Enablers and Barriers to HIV Services for Gay and Bisexual Men in the COVID-19 Era: Fusing Data Sets from Two Global Online Surveys Via File Concatenation With Adjusted Weights
Bibliographic record
Abstract
BACKGROUND: Gay and bisexual men are 26 times more likely to acquire HIV than other adult men and represent nearly 1 in 4 new HIV infections worldwide. There is concern that the COVID-19 pandemic may be complicating efforts to prevent new HIV infections, reduce AIDS-related deaths, and expand access to HIV services. The impact of the COVID-19 pandemic on gay and bisexual men's ability to access services is not fully understood. OBJECTIVE: The aim of this study was to understand access to HIV services at the start of the COVID-19 pandemic. METHODS: Our study used data collected from two independent global online surveys conducted with convenience samples of gay and bisexual men. Both data sets had common demographic measurements; however, only the COVID-19 Disparities Survey (n=13,562) collected the outcomes of interest (HIV services access at the height of the first COVID-19 wave) and only the Global Men's Health and Rights Survey 4 (GMHR-4; n=6188) gathered pre-COVID-19 pandemic exposures/covariates of interest (social/structural enablers of and barriers to HIV services access). We used data fusion methods to combine these data sets utilizing overlapping demographic variables and assessed relationships between exposures and outcomes. We hypothesized that engagement with the gay community and comfort with one's health care provider would be positively associated with HIV services access and negatively associated with poorer mental health and economic instability as the COVID-19 outbreaks took hold. Conversely, we hypothesized that sexual stigma and experiences of discrimination by a health care provider would be negatively associated with HIV services access and positively associated with poorer mental health and economic instability. RESULTS: With 19,643 observations after combining data sets, our study confirmed hypothesized associations between enablers of and barriers to HIV prevention, care, and treatment. For example, community engagement was positively associated with access to an HIV provider (regression coefficient=0.81, 95% CI 0.75 to 0.86; P<.001), while sexual stigma was negatively associated with access to HIV treatment (coefficient=-1.39, 95% CI -1.42 to -1.36; P<.001). CONCLUSIONS: HIV services access for gay and bisexual men remained obstructed and perhaps became worse during the first wave of the COVID-19 pandemic. Community-led research that utilizes novel methodological approaches can be helpful in times of crisis to inform urgently needed tailored responses that can be delivered in real time. More research is needed to understand the full impact COVID-19 is having on gay and bisexual men worldwide.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".