The case definition on a dairy is a way to define what, exactly, constitutes an intervention point for cow care employees. Monitoring of mastitis rates and events is common on dairies given the prevalence of the disease, and having a plan for developing case definitions and protocols is critical. Greg Goodell, DVM, The Dairy Authority, LLC, Greeley, Colo., says mastitis is a good, albeit complicated, example of a disease that should be monitored.
“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,” Goodell says. As far as identifying disease, establishing case definitions is important. “The case definition on a dairy is a fancy way of defining to the farm personnel what, exactly, constitutes an intervention point for them.”
Some dairies define mastitis as any visible clots, flakes or otherwise abnormal milk (true medical case definition) where other dairies define a case of mastitis as a cow who presents to the milker during the milking process a second shift or a second day with signs of clots or flakes.
“At first glance these sort of definitions may raise the eyebrows of some until it is realized that a barn of astute milkers in a herd where the most common mastitis cases presented are mild coliform cases and can quickly fill a hospital pen with cows who were seen with a single flake in the parlor but spend two days in the hospital pen with no clinical signs and a negative culture report.”
Greg Goodell, DVM. Goodell says an easier case definition would be retained placenta. “True medical case definition is described as any placenta retained after 12 hours. This isn’t practical on most dairies so we define the case definition as any cow with placenta still present 24 hours after calving. In this instance the case definition is simply based on time.”
If the case definition of mastitis is a laterally recumbent cow in the milking pen then mastitis treatments in this herd will be much different from a herd where the case definition is a single flake in a cow’s udder at milking time. “These are two extremes that wouldn’t typically be used, but it makes the point,” Goodell explains.
The case definition is different enough from dairy-to-dairy that Goodell often finds it unreliable to compare disease rates between dairies without knowing case definitions. “Some of that issue is iatrogenic due to how cases are recorded. If a cow arrives with a case of mastitis, first line therapy fails and progresses to the second line therapy it is still only one mastitis event. If the cow is recorded with a second mastitis event it would be interpreted that she is a chronic mastitis cow whereas more correctly the treatment we prescribed did not work. She may indeed turn into a chronic mastitis cow, however we need to evaluate therapies in these cases to see if what we are using works best for the disease at hand.” Also, he adds, if dairies record the second treatment event as a second case of mastitis, it can falsely inflate the mastitis rate on the dairy.
DTR, recurrence and cost
Monitoring also does not stop at disease prevalence. “More and more today we are asked to identify specific costs associated with a particular disease on one dairy,” Goodell notes. “This is used only to move the dairy ahead in achieving its own goals of reducing costs and providing effective
treatments and should never be used to benchmark against other dairies.” Goodell says benchmarking was in vogue about 10 years ago, however,it was quickly learned that simply comparing the rates between two dairies and suggesting one was good and one wasn’t was not accurate due to differences in goals, case definitions and management of those cases.
Data such as days to resolution, recurrence rate and cost of treatment is already built into many dairy management systems and is easy to retrieve. “The three biggest things we would monitor after disease incidence or prevalence would be days to resolution, recurrence rate and of course cost of treatment,” he explains. “Most of this information is already built into a dairy management system and is very easy to retrieve out of just about any software.”
Days to resolution (DTR) is used within a dairy to define treatment failure, treatment success or improved treatment success. “If DTR for one treatment is shorter or longer than for the other than we can quantitatively evaluate treatments.”
There can be two outcomes to a treatment -- short term and long term. “For example with a particular mastitis treatment we may have a shorter DTR than a previously used treatment, however, the recurrence rate may be increased. In this situation the short term outcome is more successful but will eventually be altered by the increased recurrence rate and the veterinarian has to decide if change in treatments is truly justified.”
Just as the case definition needs to be defined so does DTR and recurrence, Goodell adds. “Does resolution of mastitis occur when clinical signs are gone or when the offending quarter is CMT negative? Once again goals of the dairy are used to evaluate how to define these items.”
Recurrence is a bit simpler in most cases. “If mastitis occurs within the same quarter within two weeks does this constitute recurrence? Does it have to be the same pathogen if cultures are collected?”
The following may seem like too much for a veterinarian to get done. Nevertheless dairies are beginning to ask for this type of information. “Having this level of data available allows for a more cost-effective approach to disease management than ever before,” Goodell says. “While it may seem a bit complicated to get some of these systems in place, once done, data is usually collected automatically and can be analyzed each month or quarter.”
In the best systems only raw data (not calculated) is collected (ie: ID, dates, pen, milk, etc.) from the dairy management software and basic calculations for disease rates, cost accounting, etc., can be performed outside of the dairy management software. Almost all dairy management software systems have a way to get data out. “This is perhaps the most important aspect these days since many software companies have restricted or limited the input of data into their own program such as DC305 and others.” Goodell adds that dairy management software tends to define things differently within the program so that comparing between programs really isn’t possible.
“In some cases all a producer wants to know is the success rate of a treatment and the cost of a treatment,” he says. “Once DTR and recurrence are covered, it is a simple function to input costs.” Many dairy software packages claim to be able to get this data however to this point nothing used to date gets the job done, Goodell says.
Goodell offers this advice for setting up monitoring systems for dairy diseases. “Keep it simple and concisely defined for farm personnel. The upfront time put in to get this started is rewarded over and over again as these ‘customized’ monitoring programs are put in place for an individual dairy. It typically generates another revenue source for the veterinarian and delivers high quality decision- making tools to the producer.
“Finally remember that the resulting case definition defined for the dairy may not be the text book case definition of the disease, but when defined simply and treatment protocols defined around the case definition, the health of the animal is usually improved and goals for the dairy are more often met.”
Sidebar: Steps in monitoring disease
Greg Goodell, DVM, says that systematically, setting up a mastitis monitoring program would go something like this:
1. Identify the goals of the producer (is it BTSCC under 100K or BTSCC under 300K?).
2. Define case definition as described above (health of the cow, ability of the farm personnel to identify disease and the most prevalent presentation).
3. Define treatment protocols to treat the identified disease (additionally quality control is implemented and defined).
4. Define how this disease is recorded.
5. Define action points when disease rates are not meeting goals set by producers.