1. Monitoring Disease in Dairies
Gregory M. Goodell
The Dairy Authority, LLC

2. 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

3. Overview Setting up a Monitoring program Data sources and data capture
Analysis
Graphs and numbers
Risk calculations
Attack rate tables

4. Types of Monitoring Contemporaneous monitoring Spontaneous 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

5. 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.

6. 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)
Do we include dead/sold cows
Dry cows?
Calves?

7. 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.

8. 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.

9. Protocols Monitoring response to treatment is big part of monitoring
Answers…
Does treatment work?
What is disease recurrence?
Treatment cost.

10. 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

11. Consideration of Data Sources
Ability to capture data electronically
Ease of automation and availability
Accuracy and dependability of data source.

12. 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)

16. On Farm
Create forms for data collection
Use with protocols

17. Forms- Treatment Cards

18. Forms- Fresh Cow

19. Forms- Removal

22. Online Data Good for daily observation or spontaneous monitoring
Milk and DMI
Usually individual cow observations

24. Graphs and Numbers Numbers more definitive Graphs good as quick tool
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

25. Using the data Raw counts Percents
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.

26. 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

27. Analyze Trends Through Advanced Techniques
Risk Assessment
Relative Risk
Attributable Risk
Population Attributable Risk
Population Attributable Fraction
Attack Rate Table

28. 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.

29. 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

30. 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.

31. 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

32. Attributable Risk Example
Using previous example
Incidence of exposed was 34.8%
Incidence of non-exposed is 18.3%
AR = 34.8% % = 16.4%
What’s meaningful for the producer is that 16.5% of metabolic events are due to overweight cows.

33. 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…

34. 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 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

35. 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

36. 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

37. 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

38. 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

39. Attack Rate Table- 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%

40. 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%

41. Attack Rate Table 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%
Purchased Cow
0.8
-4.7%
-0.9%
-1.7%
Saw Dust Bedding
2.1
8.3%
2.7%
4.9%

42. Evaluation of Significance
95% Conf. Int.
Risk Factor
Variance
Min.
Max.
Dry lot Pen
0.0162
1.06
1.74
Less 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

43. 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

44. 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

45. Questions?

46. Mastitis Monitoring Cases of Mastitis Bulk Tank Somatic Cell data
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

47. 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

48. Quantify Organisms Gram positive environmentals
Gram negative environmentals
Contagious
Other

49. Bulk Tank SCC Electronic Weekly reports Milk check
Web page, creamery account
Weekly reports
Milk check

51. Individual Cow Data SCC California Mastitis Test (CMT)
DHIA services and other labs
Collect and send
Cowside
California Mastitis Test (CMT)
Individual quarters
Electrical Conductivity

52. Culture Data Most prevalent pathogen Associate SCC with pathogen
Fresh cow samples
Mastitis cow samples

53. Contemporaneous Monitoring for BVD
Routine testing best BVD monitoring program
Tested the first week of life with an individual test (ACE or IHC)
See % in our practice
Individual herds as high as 0.5%
Enough to cause problems
Test both bulls and heifers
Euthanize positives

54. 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

55. 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

56. 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 disease
Contemporaneous
All cows sampled at dry off
Manage positive cows separately
Utilize rates and incidence to evaluate trend of sub clinical disease

57. 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