Comparing the Ecological Niches of Chlamydial and Gonococcal Infections in Winnipeg, Canada: 2007–2016
Bibliographic record
Abstract
BACKGROUND: Previous studies have shown substantial differences in geographic clustering of sexually transmitted infections (STI), such as chlamydia (CT) and gonorrhea (NG), conditional on epidemic phase. Chlamydia and NG have recently shown resurgent epidemiology in the northern hemisphere. This study describes the recent epidemiology of CT and NG in Winnipeg, Canada, combining traditional surveillance tools with place-based analyses, and comparing the ecological niches of CT and NG, in the context of their evolving epidemiology. METHODS: Data were collected as part of routine public health surveillance between 2007 and 2016. Secular trends for CT and NG, and CT/NG coinfection were examined. Gini coefficients and population attributable fractions explored the distribution, and concentration of infections over time and space. RESULTS: Rates of CT increased from 394.9/100,000 population to 476.2/100,000 population from 2007 to 2016. Gonorrhea rates increased from 78.0/100,000 population to 143.5/100,000 population during the same period. Each pathogen had its own ecological niche: CT was widespread geographically and socio-demographically, while NG was clustered in Winnipeg's inner-core. CT/NG co-infections had the narrowest space and age distribution. NG was shown to be undergoing a growth phase, with clear signs of geographic dispersion. The expansion of NG resembled the geographic distribution of CT. CONCLUSIONS: We demonstrated that NG was experiencing a growth phase, confirming theoretical predictions of geographic dispersion during a growth phase. During this phase, NG occupied similar geographic spaces as CT. Knowledge of different ecological niches could lead to better targeting of resources for subpopulations vulnerable to STIs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".