Perspective: Incidence of Clinician-Diagnosed Lyme Disease in Manitoba, Canada 2009–2018
Bibliographic record
Abstract
Introduction: Lyme disease (LD) surveillance yields useful information to monitor the disease trends and spatial distribution. However, due to several factors, the Manitoba Health surveillance system, as with other systems, could be subject to underreporting. Objectives: To estimate the number and incidence of clinician-diagnosed LD over the study period in Manitoba, describe the epidemiology of clinician-diagnosed LD, and compare the findings with Manitoba Health LD surveillance data during the same period to estimate the extent of underreporting. Methods: A retrospective analysis of administrative health data was performed to calculate the number and incidence of clinician-diagnosed LD from 2009 to 2018 in Manitoba and describe the epidemiological characteristics using the International Classification of Diseases , Tenth and Ninth Revision (ICD-10-CA and ICD-9-CM) codes, and antimicrobial drug prescriptions. Conclusion: Of the 1,629,698 registrants within the Manitoba Health Insurance Registry followed over 10 years, 1658 LD events were identified. Most of the cases occurred from May to July and corresponded to the peak activity of the nymphal stage of the blacklegged tick in the province. LD events presented a bimodal distribution with a peak in children between 5 and 9 years of age for both sexes, and a peak in adults from 65 to 84 and from 50 to 74, respectively, for males and females. We estimated that an average of 165 LD events occurred annually in Manitoba (mean annual incidence of 10.17 per 100,000 population), compared with 30 cases per year reported in the surveillance system; therefore, the LD surveillance yields an important underreporting.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| 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) | 0.005 | 0.001 |
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".