Rurality, Socioeconomic Status, and Residence in Environmental Risk Areas Associated with Increased Lyme Disease Incidence in Ontario, Canada: A Case-Control Study
Bibliographic record
Abstract
Background: Lyme disease (LD) is the most common tick-borne illness in North America. LD is acquired through exposure to the tick vector, Ixodes scapularis , known as the blacklegged tick. In Canada, LD is rapidly emerging, with the establishment of I. scapularis in many newly endemic regions posing a growing risk to local communities. In the Canadian context, many environmental and socioeconomic risk factors for human LD infection are yet to be ascertained and the degree of risk associated with residential and community exposure to ticks is not well known. Methods: We conducted a matched case-control study in southeastern Ontario, using LD patient data from provincial laboratory databases and uninfected population controls from 2014 to 2018. We aimed to identify area-level risk factors for LD and associations with residence in environmental risk areas, defined as areas with high model-predicted probability of I. scapularis occurrence, using the neighborhood dissemination area as the unit of analysis. Results: Using multivariable conditional logistic regression analysis, we identified that patients with LD had higher odds (odds ratio, OR; 95% confidence interval, CI) of living in neighborhoods with high probability of tick occurrence in the environment (OR = 2.2; 95% CI: 2.0–2.5), low walkability (OR = 1.6; 95% CI: 1.2–2.1), low material deprivation (OR = 1.4; 95% CI: 1.2–1.7), and low ethnic concentration (OR = 8.1; 95% CI: 6.7–9.9). We also found that the odds of LD infection for individuals residing in environmental risk areas was highest for those living in public health units (PHUs) with <250,000 population (OR = 3.0; 95% CI: 2.4–3.9) compared to those living in PHUs with >1,000,000 population (OR = 1.5; 95% CI: 1.1–2.1). Conclusion: This study shows that odds of human LD infection in Ontario, Canada is higher in less urbanized areas with higher socioeconomic status and indicates that exposure to ticks around the home residence or neighborhood is linked to increased odds of LD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".