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The following histogram shows that, in an epidemic of hepatitis A in Ibiza, Spain, in 2001, three groups of cases were identified. The epidemic curve can be devised into 3 subgroups (restaurant attendants, customers of shop-B, and hotel C residents). Subsequently, detailed investigation and case control studies could have been conducted to identify vehicles and risk factors specific to each subgroup.
Source: Instituto Carlos III, Madrid, Spain
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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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