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Describing the longitudinal course of major depression using Markov models: Data integration across three national surveys.
- Source :
-
Population Health Metrics . 2005, Vol. 3, p11-9. 9p. 2 Diagrams, 3 Graphs. - Publication Year :
- 2005
-
Abstract
- Background: Most epidemiological studies of major depression report period prevalence estimates. These are of limited utility in characterizing the longitudinal epidemiology of this condition. Markov models provide a methodological framework for increasing the utility of epidemiological data. Markov models relating incidence and recovery to major depression prevalence have been described in a series of prior papers. In this paper, the models are extended to describe the longitudinal course of the disorder. Methods: Data from three national surveys conducted by the Canadian national statistical agency (Statistics Canada) were used in this analysis. These data were integrated using a Markov model. Incidence, recurrence and recovery were represented as weekly transition probabilities. Model parameters were calibrated to the survey estimates. Results: The population was divided into three categories: low, moderate and high recurrence groups. The size of each category was approximated using lifetime data from a study using the WHO Mental Health Composite International Diagnostic Interview (WMH-CIDI). Consistent with previous work, transition probabilities reflecting recovery were high in the initial weeks of the episodes, and declined by a fixed proportion with each passing week. Conclusion: Markov models provide a framework for integrating psychiatric epidemiological data. Previous studies have illustrated the utility of Markov models for decomposing prevalence into its various determinants: incidence, recovery and mortality. This study extends the Markov approach by distinguishing several recurrence categories. [ABSTRACT FROM AUTHOR]
- Subjects :
- *MARKOV processes
PSYCHIATRIC research
Subjects
Details
- Language :
- English
- ISSN :
- 14787954
- Volume :
- 3
- Database :
- Academic Search Index
- Journal :
- Population Health Metrics
- Publication Type :
- Academic Journal
- Accession number :
- 30094716
- Full Text :
- https://doi.org/10.1186/1478-7954-3-11