The continued rise of Lyme disease in Ontario, Canada: 2017
Bibliographic record
Abstract
BACKGROUND: . Climate change has contributed to the expansion of the geographic range of blacklegged ticks in Ontario, increasing the risk of Lyme disease for Ontarians. OBJECTIVE: To identify the number of cases and incidence rates, as well as the geographic, seasonal and demographic distribution of Lyme disease cases reported in Ontario in 2017, with comparisons to historical trends. METHODS: Data for confirmed and probable Lyme disease cases with episode dates from January 1, 2012, through December 31, 2017, were extracted from the integrated Public Health Information System (iPHIS). Data included public health unit (PHU) of residence, episode date, age and sex. Population data from Statistics Canada were used to calculate provincial and PHU-specific incidence rates per 100,000 population. The number of cases reported in 2017 by PHU of residence, month of occurrence, age and sex was compared to the 5-year averages for the period 2012-2016. RESULTS: There were 959 probable and confirmed cases of Lyme disease reported in Ontario in 2017. This was three times higher than the 5-year (2012-2016) average of 313. The provincial incidence rate for 2017 was 6.7 cases per 100,000 population, although this varied markedly by PHU. The highest incidence rates were found in Leeds-Grenville and Lanark District (128.8 cases per 100,000), Kingston-Frontenac, Lennox and Addington (87.2 cases per 100,000), Hastings and Prince Edward Counties (28.6 cases per 100,000), Ottawa (18.1 cases per 100,000) and Eastern Ontario (13.5 cases per 100,000). Cases occurred mostly from June through September, were most common among males, and those aged 5-14 and 50-69 years. CONCLUSION: In 2017, Lyme disease incidence showed a marked increase in Ontario, especially in the eastern part of the province. If current weather and climate trends continue, blacklegged ticks carrying tick-borne pathogens, such as those causing Lyme disease, will continue to spread into suitable habitat. Monitoring the extent of this geographic spread will inform future clinical and public health actions to detect and mitigate the impact of Lyme disease in Ontario.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".