An extensive re-evaluation of evidence and analyses of the Randomised Badger Culling Trial (RBCT) I: Within proactive culling areas

Here, in the first of two investigations, we evaluate and extend the analyses of the Randomised Badger Culling Trial (RBCT) to estimate the effectiveness of proactive badger culling for reducing incidence of tuberculosis (TB) in cattle within culling areas. Using previously reviewed, publicly available data, alongside frequentist and Bayesian approaches, we re-estimate culling effects for confirmed incidence of herd breakdowns (TB incidents in cattle) within proactive culling areas. We appraise the varying assumptions and statistical structures of individual models to determine model appropriateness. Our re-evaluation of frequentist models provides results consistent with peer-reviewed analyses of RBCT data, due to the consistency of beneficial effects across three analysis periods. Furthermore, well-fitting Bayesian models with weakly informative prior distribution assumptions produce high probabilities (91.2%-99.5%) of beneficial effects of proactive culling on confirmed herd breakdowns within culling areas in the period from the initial culls (between 1998 and 2002) until 2005. Similarly high probabilities of beneficial effects were observed post-trial (from 1 year after last culls until March 2013). Thus, irrespective of statistical approach or study period, we estimate substantial beneficial effects of proactive culling within culling areas, consistent with separate, existing, peer-reviewed analyses of the RBCT data.

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Publication Details

Journal
Royal Society Open Science
Published
2024-08-01
DOI
https://doi.org/10.1098/rsos.240385
Citations
6
Primary Topic
Microbial infections and disease research
Type
article
Field-Weighted Citation Impact
2.07

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article

An extensive re-evaluation of evidence and analyses of the Randomised Badger Culling Trial (RBCT) I: Within proactive culling areas

Christl Ann Donnelly, Roșie Woodroffe, Cathal Mills
6 citations
Royal Society Open Science
Microbial infections and disease research
2.07
article

An extensive re-evaluation of evidence and analyses of the Randomised Badger Culling Trial (RBCT) I: Within proactive culling areas

Christl Ann Donnelly, Roșie Woodroffe, Cathal Mills
article en
6 citations

Abstract

Here, in the first of two investigations, we evaluate and extend the analyses of the Randomised Badger Culling Trial (RBCT) to estimate the effectiveness of proactive badger culling for reducing incidence of tuberculosis (TB) in cattle within culling areas. Using previously reviewed, publicly available data, alongside frequentist and Bayesian approaches, we re-estimate culling effects for confirmed incidence of herd breakdowns (TB incidents in cattle) within proactive culling areas. We appraise the varying assumptions and statistical structures of individual models to determine model appropriateness. Our re-evaluation of frequentist models provides results consistent with peer-reviewed analyses of RBCT data, due to the consistency of beneficial effects across three analysis periods. Furthermore, well-fitting Bayesian models with weakly informative prior distribution assumptions produce high probabilities (91.2%-99.5%) of beneficial effects of proactive culling on confirmed herd breakdowns within culling areas in the period from the initial culls (between 1998 and 2002) until 2005. Similarly high probabilities of beneficial effects were observed post-trial (from 1 year after last culls until March 2013). Thus, irrespective of statistical approach or study period, we estimate substantial beneficial effects of proactive culling within culling areas, consistent with separate, existing, peer-reviewed analyses of the RBCT data.

Royal Society Open ScienceVol. 11(8)
Zoological Society of London (GB), University of Oxford (GB)
National Institute for Health Research Health Protection Research Unit, National Institute for Health and Care Research, Public Health England, Engineering and Physical Sciences Research Council
Good health and well-being
Openalex Percentile: Top 14%
Microbial infections and disease research
2.07
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