MétaCan
Menu
Back to cohort
Record W4285097923 · doi:10.1002/hsr2.704

A cross‐sectional study comparing men who have sex with men and inject drugs and people who inject drugs who are men and have sex with men in San Francisco: Implications for HIV and hepatitis C virus prevention

2022· article· en· W4285097923 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHealth Science Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsSpringboard (Canada)
FundersNational Institute on Drug AbuseFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchCenters for Disease Control and Prevention
KeywordsMen who have sex with menMedicineDemographyHuman immunodeficiency virus (HIV)Psychological interventionHomosexualityGerontologyEnvironmental healthSyphilisVirologyPsychologyPsychiatry

Abstract

fetched live from OpenAlex

People who inject drugs (PWID) and men who have sex with men (MSM) carry a high burden of HIV and hepatitis C (HCV) and represent key populations for eliminating these viral infections.1, 2 Previous studies illustrated that MSM who inject drugs (MSM-IDU) and male PWID who engage in sex with other men (PWID-MSM) have greater injecting or sexual risk behaviors and HCV and HIV prevalence than other MSM and PWID, respectively.3-6 However, it remains unclear how MSM-IDU and PWID-MSM compare to each other. People with dual risk behaviors are typically omitted from HIV/HCV programs and elimination plans or treated as a single generic risk category, with most interventions being geared towards either PWID or MSM.1, 2 Our aim was to characterize similarities and differences between MSM-IDU (i.e., men reached through affiliation with MSM) and PWID-MSM (i.e., men reached through affiliation with PWID) in San Francisco by comparing sociodemographic, drug use and sexual risk behaviors, and service access. We also compared the characteristics of MSM-IDU to MSM non-IDU and PWID-MSM to male PWID non-MSM to gain a broader understanding of these groups. We used data from the National HIV Behavioral Surveillance surveys among MSM (2017) and PWID (2018) in San Francisco. MSM were recruited using time-location sampling at MSM venues and were eligible for enrollment if ≥18 years old and either identified as MSM or had past-year sex with another man.7 PWID were recruited through respondent-driven sampling and were eligible if ≥18 years and reported past-year injection drug use.7 In both surveys, participants provided informed consent, completed the same core questionnaire, and were tested for HIV (both surveys) and HCV antibody status (PWID survey).7 Both studies were approved by the institutional review boards of the Centers for Disease Control and Prevention. We restricted the PWID sample to male participants. Those who reported past-year sex with a man were categorized as PWID-MSM. Among the MSM sample, we categorized those who reported past-year injecting drug use as MSM-IDU. We categorized the remaining groups as PWID non-MSM and MSM non-IDU. We explored differences between groups using Pearson's χ2 or Fisher's exact tests when expected cell counts were ≤5 for categorical variables and Mann–Whitney U tests for continuous variables. Among PWID, we presented sample proportions unadjusted for respondent-driven sampling since evidence suggest they may be more representative compared to adjusted estimates.8 All tests were two-sided (α = 0.05) and conducted using SAS v.9.4. Of 504 participants completing the MSM survey, 31 (6.2%) were classified as MSM-IDU. Of 311 male participants completing the PWID survey, 59 (19.0%) were classified as PWID-MSM (Table 1). MSM-IDU and PWID-MSM were different across numerous sociodemographic measures. PWID-MSM were older than MSM-IDU (57.6% vs. 35.5% were ≥40 years), more racially/ethnically diverse (61.0% vs. 35.5% identified as nonwhite), and more were bisexual (45.8% vs. 16.1%). More PWID-MSM reported a household annual income of <$25,000, current homelessness, and prior incarceration. Although a similar proportion of MSM-IDU (64.5%) and PWID-MSM (54.2%) indicated methamphetamine as the drug most often injected, other injection drug use and sexual behaviors differed. Compared to MSM-IDU, PWID-MSM began injecting drugs earlier (median age: 22 vs. 30 years), more injected ≥2 different drugs (59.3% vs. 25.8%), and injected daily (64.4% vs. 29.0%). Conversely, PWID-MSM reported fewer male sexual partners (median: 3 vs. 10), less condomless anal sex (62.7% vs. 93.6%), and more reported a female sex partner (50.9% vs. 22.6%). Service use also differed across the two groups. More PWID-MSM sought sterile syringes from a syringe service program than MSM-IDU (86.4% vs. 35.5%). Conversely, more MSM-IDU reported using pre-exposure prophylaxis (PrEP) (42.9% vs. 15.0%) and having been HCV-tested (90.3% vs. 61.0%) than PWID-MSM. HIV prevalence was similarly high for both MSM-IDU (32.3%) and PWID-MSM (39.0%). We also noted nonstatistically significant differences between MSM-IDU and PWID-MSM on receipt of medications for opioid use disorder, sharing practices, overdose, and HIV testing. Several characteristics differed between MSM-IDU and MSM non-IDU. For example, a larger proportion of MSM-IDU reported lower education (35.5% vs. 11.2%) and income (38.7% vs. 16.5%), current homelessness (25.8% vs. 2.3%), and prior incarceration (45.2% vs. 15.4%). More MSM-IDU received money or drugs in exchange for sex with a man (35.5% vs. 5.5%), had a female sex partner (22.6% vs. 6.6%), and were HIV-positive (32.3% vs. 18.4%) relative to MSM non-IDU. PWID-MSM and PWID non-MSM were comparable on several sociodemographic measures. However, more PWID-MSM reported methamphetamine as their primary drug injected (54.2% vs. 18.3%) while fewer reported heroin (22.0% vs. 58.7%) compared to PWID non-MSM. A larger proportion of PWID-MSM were aware (72.5% vs. 48.3%) and had used PrEP (15.0% vs. 0.4%). HIV prevalence was higher among PWID-MSM compared to PWID non-MSM (39.0% vs. 5.9%); no difference was found for HCV prevalence (71.2% vs. 79.4%). Overall, compared to MSM-IDU, PWID-MSM presented greater socioeconomic disadvantage and reported heavier injecting drug use but lower sexual risk practices. While MSM-IDU was more engaged in MSM-oriented prevention programs like PrEP, PWID-MSM was more engaged with syringe services programs, which primarily target PWID. Although the strength of these findings is limited by small sample sizes, taken together, our results suggest that MSM-IDU and PWID-MSM represent distinct populations that are present in different social spaces and should not be conflated with one another. More broadly, these findings suggest that harm reduction and healthcare settings catering to MSM and PWID, like syringe services programs and sexual health clinics, should adapt to reflect the complexity of risk practices and needs of these groups and provide a wider range of HIV/HCV prevention services. The extent to which people who engage in both injecting- and sexual-risk behaviors were included in the PWID- or MSM-focused studies could reflect the primary behavior which takes precedence in day-to-day life. In a qualitative study focused on people with dual risk behaviors, some participants reported engaging in male-to-male sex work to sustain injection drug use, whereas others indicated that injection drug use was used to enhance male-to-male sex.9 Varying motivations and levels of priority assigned to injecting and sexual practices have been reported in other studies10 and explain why some individuals may not identify as MSM or PWID.9 A better understanding of the reasons motivating these practices could increase the extent to which HCV and HIV prevention programs engage and help reduce risk behaviors among these populations. We also noted important differences between MSM-IDU and MSM non-IDU and PWID-MSM and PWID non-MSM, respectively. One-third of MSM-IDU indicated receiving money or drugs from a man to have sex, whereas few (5.5%) MSM non-IDU indicated this practice. While few PWID-MSM indicated heroin as the most injected drug, over half of PWID non-MSM did so. Across both MSM and PWID, HIV prevalence was higher among the dual risk groups. Collectively, these distinctions emphasize the importance of providing access to combined sexual health and harm reduction messages rather than targeting specific risk behaviors. In conclusion, our study suggests that MSM-IDU and PWID-MSM have distinct demographic, risk behavior, and healthcare access profiles. Given ongoing calls to broaden access to HCV and HIV interventions among PWID and MSM to reach 2030 elimination goals, findings indicate a need to provide access to a greater range of services to both populations. Adelina Artenie, Shelley N. Facente, Peter Vickerman, and Meghan D. Morris: Conceptualization. Adelina Artenie and Sheena Patel: Data curation. Adelina Artenie: Formal analysis. Adelina Artenie and Meghan D. Morris: Funding acquisition. Adelina Artenie and Shelley N. Facente: Writing – original draft. Adelina Artenie, Shelley N. Facente, Jack Stone, Jennifer Hecht, Perry Rhodes III, Willi McFarland, Erin Wilson, Peter Vickerman, and Meghan D. Morris: Writing – review and editing. Adelina Artenie and Meghan D. Morris had full access to all of the data in this study and take complete responsibility for the integrity of the data and the accuracy of the data analysis. All authors have read and approved the final version of the manuscript. Adelina Artenie is supported through postdoctoral fellowships through the Canadian Institute of Health Research, Fonds de recherche du Québec–Santé, and the Canadian Network on Hepatitis C. Peter Vickerman acknowledges support from the National Institute of Health Research Health Protection Research Unit in Behavioral Science and Evaluation at the University of Bristol and the National Institute for Drug Abuse (NIDA; R01DA033679, R21DA047902, and 1R21DA046809). Shelley N. Facente, Peter Vickerman, and Meghan D. Morris acknowledge support from NIDA (1R21DA046809). The parent study, National HIV Behavioral Surveillance, is funded by the US Centers for Disease Control and Prevention (5U1BPS003247). The funding sources had no role in the design, collection, analysis, and interpretation of data; writing of the report; or the decision to submit the report for publication. Peter Vickerman received an unrestricted research grant from Gilead that is not related to this study. Meghan D. Morris received investigator-sponsored research funding from Gilead Sciences for research not related to this study. Shelley N. Facente acknowledges consulting support from Gilead Sciences and from End Hep C SF; neither is related to this study. All other authors have no conflict of interest. Adelina Artenie and Meghan D. Morris affirm that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned have been explained. The data that support the findings of this study are available from the National HIV Behavioral Surveillance study. Restrictions apply to the availability of these data, which were used under license for this study. Data may be available from Meghan D. Morris with the permission of the National HIV Behavioral Surveillance study.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.033
GPT teacher head0.369
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it