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 30days 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 <200,000cells/ml to >200,000cells/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 24h 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.

Highlights

  • Mastitis remains one of the most costly endemic diseases to the dairy industry worldwide in terms of production, animal welfare and potential risks to public health (Bradley, 2002; HagnestamNielsen and Ostergaard, 2009; Halasa, 2012)

  • The clinical and subclinical mastitis data for each of the 77 herds were initially checked for completeness and any herds with incomplete records were excluded from the analysis; 73 herds out of the 77 had complete somatic cell count (SCC) data and were used for the SCC analysis and 64 herds out of the 77 had complete CM data and were used for the CM analysis

  • Interventions that were found to be cost-effective in most scenarios were reported resulting in 13 interventions for the CM model and 9 interventions for the SCC model

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Summary

Introduction

Mastitis remains one of the most costly endemic diseases to the dairy industry worldwide in terms of production, animal welfare and potential risks to public health (Bradley, 2002; HagnestamNielsen and Ostergaard, 2009; Halasa, 2012). A potential limitation with risk factor studies is that they cannot always provide evidence of causation and so there remains a large degree of uncertainty as. Intervention studies can provide evidence of causation (Rubin, 2007; Martin, 2013), but there are very few intervention studies that have sought to measure the efficacy of specific mastitis control interventions within a cost-effectiveness framework (Green et al, 2010). If potential interventions are to be prioritised in a rational and evidence-based way, cost-benefit analyses are required that capture the uncertainty of the efficacy of interventions

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