On this website you will find information on the Statistical Program to Assess Dietary Exposure (SPADE) which is developed by RIVM. With this program the habitual intake distribution for daily and episodically consumed foods or dietary components can be estimated based on intake measured on a limited number of days.
In the evaluation of dietary intake of populations, one is
often interested in the habitual (usual) intake, i.e. the long-term
average intake. For example to estimate the proportion of a
population that meets nutritional recommendations or that exceeds
safe upper intake levels.
, dietary intake is generally collected with
short-term measurements, for example 24-hr recalls or food records.
The dietary intake of an individual can vary considerably from day
to day. Consequently, intake measured over a limited number of days
will be a poor indicator of the individual habitual intake.
Statistical modelling makes it possible to estimate the
habitual intake distribution of a population from repeated
This modelling can be complicated if the intake is derived
from several sources; for example micronutrients are naturally
present in foods, but they are also available in fortified foods
and dietary supplements. For the evaluation of both the adequacy of
intakes and the risk of excessive intakes of micronutrients, all
potential sources should be included. In the estimation of the
habitual intake, this may cause specific challenges like multimodal
distributions and heterogeneous variances between the
Statistical Program to Assess Dietary Exposure
was developed by RIVM
It can be used to estimate the habitual intake distribution for
daily and episodically consumed foods or dietary components,
similar as other available methods. On top of that, SPADE
provides models to estimate habitual intake distributions from
different sources separately and adds these habitual intakes in
order to get the overall habitual intake distribution.
In order to use SPADE
the program can be requested
The current version of SPADE
3.1" (March 2016).