Monitoring Disease in Dairies

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

21. 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

22. 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.

23. 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

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

25. 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.

26. 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

27. 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.

28. 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

29. 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.

30. 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…

31. 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

32. 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

33. Population Attributable Fraction PAF = PAR / Incidence PAF = 7.6% x25.9% = 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

34. 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

35. 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

36. Attack Rate Table- Exposed Cows

37. Attack Rate Table- Non-exposed cows

38. Attack Rate Table Risk Calculations

39. Evaluation of Significance

40. 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

41. 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

42. Questions?

43. 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

44. 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

45. Quantify Organisms • Gram positive environmentals • Gram negative environmentals • Contagious • Other

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

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

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

50. 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