Making Good Decisions on Dry Cow Management to Improve Udder Health - Synthesising Evidence in a...

Making Good Decisions on Dry Cow Management to ImproveUdder Health – Synthesising Evidence in a Bayesian Framework

Martin Green1,2,4, Jon Huxley2,4, Aurelien Madouasse2, William Browne3, Graham Medley5,Andrew Bradley3, Andrew Biggs4, James Breen4, Mark Burnell4, Alistair Hayton4, JamesHusband4, Jon Reader4, Jon Statham4, and Mike Thorne4

1School of Mathematical Science, University of Nottingham, Sutton Bonington Campus, SuttonBonington, LE12 5RD2School of Veterinary Medicine and Science, University of Nottingham, Sutton BoningtonCampus, Sutton Bonington, LE12 5RD3Department of Clinical Veterinary Science, University of Bristol, Langford House, Langford,Bristol, BS40 5DT4“The Nottingham Dairy Herd Health Group”, School of Veterinary Medicine and Science,University of Nottingham, Sutton Bonington Campus, Sutton Bonington, LE12 5RD5 Ecology and Epidemiology Group, Department of Biological Sciences, University of Warwick,Coventry, CV4 7AL, UK

AbstractThe dry period is now recognised as a critical time for the control of clinical and sub-clinicalmastitis in dairy cattle. Infections that occur, or that are not cured, during the dry period oftenresult in clinical mastitis or raised somatic cell counts in early lactation. There is known to belarge variability between herds in the patterns of dry period intramammary infections (IMI) andyet, until recently, there has been no information on farm determinants of the risk of IMI, otherthan in relation to dry cow treatments. In this paper we consider new research on cowcharacteristics, farm facilities and herd management strategies during the dry period in relation toclinical mastitis and raised somatic cell counts (SCC) in early lactation. We then describe, within aBayesian framework, the concept of synthesising existing knowledge with new data to facilitatedecision-making on dry cow management for individual farms.

KeywordsDry period; mastitis; somatic cell count; decision-making; Bayesian analysis; evidence synthesis

INTRODUCTIONThe importance of the dry period in the dynamics of intramammary infections in dairy cowsis well established. Research in the UK over the last ten years has clearly demonstrated theimportance of this period to mammary gland health in the context of UK dairy productionand this has lead to practical recommendations, particularly in the field of preventivetreatments, (Bradley and Green, 2000, Bradley and Green, 2001, Bradley et al., 2002, Berryand Hillerton, 2002, Bradley and Green, 2002, Huxley et al., 2002, Green et al., 2002a,Green et al., 2002b, Bradley and Green, 2005).

Infections present during the dry period are usefully split into those that persist from theprevious lactation (existing infections), and those that enter between the time of drying off

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Published in final edited form as:Cattle Pract. 2008 ; 16: 200–208.

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and calving (new infections), (Bradley and Green, 2004). It is helpful to consider theseinfection processes separately since in terms of prevention, different decisions are needed.Existing infections require decisions relating to effectiveness of a cure whilst new infectionsrequire decisions relating to the effectiveness of preventive strategies. The risk of a new dryperiod IMI is thought to be influenced by:-

- the efficacy of protection from medical interventions (such as internal sealants orantibiotics)

- the rate of exposure to potential pathogens (for example from the housing environmentor during the administration of dry cow therapy)

- factors that affect cow susceptibility to infection (for example anatomical or immunerelated components)

Importantly, large differences have been identified in patterns of IMI during the dry periodbetween farms, and within farms over time, (Cook et al., 2002, Dingwell et al., 2004, Greenet al., 2005). However, reasons for these differences have not been identified or quantified.Differences in dry period IMI will lead to differences in the patterns of clinical mastitis(CM) and SCC in early lactation between farms (Green et al., 2002a). The large variabilitybetween herds in the rate of CM and the proportion of cows with an SCC ≥ 200,000 cell/mLin the first 30 days of lactation is illustrated in Figures 1 to 4.

Differences in dry period infection rates between farms are thought to arise, in part, fromdifferent management policies. This has been the subject of recent research in which cowcharacteristics, farm facilities and herd management strategies during the dry period havebeen investigated to assess their joint influence on the rate of CM and the proportion of SCC≥ 200,000 cell/mL, in early lactation.

OUTLINE OF RECENT STUDIES ON DETERMINANTS OF CLINICALMASTITIS AND HIGH SCC IN EARLY LACTATION

Fifty two commercial dairy farms located throughout England and Wales were included inthe research (Green et al, 2007) and data were collected over a two year period, May 2003 toMay 2005. To characterize farm facilities and management policies, data were obtainedfrom farmer interviews and through on-farm observation, using structured, pre-testedquestionnaires. Information was collected in May or June 2004 (for year one) and again inMay or June 2005 (for year two) and covered details of dry cow facilities and dry cowmanagement. Cow information was obtained from National Milk Records (Chippenham,England). Dates of clinical mastitis cases were obtained directly from farm records. Dryperiods included in the analysis were from cows that were dried off after May 2003 and thatcalved before May 2005.

The outcomes of interest were the incidence of CM (first occurrence in lactation) and themagnitude of cow SCC, both within 30 days of calving. CM was also investigated as a timeto the first occurrence during lactation (discrete time survival response). The data werehighly correlated, consisting of a dry period for each cow (cow-dry period), within a 12month time span on a farm (farm-year), within a farm and conventional multilevel (randomeffects) models (Goldstein, 1995) were specified so that these correlations were accountedfor appropriately. Model building, fit and posterior predictive checks have been describedpreviously (Green et al, 2007).

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Outline of ResultsA total of 8,710 cow-dry periods (6,852 cows) were used in analysis for cows housed for thedry period and 9,964 cow-dry periods (7,576 cows) for cows at pasture. It was notable thatmanagement factors throughout the dry period, from the point of drying off untilimmediately post calving, were linked to CM and raised SCC in early lactation. A summaryof significant factors that influenced clinical mastitis in the first 30 days of lactation arepresented in Figure 5. Results of SCC analysis will be presented at the conference. Factorsthat had no impact on either clinical mastitis or SCC included the length of the dry period(the trend was for dry periods under 50 days to be associated with a higher risk of clinicalmastitis and raised SCC in the following lactation) and specific therapeutic products(although a farm policy of appropriate product selection for different cows was protective).

BAYESIAN EVIDENCE SYNTHESIS WITHIN A DECISION MODELFRAMEWORK

The dry period in relation to udder health is an example of where, as clinicians, we makepreventive medicine decisions based on information from a variety of sources. Aquantitative analysis of multiple (nearly identical) studies is often termed a meta-analysisand is a useful technique for summarising information for use in ‘evidence-based’ medicine.This is particularly appropriate for multiple clinical trial analysis (although unfortunatelythese are rarely available in veterinary medicine) and techniques for this have beenformalised (e.g see The Cochrane Collaboration, www.cochrane.org).

In some circumstances, we may need to move beyond a comparison of similar studies andcombine the evidence of multiple studies that relate to different aspects of a particulardisease process. We may also want to include current ‘expert’ clinical opinion within ourmodels and possibly also assess the likely effect of new research on current clinical beliefs.

The essence of a Bayesian approach is to combine reasonable known evidence/informationof a parameter (prior knowledge) with new research data, to determine a final probability fora parameter of interest (posterior distribution). The combination is carried out using BayesTheorem and effectively weights the new data by the prior information. The method isflexible, is able to include diverse sources of evidence and is particularly useful for allowingpredictions of future events. In this context, the results represent the probability of futureevents, given the prior and new data. The Bayesian approach provides an ideal framework tocarry out such evidence synthesis and has been described in detail within a medical context(Parmigiani, 2002; Spiegelhalter et al., 2004).

In this case we consider the dry period and illustrate (Figure 6) a simplified pathway todescribe the main processes of dry period intramammary infection. To understand and makedecisions relating to this infection process, we need to combine knowledge of the following;

■ the predictive value of SCC for IMI at drying off

■ the cure and new infection rates during the dry period under different treatmentregimes

■ the impact of different management strategies at different times throughout the dry/calving period on the risk of new infection

■ the cost or effort associated with implementing different management strategies

■ the losses that arise from the resultant IMI

Each of the parameters used to describe the infection process during the dry period has someuncertainty associated with it, and we include this, as probability distributions, to assess the

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uncertainty of our model outputs. Using this approach we can make estimates, (for a givenfarm situation, for an ‘average’ farm or for a farm ‘at random’) of the impact of differentdecisions with an associated degree of uncertainty attached.

In a Bayesian context, we can extend this into a decision-based framework and ask questionssuch as, given specific management interventions, “what is the probability of reducingclinical mastitis within 30 days of calving by 20%” or “what is the probability of a return oninvestment from the intervention”. We can combine evidence from experts (if we wish) andliterature as prior distributions, update this with new evidence, determine predictivedistributions for future events and design outputs that facilitate decision-making.

An example of this is provided using a model of the infection process in Figure 6. ‘Expert’clinical opinion for this model was obtained from a group of 9 experienced veterinarysurgeons comprising the Nottingham Dairy Herd Health Group, using described techniques(O'Hagan et al 2006). After amalgamating the group opinions into probability distributionsthese were incorporated into a dry period mastitis model alongside new research evidence.The model can be used to estimate the consequences of new interventions (from researchevidence) in the context of current clinical beliefs. Examples of model outputs are presentedin Figures 7 and 8 and further results will be discussed at the conference.

The uncertainty in estimated parameters reflects uncertainty in the likely effect of differentmanagement interventions in different herd situations. Such variability is seen in practicewhen similar interventions result in different results on different farms. Estimating theexpected variability in an outcome is important and useful since it helps decision-making inthat an informed assessment can be based on resources required (time, money etc), theprobability of improved health and therefore the probability of return on investment.Whether a probable return on investment is acceptable at (say) 50%, 75% or 90% is a farmbased decision that depends in part, on the risk attitude of the farmer and vet involved.However, the aim of this modeling approach is to use a variety of sources of evidence toexamine different ‘what if’ scenarios and thus to enlighten the decision-making process.

SUMMARY: SALIENT POINTS ON THE DRY PERIOD AND MASTITIS■ Clinical mastitis and raised somatic cell counts in the first 30 days of lactation mostlyarise from infections that are present during the dry period.

■ In many herds, clinical mastitis and raised SCC from dry period infections comprisethe majority of the total herd clinical/sub-clinical infections. In these herds, preventionof dry period IMI leads to a huge reduction in mastitis incidence and high SCC cows.Problem herds can be identified by monitoring the incidence rates of clinical mastitisand cows with SCC >199,000 cells/mL in the first 30 days of lactation.

■ New infections that occur during the dry period generally have a much larger impacton clinical and sub-clinical mastitis in early lactation than infections existing at theprevious drying off.

■ Management throughout the dry period (drying off, early dry period, transition dryperiod and calving period) has a crucial influence on dry period IMI. Environment andcow management have a larger influence on clinical and sub-clinical mastitis in earlylactation than choice of dry cow treatment. Therefore a holistic approach to improvingherd management during the dry period will reap the largest rewards for control ofmastitis in early lactation.

■ Short dry periods have not been shown to lead to a reduced incidence of clinical orsub-clinical mastitis in early lactation and they may cause increased problems with

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mammary gland health. Thus, it appears to be ‘how cows are managed’ and not ‘howlong they are dry’ that is important in determining the incidence of clinical and sub-clinical mastitis in early lactation

■ Cure of existing IMI is enhanced with the use of dry cow therapy and selection of anappropriate product for the commonest pathogens present on a farm is beneficial. Curerates of over 80% are possible and the combination use of antibiotics with internal teatsealants will tend to reduce subsequent new infections in ‘cured’ cows.

AcknowledgmentsThis research is funded by the Wellcome Trust (Grant Number WT076998 “Use of Bayesian statistical methods toinvestigate farm management strategies, cow traits and decision- making in the prevention of clinical and sub-clinical mastitis in dairy cows”): Martin Green is a Wellcome Trust Clinical Fellow. We would like to thank theMilk Development Council for funding earlier research and NMR for providing data data.

REFERENCESBerry E, Hillerton JE. The Effect of an Intramammary Teat Seal on New Intramammary Infections.

Journal of Dairy Science. 2002; 85:2512–2520. [PubMed: 12416803]

Bradley AJ, Green MJ. A study of the incidence and significance of intramammary enterobacterialinfections acquired during the dry period. Journal of Dairy Science. 2000; 83:1957–65. [PubMed:11003224]

Bradley AJ, Green MJ. An investigation of the impact of intramammary antibiotic dry cow therapy onclinical coliform mastitis. Journal of Dairy Science. 2001; 84:1632–9. [PubMed: 11467813]

Bradley AJ, Green MJ. An investigation of the impact of intramammary antibiotic dry cow therapy onclinical coliform mastitis. Prakt Tierarzt. 2002; 83:798.

Bradley AJ, Green MJ. The importance of the nonlactating period in the epidemiology ofintramammary infection and strategies for prevention. [Review] Veterinary Clinics of NorthAmerica - Food Animal Practice. 2004; 20:547–68. 2004.

Bradley AJ, Green MJ. Use and interpretation of somatic cell count data in dairy cows. In Pract. 2005;27:310–315.

Bradley AJ, Huxley JN, Green MJ. A Rational Approach to Dry Cow Therapy II - Making LogicalTreatment Decisions. In Pract. 2002; 25:12–17.

Cook NB, Bennett TB, Emery KM, Nordlund KV. Monitoring nonlactating cow intramammaryinfection dynamics using DHI somatic cell count data. J. Dairy Sci. 2002; 85:1119–1126. [PubMed:12086046]

Dingwell RT, Leslie KE, Schukken YH, Sargeant JM, Timms LL, Duffield TF, Keefe GP, Kelton DF,Lissemore KD, Conklin J. Association of cow and quarter-level factors at drying-off with newintramammary infections during the dry period. Preventive Veterinary Medicine. 2004; 63:75–89.[PubMed: 15099718]

Goldstein, H. Multilevel Statistical Models. 2nd edition. Edward Arnold; London, UK: 1995.

Green MJ, Bradley AJ, Medley GF, Browne WJ. Cow, Farm and Management Factors during the DryPeriod that Determine the Rate of Clinical Mastitis after Calving. J. Dairy Sci. 2007; 90:3764–3776. [PubMed: 17638988]

Green MJ, Green LE, Bradley AJ, Burton PR, Schukken YH, Medley GF. Bacterial isolates in the drybovine mammary gland: Prevalence and Associations. Veterinary Record. 2005; 156:71–77.[PubMed: 15689035]

Green MJ, Green LE, Medley GF, Schukken YH, Bradley AJ. Influence of dry period bacterialintramammary infection on clinical mastitis in dairy cows. J Dairy Sci. 2002a; 85:2589–99.[PubMed: 12416812]

Green MJ, Huxley JN, Bradley AJ. A Rational Approach to Dry Cow Therapy I - Background andCurrent Perspectives. In Practice. 2002b; 24:582–587.

Huxley JN, Green MJ, Green LE, Bradley AJ. Evaluation of the efficacy of an internal teat sealerduring the dry period. J Dairy Sci. 2002; 85:551–561. [PubMed: 11949859]

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O'Hagan, A.; Buck, CE.; Daneshkhah, A.; Eiser, R.; Garthwaite, P.; Jenkinson, D.; Oakley, J.; Rakow,T. Uncertain Judgements: Eliciting Experts' Probabilities. John Wiley and sons; Chichester, UK:2006. Statistics in Practice.

Parmigiani, G. Modelling in Medical Decision-Making, A bayesian Approach. John Wiley and sons;Chichester, UK: 2002. Statistics in Practice.

Spiegelhalter, DJ.; Abrams, KR.; Myles, JP. Bayesian Approaches to Clinical Trials and Health-CareEvaluation. John Wiley and sons; Chichester, UK: 2004. Statistics in Practice.

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Figure 1.Incidence rates of clinical mastitis in the first 30 days of lactation, in 103 farm-years in 52UK dairy herds (2003-2005), (after Green et al., 2007).

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Figure 2.Proportion of cows with SCC > 199,000 cell/mL in the first 30 days of lactation in 103farm-years in 52 dairy herds (2003-2005), (after Green et al., 2007).

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Figure 3.Proportion of cows with SCC > 199,000 cell/mL in the first 30 days of lactation, inapproximately 3,500 dairy herds in 2006, (Madouasse, A., PhD data).

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Figure 4.Kaplan-Meier survival curves illustrating the variation in cow ‘survival’ times to the firstcase of clinical mastitis after calving, in each of 103 farm-years, for cattle housed during thedry period (after Green et al, 2007).

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Figure 5.Summary of the major cow, farm and management factors associated with a significantlyREDUCED risk of clinical mastitis (after Green et al., 2007). “rest 4 graze2” is a pasturerotation method such that dry cows graze the pasture for a maximum of 2 weeks followed bystock not grazing the pasture for a minimum of 4 weeks.

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Figure 6.An Illustration of the Infection Processes during the Dry Period.

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Figure 7. The amalgamated probability distribution from 9 experienced dairy vets for theincidence rate of clinical mastitis within 30 days of calving in UK dairy herds(Cases /100 cows /yr; median = 16.9, 95% credibility interval = 6.4 – 33.1)

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Figure 8 (i and ii). Examples of estimated probabilities from a Bayesian model that combinesclinical opinion of the incidence rate of clinical mastitis within 30 days of calving in UK herds(Figure 7) and new research data on the efficacy of different management interventions8i. Use of pasture rotation for dry cows such that cows graze the pasture for a maximum of 2weeks followed by stock not grazing the pasture for a minimum of 4 weeks - compared to anon-rotation policy.a: Probability distribution for the reduction in cases/100 cows/yr of clinical mastitis within30 days of calving (median = 11.0, 95% credibility interval = 1.8 - 25.8), followingimplementation of the pasture rotation policy.b: Probability distribution for a financial saving from reduced clinical mastitis @ £177 perclinical case (£s/100 cows in the herd/yr, median = £1,959, 95% credibility interval = £326 -£4,573) following implementation of the pasture rotation policy.8ii. Use of dry cow treatments selected at cow level - compared to one blanket treatment forthe herda: Probability distribution for the financial saving from the reduced clinical mastitis @ £177per clinical case (£s/100 cows/yr, median = £656, 95% credibility interval = £28 - £1727)b: Probability of a financial saving of at least £5.00 per cow in the herd/yr = 0.65)

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