Incident syphilis infection among people who inject drugs in Tijuana, Mexico
Bibliographic record
Abstract
Given that syphilis is associated with HIV infection among people who inject drugs (PWID), we examined syphilis incidence among PWID in Tijuana, Mexico. From 2006 to 2007, 940 PWID (142 women and 798 men) were recruited via respondent-driven sampling and followed for 18 months. At semi-annual visits, participants were tested for syphilis and completed surveys, which collected information on socio-demographics, sexual behaviours, substance use and injection behaviours. Poisson regression was used to estimate syphilis incidence rates (IRs), incidence rate ratios (IRRs) and 95% confidence intervals (CIs). Twenty-one participants acquired syphilis during follow-up (IR = 1.57 per 100 person-years, 95% CI: 1.02-2.41). In a multivariate analysis, syphilis incidence was higher among women (IRR = 3.90, 95% CI: 1.37-11.09), HIV-positive participants (IRR = 4.60, 95% CI: 1.58-13.39) and those who reported ever exchanging sex for drugs, money, or other goods (IRR = 2.74, 95% CI: 0.97-7.76), while syphilis incidence was lower among those living in Tijuana for a longer duration (IRR = 0.95 per year, 95% CI: 0.91-1.00) and those reporting at least daily injection drug use (past 6 months) (IRR = 0.22, 95% CI: 0.09-0.54). Our findings suggest interventions that address the destabilising conditions associated with migration and integrate sexual and drug-related risk reduction strategies may help reduce syphilis incidence among PWID along the Mexico-US border.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".