Monitoring Disease in Dairies

Gregory M. GoodellThe Dairy Authority, LLC

Why is Disease Monitoring Important?

Basis of sound animal husbandryPractical and methodical approach to health

in herds with more than a couple of care-takers

Identifies trendsIncreases profitability

Overview

Setting up a Monitoring programData sources and data captureAnalysis

Graphs and numbers Risk calculations Attack rate tables

Types of Monitoring

Contemporaneous monitoring Common health events such as mastitis, pneumonia,

diarrhea, etc. Done to identify health trends in a herd Identify problems as they arise

Spontaneous monitoring NEFA, BHB, Rumen taps Done to rule-in/out specific disease Not performed on a routine basis

Monitoring

The veterinarian must combine the health of the cow, ability of the farm personnel to identify disease and the most prevalent presentation of the disease with goals of the dairy in order to define the case definition and create the protocols that go along with the case definition.

Case Definition

Fundamental basis of disease identificationDefines the diseaseDecreases case-to-case variabilityDecreases variability when multiple people

identifying disease within a single herd.Need to clearly define the cows at risk

(denominator) Do we include dead/sold cows Dry cows? Calves?

Case Definition for Retained Placenta

Is a placenta retained at 12 hrs? 24 hr? or 48 hrs?

Numerator Include RPs found only fresh cows? What about

aborted cows?Denominator

Based on trend trying to identify. Typically fresh cow diseases are defined only in fresh

cows Fresh cow defined as calving at 1 month or less.

Protocols

Protocols required to treat cows consistentlyCreated based on case definitionFor example a treatment protocol for a cow

that has one flake identified in her milk will be different than a protocol for a cow laterally recumbent from mastitis.

Protocols

Monitoring response to treatment is big part of monitoring

Answers… Does treatment work? What is disease recurrence? Treatment cost.

Frequency of Monitoring

Frequency of observations or the time allowed in the denominator is a compromise between time enough to get accurate numbers yet soon enough to intervene when change is needed.

Diseases typically weekly or monthlyProduction indices such as milk/cow or

DMI/cow monitored daily

Consideration of Data Sources

Ability to capture data electronicallyEase of automation and availabilityAccuracy and dependability of data source.

3 Places for Data Capture

Off-Farm (Coop, DHIA, DLab)On-Farm (cow counting, event counting,

treatment cards, clip boards)Online computerized data (milk meters,

conductivity, temperatures, podometers)

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On Farm

Create forms for data collectionUse with protocols

Forms- Treatment Cards

Evt. Area Other

Date Code Remark Trtmt Drug Dosage Rte Script Clrm Clrb

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Evt. Area Other

Date Code Remark Trtmt Drug Dosage Rte Script Clrm Clrb

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Drug

TEMP

Date

Drug

TEMP

Pen in Card #

AM PM

AM PM

AM PM AM PM AM PM AM PM AM PM AM PM AM PM

Cow ID

AM PM

(Date) Notes:

AM PM AM PM AM PM AM PM AM PM AM PM AM PM AM PM

FRESH COWS

COW ID BCS

FRESH DATE H/B TIME

AM\ PM

COW BREED CALF ID

CALF BREED

CALF PRES

CALF DIFF

CALF NORM

CALF LISTO

CALF SIZE

HRD MN PEN

Cow/Calf Breed Calf Presentation Calf Diff Calf Normal Calf Listo Calf Size:

H=Holstein 1 = Normal 1 = Unassisted 1 = OK 1 = OK 1. <75 lbs

B=Brown Swiss 2 = Backwards 2 = Slight Assist (1 person) 2 = Red/White 2 = DOA 2. 75-84 lbs

O=Crossbred 3 = Breach 3 = Moderate Assist (2+ people) 3 = Deformed 3 = Inj/Alive 3. 85-95 lbs

J=Jersey 4 = Excessive Assist (3+ people/mech/vet) 4 = Other 4 = Abort 4. 95-105 lbs5 = Extreme Assist (+mech/c-sect) 5. >105 lbs

Forms- Fresh Cow

Online Data

Good for daily observation or spontaneous monitoring

Milk and DMIUsually individual cow observations

Graphs and Numbers

Numbers more definitive Counts, averages, rates Rates the best

Graphs good as quick tool Draw gross observations Helpful but can be misleading in general observations Excellent for demonstrating derived numbers

Combo often the best for producer

Using the data

Raw counts Easier for lay personnel to understand Easy to calculate Ie: how many milk fevers were there last week?

Percents Most common Often more meaningful especially for disease Defining time can provide disease incidence rates

helpful for goal setting.

Herd Level Proportions

Prevalence Snapshot in time Good for broad assessment Answers how well we’ve done or how bad the problem

isIncidence

# cases/# lactating cows over a specific period Best number to look at Adjusts for seasonality

Analyze Trends Through Advanced Techniques

Risk Assessment Relative Risk Attributable Risk Population Attributable Risk Population Attributable Fraction

Attack Rate Table

Relative Risk

Incidence of disease for individuals exposed to risk factor divided by Incidence of disease for individuals not exposed to risk factor

An index of strength of the association between the risk factor and the disease

Calculate Confidence Interval (CI). If it contains 1 then it is not significant.

95% CI is best. 90% CI is okay.

Relative Risk Example

Do dry cows considered to be over conditioned have more metabolic issues?

405 cows calved in the last 30 days with 105 metabolic events (Milk Fever, Das and RP). 65 cows with metabolic disease considered overweight. There were 187 total cows considered overweight.

Incidence in fat cows = 65/187 = 34.8%Incidence in normal cows = 40/218 = 18.3%Relative Risk = 34.8% / 18.3% = 1.9

Relative Risk Example

Relative Risk = 34.8% / 18.3% = 1.995% CI = (1.35, 2.67)If includes 1 not significantIf greater then 1 than risk factor adding to dzIf less than one then risk factor is protectiveProducer interpretation: A overweight cow is

90% more likely to experience a metabolic event than a cow that is not over conditioned.

Attributable Risk

Incidence of disease for individuals exposed to risk factor MINUS Incidence of disease for individuals not exposed to risk factor

Removes background incidenceThe additional incidence of disease

attributable to specific risk factor

Attributable Risk Example

Using previous exampleIncidence of exposed was 34.8%Incidence of non-exposed is 18.3%AR = 34.8% - 18.3% = 16.4%What’s meaningful for the producer is that

16.5% of metabolic events are due to overweight cows.

Population Attributable Risk

Attributable Risk x prevalence of risk factorDescribes what part of the disease incidence

is associated with the risk factorHelps us decide on how impactful the risk

factor is on the herd. Using same example then…

Population Attributable Risk

405 cows calved in the last 30 days with 105 metabolic events (Milk Fever, DAs and RP). 65 cows with metabolic disease considered overweight.

PAR = AR x PrevalencePrevalence of the risk factor= 187 / 405 =

46.2%PAR = 46.2% x 16.5% = 7.6%Important to the producer: 7.6% of your herd

will suffer from metabolic disease due to obese dry cows

Population Attributable Fraction

Population Attributable Risk divided by total incidence of disease in population

Predicts proportion of disease eliminated through control of risk factor

Usually used when more than 1 risk factor present

Population Attributable Fraction

PAF = PAR / IncidencePAF = 7.6% x 25.9% = 29.3%Shows us what fraction of the disease

occurrence is associated with the risk factorFor producer then we can say that rate of

metabolic disease will be reduced by 29.3% if we eliminate obese cows

Attack Rate Tables

Used in acute outbreaksProvide top 3-5 risk factorsCalculate risk statistics for exposed and non-

exposed cows by risk factorEvaluate confidence intervalsAssess biological importance!!Calculate economic importance

Attack Rate Table- Mastitis Outbreak

Mastitis rate has increased by 20% in the past 6 months

Risk Factors Dry lot pen Early lactation cows (<100 DIM) Purchased cows Saw dust bedding

Attack Rate Table- Exposed Cows

Exposed Cows

Risk Factor Disease No Disease Total Incidence

Drylot Pen 113 779 892 12.7%

Less than 100 DIM 102 388 490 20.8%

Purchased Cow 68 337 405 16.8%

Saw Dust Bedding 61 315 376 16.2%

Attack Rate Table- Non-exposed cows

Cows NOT Exposed

Risk Factor Disease No Disease Total Incidence

Drylot Pen 107 1040 1147 9.3%

Less than 100 DIM 270 1279 1549 17.4%

Purchased Cow 351 1283 1634 21.5%

Saw Dust Bedding 61 710 771 7.9%

Attack Rate Table Risk Calculations

Risk Calculations

Risk Factor RR AR PAR PAF

Drylot Pen 1.4 3.3% 1.5% 2.6%

Less than 100 DIM 1.2 3.4% 0.8% 1.5%

Purchased Cow 0.8 -4.7% -0.9% -1.7%

Saw Dust Bedding 2.1 8.3% 2.7% 4.9%

Evaluation of Significance

95% Conf. Int.

Risk Factor Variance Min. Max.

Dry lot Pen 0.0162 1.06 1.74Less than 100 DIM 0.0108 0.97 1.46

Purchased Cow 0.0145 0.62 0.99

Saw Dust Bedding 0.0288 1.47 2.86

Interpretation

Dry lot pen has contributed to the mastitis rate Indicates we will eliminate 2% of mastitis rate

Cows less than 100 DIM is not a risk factorPurchased cows- risk analysis says that this is

protective. Biological significance? New cows probably haven’t been exposed to facility

long enough.Sawdust bedding has highest PAF (population

attributable fraction). Indicates we will eliminate 5% of mastitis rate

End of Stats Lesson

Advanced techniques very helpful when multiple risk factors present

Useful to show strength on how much relief a risk factor may provide

Helps convince producer (and you) of the importance of the risk factor

Can assess economics to the decision

Questions?

Mastitis Monitoring

Cases of Mastitis Measured per month (incidence) All cases in herd (prevalence)

Bulk Tank Somatic Cell data Weekly to observe for trends

Individual Somatic Cell data Monthly/quarterly to look at % lactating cows below

200KCulture data

Monthly to look for change in organism type or amount

Mastitis Case Data

Dairy management softwareTreatment cards

Allows assessment of duration Allows assessment of efficacy

Clip board Place to start if nothing else Calculate prevalence/incidence

Quantify Organisms

Gram positive environmentalsGram negative environmentalsContagiousOther

Bulk Tank SCC

Electronic Web page, creamery account

Weekly reportsMilk check

Bulk Tank SCC

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Individual Cow Data

SCC DHIA services and other labs Collect and send Cowside

California Mastitis Test (CMT) Individual quarters

Electrical Conductivity

Culture Data

Most prevalent pathogenAssociate SCC with pathogenFresh cow samplesMastitis cow samples

Contemporaneous Monitoring for BVD

Routine testing best BVD monitoring program Tested the first week of life with an individual test

(ACE or IHC) See 0.05-0.1% in our practice Individual herds as high as 0.5% Enough to cause problems Test both bulls and heifers Euthanize positives

Spontaneous BVD Monitoring

PCR on milk samples by lactating penRecommend putting as few cows as possible

in milk sampleEar notch aborted and DOA calves

Spontaneous BVD Monitoring

Advantages Less expensive

Disadvantages Will not identify BVD very quickly Often missed since only notching aborted and DOA

calvesIf BVD present result is increase in

generalized disease rates especially in youngstock

Johnes Monitoring

Many herds take long term approach of test and manage

Spontaneous monitoring Sample cows with diarrhea Utilize rates and incidence to evaluate trend of clinical

diseaseContemporaneous

All cows sampled at dry off Manage positive cows separately Utilize rates and incidence to evaluate trend of sub

clinical disease

Test Methods

Pooled fecal by PCRPooled individual by PCRELISAIn our practice most dairies will conduct long

term monitoring by sampling cows at dry off using ELISA

Clinical cows are culled and non-clinical positive cows managed separately