A relevant dispensation is a dispensation delivered on a basket of products defined as markers of a syndrome.
This type of monitoring makes it possible to both anticipate epidemiological peaks and also their decreases, in advance of their phase. Indeed, there is a time lag between the moment when the patient is treated and the moment when he is treated.
But also to not take into account products that are not relevant to the syndrome studied.
Thanks to time series modelling (of which you will find more details on the following link: http://success.openhealth.fr/fr/articles/3633991-etude-des-series-temporelles), it is also possible to assess the number of relevant dispensations that are supernumerary compared to a "normal" situation.

For week 1 of a given year, we have 22,000.00 dispensations, all products combined.
For week 2 of the same year, we have 23,000.00 dispensations, all products combined.
We therefore have an evolution of +5.2% [ (23,000,000 / 22,000,000) - 1 ].
But this evolution is not relevant to the syndrome studied because it includes all products and does not allow us to study any statistical correlation with this syndrome.

Defining a basket of marker products allows us to analyse these correlations more finely.
For week 1 of a given year, we have 4,000,000 dispensations, all products included.
For week 2 of the same year, we have 5,000,000 dispensations, all products combined.
We have an evolution of +25.0% (5,000,000 / 4,000,000) of relevant dispensations.

To estimate the number of supernumerary relevant dispensations related to this syndrome, we need to model the situation without the last week, the start of the syndrome inflection.
Let's assume that modelling the number of dispensations in week 2 leads us to a result of 4.100,000 relevant dispensations, then:
​Nb supernumerary relevant dispensations=
Nb relevant dispensations observed - Nb relevant dispensations modelled
Thus 900,000 supernumerary relevant dispensations (5,000,000 - 4,100,000)

PHARMA only, mainland France excluding Corsica

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