P.M. Down
A Bayesian micro-simulation to evaluate the cost-effectiveness of interventions for mastitis control during the dry period in UK dairy herds
Down, P.M.; Bradley, A.J.; Breen, J.E.; Browne, W.J.; Kypraios, T.; Green, M.J.
Authors
Professor ANDREW BRADLEY andrew.bradley@nottingham.ac.uk
PROFESSOR OF DAIRY HERD HEALTH AND PRODUCTION
Dr JAMES BREEN JAMES.BREEN@NOTTINGHAM.AC.UK
CLINICAL ASSOCIATE PROFESSOR
W.J. Browne
Professor THEODORE KYPRAIOS THEODORE.KYPRAIOS@NOTTINGHAM.AC.UK
PROFESSOR OF STATISTICS
M.J. Green
Abstract
Importance of the dry period with respect to mastitis control is now well established although the precise interventions that reduce the risk of acquiring intramammary infections during this time are not clearly understood. There are very few intervention studies that have measured the clinical efficacy of specific mastitis interventions within a cost-effectiveness framework so there remains a large degree of uncertainty about the impact of a specific intervention and its costeffectiveness. The aim of this study was to use a Bayesian framework to investigate the cost-effectiveness of mastitis controls during the dry period. Data were assimilated from 77 UK dairy farms that participated in a British national mastitis control programme during 2009–2012 in which the majority of intramammary infections were acquired during the dry period. The data consisted of clinical mastitis (CM) and somatic cell count (SCC) records, herd management practices and details of interventions that were implemented by the farmer as part of the control plan. The outcomes used to measure the effectiveness of the interventions were i) changes in the incidence rate of clinical mastitis during the first 30 days after calving and ii) the rate at which cows gained new infections during the dry period (measured by SCC changes across the dry period from <200,000 cells/ml to >200,000 cells/ml). A Bayesian one-step microsimulation model was constructed such that posterior predictions from the model incorporated uncertainty in all parameters. The incremental net benefit was calculated across 10,000 Markov chain Monte Carlo iterations, to estimate the cost-benefit (and associated uncertainty) of each mastitis intervention. Interventions identified as being cost-effective in most circumstances included selecting dry-cow therapy at the cow level, dry-cow rations formulated by a qualified nutritionist, use of individual calving pens, first milking cows within 24 h of calving and spreading bedding evenly in dry-cow yards. The results of this study highlighted the efficacy of specific mastitis interventions in UK conditions which, when incorporated into a costeffectiveness framework, can be used to optimize decision making in mastitis control. This intervention study provides an example of how an intuitive and clinically useful Bayesian approach can be used to form the basis of an on-farm decision support tool.
Citation
Down, P., Bradley, A., Breen, J., Browne, W., Kypraios, T., & Green, M. (2016). A Bayesian micro-simulation to evaluate the cost-effectiveness of interventions for mastitis control during the dry period in UK dairy herds. Preventive Veterinary Medicine, 133, 64-72. https://doi.org/10.1016/j.prevetmed.2016.09.012
Journal Article Type | Article |
---|---|
Acceptance Date | Sep 13, 2016 |
Online Publication Date | Sep 14, 2016 |
Publication Date | Oct 1, 2016 |
Deposit Date | Oct 20, 2016 |
Publicly Available Date | Oct 20, 2016 |
Journal | Preventive Veterinary Medicine |
Print ISSN | 0167-5877 |
Electronic ISSN | 1873-1716 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 133 |
Pages | 64-72 |
DOI | https://doi.org/10.1016/j.prevetmed.2016.09.012 |
Keywords | Dairy cow; Mastitis control; Bayesian; Cost-effectiveness; Decision making; Dry-period |
Public URL | https://nottingham-repository.worktribe.com/output/807827 |
Publisher URL | http://www.sciencedirect.com/science/article/pii/S0167587716303579 |
Contract Date | Oct 20, 2016 |
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Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0
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