With the conceptual framework in mind, the steps to investigating a disease event are as follows:
1. Define the extent of disease. The process of disease investigation begins with defining the extent of the disease - how many animals are at risk, how many are sick and how many have died.
2. Formulate a case definition. The next step is to form a case definition. The case definition is a set of conditions that must be met before an animal can be counted as a case. This is necessary because animals may get sick or die from any of a number of different conditions and some of these different conditions may look similar. Assessing each animal against a formal case definition is an attempt to ensure that cases all have the same condition and that non-cases do not have the condition.
Case definitions are generally based on examination of affected and unaffected animals (clinical examination, laboratory testing, and necropsy), and may be informed by information about environmental and management circumstances. Case definitions are imperfect and will need to be revised as new information becomes available. To avoid missing any cases, it is usually better to have a broad case definition to begin with, then consider later adjustments to the case definition if necessary.
It is important to note that it is not essential to reach a diagnosis for case animals in a disease event. A diagnosis is useful for deciding treatment(s), prognosis and control based on knowledge about the diagnosed condition. However, even when a diagnosis is not reached, preliminary conclusions about likely involvement of an infectious or non-infectious process, and about possible risk factors, may still be made based on the initial investigation. These conclusions can then be used to guide initial management of cases and to implement preventive measures in an attempt to prevent further cases.
3. Map the disease event. The next step is to map the disease clusters using a timeline, map and table of animal characteristics as previously described. When defining the clusters, use your case definition.
On a ship, the timeline may extend over days or weeks. A line with time intervals marked can be drawn vertically or horizontally on paper and the cases marked on one side of the line. Later, management and environmental events will be marked on the other side of the line. An example is shown in Figure 2.3.
Figure 2.3: Example of a timeline showing dates, events and cases
The map may be the ship’s load plan. The location of individual or groups of cases can be marked on the load plan. Overlays can be used to show, for example, movement of animals to different pens over time or changes in a ship's orientation to the direction of wind or sun.
The table of animal characteristics of cases and non-cases may include columns for attributes such as age, sex, breed, type, body condition, physiological status, vaccination status, origin and so on. Columns recording exposure to management and environmental risk factors may also be added to the table.
While the timelines and maps are excellent for visual detection or highlighting of clusters, tables do not lend themselves to visual appraisal - instead, clusters are detected in tables using calculations of attack rates and relative risks.
Attack rate = no. of cases/no. exposed
Relative risk = attack rate 1/attack rate 2
Relative risks are used to summarise associations in tabulated data. High relative risks may identify possible causes (key contributing factors) of the disease event.
As an example, if on the deck of a ship and 5 of 1000 Bos indicus cattle died and 50 of 500 Bos taurus cattle died, the attack rate in Bos indicus is 0.5%, and in Bos taurus it is 10%. The relative risk of Bos taurus dying compared to Bos indicus is then 1 in 20.
In another example, if on a ship 10% of vaccinated animals had snotty noses and 50% of unvaccinated animals had snotty noses, then the relative risk of an unvaccinated animal having a snotty nose compared to a vaccinated animal is 1 in 5.
Identification of animal characteristics and possible risk exposures for inclusion in an initial investigation table may incorporate a combination of initial knowledge of the disease event and likely risk factors/characteristics that may be important, as well as practical issues based on availability and feasibility of collecting data on identified factors.
4. Link the clusters to potential risk factors. The next step is to systematically work through the management, environmental, and animal factors (using the risk factor library as a reference if necessary), to identify what may have changed to reduce resistance or increase challenge among the animals to produce the cases. Mark the timeline, map and table with notes and arrows as your theories develop. If there is no apparent explanation for cases occurring then consider redefining the case definition and redoing the timeline, map and table. This may produce a more revealing set of clusters.
5. Check criteria for determining causation. By following the steps above you will hopefully have formed a hypothesis (theory) as to why the disease event happened. Once a hypothesis is formed it should be tested against some preliminary criteria for causation just to make sure we are not misleading ourselves.
This can be done as follows:
- Use the timeline to check the suspected 'cause' preceded the event.
- If sufficient information is available, use the timeline to check that the temporal association occurred consistently at other times and that disease was absent when the causes were absent.
- Use the map to check the suspected 'causes' were in proximity to the location of the disease event. If sufficient information is available, use the map to check the spatial association was consistently present in other locations including that disease was absent where the 'causes' were absent.
- Use the table to check that increased exposure to the 'causes' was associated with more disease and less exposure was associated with less disease.
This systematic process will help ensure that cases and non-cases are distinguished using repeatable and defined criteria and that hypotheses about causes are based on available evidence and assessed and revised if necessary. The process reduces the chance of mistaken identification of possible causes and increases the chance of identifying the necessary changes the farmer/feedlot manager/veterinarian must make to stop a disease event continuing and to prevent its recurrence, even in the absence of a diagnosis.