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Good Health Records: The Foundation of Consistent, Effective Dairy Health Management- Dr. John Wenz


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Dr. John Wenz presented this material for DAIReXNET on Monday, March 4, 2013. For more information, please see our archived webinars page at

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Good Health Records: The Foundation of Consistent, Effective Dairy Health Management- Dr. John Wenz

  1. 1. “Good Health Records” The Foundation of Consistent,Effective Dairy Health Management John R. Wenz DVM, MS
  2. 2. Despite all our Knowledge% Cows with Disease is Rising USDA, 2008 #N481.0308
  3. 3. Dairy Health Management You know what to do… You don’t always know how well it’s getting done “Good” Health Records are the Foundation ofConsistent, Effective Dairy Health Management
  4. 4. Good Health Records Programin WA and ID• Survey Dairy Producers (243)• Develop Software to Evaluate Outcomes• Demonstration Herd Project (43 herds) Dr. Sarah Giebel – Impact of Standardized Health Records • Time for recordkeeping • Importance of Feedback • Value
  5. 5. Dairy Health Record Quality is Poor• Lack Accuracy and Consistency needed for efficient summary and analysis Kelton et al., 1998; LeBlanc et al., 2006; Appuhamy et al., 2007; Wenz and Giebel, 2012• “…many health record entries are focused on treatment, rather than diagnosis...” Nordlund and Cook, 2004
  6. 6. WA and ID Survey of Health Records Wenz et al., 2012 J.Dairy Sci. 95 Suppl.2:260• Only 17% dissatisfied with quality and utility of their computer health records.
  7. 7. Perception of Health Record Quality is Based on Intended Use• producers generally utilize individual animal data, whereas practitioners … are more concerned with herd summary information” (Etherington et al., 1995)• Health Records are USER – DEFINED Why?• Variable Disease Definitions
  8. 8. User-defined Records  Inconsistency Between and Within Dairies Metritis User-defined events used to record metritis on 50 dairies METR using Dairy Comp 305® METRITS Wenz and Giebel, 2012 HOSP ILL TEMPHow was health data recording decided? DIRTY • 52% “Made it up as we went along” TREATED Wenz et al., 2012 J.Dairy Sci. 95 Suppl.2:260 FLUSHED INFUSED PEN
  9. 9. The PMO Provides a Standard for Complete Treatment Records• Identity of animal(s) treated• Date(s) of treatment• Drug(s) administered• Dosage administered• Milk discard time; and• Withdrawal time prior to slaughter• Disease being treated (Not in PMO)*• Who administered treatments (Not in PMO) *But, Ailment treated is determined when a residue violation is investigated
  10. 10. 3+4 Meeting FDA Treatment Records Requirements • Can be a combination of Electronic and Written (Confirm in your area) • Example: – Computer: • ID, Date, Disease, Drug, Withhold times and dates – Paper/Written: • Treatment protocol book: Disease, Drug, Route, Dose • Daily Hospital List: ID, Date, Disease, Drug, WHO treated 10
  11. 11. FDA Warning Letter June 2010• “Our Investigation also found that you hold animals under conditions that are so inadequate that medicated animals…are likely to enter the food supply. For example, you failed to maintain complete treatment records.” 11
  12. 12. FDA Turning up the Heat on Residues in Milk Results due any day now
  13. 13. USDA Turning up the Heat on Residues in Meat August 2012
  14. 14. New Multi-Residue Method (MRM)Testing is More Sensitive, More Drugs DRUG (Partial List) Bovine Kidney Old Test ug/g ug/g Ampicillin (Polyflex) 0.02 0.05 Beta-dexamethasone 0.05 - DCCD (Excenel/Excede) 0.2 - Enrofloxacin (Baytril) 0.025 - Florfenicol (Nuflor/Resflor) 0.1 - Flunixin (Banamine) 0.0125 - Lincomycin (LS-50) 0.05 1.5 Oxytetracycline 0.5 0.4 Penicillin G 0.1 0.05 Sulfadimethoxine (DI-METHOX) 0.05 -One of 17 drugs other than intramammary, used to treat mastitis on 102 dairiesNo Tolerance for any residue in lactating dairy cattleILLEGAL use (not labeled for mastitis, extra-label use not allowed)
  15. 15. “Creative” drug use is fostered bymisconceptions of the uninformed
  16. 16. Need to Change the Focus of Dairy Health Management Perception at the Cow Level What Drug? When can milk or the cow be shipped? Evidence at the Herd Level Outcomes of Health Management Good Health Records
  17. 17. You need Health Records more like Repro Records Standardized Fast and Easy Evaluation of OutcomesRepro Records Health Records• System defined • User-defined – Standard data entry – Whatever you want – Consistent remarks – However you want• Outcomes evaluation • Count keeper – Conception rate – # of Events – Pregnancy rate• Accurate and Consistent • Variable accuracy and consistency
  18. 18. To be “Good”, Health Records Should Support:• Individual cow management decisions• Residue avoidance/Regulatory compliance• Outcomes-based health management decisions
  19. 19. 3 Simple Rules for Good Health Records*1. Record ALL Disease Episodes – Regardless of severity or therapy – Each Quarter or Foot as separate episode2. Use a SINGLE, SPECIFIC Event for Each Disease – Record Diseases not Treatments – Differentiate Clinical from Subclinical/Screening3. Record CONSISTENT Event Remarks – Same INFO (Treatment, Quarter, Severity)in the – Same ORDER using the – Same ABBREVIATIONS*
  20. 20. Good Records are achieved through Standard Protocol Implementation* Protocols must Balance 3 Key Functions of Health Records NT – No treatment intend to keep BF – No treatment will be sold*Until dairy management software companies make disease entrythe same as entry of breedings (standardized)
  21. 21. Keeping Good Health Records Doesn’t Take Longer Time Budget Analysis Time to Capture and Enter Data Before and After Standard Protocol Implementation… Capture EntryIncrease 4.5% (1/22) 22.2% (4/18)Decrease or 95.5% (21/22) 77.8% (14/18)No Change Giebel et al., 2012 JDS 95 Suppl. 2:9
  22. 22. Veterinarians Need to be More Involved in Health (Records) Management• WA and ID Health Records Survey Respondents – 80% - Vet Valuable Resource for Health Records – 30% - Vet helps decide how to record health data – 35% - Vet evaluates health record summaries
  23. 23. HEALTHSUM Dairy Health Database A tool available through veterinarians to Evaluate the Outcomes of YOUR Management, YOUR Cows on YOUR Dairy• Is Your Prevention Program Effective? – Disease incidence Monitoring and Alerts • By risk group (Pen, Lactation…)• Is Your Treatment Program Effective? – Retreatment – Recurrence – Removal
  24. 24. Prevention Efficacy Example: Clinical Mastitis Incidence• New vs. Recurrent*, ALL Clinical Mastitis (% Milking Herd) * *Cow had mastitis episode 15-60 days prior
  25. 25. Pen Level Incidence Example:A Story of Two Identical Open Lot PensPen 56 (Avg.) – 120 cows, Lact – 2.9, DIM – 191, CM* – 10%Pen 58 (Avg.) – 123 cows, Lact – 2.8, DIM – 200, CM* – 5.2% * CM – Clinical Mastitis
  26. 26. Treatment Efficacy Example:OUTCOMES of Clinical Mastitis Episodes that Occur in a Month• Evaluated by Risk Groups – Lactation group – Initial treatment – Culture Result• Retreatments 24 WA+ID herds Median (5.8%)
  27. 27. Treatment Efficacy ExampleOUTCOMES of Clinical Mastitis (CM) Episodes that Occur in a Month RECURRENCE – CM Same Quarter 15-60 days later 24 herd Median (7.4%) LOST QUARTER – Dried or Killed quarter 17 herd Median (1.8%) REMOVAL – Sold or Died within 14 days of CM 24 herd Median (12%)
  28. 28. Clinical Mastitis Treatment Efficacy• Recurrence by Culture Result Example Coliforms No Growth Coag. Neg. Staph. Env. Strep.
  29. 29. Take Homes• Need Dairy Health Management more like Repro Management – Change from Perception at the cow-level to Evidence (Outcomes) at the herd-level• “Good” Health Records Support: – Individual cow management decisions – Residue avoidance/Regulatory compliance – Outcomes-based health management decisions
  30. 30. Take Homes• Don’t need standard disease definitions to Standardize Health Records• Each dairy needs Standard Data Recording Protocols – Guided by the 3 Simple Rules of Good Records• Resources at
  31. 31. Questions?• Acknowledgements – USDA NIFA for Funding – WA and ID Dairies and Veterinarians – Dr. Sarah Giebel and Sandy Poisson – Drs. Dale Moore, Chris Schneider, David Galligan, Ray Jussuame