Lessons from management of syphilis in Nunavut, Canada, 2012–2020
Bibliographic record
Abstract
Background: Nunavut, part of Inuit Nunangat, is a geographically vast territory in northern Canada, with a population of over 38,000 people. Most (85%) of the population identify as Inuit. Nunavut has experienced a significant rise in heterosexual infectious syphilis cases since 2012. Management of communicable diseases, including syphilis, is challenging due to high staff turnover and long delays in specimen transport times. Social determinants of health are also an important contributor. The aim of this study is to describe the epidemiology and program elements for infectious syphilis from 2012-2020 and to highlight beneficial interventions. Methods: Syphilis is a notifiable disease in Nunavut with all cases reported to the Territorial Department of Health. Cases were staged by a medical consultant. Data were analyzed and released in public reports as part of the public health program. Results: From 2012 to 2020, 655 infectious syphilis cases were reported, with 53% of reported cases among females. Infection rates were highest in 20 to 39-year-olds. There was significant variability in reported cases over this time period by geographic region, with the majority of infectious cases reported from the Kivalliq region. Despite 48 reported cases in pregnancy, no confirmed congenital syphilis cases were identified. Program staff identified strengths of the response as well as ongoing needs, such as plain language resources available in multiple languages. Conclusion: Despite the logistical challenges with syphilis management in the territory, the overall outcomes have been positive, with no confirmed congenital cases identified. We attribute this to a coordinated effort by multiple partners including key actions by public health nurses and community health representatives.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".