Seroprevalence of Babesia bigeminain smallholder dairy cattle in Tanzania and associated risk factors
Bibliographic record
Abstract
Variations in the seroprevalence of antibody to Babesia bigemina infection by farm and animal level risk factors were investigated for 2 contrasting regions of Tanga and Iringa in Tanzania. Tanga is situated in the eastern part of the country and has typical tropical coast climate while Iringa is situated in the Southern Highlands and has a tropical highland climate. Two hundred farms from each region were selected using simple random sampling procedure and visited once between January 1999 and April 1999. Blood samples were collected from 1329 smallholder dairy animals on selected farms for harvesting serum which was subsequently used for serodiagnosis of B. bigemina using an indirect enzyme linked immuno-sorbent assay (ELISA). Of the 1329 sera samples screened, 34.9 % were positive for B. bigemina. The prevalence was higher in Iringa Region [43%, 95% confidence intervals (CI) = 39.5-47.3] than in Tanga Region (27%, CI = 23.6-30.5). Using a logistic binomial regression model as an analytical method for predicting the likelihood of animal seropositivity, we found (in both regions) that the risk of positive reaction varied with the animal's age, history of grazing and geographical location. Seroprevalence increased with age (beta = 0.01 and 0.01 per year of age, P < 0.005 in Tanga and Iringa, respectively). Animals located in Lushoto and Iringa urban district were associated with increased risk of seropositivity [Odds ratio (OR) = 4.24, P = 0.001, for Lushoto, and OR = 1.81, P = 0.040, for Iringa Urban, respectively). Animals grazed 3 months prior to sampling had higher odds for seropositivity than zero/semi-grazed, despite farmer-reported high frequency of tick control (OR = 2.71, P = 0.0087, for Tanga, and OR = 4.53, P = 0.001, for Iringa). Our study suggests that even though herd sizes are small, B. bigemina infection is widespread in many smallholder dairy farms and endemic stability with respect to this disease has not yet been attained, but the observed levels are sufficiently high to ensure that clinical disease would be a risk.
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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 | 0.002 |
| 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".