%0 Journal Article
%T Problems encountered with the use of simulation in an attempt to enhance interpretation of a secondary data source in epidemiologic mental health research
%A Scott B Patten
%J BMC Research Notes
%D 2010
%I BioMed Central
%R 10.1186/1756-0500-3-231
%X The data source was a Canadian longitudinal study called the National Population Health Survey (NPHS). A simulation model representing the course of depressive episodes was used to reshape estimates deriving from binary and ordinal logistic models (fit to the NPHS data) into equations more capable of informing clinical and public health decisions. Discrete event simulation was used for this purpose. Whereas the intention was to clarify a complex epidemiology, the models themselves needed to become excessively complex in order to provide an accurate description of the data.Simulation methods are useful in circumstances where a representation of a real-world system has practical value. In this particular scenario, the usefulness of simulation was limited both by problems with the data source and by inherent complexity of the underlying epidemiology.Major Depressive Disorder (MDD) is a mood disorder that is characterized by one or more major depressive episodes (MDE). Clinical practice guidelines for MDD have historically regarded the diagnosis as a de facto indication of treatment need (e.g.[1]). However, in community studies application of diagnostic criteria for MDD has been shown to identify some short lived episodes that may not be associated with a need for treatment [2]. This has led to more recent recommendations acknowledging the apparent heterogeneity of this condition. For example, in the strategy of "watchful waiting" treatment may be delayed for several weeks while there is ongoing monitoring in order to determine whether an episode will resolve without active treatment [3]. For mild episodes, guided self-management has also been proposed as a reasonable intervention [3,4].It would be helpful to make use of epidemiologic data in order to quantify the probability of various outcomes and ultimately to use this information as a means of supporting clinical decisions. Recently, the predictD study has reported predictive algorithms for the risk of MDE in genera
%U http://www.biomedcentral.com/1756-0500/3/231