Research
Describing the longitudinal course of major depression using Markov models: Data integration across three national surveys
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* Corresponding author: Scott B Patten patten@ucalgary.ca
1 Department of Community Health Sciences, University of Calgary, 3330 Hospital Drive NW, Calgary, Canada
2 Health Technology Implementation Unit, Calgary Health Region. Foothills Medical Centre, South Tower, Room 602. 1403 29th Street NW, Calgary, Canada
3 Department of Psychiatry, University of Calgary, 1403 – 29 Street NW Calgary, Canada
4 Department of Community Health Sciences. University of Calgary, 3330 Hospital Drive NW, Calgary, Canada
Population Health Metrics 2005, 3:11 doi:10.1186/1478-7954-3-11
Published: 15 November 2005Abstract
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.