P1-S1.07 Multiple bacterial sexually transmitted infections in Ontario, Canada
Bibliographic record
Abstract
Background In 2009, there were 33 000 reported cases of chlamydia, gonorrhoea and infectious syphilis in Ontario. These reportable bacterial sexually transmitted infections (STIs) represented approximately 48% of all reportable disease cases reported in Ontario that year. A significant amount of resources is expended on public health case and contact management of bacterial STIs. The objective of our study was to use routine surveillance data from Ontario to quantify the incidence of persons with reported multiple bacterial STIs in Ontario from 2006 to 2009 in order to inform future public health interventions. Methods All bacterial STI records from 2006 to 2009 were extracted from Ontario's integrated Public Health Information System (iPHIS). Multiple STIs were defined as repeat bacterial STIs or infection with a different bacterial STI in the same individual. Repeat STIs were defined as diagnoses of chlamydia or gonorrhoea more than 28 days after the previous infection. The data were analysed using PASW 18 for Windows (SPSS Inc.). Results There were almost 100 000 unique clients with at least one bacterial STI from 2006 to 2009 representing 113 097 STI cases. Approximately 24% of STI cases reported from 2006 to 2009 occurred in individuals with multiple STIs during the same timeframe. However, clients with multiple STIs accounted for only 12% of the total number of clients reporting at least one STI in this 4-year time period and clients with three or more STIs only accounted for 2.4% of all clients. The majority of clients (∼60%) with multiple STIs were infected within one year of their first STI. On average clients with multiple STIs had more sexual contacts recorded for their first STI than those clients who only had one STI (1.44 contacts vs 1.33 contacts, p<0.001). There was no difference in the number of clients lost to follow-up when comparing clients with only one STI to those with multiple STIs (p>0.9). Conclusions Public health resources may be well spent in case and contact management because almost 90% of individuals only have one STI reported. However, individuals who have multiple STIs account for a substantial proportion of reported bacterial STI cases in Ontario. Further work to identify additional characteristics of individuals at risk for multiple STIs would aid in informing future public health interventions aimed at these high-risk individuals.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".