PofoliaShared via Pofolia

BMC Veterinary Research· 2026Q1

Residency time and other risk factors for large bovine tuberculosis breakdowns in Irish cattle herds

Miriam Casey-Bryars, Jamie A. Tratalos, Andrew William Byrne, Simon J. More et al.

Short summary

Increasing cattle residency times (IRR 3.25) and herd size (IRR 3.20) are the strongest predictors of large bovine tuberculosis (bTB) breakdowns in Irish cattle herds, suggesting cattle-to-cattle transmission within herds amplifies infection.

AI-generated from the title and abstract; the full text is not read.

Key points

  • Longer cattle residency times (IRR 3.25) and larger herd sizes (IRR 3.20) are the strongest predictors of increased bTB case counts in Irish cattle herds.
  • Dairy (IRR 1.42) and fattener (IRR 1.32) herd types have higher bTB case counts compared to beef breeding herds.
  • Risk-based skin tests (IRR 1.52) detected more cases than annual skin testing.
  • Slaughterhouse-initiated breakdowns had lower odds of multiple cases (OR 0.30) compared to annual skin testing.
  • Neighbourhood burden was the least explanatory factor (LRT χ² = 4.5, p = 0.21).

AI-generated from the title and abstract; the full text is not read.

Abstract

Abstract Bovine tuberculosis (bTB) poses challenges to sustainable livestock production and to animal and human health globally. Between 2016 and 2024, annual herd-level incidence of bTB in Irish cattle herds increased from 3.27% to 6.04%. To inform control, we investigated risk factors for large bTB breakdowns (periods of trade restriction and enhanced testing in herds associated with detection of bTB cases), including the length of time cattle resided in herds (residency times). For breakdowns ending between 2012 and 2023 ( N = 37,176 breakdowns in 24,730 herds), we described associations between herd size, residency times, breakdown history, initiating test type, year, inward movements, neighbourhood burden, and case counts of greater than one, four, and ten cases, using logistic regressions. For a subpopulation of 15,834 skin-test-initiated breakdowns, we investigated the same risk factors for increasing standard skin reactor counts using zero-truncated negative binomial regression. Increasing herd size (incidence rate ratio (IRR) 3.20, 95% CI [2.88–3.57] for highest compared to lowest quintile) and cattle residency times (IRR 3.25 [95% CI 2.94–3.59] for highest compared to lowest quintile) are the strongest predictors of increased case counts. Dairy (IRR 1.42 [95% CI 1.31–1.54]) and fattener (IRR 1.32 [95% CI 1.20–1.44]) herd types have higher case counts compared to beef breeding. Risk-based skin tests (IRR 1.52 [95% CI 1.38–1.68]) have more cases compared to annual skin testing. Logistic regression estimates that slaughterhouse-initiated breakdowns have lower odds of having greater than a single case compared to annual skin testing (odds ratio 0.30 [95% CI 0.29–0.32]). When cases detected by interferon-γ testing are excluded, year accounts for less variation within the estimated models. Neighbourhood burden adds the least explanatory ability to our models (Likelihood Ratio Test χ 2 = 4.5, p = 0.21 in negative binomial regression). While herd size and residency times are strongly associated with large case counts, metrics of neighbourhood risk are not. This suggests, following initial introduction of infection, cattle-to-cattle transmission within herds may play an important role in amplifying infections. Preventing introduction of infection to larger herds may assist with control. Consequences of larger breakdowns in fattener herds warrant further research.

The authors' abstract, as published at the source. BMC Veterinary Research, 2026 · DOI ↗

TakeawaysPremium
Ask the paperFree account

Continue with a free account

Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.

Continue free on the web

Sign in with Google or Apple; no card needed. You come back to this paper.

On your phone:

Field: Agronomy and Crop Science

Agronomy and Crop ScienceAgricultural and Biological Sciences