These are the sildes of a presentation I gave at the NMC Annual Meeting, held in Fort Worth Texas on January 27, 2014. I was asked to tell something on the economics of mastitis treatment. I broadened that to balancing. Economics is about optimization, but nowadays in antibiotic treatment in animals factors such as animal welfare and a reduction in the use of antibiotics play also a role. The farmer and the veterinary advisor have to balance this. The presentation aims at setting up spreadsheet to support decision making

1. Treating mastitis: Balancing cure, money,
welfare and resistance
Henk Hogeveen
With input from Wilma Steeneveld and Claudia Kamphuis

2. My paper gives latest results from literature
But ...session on analytics:
The modern dairy farm has at its fingertips an endless array of
data. When managed properly, these data can be used to create
a competitive advantage. This session will explore the potential
of analytical approaches to managing mastitis through the use of
on-farm records, decision support for mastitis treatment, and
statistical processing of information.
My presentation will have an emphasis on analytics, not on
results

3. My presentation
 Balancing treatments
 Analytics of lactational treatment
 Example of published results
 Analytics of dry-cow therapy (optimization)
 New possibilities with automatic milking
 Concluding remarks

4. Important in treatment decisions
 Cure
 Much knowledge
available
 Keywords “Cure rate”
and “mastitis”
 11 scientific papers in
2013

5. There is more than cure rates: Welfare
 Clinical mastitis gives pain (e.g., Kemp et al. 2008 VetRec)
 Behaviour is also affected (Medrano-Galarza et al., 2012 JDairySci)
 So: Better cure is better welfare

6. Antibiotic resistance
 Heavily in discussion
 Resistance of mastitis pathogens
● Self-interest
● No increase seen
(Hogan, IDF-factsheet)
 Antibiotic resistance in humans
● Externality
● Dairy cattle has very minor contribution
● In the Netherlands (self) regulations
(Oliver et al., 2011)

7. Economics
 A farm is a business
 Self interest
 Costs of antibiotics vs benefits of higher cure rates or
better prevention

8. Difficult task of herd manager
 There is:
● Cure rate (welfare)
● Money
● Antibiotic resistance
 Should be balanced

9. Not much knowledge on balancing
 11 scientific papers in
2013
 One with economics
(related to
transmission);
Down et al., JDairySci

10. My presentation
 Balancing treatments
 Analytics of lactational treatment
 Example of published results
 Analytics of dry-cow therapy (optimization)
 New possibilities with automatic milking
 Concluding remarks

11. Lactational treatment
 Much knowledge available on cure, e.g., reviews
● Barkema et al. 2006 JDairySci
● Roberson, 2012 VetClinFoodAnim
● Roy and Keefe, 2012 VetClinFoodAnim
● Suojala et al. 2013 JVetPharmTherap
 Some papers on economics
● Steeneveld et al., 2011 JDairySci
● Halasa et al., 2012 JDairySci
● Down et al., 2013 JDairySci
 These are averages
 Make your own calculations

12. Straightforward spreadsheet
Two scenarios

13. Straightforward spreadsheet
Cure rates
(input)

14. Straightforward spreadsheet
Calculation of
proportion of
cured cows

15. Straightforward spreadsheet
What if
animals do
not cure?
We have not
really thought
that out very
well
Farm specific
Role of
advisor

16. Let’s add economics
Proportion of
proportion

17. Let’s add economics
But we are
missing input

18. Let’s add economics
That depends
on cure and
lactation
stage

19. Let’s add economics
Formula for
the average
nr of days
We are
missing some
price levels

20. Let’s add economics
Let’s make
final
calculations

21. Let’s add economics

22. And finally, what are the costs?

23. What’s the point
 Specific situation -> no papers available
 Creation of a tool is not too difficult
 Input needed
● Price levels – farmers know these
● Specific situation of the cow – farmers know these
● Cure rates – this is a problem
 Cure rates might be available from farm records
● Large farms
● Available data
 Evaluate previous assumptions

24. My presentation
 Balancing treatments
 Analytics of lactational treatment
 Example of published results
 Analytics of dry-cow therapy (optimization)
 New possibilities with automatic milking
 Concluding remarks

25. Treatment of clinical mastitis
 Causal pathogen
● Streptococci (40%), S. aureus (30%), E. coli
(30%)
 Parity
 Day in milk
 Calving interval
 Most recent SCC-value
 Repeated CM case yes/no
 Systematically ill yes/no
 305-day milk production
All stochastic
● Wood curve to determine daily milk production at
moment CM and remaining milk production
during lactation
Steeneveld et al., 2011, JDairySci

26. Defined treatments
 3-day intramammary antibiotic treatment (IMM3)
 5-day intramammary antibiotic treatment (IMM5)
 3-day intramammary antibiotic treatment + systemic
antibiotic treatment (IMM3_S)
 5-day intramammary antibiotic treatment
+ systemic antibiotic treatment (IMM5_S)
 Immediately culling

27. Simulating follow-up of treatment
Treatment
CM1
No bact. cure,
clin. cure
Bact. + clin.
cure
End
lactation
No bact. cure,
no clin. cure
Culling
No bact. cure,
clin. cure
Culling
End
lactation
Treatment
CM2
Extended
treatment
Bact. + clin.
cure
End
lactation
Culling
No bact. cure,
no clin. cure
Dying
End
lactation
etc.
Dry-off
quarter
Culling

28. Cow-specific cure
 Probability of bacteriological cure (%) defined
● for heifers, SCC<200, <60 DIM, no CM before, not
systematically ill
IMM3
Streptococci
S. aureus
E. coli
IMM5
IMM3_S
IMM5_S
70
40
80
80
60
85
80
60
85
90
70
95
Defined effects of cow factors
• Older cow:
10%
• SCC 200-500:
10%
• SCC >500:
20%
• >60 DIM:
• Repeated case:
• Systematically ill:
10%
20%
20%

29. Average costs ($US)
IMM3
IMM5
IMM3_S
IMM5_S
Culling
Overall
245
270
275
295
766
Daily milk
production (kg)
<20
20-25
25-30
30-35
35-40
>40
158
189
200
216
253
307
174
206
223
238
279
326
192
216
230
244
284
332
207
230
255
266
301
351
478
578
569
577
732
1,026
Causal pathogen
Streptococci
S. aureus
E. coli
216
283
253
238
296
284
238
296
284
266
318
310
758
749
789
Original calculations in €; € 1 = $US 1.37

30. Least cost frontiers
High cure cow
E. coli cow
Average cow
S. aureus cow
Low cure cow
100
IMM3
IMM5
IMM3_S
IMM3_N_S
IMM5_S
90
Probability of cure (%)
80
70
60
50
40
30
20
130
150
170
190
210
Total costs (€)
230
250
270
290

31. My presentation
 Balancing treatments
 Analytics of lactational treatment
 Example of published results
 Analytics of dry-cow therapy (optimization)
 New possibilities with automatic milking
 Concluding remarks

32. The ongoing debate on dry-cow therapy
 Dry-cow therapy has two uses
● Curative
● Preventive
 “We” do not want preventive use of antibiotics (anymore)
 Which cows to dry-off with antibiotics?
 Let’s create another spreadsheet
● Basically the same as the previous one

33. Probabilities
depend on
risk group
Very basic
input
Costs with
and without
DCT
Probabilities from: Scherpenzeel et al., 2014,
in preparation

34. Average costs drying-off per cow
Costs per cow

35. We’re also interested in amount of AB
Daily doses
Totals

36. Let’s optimize (linear programming)

37. Minimize total
costs of mastitis
By the numbers
of treated cows
Under constraints:
- Nr of cows equal
- Less AB than threshold

38. The constraint

39. What’s the point
 Literature never fits the individual farmer’s situation
 Probabilities can be based on farmers on data
 Quite straightforward economic modelling
 Evaluate previous assumptions

40. My presentation
 Balancing treatments
 Analytics of lactational treatment
 Example of published results
 Analytics of dry-cow therapy (optimization)
 New possibilities with automatic milking
 Concluding remarks

41. Automatic milking
 On-line mastitis monitoring (Electrical conductivity,
colour, SCC)
 Great possibilities for therapy evaluation
 But ….. Sensitivity & specificity of mastitis detection
 Farmer’s confirmation is needed
● Time consuming
● Mostly negative
● Not the nicest of work
 3.5 % of alerts are checked

42. Study on farmer’s handing of alerts
 7 farmers, student checked all alerts
● 60 % of alerts false positive
● 3.5 % of alerts is checked by farmers
● checked alerts are often clinical cases
● 74 % of clinical cases is missed
 How bad is this?

43. Options
1. Maintain the “old” paradigm of treating clinical mastitis
cases hold in automatic milking
….. and educate our farmers better to check
….. or have better sensors (less false positives)
2. Use the daily sensor measurements differently ->
detect acute severe mastitis, for mild mastitis look at
chronicity, use on-farm culturing before treatment
 Lot’s of questions, no answers (yet)

44. My presentation
 Balancing treatments
 Analytics of lactational treatment
 Example of published results
 Analytics of dry-cow therapy (optimization)
 New possibilities with automatic milking
 Concluding remarks

45. I did not present all knowledge
 There is more knowledge out there
 Mostly economics
 Welfare ≈ cure rate
 On-farm culture systems
● Lago et al., 2011, JDairySci
● Cameron et al., 2013, PrevVetMed
● Pinzón-Sánchez et al., 2011 JDairySci (Economics)
 Use of antibiotics only through dry cow therapy

46. On-farm analyses
 Use straightforward calculation tools
 Use farm-specific input
● Price levels
● Incidences
● Cure rates
 Use those farm data!!!
 Operational use should be automated
 There is a future for tailor-made treatment decisions
 PS Example models and ppt are available

47. Thank you for your attention
@henkhogeveen
animal-health-management.blogspot.com
On-line courses on
Veterinary Economics on:
www.elevatehealth.eu