Describing the longitudinal course of major depression using Markov models: Data integration across three national surveys
<p>Abstract</p> <p>Background</p> <p>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...
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| Format: | Article |
| Language: | English |
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BMC
2005-11-01
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| Series: | Population Health Metrics |
| Subjects: | |
| Online Access: | http://www.pophealthmetrics.com/content/3/1/11 |
| _version_ | 1830213635285188608 |
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| author | Lee Robert C Patten Scott B |
| author_facet | Lee Robert C Patten Scott B |
| author_sort | Lee Robert C |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p> |
| first_indexed | 2024-12-18T06:26:56Z |
| format | Article |
| id | doaj.art-0d635a59fce248fe88ef2be57996692b |
| institution | Directory Open Access Journal |
| issn | 1478-7954 |
| language | English |
| last_indexed | 2024-12-18T06:26:56Z |
| publishDate | 2005-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Population Health Metrics |
| spelling | doaj.art-0d635a59fce248fe88ef2be57996692b2022-12-21T21:18:00ZengBMCPopulation Health Metrics1478-79542005-11-01311110.1186/1478-7954-3-11Describing the longitudinal course of major depression using Markov models: Data integration across three national surveysLee Robert CPatten Scott B<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p>http://www.pophealthmetrics.com/content/3/1/11Depressive DisorderEpidemiologic MethodsMarkov Chain |
| spellingShingle | Lee Robert C Patten Scott B Describing the longitudinal course of major depression using Markov models: Data integration across three national surveys Population Health Metrics Depressive Disorder Epidemiologic Methods Markov Chain |
| title | Describing the longitudinal course of major depression using Markov models: Data integration across three national surveys |
| title_full | Describing the longitudinal course of major depression using Markov models: Data integration across three national surveys |
| title_fullStr | Describing the longitudinal course of major depression using Markov models: Data integration across three national surveys |
| title_full_unstemmed | Describing the longitudinal course of major depression using Markov models: Data integration across three national surveys |
| title_short | Describing the longitudinal course of major depression using Markov models: Data integration across three national surveys |
| title_sort | describing the longitudinal course of major depression using markov models data integration across three national surveys |
| topic | Depressive Disorder Epidemiologic Methods Markov Chain |
| url | http://www.pophealthmetrics.com/content/3/1/11 |
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