Prevalence of And Risk Factors for Canine Tick Infestation in The United States, 2002–2004
Bibliographic record
Abstract
Knowledge of the geographic range and seasonal activity of vector ticks is important for determining which people or animals are at risk of acquiring tick-borne infections. Several time-consuming methods requiring large-scale organization are used to map geographic and seasonal variations in tick distribution. A cost-effective, complementary approach to study tick distribution using a large nation-wide veterinary database is described in this paper. Prevalence of canine tick infestation in 40 states was estimated by analyzing electronic medical records of more than 8 million dog visits to Banfield veterinary hospitals in 2002-2004. Prevalence was defined as the proportion of dog visits in which tick infestation was recorded, and was expressed per 10,000 dog visits with 95% confidence intervals (CI). The overall prevalence (CI) of tick infestation was 52 (51, 53) dogs per 10,000 dog visits. Among states, Oklahoma (249 [229, 271) dogs with ticks per 10,000 dog-visits), Arkansas (242 [213, 274]), Connecticut (136 [119, 155]), West Virginia (130 [105, 161]), and Rhode Island (122 [97, 154]) ranked highest in prevalence of canine tick infestation. Overall prevalence peaked from May through July, although monthly prevalence varied by geographic region. In multiple logistic regression, younger dogs, male dogs, and sexually intact dogs, were at increased risk of tick infestation. Toy breeds were least likely to be infested, but no linear pattern of risk was evident with body weight. Identified risk factors should enable veterinarians to prevent tick infestation in pet dogs although differences in risk of tick infestation may be related to outdoor activity of dogs. Feasibility of collecting information for surveillance of vectors ticks on a national level using this large, electronic veterinary database is discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".