Healthcare costs and outcomes associated with laboratory-confirmed Lyme disease in Ontario, Canada: A population-based cohort study
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to estimate the economic burden attributable to laboratory-confirmed Lyme disease (LD) in Ontario, Canada and assess health outcomes associated with LD. METHOD: We conducted a cohort study using laboratory-confirmed LD cases accrued between 2006 and 2018. The exposed cohort was matched 1:3 to the unexposed cohort using a combination of hard and propensity score matching. We used phase-of-care costing methods to calculate attributable costs for four phases of illness: pre-diagnosis, acute care, post-acute care, and continuing care in 2018 Canadian dollars. We used ICD-10-CA and OHIP billing codes to identify emergency department visits, physician billings and hospitalizations related to LD sequelae to assess health outcomes. RESULTS: A total of 2,808 cases were identified with a mean age of 46.5 (20.7) years and 44% female. Within 30-days, 404 (14.3%) cases required an ED visit and 63 (2.4%) cases required hospitalization. The mean (95% CI) total costs for LD cases in pre-diagnosis, acute, and post-acute care phases were $209 ($181, 238), $1,084 ($956, $1,212), and $1,714 ($1,499, $1,927), respectively. The highest mean attributable 10-day cost was $275 ($231, $319) during acute care. At 1-year post-infection, LD increased the relative risk of nerve palsies by 62 (20, 197), and polyneuropathy by 24 (3.0, 190). LD resulted in 16 Lyme meningitis events vs. 0 events in the unexposed. CONCLUSION: Individuals with laboratory-confirmed LD have increased healthcare resource use pre-diagnosis and up to six months post-diagnosis, and were more likely to seek healthcare services related to LD sequelae.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".