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dc.contributor.authorPatten, Scott B.en
dc.descriptionThis animation accompanies a paper that may be published in the journal BMC Psychiatry.en
dc.description.abstractBackground: Epidemiologic estimates are now available for a variety of parameters related to major depression epidemiology (incidence, prevalence, etc.). These estimates are potentially useful for policy and planning purposes, but it is first necessary that they be synthesized into a coherent picture of the epidemiology of the condition. Several attempts to do so have been made using mathematical modeling procedures. However, this information is not easy to communicate to users of epidemiological data (clinicians, administrators, policy makers). Methods: In this study, up-to-date data on major depression epidemiology were synthesized using a discrete event simulation model. The mathematical model was animated in Virtual Reality Modeling Language (VRML) to create a visual, rather than mathematical, depiction of the epidemiology. The animation is available in DSpace ( Results: Consistent with existing literature, the model highlights potential advantages of population health strategies that emphasize access to effective long-term treatment. Conclusions: Visual animation of epidemiological results may be an effective knowledge translation tool. In clinical practice, such animations could potentially assist with patient education and enhanced long-term compliance.en
dc.description.sponsorshipDr. Patten is a Health Scholar with the Alberta Heritage Foundation for Medical Research and a Research Fellow with the Institute of Health Economics.en
dc.rightsAttribution Non-Commercial No Derivatives 3.0 Unported*
dc.subjectHealth Sciencesen
dc.subject.otherDepressive Disordersen
dc.subject.otherMajor Depressionen
dc.titleLonger Simulations of Major Depression Epidemiologyen
dc.typelearning objecten
dc.publisher.corporateUniversity of Calgaryen
dc.identifier.doi Health Sciences and Psychiatryen

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Attribution Non-Commercial No Derivatives 3.0 Unported
Attribution Non-Commercial No Derivatives 3.0 Unported