Incidence of hospitalizations related to Lyme disease and other tick-borne diseases using Discharge Abstract Database, Canada, 2009−2021
Bibliographic record
Abstract
To estimate rates of hospitalizations for tick-borne diseases (TBDs) in Canada, retrospective analysis was conducted to determine the incidence of patients diagnosed with TBDs during their hospital stay in Canada, and describe demographic characteristics, temporal trends and geographic distributions, from 2009 through 2021. Codes from the International Classification of Diseases, Tenth Revision (ICD-10-CA) were used to capture diagnoses of TBDs in the Discharge Abstract Database (DAD) in Canadian hospitals. From 2009 through 2021, 1,626 patients were diagnosed with TBDs during their hospital stay. Of these, 1,457 were diagnosed with Lyme disease (LD), 162 with other TBDs, and seven were diagnosed with more than one TBD. Annual hospitalization counts for LD showed a significant increase from 50 in 2009 to 259 in 2021 (incidence rate per 100,000 population of 0.1 and 0.7, respectively). Epidemiologic patterns for hospitalized LD cases, including increases and variation in annual incidences, seasonality, demographics and geographic distribution, are consistent with those elucidated in national LD surveillance data. Amongst 162 patients diagnosed with other tick-borne diseases, discharge diagnoses were: rickettsiosis (32.7%), spotted fever due to rickettsia rickettsii (23.5%), tularemia (21.0%), babesiosis (8.6%), other tick-borne viral encephalitis (6.2%), tick-borne relapsing fever (4.9%), and Colorado tick fever (0.6%). Annual incidence increased only for rickettsiosis from 3 to 12 patients over the study period. Monitoring the data of hospitalizations using the DAD provided insights into the burden of emerging TBDs, the severity of illnesses and the population most at risk.
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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) | 0.002 | 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".