The Unexpected Impact of a <i>Chlamydia trachomatis</i> Infection Control Program on Susceptibility to Reinfection
Bibliographic record
Abstract
BACKGROUND: After the introduction of a program to control Chlamydia trachomatis infection in British Columbia, Canada, case rates fell from 216 cases/100,000 population in 1991 to 104 cases/100,000 population in 1997. Since 1998, rates have increased, and case counts now exceed those recorded before the intervention. METHODS: We used Cox proportional-hazards survival analysis and developed a compartmental mathematical model to investigate the cause of resurgence in chlamydia cases. RESULTS: Cox proportional-hazards survival analysis showed that the relative risk of C. trachomatis reinfection has increased 4.6% per year since 1989, with the increased risk greatest among the young and greater among women than men. A compartmental mathematical model of C. trachomatis transmission showed that a control strategy based on shortening the average duration of infection results in an early reduction in prevalence followed by a rebound in prevalence, reproducing the observed trends. CONCLUSIONS: We speculate that a C. trachomatis infection control program based on early case identification and treatment interferes with the effects of immunity on population susceptibility to infection and that, in the absence of strategies to alter sexual networks, a vaccine will be needed to halt the spread of infection at the population level.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".