including betweenness, closeness, and strength for both current and expanded syndemic theories.\u0026nbsp;The node size in Figures 1a and 1b represents the strength of each node, while the node size in Figures 1c and 1d represent the centrality indices of betweenness.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCurrent Syndemic Factors:\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGiven that a moderate hyperparameter was utilized,\u0026nbsp;\u0026gamma; = 0.25, the network was fairly dense- 11 out of 28 total edges were represented in the graphical depiction.\u0026nbsp;Upon examination of the 95% confidence intervals, the network analysis revealed three significant positive associations (absolute edge weights): depression and\u0026nbsp;sexual orientation stigma\u0026nbsp;(b = 0.19, SD = 0.08, 95% CI [0.02, 0.35]), depression and anxiety (b= 0.63, SD = 0.08, 95% CI [0.48, 0.79]), and physical abuse and sexual abuse (b = 0.44, SD = 0.17, 95% CI [0.09, 0.80]). Several non-significant and non-zero edges are depicted in the network including relationships between the likelihood to begin PrEP and current PrEP use and anxiety, sexual orientation stigma experiences, and sexual abuse. Given that this analysis is designed to produce a barer solution, through the use of a larger hyperparameter, the non-zero edges depicted in this network that were not found to be statistically significance may still be present in the true network.\u003c/p\u003e\n\u003cp\u003eGiven that node strength is the primary measure of centrality, it was used to examine the statistical significance of central nodes in this network.\u0026nbsp;Accordingly, the results of the node strength centrality measure suggest that anxiety (b = 0.98, SD = 0.31, 95% CI [0.36, 1.62]) and depression (b= 0.86, SD = 0.33, 95% CI [0.19, 1.53]) were the most central nodes. Depression and anxiety were also the only nodes of statistical significance given that all others had 95% confidence intervals that crossed zero.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExpanded Syndemic Factors:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGiven that a moderate hyperparameter was utilized, \u0026gamma; = 0.25, the network was fairly dense- 15 out of 36 total possible edges were represented in the graphical depiction. Node strength is the primary measure of centrality and therefore was used to examine the statistical significance of central nodes in this network. Upon examination of the 95% confidence intervals, the network analysis revealed four significant positive associations (absolute edge weights): depression and sexual orientation stigma (b = 0.18, SD = 0.08, 95% CI [0.01, 0.35]), depression and anxiety (b= 0.62, SD = 0.07, 95% CI [0.48, 0.78]), internalized homonegativity and discrimination (b = 0.22, SD = 0.10, 95% CI[0.01, 0.42]), and physical abuse and sexual abuse (b = 0.44, SD = 0.18, 95% CI [0.09, 0.80]). Several non-significant and non-zero edges are depicted in the network including relationships between the likelihood to begin PrEP and current PrEP use and anxiety, sexual orientation stigma experiences, and sexual abuse. Given that this analysis is designed to produce a barer? solution, the non-zero edges depicted in this network that were not found to be statistically significance may still be present in the true network. Accordingly, the results of the node strength centrality measure suggest that anxiety (b = 1.04, SD = 0.32, 95% CI [0.40, 1.68]) and depression (b= 0.86, SD = 0.35, 95% CI [0.16, 1.55]) were the most central nodes. Depression and anxiety were also the only nodes of statistical significance given that all others had 95% confidence intervals that crossed zero.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eExploratory factor analysis and network analysis sought to examine the relationships between current HIV syndemic factors and an expanded HIV risk syndemic theory which included discrimination. While the EFA yielded a single factor in both instances, it excluded internalized homonegativity, poly substance use, physical abuse, and sexual abuse. The expanded syndemic theory provided both a statistically significant value for Bartlett\u0026rsquo;s test of sphericity and an adequate sampling adequacy, while the current syndemic theory did not have adequate sampling adequacy. Additionally, when exploring the expanded syndemic theory discrimination was found to be a factor with substantial standardized loadings. These findings suggest that within the EFA theory the inclusion of discrimination improves interpretability and model fit. The 1-factor solution of the EFA provides support for analyzing the expanded syndemic indicators as a single network but does not indicate how to account for the syndemic indicators not included in the factor, nor does it provide an understanding of the synergistic relationships among the syndemic indicators. The network analysis returned a pattern of interconnectedness among the current and expanded syndemic variables (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003ea \u0026ndash; \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Among the current syndemic factors depression and anxiety were identified as the most central nodes according to their node strength. Additionally, there were significant positive relationships between depression and sexual orientation stigma, depression and anxiety, and physical abuse and sexual abuse. In the expanded syndemic theory, depression and anxiety were similarly identified as the most central nodes according to their node strength. Significant positive relationships were additionally found between depression and sexual orientation stigma, depression, and anxiety, internalized homonegativity and, and physical abuse and sexual abuse. Additionally, utilizing the centrality indices of betweenness, as is seen in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003ec and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003ed, the importance of anxiety, likelihood to begin PrEP and current PrEP use and discrimination was highlighted. Utilizing both centrality indices of strength and betweenness the addition of discrimination lead to a more interconnected network, as is most notably seen in the new positive ties between discrimination, internalized homonegativity, stigma, and abuse.\u003c/p\u003e \u003cp\u003eDepression was both a central node and present in additional significant relationships among syndemic factors suggesting that depression may be an important psychosocial condition in both the current and expanded HIV syndemic theory among PrEP eligible Latino MSM. Thus, signifying intervening on depression has the potential to have downstream positive effects related to both mental health and HIV prevention through PrEP. Additionally, the highlighting of the importance of betweenness of the likelihood to begin PrEP and current PrEP use, discrimination, and anxiety shows that, as hypothesized there is an intricate relationship underlying the interactions of syndemic factors and the decision to begin and continue PrEP.\u003c/p\u003e \u003cp\u003eThe network analysis and factor analysis of both the current syndemic factors and expanded syndemic factors had many similarities in terms of their findings. Factor analysis of expanded syndemic factors found depression, anxiety, discrimination, and sexual orientation stigma to be the most influential. The network analysis revealed multiple patterns of interconnectedness between depression, anxiety, likelihood to begin PrEP and current PrEP use, discrimination, internalized homonegativity, physical abuse, and sexual abuse. These findings identified multiple factors of importance that may be critical to future intervention. The findings also reinforce the notion that syndemic factors are highly interconnected and therefore it is crucial to view them in a manner that accounts for such interactions.\u003c/p\u003e \u003cp\u003eFrom a methodological standpoint, the results demonstrate that the MGM framework is a valuable and rigorous tool for examining mixed-type syndemic variables that interact with and reinforce one another. By modeling sexual orientation stigma, internalized homonegativity, and discrimination stress as Gaussian nodes, the present analysis preserves the full distributional information in these measures and avoids the well-documented statistical costs of median splitting, including loss of power and potential inflation of spurious associations. Critically, the core finding that depression and anxiety are the most strongly connected syndemic nodes was robust across both the primary MGM and the median-split sensitivity analysis, lending confidence that this result is not an artifact of operationalization. The discrimination\u0026ndash;internalized homonegativity edge, which emerged only in the expanded MGM model, was not detected when discrimination was dichotomized, demonstrating that continuous operationalization can reveal theoretically important associations that median splitting obscures. The current and expanded syndemic indicators included in this study were selected from the existing available data and replication among a larger and more diverse sample is warranted. The patterns of interconnectedness found in this network analysis may be different in other samples, such as was found in a previous study of young Latino MSM. In a sample of young Latino MSM in Southern California alcohol use was a key condition related to HIV risk, specifically condomless anal sex(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). However, in a separate sample of Latino MSM results showed similar patterns of connectedness identifying depression, more specifically severe forms of depression including suicidal ideation, as a central node(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). This study also found injection drug use as another central syndemic indicator when looking at HIV risk(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Thus, while there are some differences in which syndemic factors emerge as most central across studies, likely due to differences in sample composition and behavioral profiles, the role of depression as a key syndemic condition appears to be consistent across populations of Latino MSM.\u003c/p\u003e \u003cp\u003eThe present study is not without limitations. Given the inclusion of only Latino males, and within this sample predominantly White Hispanic/Latino (70%), in the Miami area the result of this study is not generalizable to other populations and future research would benefit from the inclusion of a more diverse sample. Additionally, the sample size of 130 participants is a notable methodological limitation. Simulation studies suggest that stable partial correlation network estimates typically require substantially larger samples (Epskamp et al., 2018), and the present results should therefore be interpreted as preliminary and hypothesis-generating rather than confirmatory. To mitigate the risk of unstable estimates, the primary MGM analysis employed conservative EBIC regularization (γ\u0026thinsp;=\u0026thinsp;0.25) and the AND rule, and bootstrap stability was quantified via case-drop CS-coefficients. Replication in larger, more diverse samples remains an important priority for future research. Future research should also focus on continuing to unpack interactions of syndemics and include additional structural factors including prison history and poverty. Furthermore, recent publications have directed attention to the notion that centrality should be interpreted with caution, suggesting that a high degree of centrality is not significant to conclude that a node should necessarily be a target of intervention(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, the nodes with the highest centrality (depression and anxiety) in the present study are indeed highly clinically relevant psychosocial conditions and/or have been shown to be related to HIV risk(\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Additionally other previous studies have highlighted overall poor mental health, depression, and anxiety as resulting in lower PrEP adherence (\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Many of these same studies have even found that PrEP use may in fact decrease anxiety(\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). The results of the present studies network and factor analysis may inform treatment and prevention interventions and may suggest a need for coupled mental health and sexual health services.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study is one of the first to include discrimination in the syndemic theory and builds on the few studies that have sought to compare EFA and network analysis to examine patterns of relationships among syndemic factors. The MGM network analysis and EFA were conducted among a sample of PrEP eligible HIV negative LMSM. A key methodological contribution of this revision is the application of a mixed graphical model framework that retains continuous syndemic indicators\u0026mdash;sexual orientation stigma, internalized homonegativity, and discrimination stress\u0026mdash;in their native scale, avoiding information loss from median dichotomization. The results of both the network and factor analyses consistently identified depression and anxiety as the most strongly connected syndemic nodes. The discrimination\u0026ndash;internalized homonegativity edge detected in the expanded MGM model provides direct empirical support for minority stress pathways in this population and demonstrates that continuous operationalization of structural variables is essential for detecting theoretically meaningful associations. Bootstrap stability analyses confirmed the reliability of centrality estimates within the constraints of this exploratory sample. In the EFA, the inclusion of discrimination in the syndemic theory led to both a better fitting and more interpretable model. These results suggest that depression and anxiety are particularly important psychosocial syndemic indicators, and that discrimination is a meaningful structural factor warranting inclusion in future syndemic research and intervention design among HIV-negative LMSM.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors:\u0026nbsp;\u003c/strong\u003eAriana L. Johnson, WayWay M. Hlaing, Raymond Balise, Adam Carrico, Mariano Kanamori\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author:\u0026nbsp;\u003c/strong\u003eAriana L. Johnson (corresponding author), [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis secondary analysis received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuman Ethics and Consent to Participate declarations: not applicable due to secondary data analysis; all parent studies were IRB approved and individuals consented.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent to Publish declaration: not applicable.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAriana L. Johnson, Ph.D., MPH: Conceptualization, methodology, formal analysis, investigation, data curation, writing \u0026ndash; original draft, writing \u0026ndash; review and editing, visualization, project administration.WayWay M. Hlaing, MBBS, M.S., Ph.D.: Methodology, formal analysis, writing \u0026ndash; review and editing, supervision.Raymond Balise, Ph.D.: Methodology, formal analysis, software, writing \u0026ndash; review and editing.Adam Carrico, Ph.D.: Conceptualization, resources, data curation, writing \u0026ndash; review and editing, funding acquisition.Mariano Kanamori, Ph.D.: Conceptualization, resources, data curation, writing \u0026ndash; review and editing, supervision, funding acquisition.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHealth FDo. Men with an HIV Diagnosis in Florida. 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHealth FDo. 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Am J Public Health. 2012;102(1):156\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParsons JT, Millar BM, Moody RL, Starks TJ, Rendina HJ, Grov C. Syndemic conditions and HIV transmission risk behavior among HIV-negative gay and bisexual men in a U.S. national sample. Health Psychol. 2017;36(7):695\u0026ndash;703.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinger M, Clair S. Syndemics and public health: reconceptualizing disease in bio-social context. Med Anthropol Q. 2003;17(4):423\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStall R, Mills TC, Williamson J, Hart T, Greenwood G, Paul J, et al. Association of co-occurring psychosocial health problems and increased vulnerability to HIV/AIDS among urban men who have sex with men. Am J Public Health. 2003;93(6):939\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinger MC, Erickson PI, Badiane L, Diaz R, Ortiz D, Abraham T, et al. Syndemics, sex and the city: understanding sexually transmitted diseases in social and cultural context. Soc Sci Med. 2006;63(8):2010\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuadamuz TE, McCarthy K, Wimonsate W, Thienkrua W, Varangrat A, Chaikummao S, et al. Psychosocial health conditions and HIV prevalence and incidence in a cohort of men who have sex with men in Bangkok, Thailand: evidence of a syndemic effect. AIDS Behav. 2014;18(11):2089\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMimiaga MJ, O'Cleirigh C, Biello KB, Robertson AM, Safren SA, Coates TJ, et al. The effect of psychosocial syndemic production on 4-year HIV incidence and risk behavior in a large cohort of sexually active men who have sex with men. J Acquir Immune Defic Syndr. 2015;68(3):329\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBauermeister JA, Meanley S, Pingel E, Soler JH, Harper GW. PrEP awareness and perceived barriers among single young men who have sex with men. Curr HIV Res. 2013;11(7):520\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrov C, Rendina HJ, Whitfield TH, Ventuneac A, Parsons JT. Changes in Familiarity with and Willingness to Take Preexposure Prophylaxis in a Longitudinal Study of Highly Sexually Active Gay and Bisexual Men. LGBT Health. 2016;3(4):252\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHojilla JC, Vlahov D, Crouch PC, Dawson-Rose C, Freeborn K, Carrico A. HIV Pre-exposure Prophylaxis (PrEP) Uptake and Retention Among Men Who Have Sex with Men in a Community-Based Sexual Health Clinic. AIDS Behav. 2018;22(4):1096\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJackson T, Huang A, Chen H, Gao X, Zhong X, Zhang Y. Cognitive, psychosocial, and sociodemographic predictors of willingness to use HIV pre-exposure prophylaxis among Chinese men who have sex with men. AIDS Behav. 2012;16(7):1853\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehrotra ML, Glidden DV, McMahan V, Amico KR, Hosek S, Defechereux P, et al. The Effect of Depressive Symptoms on Adherence to Daily Oral PrEP in Men who have Sex with Men and Transgender Women: A Marginal Structural Model Analysis of The iPrEx OLE Study. AIDS Behav. 2016;20(7):1527\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrauss BB, Greene GJ, Phillips G 2nd, Bhatia R, Madkins K, Parsons JT, et al. Exploring Patterns of Awareness and Use of HIV Pre-Exposure Prophylaxis Among Young Men Who Have Sex with Men. AIDS Behav. 2017;21(5):1288\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlashill AJ, Brady JP, Rooney BM, Rodriguez-Diaz CE, Horvath KJ, Blumenthal J, et al. Syndemics and the PrEP Cascade: Results from a Sample of Young Latino Men Who Have Sex with Men. Arch Sex Behav. 2020;49(1):125\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMillett GA, Flores SA, Peterson JL, Bakeman R. Explaining disparities in HIV infection among black and white men who have sex with men: a meta-analysis of HIV risk behaviors. AIDS. 2007;21(15):2083\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMillett GA, Peterson JL. The known hidden epidemic HIV/AIDS among black men who have sex with men in the United States. Am J Prev Med. 2007;32(4 Suppl):S31\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarofalo R, Mustanski B, Johnson A, Emerson E. Exploring factors that underlie racial/ethnic disparities in HIV risk among young men who have sex with men. J Urban Health. 2010;87(2):318\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoung RM, Meyer IH. The trouble with MSM and WSW: erasure of the sexual-minority person in public health discourse. Am J Public Health. 2005;95(7):1144\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanamori M, Shrader CH, Johnson A, Arroyo-Flores J, Rodriguez E, Skvoretz J et al. The Association Between Homophily on Illicit Drug Use and PrEP Conversations Among Latino Men Who Have Sex with Men Friends: A Dyadic Network and Spatially Explicit Study. Arch Sex Behav. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShrader CH, Arroyo-Flores J, Stoler J, Skvoretz J, Carrico A, Doblecki-Lewis S, et al. The Association Between Social and Spatial Closeness With PrEP Conversations Among Latino Men Who Have Sex With Men. J Acquir Immune Defic Syndr. 2021;88(4):366\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShrader CH, Arroyo-Flores J, Skvoretz J, Fallon S, Gonzalez V, Safren S, et al. PrEP Use and PrEP Use Disclosure are Associated with Condom Use During Sex: A Multilevel Analysis of Latino MSM Egocentric Sexual Networks. AIDS Behav. 2021;25(5):1636\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCenters for Disease. Control and Prevention. 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKroenke K, Strine TW, Spitzer RL, Williams JB, Berry JT, Mokdad AH. The PHQ-8 as a measure of current depression in the general population. J Affect Disord. 2009;114(1\u0026ndash;3):163\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSwinson RP. The GAD-7 scale was accurate for diagnosing generalised anxiety disorder. Evid Based Med. 2006;11(6):184.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMayfield W. The development of an Internalized Homonegativity Inventory for gay men. J Homosex. 2001;41(2):53\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStahlman S, Sanchez TH, Sullivan PS, Ketende S, Lyons C, Charurat ME, et al. The Prevalence of Sexual Behavior Stigma Affecting Gay Men and Other Men Who Have Sex with Men Across Sub-Saharan Africa and in the United States. JMIR Public Health Surveill. 2016;2(2):e35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCervantes RC, Fisher DG, Padilla AM, Napper LE. The Hispanic Stress Inventory Version 2: Improving the assessment of acculturation stress. Psychol Assess. 2016;28(5):509\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeam RC. R: A language and environment for statistical computing. R Foundation for Statistical Computing; 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLedesma R, Molina JG, Young FW, Valero-Mora P. [Multiple visualisation in data analysis: a ViSta application for principal component analysis]. Psicothema. 2007;19(3):497\u0026ndash;505.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDinno A. Exploring the Sensitivity of Horn's Parallel Analysis to the Distributional Form of Random Data. Multivar Behav Res. 2009;44(3):362\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCattell RB. The Scree Test For The Number Of Factors. Multivar Behav Res. 1966;1(2):245\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMazumder R, Hastie T. The graphical lasso: New insights and alternatives. Electron J Stat. 2012;6:2125\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorsboom D, Cramer AO. Network analysis: an integrative approach to the structure of psychopathology. Annu Rev Clin Psychol. 2013;9:91\u0026ndash;121.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEpskamp S, Fried EI. A tutorial on regularized partial correlation networks. Psychol Methods. 2018;23(4):617\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEpskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: A tutorial paper. Behav Res Methods. 2018;50(1):195\u0026ndash;212.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRhemtulla M, Fried EI, Aggen SH, Tuerlinckx F, Kendler KS, Borsboom D. Network analysis of substance abuse and dependence symptoms. Drug Alcohol Depend. 2016;161:230\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee JS, Safren SA, Bainter SA, Rodriguez-Diaz CE, Horvath KJ, Blashill AJ. Examining a Syndemics Network Among Young Latino Men Who Have Sex with Men. Int J Behav Med. 2020;27(1):39\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee JS, Bainter SA, Carrico AW, Glynn TR, Rogers BG, Albright C, et al. Connecting the dots: a comparison of network analysis and exploratory factor analysis to examine psychosocial syndemic indicators among HIV-negative sexual minority men. J Behav Med. 2020;43(6):1026\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFried EI, von Stockert S, Haslbeck JMB, Lamers F, Schoevers RA, Penninx B. Using network analysis to examine links between individual depressive symptoms, inflammatory markers, and covariates. Psychol Med. 2020;50(16):2682\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSafren SA, Traeger L, Skeer MR, O'Cleirigh C, Meade CS, Covahey C, et al. Testing a social-cognitive model of HIV transmission risk behaviors in HIV-infected MSM with and without depression. Health Psychol. 2010;29(2):215\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSafren SA, Reisner SL, Herrick A, Mimiaga MJ, Stall RD. Mental health and HIV risk in men who have sex with men. J Acquir Immune Defic Syndr. 2010;55(Suppl 2):S74\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoung LB, Lalley-Chareczko L, Clark D, Ramos MT, Nahan RA, Troutman GS, et al. Correlation of pre-exposure prophylaxis adherence to a mental health diagnosis or experience of childhood trauma in high-risk youth. Int J STD AIDS. 2020;31(5):440\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIkeda DJ, Kidia K, Agins BD, Haberer JE, Tsai AC. Roll-out of HIV pre-exposure prophylaxis: a gateway to mental health promotion. BMJ Glob Health. 2021;6(12).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiller SJ, Harrison SE, Sanasi-Bhola K. A Scoping Review Investigating Relationships between Depression, Anxiety, and the PrEP Care Continuum in the United States. Int J Environ Res Public Health. 2021;18(21).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"617\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"3\" valign=\"bottom\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1: Characteristics of Participants (n=130)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e28.31 (4.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\n \u003cp\u003e21 - 38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eSexual Orientation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eGay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e122 (93.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eBisexual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e5 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e3 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eRelationship Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eSingle/Never Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e110 (84.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eDomestic Partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e7 (5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e10 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eOther\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e3 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eBlack/African American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e5 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e91 (70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eAmerican Indian or Alaskan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e1 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eAsian or Pacific Islander\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e1 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eMulti-Racial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e24 (18.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e8 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eSome High School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e2 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eHigh School Graduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e14 (10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eTrade School\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e3 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eSome College\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e67 (51.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eBachelor\u0026rsquo;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e40 (30.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003ePostgraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e4 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eCountry of Birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eUnited States\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e87 (66.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eEl Salvador\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e1 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eNicaragua\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e5 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eDominican Republic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e5 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eCuba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e22 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003ePuerto Rico\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e3 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eHonduras\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e4 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003ePer\u0026uacute;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e3 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eIncome (Annual, USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eLess than $4,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e4 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003e$5,000 - $11,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003e$12,000 - $15,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e3 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003e$16,000 - $24,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e9 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003e$25,000 - $34,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e54 (41.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003e$35,000 - $49,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e35 (26.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003e\u0026nbsp;$50,000 - $74,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e14 (10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003e$75,000 - $99,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e4 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003e$100,000 or greater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e4 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 58.671%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 26.2561%;\"\u003e\n \u003cp\u003e3 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 15.0729%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"653\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"3\" valign=\"bottom\" style=\"width: 567px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2: Descriptive Statistics of Syndemic Indicators and PrEP Use (n=130)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 380px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 380px;\"\u003e\n \u003cp\u003eDepression (PHQ9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3.28 (4.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0 - 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eCategorical\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 380px;\"\u003e\n \u003cp\u003eAnxiety (GAD-7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3.44 (4.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0 - 28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 380px;\"\u003e\n \u003cp\u003eSexual orientation stigma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2.97 (3.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0 - 13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 380px;\"\u003e\n \u003cp\u003eInternalized Homonegativity (IHP)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e17.12 (7.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0 - 39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 380px;\"\u003e\n \u003cp\u003eDiscrimination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e10.12 (11.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0 - 35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 380px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 380px;\"\u003e\n \u003cp\u003ePhysical Abuse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eDichotomous\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 380px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e97 (74.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 380px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e33 (25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 380px;\"\u003e\n \u003cp\u003eSexual Abuse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eDichotomous\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 380px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e114 (87.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 380px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e16 (12.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 380px;\"\u003e\n \u003cp\u003ePolydrug Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eDichotomous\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 380px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e72 (55.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 380px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e58 (44.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 380px;\"\u003e\n \u003cp\u003ePlan to Begin PrEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eDichotomous\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 380px;\"\u003e\n \u003cp\u003eNo, definitely will not begin PrEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2 (1.6%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 380px;\"\u003e\n \u003cp\u003eProbably will not begin PrEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 380px;\"\u003e\n \u003cp\u003eMight begin PrEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e25 (19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 380px;\"\u003e\n \u003cp\u003eWill probably begin PrEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e23 (17.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 380px;\"\u003e\n \u003cp\u003eYes, definitely will begin taking PrEP/ On PrEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e77 (59.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 80px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"622\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"3\" valign=\"bottom\" style=\"width: 622px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e \u003cstrong\u003eExploratory Factor Analyses Factor Loadings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 215px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 195px;\"\u003e\n \u003cp\u003eCurrent Syndemic Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003eExpanded Syndemic Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 215px;\"\u003e\n \u003cp\u003ePolysubstance Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 195px;\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 215px;\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 195px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.83\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 215px;\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 195px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.92\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 215px;\"\u003e\n \u003cp\u003ePhysical Abuse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 195px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 215px;\"\u003e\n \u003cp\u003eSexual Abuse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 195px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 215px;\"\u003e\n \u003cp\u003eSexual orientation stigma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 195px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.59\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 215px;\"\u003e\n \u003cp\u003eInternalized Homonegativity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 195px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 215px;\"\u003e\n \u003cp\u003eDiscrimination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 195px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.41\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 215px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 195px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 211px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 215px;\"\u003e\n \u003cp\u003eSS loadings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 195px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 215px;\"\u003e\n \u003cp\u003eProportional Variance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 195px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 215px;\"\u003e\n \u003cp\u003eDegrees of freedom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 195px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 215px;\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 195px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 215px;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 195px;\"\u003e\n \u003cp\u003e-45.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 211px;\"\u003e\n \u003cp\u003e-47.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBold numbers indicate significance (p \u0026lt;0.05)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"688\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 688px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4: Expanded Syndemic Framework Polychoric Correlation Matrix\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePolydrug\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiscrimination\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical Abuse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSexual Abuse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSexual Orientation Stigma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternalized Homonegativity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrEP Likelihood to Use/ Current Use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePolydrug\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.81**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiscrimination\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbuse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex Abuse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSexual Orientation Stigma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.51**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.5**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.34**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternalized Homonegativity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.08**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.16**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.34**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.11**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrEP Likelihood to Use/ Current Use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.27**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBold numbers indicate significance (p \u0026lt;0.05)\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"discpsy","sideBox":"Learn more about [Discover Psychology](https://www.springer.com/44202)","snPcode":"","submissionUrl":"","title":"Discover Psychology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Social Network Analysis, Pre-Exposure Prophylaxis, Syndemic","lastPublishedDoi":"10.21203/rs.3.rs-9179799/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9179799/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eSyndemic theory posits that social and structural inequities enable health conditions to cluster and interact, worsening outcomes. Despite widespread use in HIV research, most syndemic studies rely on additive indices without rigorously testing factor structure or interconnections. This study compares exploratory factor analysis (EFA) and network analysis to examine the structure of syndemic conditions \u0026mdash; including depression, anxiety, internalized homophobia, sexual orientation stigma, racial discrimination, polydrug use, childhood sexual abuse, and physical abuse \u0026mdash; influencing PrEP uptake among Latino men who have sex with men (LMSM) in Miami-Dade County, Florida.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eSecondary data from 130 LMSM recruited through community organizations were analyzed. Continuous syndemic indicators (sexual orientation stigma, internalized homonegativity, discrimination stress) were retained in their native scale and modeled using a mixed graphical model (MGM) framework (Haslbeck \u0026amp; Waldorp, 2018), which natively accommodates mixed variable types without requiring dichotomization. Binary and count variables were modeled accordingly. The original median-split EBICglasso approach was retained as a sensitivity analysis. EFA with oblique rotation was conducted to assess latent structure. Bootstrap stability of node centrality was quantified via case-drop CS-coefficients (Epskamp et al., 2018).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBoth methods revealed convergent syndemic patterns. The primary MGM analysis identified three retained edges in the current syndemic model: depression\u0026ndash;anxiety (edge weight\u0026thinsp;=\u0026thinsp;4.53), depression\u0026ndash;sexual abuse (1.63), and physical abuse\u0026ndash;sexual abuse (0.64). In the expanded model incorporating discrimination, a fourth edge emerged between discrimination and internalized homonegativity (0.20), consistent with minority stress theory. Depression and anxiety were the most strongly connected nodes across both models, and findings were robust in the median-split sensitivity analysis. EFA grouped depression, anxiety, sexual orientation stigma, and discrimination on a shared latent dimension. The expanded model demonstrated improved sampling adequacy (KMO\u0026thinsp;=\u0026thinsp;0.80) and model fit (lower BIC).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eDepression and anxiety function as central syndemic indicators among LMSM. Interventions targeting these mental health conditions may reduce broader syndemic burden and improve PrEP uptake in this population.\u003c/p\u003e","manuscriptTitle":"Examining factors and network analysis to validate and expand the HIV risk syndemic theory","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 16:58:06","doi":"10.21203/rs.3.rs-9179799/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-06T15:37:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T05:01:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T04:23:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-23T14:56:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-20T19:27:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"296460471489546433602844390178649294530","date":"2026-04-20T19:16:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112393641075011319206402104120391032915","date":"2026-04-15T15:17:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7404561109409852091900968800998846570","date":"2026-04-15T12:17:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14029813774224599436838695330500145201","date":"2026-04-13T18:17:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-13T07:53:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-10T09:40:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-01T22:39:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-01T20:36:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Psychology","date":"2026-04-01T20:31:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"discpsy","sideBox":"Learn more about [Discover Psychology](https://www.springer.com/44202)","snPcode":"","submissionUrl":"","title":"Discover Psychology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"132822f7-c875-4baa-8c6c-b7ed0dd29155","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-06T15:37:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T05:01:52+00:00","index":71,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T04:23:04+00:00","index":69,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-17T23:53:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 16:58:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9179799","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9179799","identity":"rs-9179799","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}