Incidence rate of sexually transmitted infections among HIV infected patients on long-term ART in an urban and a rural clinic in Uganda
Bibliographic record
Abstract
BACKGROUND: HIV immunosuppression increases susceptibility to other STIs and STIs can enhance HIV transmission, reduce CD4 cell count and increase viral load. Co-infections of HIV and STIs may thus reduce the preventive benefits of ART. Little is known about the incidence rate of STIs among long-term patients on ART. METHOD: We conducted a secondary data analysis of all patients enrolled in a rural and an urban longitudinal cohort studies who initiated ART between April 2003 and July 2007 followed up to 2016. Patients were screened for STI every three months using "a syndromic and case management approaches". STI incidence rate, was defined as the number of new cases per population at risk over the follow-up review period. We performed a time-to-event and Kaplan Meier analysis. We used a multivariable Cox proportional hazards regression model to assess for factors associated with STI incidence. RESULT: Of 1012 participants, 402 (39.8%) were urban and 610 (60.2%) rural residents. Mean age was 42.8 years (SD 8.5). The total number of follow up time was 44,304 person years. We observed STI incidence rate of 2.1 per 1000 person-years after follow-up. Rural residence (adjusted hazard ratio [aHR] 3.53, 95% CI: 1.95-6.39), younger age (aHR 2.05, 95% CI: 1.02-4.12 for 18-34 years and aHR 1.65, 95% CI: 1.00-2.72 for 35-44 years) were factors associated with higher incidence of STIs. Being male (aHR 0.51, 95% CI: 0.27-0.93) was associated with a lower incidence of STIs. CONCLUSION: We found STIs incidence rate of approximately 3 per 1000 person-years among patients on long-term (≥ 4 years) ART followed up-to 3.5 years. Rural and younger persons on ART should be routinely screened for STIs because high incidence of STIs may undo the preventative effects of ART for all.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".