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Outbreak investigations are often seen as key examples of field epidemiology.
Definition of an outbreak
What exactly is an outbreak? Different definitions of an outbreak can be found in literature. Here we will define an outbreak simply as 'the occurrence of more cases than expected in a particular population, in a specific geographical area and over a specified period of time'.
Difference between an outbreak and an epidemic
The word 'epidemic' is often used interchangeably with 'outbreak' in professional communication. In the media, the word epidemic usually has a more threatening ring to it, which is why most communication experts tend to avoid using it. Outbreak investigators may decide to use the word 'epidemic' or not depending on whether they want to attract or deflect attention. In September 2014, president Obama made a careful distinction between the two terms to attract attention to the emergency that Ebola constituted. (1)
From the identification of a cluster to the establishment of the existence of an outbreak
To establish the existence of an outbreak, we first of all need to understand what is meant by 'cases'. This needs to be defined. Usually the first signal of an outbreak can come from a telephone call or report from the health care system about a cluster of cases. We may or may not yet know if such a cluster is 'more than expected', so a systematic approach is required.
At this stage it is important to understand the distinction between a cluster of cases (2 or more cases that are related by sharing similar characteristics in time and/or place and or personal characteristics) and an outbreak (more cases than expected). For example 5 cases of respiratory illness occurring in the same week can be considered 'a cluster in time', yet this may be the usual number of such cases that one would expect in that week of the year.
Similarly cases can be clustered in place (same village / neighbourhood etc) or according to personal characteristics (e.g. cases sharing the same age-category). In each of these examples of clusters, a key question will be: 'is this number of cases more than we would expect?'. If the answer is 'yes' then the cluster can be considered an 'outbreak'.
"More cases than expected", implies that we need to have knowledge of the 'normal' number of cases (or baseline). This knowledge may come from surveillance or surveys. The increase in the number of cases is best documented as a population-based incidence rate. Investigators may want to examine possible artifact in the numerator (e.g., batch reporting of old cases or of prevalent cases) on in the denominator (e.g., population movements, mass gathering).
Steps of an outbreak investigation
The various lists of various steps
Investigating an outbreak requires a systematic approach that is summarized as a number of steps. Unfortunately, various groups have generated various lists where steps differ in sequence and number. One of the most classical lists (2) includes Ten steps summarized as below:
Some lists have referred to 13 steps (4), adding additional steps such as logistical aspects (e.g., Prepare for field work) while another prepared for foodborne outbreaks was more conceptual with only 7 steps (1. Detecting a possible outbreak, 2. Defining and finding cases, 3. Generating hypotheses about likely sources, 4. Testing the hypotheses, 5. Finding the point of contamination, 6. Controlling the outbreak and 7. Deciding an outbreak is over).
Since 2012, the EPIET and EUPHEM fellowships have used an adaptation of the original 10 steps for teaching and supportive supervision. These 10 steps are very similar from the one above, with minor adjustments derived from an analysis of the common errors in outbreak investigations (5). This adaptation has the advantage of disentangling (a) case definition (step 3) from case search (step 4) and (b) generation of hypothesis (step 5) from hypothesis testing (step 6). Case definition and case search on one side and hypothesis formulating and testing on the other side are quite different processes that can suffer from specific pitfalls and benefit from specific guidance (Hence the benefit in the split). In addition, the 10 steps adapted in such a way places the 'middle' of an investigation between hypothesis generating and testing. This reflects the pivotal thought process that needs to take place at that critical phase of the outbreak investigation when the outbreak investigation team may have to write a mini-protocol.
How to understand the lists of steps of an outbreak investigation?
The lists of steps for outbreak investigations must not be taken to literally. First, they are ordered in a sort of logical sequence that does not necessarily match the temporal sequence. For example, some outbreak investigations may start with the enforcement of control measures (e.g., implementation of infection control in health care facilities to prevent secondary spread). Second, they summarize a number of steps that should take place for most investigations. However, some this may vary from investigation to investigation. Overall, they can be thought of as a list of 'things one wants to consider' while investigating an outbreak.
The outbreak team
Key to the investigation and control of an outbreak is the constitution of a team.
Epidemiological Outbreak Investigation
Operational Aspects of Outbreak Investigations
Join the discussion about this article in the forum!
Arnold Bosman posted on 9/19/2011 8:00:05 PM:
Mumtaz Ali Laghari replied on 9/27/2011 5:55:39 PM:
Dear Arnold Thanks a lot for lot of Contribution and Guidance.
Regarding Outbreak investigation it is pertinent to note that we should have Denominator means the base line population or to calculate rates.Problem with developing countries is that Population is not registered adequately so most of times it is estimated.Estimations can be wrong as well.What is solution You will suggest in this regard?
Arnold Bosman replied on 9/27/2011 9:15:13 PM:
Thanks a lot for the question.
Indeed, for a good understanding of "the expected occurrence" of a disease, it helps a lot when we know numerator (cases) and denominator (population at risk).
In some outbreaks, that occur in circumscript, closed populations, we may be able to study the whole cohort, in particular if the attack rate is high. In such cases we can count our denominator by our own observations.
If the disease is not so frequent and when denominator information is absent, then we can still look at the absolute number of cases in a given period and region, and compare that to previous periods in that region.
Any increase could then mean several things:
1. An increase in population at risk (=denominator), for example due to an influx of migrants. If the number of cases doubles, because the population has doubled, then we would have expected that, and in the light of our definition, it would not be an outbreak (however, it could still require public health attention).
2. Random fluctuations. If in a city of 1 million inhabitants a rare disease occurs in only 1 patient per year, and suddenly we have 2 cases in a year, then this could still be within the variance of the expected. Again here. it helps to have the denominator
3. Registration artefact: it could be a classification error, or a better diagnostics was used, or a screening programme introduced. Any kind of change to 'the system' that suddenly detects more cases, while the 'true' number of cases in the population has not changed
4. A true rise in cases (i.e. an outbreak).
When we have no denominator information whatsoever, then we still want to try to rule out causes 1,2 and 3 so that we are more confident that it is 'more cases than expected'
Explanation 1 is difficult to assess without information about the size of the denominator. However it should not be impossible. If we know that migration did not take place, and that birth rate and death rate have not changed significantly, then we can assume that the population has been stable. So without knowing the exact rate, we could rule out population changes as cause of the increase
Explanation 2 is easier, because even in absence of accurate denominator counts, we could still make reliable estimates of the total population size. So we would have a good 'educated guess' if a disease would be rare enough to have a large variance.
Explanation 3 is independent of population size: we need to now the system very well.
So this would suggest that even in absence of accurate denominator information, we should be able to make a reliable assessment if n increase in cases could reflect an outbreak or one of the other three explanations.
I am not sure if this is convincing: please share with me some possible alternative views
Mumtaz Ali Laghari replied on 9/28/2011 5:44:12 PM:
Excellent Reply !! almost explaining all possible situations faced in Filed and Solutions ...
Arnold Bosman replied on 9/28/2011 6:47:24 PM:
Mumtaz Ali, should we consider adding some part of this discussion to the current Wiki page, as an annex or example? What do you think?
Mumtaz Ali Laghari replied on 9/29/2011 2:49:47 PM:
Yes ,I think it shall be useful to add it as an example ..
Vladimir Prikazsky replied on 10/31/2011 10:31:54 PM:
Epidemie inteligence (including indicator and event based surveillances) is an important source of signals tha trigger first of the ten steps of outbreak investigation.
Arnold Bosman replied on 3/1/2014 8:47:13 PM:
A great article by Werber and Bernard published in Eurosurveillance describes how they developed a toolbox consisting to increase the use of analytical studies in the investigation of outbreaks of foodborne diseases.
The Linelist Tool is available at the RKI website. A real recommendation!
chwilliams replied on 10/2/2015 8:40:14 AM: The modified 10 steps lose the step to define the population at risk. It can be easier to define a case in terms of a population at risk plus clinical / laboratory findings. This ensures inclusion of time , place , person in the definition.
In reality the case definition and population at risk are developed in an iterative loop, as once cases have been found and described, this may lead to a hypothesis about the cause and the population at risk. So an early case definition may be broad in both disease syndrome and population , a later one has a more clearly demarcated population at risk.
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