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If the magnitude of the risk ratio, rate ratio, odds ratio or risk difference) varies in different sub groups (strata) of the study population, there is effect modification. This differs from confounding, where we generally believe that the measure of effect (i.e. the RR, OR etc.) will be the same in each of the strata defined by levels of the confounding variable. Where the measure of effect does differ by the effect modifying variable, it is unreasonable to combine the results from the different strata (as is the case using the Mantel-Haenzel methods for a confounding variable).
In the hypothetical cohort study carried out to measure the effectiveness of a vaccine on preventing occurrence of disease X; vaccine effectiveness (VE) can be derived from the risk ratio (RR) using the formula: VE = (1 - RR) *100 (to express VE as a percentage).
The risk ratio for the entire cohort of 0.29 implies a VE of 71%. But we also looked at VE across different age groups of the population. Within each age group, a risk ratio comparing the risk of disease X between the vaccinated and unvaccinated was computed.
< 1 year
1 - 4 years
5 - 9 years
10 - 24 years
> 24 years
We observe that the risk ratio ranges from 0.15 to 0.87 according to various age groups, consequently, neither is VE equal for the various age groups. This suggests that age is modifying the protective effect of the vaccine. Age is called an effect modifier.
Since the data suggest different vaccine effectiveness by age group it would not be logical to summarise the table and give only an overall vaccine effectiveness (e.g. 71%). It is important to describe the VE by age groups. When effect modification is suggested by the data, it is important to present stratum specific results that provide more information than an overall effect.
The same reasoning can be applied to a case control study. A case control study conducted in France in 1995 suggest that storing eggs for longer than 2 weeks in the home increases the risk of gastroenteritis (OR = 3.8) in children . However if the analysis is stratified in two seasons, summer and others, the odds ratio is higher in summer (OR = 6) than in other seasons (OR = 2.3), suggesting that the increased risk of gastroenteritis with duration of home eggs storage expresses itself differently according to the season. Here, season is an effect modifier of the association between duration of storage of eggs in the home and the occurrence of Salmonella enteritidis gastroenteritis.
Duration of storage
≥ 2 weeks
1.3 - 26.8
< 2 weeks
1.4 - 10.2
Seasons other than summer
0.6 - 9.0
Specific statistical methods are used to look for effect modification and test the homogeneity of stratum specific risk ratios or odds ratios. The most popular tests include the Woolf test, Breslow-Day, Χ2 for trends, etc. Details of the various methods can be found in referenced books and articles .
In the two above examples, effect modification was assessed by comparing the risk ratios or odds ratios between different sub-groups (strata) of a population. However we sometimes use risk difference to identify how risk varies between exposed and unexposed cohorts.
The following example is a classic illustration of the difficulty to conclude on the presence or not of effect modification according to the type of effect measure we use (risk ratio or risk difference). In the figure, the risk of hypothetical disease X is compared between exposed and unexposed according to age. The risk increases with age linearly among unexposed (bold line). For the exposed groups two alternatives are presented. First the line representing the increase of risk with age among exposed (plain line) is parallel to that of unexposed. The risk difference (RD) is constant and the RR decreases with age. Alternatively (doted line) if risk increases with a bigger slope among exposed, RD increases with age and RR is constant. This is why some authors would use the term effect-measure modification rather than effect modification to make sure that the type of effect measure (RR or RD) is specified [3;4]. Some also refer to "an effect modifier of the risk difference" or alternatively of the risk ratio.
A cohort study collects information on drinking, exposure to ceramic dust and subsequent liver cancer. The table shows the risk (over 1 year, per 100,000 persons) derived from the study.
No ceramic dust
Among those exposed to ceramic dust, the relative risk of liver cancer between drinkers and non-drinkers is 10 (50/5). Among the unexposed, the relative risk between drinkers and non-drinkers is 10 (10/1).
The risk difference between the drinkers and non-drinkers who are not exposed to ceramic dust is 10-1=9/100,000 persons. The risk difference between drinkers and non-drinkers who are exposed to ceramic dust is 50-5=45/100,000 persons.
The difference in effect modification between these scales reflects statistical interaction - which refers to the deviation from the underlying model. This is different from biological interaction.
1. Delarocque-Astagneau, E., Desenclos JC, Bouvet P, Grimont PA. Risk factors for the occurrence of sporadic Salmonella enterica serotype enteritidis infections in children in France: a national case-control study. Epidemiol Infect 1998 Dec;121(3):561-7.
2. B.R.Kirkwood, J.A.C.Sterne. Medical Statistics. Second ed. Massachusetts: Blackwell Science Ltd; 2003.
3. A Dictionary of Epidemiology. Fifth ed. New York: Oxford University Press; 2008.
4. K.J.Rothman, S.Greenland, T.L.Lash. Modern Epidemiology. Third ed. Philadelphia, USA: Lipincott Williams and Wilkins; 2008.
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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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