Sexually Transmitted Infections Diagnosed Among Sexual and Gender Minority Communities During the First 11 Months of the COVID-19 Pandemic in Midwest and Southern Cities in the United States
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic adversely affected sexual health services. Given the burden of sexually transmitted infections (STIs) on sexual and gender minorities (SGMs), we estimated incidence of self-reported STI diagnoses and factors associated with STI diagnoses among SGMs during the pandemic's first year. METHODS: A cohort of 426 SGM persons, 25 years or older, recruited in Chicago, Milwaukee, Detroit, Minneapolis, and Houston completed 5 online surveys from April 2020 to February 2021. Persons self-reported on each survey all health care provider STI diagnoses. Kaplan-Meier was used to estimate the cumulative risk of STI diagnoses, stratified by human immunodeficiency virus (HIV) status. Factors associated with STI diagnoses were assessed with a longitudinal negative binomial regression. RESULTS: Median age was 37 years, and 27.0% were persons living with HIV (PLH). Participants reported 63 STIs for a cumulative incidence for PLH and HIV-negative persons of 0.19 (95% confidence interval [CI], 0.13-0.29) and 0.12 (95% CI, 0.09-0.17), respectively. Regardless of HIV, a younger age and changes in health care use were associated with STI diagnoses. Among HIV-negative persons, the rate of STI diagnoses was higher in Houston than the Midwest cities (adjusted relative risk, 2.37; 95% CI, 1.08-5.20). Among PLH, a decrease in health care use was also associated with STI diagnoses (adjusted relative risk, 3.53; 95% CI, 1.01-12.32 vs no change in health care services), as was Hispanic ethnicity and using a dating app to meet a sex partner. CONCLUSIONS: Factors associated with STI diagnoses during the COVID-19 pandemic generally reflected factors associated with STI incidence before the pandemic like geography, HIV, age, and ethnicity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".