P1-S1.02 Assessing heterogeneity in the incidence of chlamydia and gonorrhoea in an urban Canadian setting—a population-based analysis
Bibliographic record
Abstract
Introduction A combination of increased testing and improved testing modalities has contributed to substantially higher incidences of chlamydia (CT) and gonorrhoea (GC) infections globally. Previous studies have noted the differing epidemiological profiles of CT and GC, resulting in different public health prevention and intervention strategies. Using a population-based perspective, this study describes recent trends, and assesses inequalities in the geographic distribution of CT and GC in a Canadian city. Methods Analyses included all laboratory-confirmed cases of CT and GC among residents of the Winnipeg Health Region from 1996 to 2008. Annual CT and GC incidence rates were directly age-standardised to the 2000 Winnipeg Health Region population. Adjusted rate ratios (ARR) for the effect of age and gender, and 95% 95% CI, were estimated by negative binomial regression models. Generalised estimating equations (GEE) were used to correct for clustering within neighbourhood areas (n=25). Inequalities in the neighbourhood area distribution of annual CT and GC rates were measured by the Gini coefficient, with 95% CI estimated by bootstrapped samples. For regression and Gini analyses, cases were grouped into 10–19, 20–29 and 30+ age groups. Results The age-adjusted incidence of CT increased from 198/100 000 in 1996 to 513/100 000 in 2008. During this same time period, GC increased from 62/100 000 to 89/100 000, with a peak of 133/100 000 in 2006. For females, and compared to those aged 30 or over, equally high rates of CT were observed in those 10–19 (ARR 24.2; 95% CI 18.7 to 31.4, p<0.0001) and 20–29 years (ARR 24.4; 95% CI 18.8–31.7, p<0.0001). For males, differences in rates were observed in those aged 10–19 (ARR 5.3; 95% CI 4.1 to 6.9, p<0.0001) and 20–29 (ARR 10.5; 95% CI 8.2 to 13.5, p<0.0001) years. A similar pattern was detected for GC infections. Inequality by neighbourhood area, as measured by Gini coefficients decreased over time for CT infections, regardless of age group. For GC infections, increases were observed in those 30 years or older, for both males and females. Conclusion Since 1996, the distribution of CT incidence is suggestive of a generalised spread; while for GC the distribution has narrowed in those aged 30 or over, suggestive of concentration among core populations within this age group. CT and GC continue to display differing and dynamic epidemiological profiles.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".