Correlations Between Environmental Factors and Increasing Lyme Disease Incidence in Canada
Bibliographic record
Abstract
Abstract Background Lyme disease (LD) is an emerging infectious disease in Canada due to northward expansion of the geographic range of Ixodes scapularis, the principal tick vector for the LD agent Borrelia burgdorferi, into central and eastern Canada. This study aims to i) summarize the surveillance data for LD cases reported in Canada between 2009 and 2015, ii) identify potential environmental risk factors and iii) develop an acarological risk indicator from passive surveillance for occurrence of human cases. Methods We described the distribution, trends, demographic and clinical characteristics of cases of the disease. Logistic regression models were used to identify risk factors for the occurrence of LD: 1) demographic (age and sex), and 2) environmental (type of forest cover, temperature and abundance of ticks). Passive surveillance data were used to develop an acarological indicator of at-risk areas for LD. Results The number of reported LD cases increased more than six-fold overall, from 144 cases in 2009 to 917 cases in 2015, mainly due to locally acquired infections. LD incidence in Nova Scotia has risen sharply since 2013 and was the highest in Canada over the study period. Children below 15 years and adults of the 55–74 age groups reported highest incidence. Significantly more men than women were infected and men had significantly more symptoms of late disseminated LD than women. Variability in clinical manifestations is observed between provinces, years, for children below 15 years and between age groups. The majority of cases were reported between April and November and there was an increase in risk areas. The abundance of Ixodes scapularis ticks collected on humans and deciduous forest cover were significantly associated with the occurrence of LD cases at the municipality scale. Passive surveillance provides a cost-effective alert tool for public health authorities to timely identify risk areas and target vulnerable populations and front-line health professionals by means of awareness campaigns. Conclusion These findings showed that LD continues to increase in Canada, both over time and geographically, underlining the need to implement better preventive strategies, early disease recognition and treatment and efficient surveillance systems. Disclosures All authors: No reported disclosures.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".