Spatiotemporal Dynamics and Demographic Profiles of Imported Plasmodium falciparum and Plasmodium vivax Infections in Ontario, Canada (1990–2009)
Bibliographic record
Abstract
We examined malaria cases reported to Ontario's public health surveillance systems from 1990 through 2009 to determine how temporal scale (longitudinal, seasonal), spatial scale (provincial, health unit), and demography (gender, age) contribute to Plasmodium infection in Ontario travellers. Our retrospective study included 4,551 confirmed cases of imported malaria reported throughout Ontario, with additional analysis at the local health unit level (i.e., Ottawa, Peel, and Toronto). During the 20-year period, Plasmodium vivax accounted for 50.6% of all cases, P. falciparum (38.6%), Plasmodium sp. (6.0%), P. ovale (3.1%), and P. malariae (1.8%). During the first ten years of the study (1990-1999), P. vivax (64% of all cases) was the dominant agent, followed by P. falciparum (28%); however, during the second ten years (2000-2009) the situation reversed and P. falciparum (55%) dominated, followed by P. vivax (30%). The prevalence of P. falciparum and P. vivax cases varied spatially (e.g., P. falciparum more prevalent in Toronto, P. vivax more prevalent in Peel), temporally (e.g. P. falciparum incidence increased during the 20-year study), and demographically (e.g. preponderance of male cases). Infection rates per 100,000 international travellers were estimated: rates of infection were 2× higher in males compared to females; rates associated with travel to Africa were 37× higher compared to travel to Asia and 126× higher compared to travel to the Americas; rates of infection were 2.3-3.5× higher in June and July compared to October through March; and rates of infection were highest in those 65-69 years old. Where exposure country was reported, 71% of P. falciparum cases reported exposure in Ghana or Nigeria and 63% of P. vivax cases reported exposure in India. Our study provides insights toward improving pre-travel programs for Ontarians visiting malaria-endemic regions and underscores the changing epidemiology of imported malaria in the province.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".