The prevalence and risk factors for human Brucella species infection in a cross-sectional survey of a rural population in Punjab, India
Bibliographic record
Abstract
BACKGROUND: Brucellosis is an important neglected zoonosis. Effective cattle vaccines are available but are infrequently used in India, where rural households commonly own one or two cattle as sources of protein and income. We assessed the prevalence of infection and risk factors in humans. METHODS: We conducted a cross-sectional sero-survey in randomly selected individuals in 60 villages in Punjab. Infection prevalence was assessed by positive Rose Bengal testing or immunoglobulin G enzyme-linked immunosorbent assay. Risk factors were adjusted for potential confounding using multivariable analyses. RESULTS: Of the 1927 subjects who were approached, 93% agreed to participate. Age-standardised prevalence for Brucella infection was 2.24% (95% confidence interval [CI] 1.61 to 3.11). More than 60% of households kept cattle and 10% assisted with calving or abortions. Nearly all individuals consumed boiled cow/buffalo milk from their own or neighbours' cattle and 3.3% consumed goat's milk. There was a 2.18 times increased odds (95% CI 0.96 to 4.95) of infection with calving/abortions and a 4.26 times increased odds (95% CI 1.33 to 13.6) with goat's milk but not bovine milk consumption. CONCLUSIONS: An association with calving/abortions and goat's milk consumption was seen. Brucella vaccination of household livestock would reduce the risk to humans in such settings. Additional measures include biosecurity training around calving/abortions, education to boil all milk and for healthcare workers to test for brucellosis.
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 | 0.001 | 0.000 |
| 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".