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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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References

REFERENCES

Ayuso-Mateos, J.L. (2003). Global Burden of Unipolar Depressive Disorders in the Year 2000. Global Burden of Disease Draft 28-05-03. World Health Organization Global Program on Evidence for Health Policy (GPE).Google Scholar
Blazer, D. G., Kessler, R.C., McGonagle, K.A. & Swartz, M.S. (1994). The prevalence and distribution of Major Depression in a national community sample: the National Comorbidity Survey. American Journal of Psychiatry 151, 979–986.Google Scholar
Coryell, W., Akiskal, H., Leon, A.C., Winokur, G., Maser, J.D., Mueller, T.I. & Keller, M.B. (1994). The time course of nonchronic major depressive disorder. Archives of General Psychiatry 51, 405–410.Google Scholar
Evans, M.D., Hollon, S.D., DeRubeis, R.J., Piasecki, J.M., Grove, W.M., Garvey, M.J. & Tuason, V.B. (1992). Differential relapse following cognitive therapy and pharmacotherapy for depression. Archives of General Psychiatry 49, 802–808.CrossRefGoogle ScholarPubMed
Kessler, R.C, Andrews, G., Colpe, L.J., Hiripi, E., Mroczek, D.K. & Normand, S.L. (2002). Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychological Medicine 32, 959–976.CrossRefGoogle ScholarPubMed
Kessler, R.C., Andrews, G., Mroczek, D., Ustiin, B. & Wittchen, H.U. (1998). The World Health Organization Composite International Diagnostic Interview Short-Form (CIDI-SF). International Journal of Methods in Psychiatric Research 7, 171–185.CrossRefGoogle Scholar
Patten, S.B. (2002a). A framework for simulating the impact of antidepressant medications on population health status. Pharmacoepidemiology and Drug Safety 11, 549–559.CrossRefGoogle ScholarPubMed
Patten, S.B. (2002b). Progress against major depression in Canada. Canadian Journal of Psychiatry 47, 775–780.CrossRefGoogle ScholarPubMed
Patten, S.B., Brandon-Christie, J., Devji, J. & Sedmak, B. (2000). Perfomance of the Composite International Diagnostic Interview Short Form for Major Depression in a community sample. Chronic Diseases in Canada 21, 68–72.Google Scholar
Sonnenberg, F.A. & Beck, J.R. (1993). Markov models in medical decision making: a practical guide. Medical Decision Making 13, no. 4, 322–338.Google Scholar
Tansella, M. & Thornicroft, G. (1998). A conceptual framework for mental health services: the matrix model. Psychological Medicine 28, 503–508.CrossRefGoogle ScholarPubMed
Von Korff, M. & Parker, R.D. (1980). The dynamics of the prevalence of chronic episodic disease. Journal of Chronic Diseases 33, 79–85.CrossRefGoogle ScholarPubMed
Wells, K.B., Stewart, A. & Hays, R.D. (1989). The functioning and wellbeing of depressed patients. Results from the Medical Outcomes Study. Journal of American Medical Association 262, 914–919.Google Scholar
Wulsin, L.R., Vaillant, G.E. & Wells, V.E. (1999). A systematic review of the mortality of depression, Psychosomatic Medicine 61, 6–17.Google Scholar
Young, A.S., Klap, R., Sherbourne, C.D. & Wells, K.B. (2001). The quality of care for depressive and anxiety disorders in the United States. Archives of General Psychiatry 58, 55–61.Google Scholar