Epidemiology of Canine Haemoprotozoan Diseases in Chennai, India
Bibliographic record
Abstract
Background: Haemoprotozoan infections are common in canine in tropical countries. The present work was based on retrospective study of 11,000 blood smears of dogs received over a period of nine years (2010 to 2019) in and around Chennai at Madras Veterinary College Teaching Hospital. Methods: The year-wise incidence, percentage increase year-wise, season-wise, breed-wise and age-wise prevalence and spatial distribution were recorded from the case reports. Diagnosis was made by whole blood and buffy coat smear examination using Geimsa’s stain, wet film examination was done for cases suspected for Trypanosoma sp. and PCR for ruling out Babesia, E.canis and Trypanosoma sp. The collected data were entered into Excel sheets, which were imported and analyzed using Descriptive statistics (frequency and percentage). Result: On blood smear examination 3,844 blood smears were found to be positive for various Haemoprotozoan diseases. Among the recorded positive Haemoprotozoan diseases, the highest incidence was of Ehrlichia canis of 2167 cases (56.37%) followed by Babesia gibsoni with 837 cases (21.77%), Hepatozoan canis with 399 cases (10.37%), Babesia canis with 350 cases (9.10%), Trypanosoma sp. With 46 cases (1.19%), Microfilaria with 45 cases (1.12%). The prevalence of Canine Haemoprotozoan diseases were highest in Non-descript Dogs (ND) with 1948 cases (50.67%), Labrador retriever 1665 cases (43.31%), Spitz 909 cases (23.64%), German Shepherd 543 cases (14.12%) and others 219 cases (5.69%) respectively. Maximum number of cases reported were 43.7% in the age group of 2-6 years followed by 38% cases in 0-2 years, 10.58% cases in 6-10 years and 7.51% cases above 10 years of age. Maximum number of cases were recorded during Monsoon season (June to September) with 1337 cases (34.78%) followed by 1001 cases (26.04%) during Summer season (March to May), 838 cases (21.80%) during winter season (December to February) and 668 cases (17.37%) Autumn season (October and November). The percentage change in occurrence of Canine Haemoprotozoan diseases follow the pattern of 2012 (-31.9%), 2013 (-23.11%), 2014 (+19.3%), 2015 (+39.06%), 2016 (+12.05%), 2017 (+104.11%), 2018 (+22.64%) and 2019 (+16.81%). The spatial distribution of the same diseases was plotted in Chennai geographical map. The epidemiological study would help the veterinary physician to identify the trends in occurrence of disease and clinical pattern followed by the protozoa, which helps in treatment and control of haemoprotozoan diseases in dogs.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".