Local Landscape Change, Personal Practices, and Lyme Disease Risk in Ottawa, Ontario
Bibliographic record
Abstract
In Canada, Lyme disease is an emerging tick-borne illness with a number and extent of risk areas that continue to expand due to the effects of climate change. Nationally, the incidence of Lyme disease increased more than 17-fold since its designation as a notifiable disease in 2009. While Ontario's incidence rate is one of the highest among Canadian provinces, Ottawa was declared an at-risk region for the first time in 2017. Amidst the emergence of reproducing blacklegged tick populations and continued urban expansion of new communities into natural areas throughout this municipal region, the local incidence of Lyme disease has more than tripled. Using the municipal region of Ottawa as a study setting, the objective of my thesis was to examine local Lyme disease risk as a function of environmental hazard and population-level vulnerability. I achieved this by (1) exploring socioeconomic and landscape factors associated with local Lyme disease risk at the neighbourhood scale, (2) identifying population-level characteristics associated with high knowledge and protective behaviour adoption, and (3) examining the Lyme disease transmission risk across a gradient of residential to woodland land use. Count models of Lyme disease cases arising from exposures within patients' home neighbourhoods highlighted associations between forested landscape configurations and elevated local risk. Analysis of survey results identified population-level characteristics and high levels of both knowledge regarding Lyme disease and adoption of preventive and protective behaviours, from which several dynamics among population and exposure risk subgroups emerged. Finally, an analysis of environmental hazard along the residential-woodland gradient in western Ottawa neighbourhoods identified significant risk in the shared ecotone. Together, the results present strong evidence for factors associated with increased potential for human-tick encounters in fine-scale local settings and opportunities for targeted Lyme disease prevention efforts.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 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) | 0.003 | 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".