Assessing human exposure to spotted fever and typhus group rickettsiae in Ontario, Canada (2013–2018): a retrospective, cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Assessing the burden of rickettsial infections in Ontario, Canada, is challenging since rickettsial infections are not reportable to public health. In the absence of reportable disease data, we assessed the burden of rickettsial infections by examining patient serological data and clinical information. METHODS: Our retrospective, cross-sectional study included patients who had Rickettsia serological testing ordered by their physician, in Ontario, from 2013 to 2018. We tested sera from 2755 non-travel patients for antibodies against spotted fever group rickettsiae (SFGR) and typhus group rickettsiae (TGR) using an indirect immunofluorescence assay (IFA) (positive IgG titers ≥1:64). We classified cases using a sensitive surveillance case definition: confirmed (4-fold increase in IgG titers between acute and convalescent sera with clinical evidence of infection), possible (single positive sera with clinical evidence) and previous rickettsial infection (single positive sera without clinical evidence). We classified cases seropositive for both SFGR and TGR as unspecified Rickettsia infections (URIs). RESULTS: Less than 5% of all patients had paired acute and convalescent sera tested, and of these, we found a single, laboratory-confirmed SFGR case, with a 4-fold increase in IgG titers and evidence of fever, maculopapular rash and headache. There were 45 possible (19 SFGR, 7 TGR, 19 URI) and 580 previous rickettsial infection (183 SFGR, 89 TGR, 308 URI) cases. The rate of positive tests for SFGR, TGR and URI combined (all case classifications) were 4.4 per 100,000 population. For confirmed and possible cases, the most common signs and symptoms were fever, headache, gastrointestinal complaints and maculopapular rash. The odds of having seropositive patients increased annually by 30% (odds ratio = 1.3, 95% confidence interval: 1.23-1.39). CONCLUSIONS: The rates of rickettsial infections in Ontario are difficult to determine. Based on confirmed and possible cases, rates are low, but inclusion of previous rickettsial infection cases would indicate higher rates. We highlight the need for education regarding the importance of testing acute and convalescent sera and consistent completion of the laboratory requisition in confirming rickettsial disease. We suggest further research in Ontario to investigate rickettsial agents in potential vectors and clinical studies employing PCR testing of clinical samples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".