Monitoring Disease
in Dairies
Gregory M. Goodell
The Dairy Authority, LLC
Why is Disease Monitoring Important?
 Basis of sound animal husbandry
 Practical and methodical approach to health in herds
with more than a couple of care-takers
 Identifies trends
 Increases profitability
Overview
 Setting up a Monitoring program
 Data sources and data capture
 Analysis
 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 identification
 Defines the disease
 Decreases case-to-case variability
 Decreases variability when multiple people
identifying disease within a single herd.
 Need to clearly define the cows at risk (denominator)
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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 consistently
 Created based on case definition
 For 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…
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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 monthly
 Production indices such as milk/cow or DMI/cow
monitored daily
Consideration of Data Sources
 Ability to capture data electronically
 Ease of automation and availability
 Accuracy 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 collection
 Use with protocols
Forms- Treatment Cards
Date
Evt.
Area
Code
Remark
Other
Trtmt Drug Dosage Rte
Script
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Code
Remark
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Trtmt Drug Dosage Rte
Script
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Clrm
Clrb
Date
Drug
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TEMP
Date
Drug
TEMP
(Date) Notes:
Pen in
Cow ID
Card #
Forms- Fresh Cow
FRESH COWS
COW ID
Cow/Calf Breed
H=Holstein
B=Brown Swiss
O=Crossbred
J=Jersey
BCS
FRESH
AM\ COW
DATE H/B TIME PM BREED
Calf Presentation
1 = Normal
2 = Backwards
3 = Breach
CALF ID
CALF CALF CALF CALF CALF CALF
BREED PRES DIFF NORM LISTO SIZE
Calf Diff
1 = Unassisted
2 = Slight Assist (1 person)
3 = Moderate Assist (2+ people)
4 = Excessive Assist (3+ people/mech/vet)
5 = Extreme Assist (+mech/c-sect)
Calf Normal
1 = OK
2 = Red/White
3 = Deformed
4 = Other
Calf Listo
1 = OK
2 = DOA
3 = Inj/Alive
4 = Abort
HRD
MN
PEN
Calf Size:
1. <75 lbs
2. 75-84 lbs
3. 85-95 lbs
4. 95-105 lbs
5. >105 lbs
Online Data
 Good for daily observation or spontaneous
monitoring
 Milk and DMI
 Usually 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 is
 Incidence
 # 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
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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% = 1.9
 95% CI = (1.35, 2.67)
 If includes 1 not significant
 If greater then 1 than risk factor adding to dz
 If less than one then risk factor is protective
 Producer 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 incidence
 The additional incidence of disease attributable to
specific risk factor
Attributable Risk Example
 Using previous example
 Incidence 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 factor
 Describes what part of the disease incidence is
associated with the risk factor
 Helps 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
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metabolic events (Milk Fever, DAs and RP). 65 cows
with metabolic disease considered overweight.
PAR = AR x Prevalence
Prevalence 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 / Incidence
 PAF = 7.6% x 25.9% = 29.3%
 Shows us what fraction of the disease occurrence is
associated with the risk factor
 For 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 outbreaks
 Provide top 3-5 risk factors
 Calculate risk statistics for exposed and non-exposed
cows by risk factor
 Evaluate confidence intervals
 Assess biological importance!!
 Calculate economic importance
Attack Rate Table- Mastitis Outbreak
 Mastitis rate has increased by 20% in the past 6
months
 Risk Factors
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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%
102
388
490
20.8%
68
337
405
16.8%
61
315
376
16.2%
Less than 100
DIM
Purchased
Cow
Saw Dust
Bedding
Attack Rate Table- Non-exposed cows
Cows NOT Exposed
Risk Factor Disease
Drylot Pen
Less than
100 DIM
Purchased
Cow
Saw Dust
Bedding
No Disease
Total
Incidence
107
1040
1147
9.3%
270
1279
1549
17.4%
351
1283
1634
21.5%
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%
1.2
3.4%
0.8%
1.5%
0.8
-4.7%
-0.9%
-1.7%
2.1
8.3%
2.7%
4.9%
Less than
100 DIM
Purchased
Cow
Saw Dust
Bedding
Evaluation of Significance
95% Conf. Int.
Risk Factor
Variance
Min.
Max.
Dry lot Pen
0.0162
1.06
1.74
0.0108
0.97
1.46
0.0145
0.62
0.99
0.0288
1.47
2.86
Less than
100 DIM
Purchased
Cow
Saw Dust
Bedding
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 factor
 Purchased 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 200K
 Culture data
 Monthly to look for change in organism type or amount
Mastitis Case Data
 Dairy management software
 Treatment cards
 Allows assessment of duration
 Allows assessment of efficacy
 Clip board
 Place to start if nothing else
 Calculate prevalence/incidence
Quantify Organisms
 Gram positive environmentals
 Gram negative environmentals
 Contagious
 Other
Bulk Tank SCC
 Electronic
 Web page, creamery account
 Weekly reports
 Milk check
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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 pathogen
 Associate SCC with pathogen
 Fresh cow samples
 Mastitis 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 pen
 Recommend putting as few cows as possible in milk
sample
 Ear 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 calves
 If 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
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Sample cows with diarrhea
Utilize rates and incidence to evaluate trend of clinical disease
 Contemporaneous
 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 PCR
 Pooled individual by PCR
 ELISA
 In 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