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Towards a dynamic description of major depression epidemiology

Published online by Cambridge University Press:  11 October 2011

Scott B. Patten  and
Robert C. Lee
Scott B. Patten*
Affiliation:
Departments of Community Health Sciences and Psychiatry, University of Calgary (Canada)
Robert C. Lee
Affiliation:
2Department of Community Health Sciences. University of Calgary. Health Technology Implementation Unit, Calgary Health Region (Canada)
*
Address for correspondence: Professor S.B. Patten, Departments of Community Health Sciences and Psychiatry, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta (Canada). Fax: +1-403-270-7307 E-mail: [email protected]
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Abstract

Summary

Aims – The substantial impact of major depression on population health is widely acknowledged. To date, health system responses to this condition have been largely shaped by observational findings. In the future, health policy decisions will benefit from an increasingly integrated and dynamic understanding of the epidemiology of this condition. Policy decisions can also be supported by the development of decision-support tools that can simulate the impact of alternative policy decisions on population health. Markov models are useful both in epidemiological modelling and in decision analysis. Methods – In this project, a Markov model describing major depression epidemiology was developed. The model employed a Markov Tunnel in order to depict the dependence of recovery probabilities on episode duration. Transition probabilities, including incidence, recovery and mortality were estimated from Canadian national survey data. Results – Episode incidence was approximately 3% per year. Recovery rates declined exponentially over time. The model predicted point prevalence at slightly less than 1%, agreeing closely with observed prevalence data. Conclusions – Epidemiological models describing the dynamic relationships between major depression incidence, prevalence, recovery and mortality can help to integrate available epidemiological data. Such models offer an attractive option for support of health policy decisions.

Declaration of Interest

Acknowledgement: Both authors are Research Fellows with the Institute of Health Economics (www.ihe.ab.ca). This study was supported by an operating grant from the Canadian Institutes of Health Research (www.cihr.ca).

Keywords

depressionepidemiologypublic healthMarkov Chains
Type
Invited Papers
Information
Epidemiology and Psychiatric Sciences , Volume 13 , Issue 1 , March 2004 , pp. 21 - 28
Copyright
Copyright © Cambridge University Press 2004

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