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Prodromes and precursors: Epidemiologic data for primary prevention of disorders with slow onset
journal contribution
posted on 1995-01-01, 00:00 authored by W W Eaton, M Badawi, B MeltonObjective: The concepts of prodrome and precursor are used to show how epidemiologic data on age at onset can be used in timing preventive interventions and selecting target populations. Method: Data concerning onset of DSM-III major depression and panic disorder were taken from the Epidemiologic Catchment Area Program. Cumulative distributions of ages at onset of diagnosis and onset of precursors are presented, and the concept of attributable risk is introduced. Attributable risk is the maximum proportion of cases that would be prevented if an intervention were 100% effective in eliminating a specific precursor. Results: Illustrative results for depression and panic are presented. Precursors vary in the degree to which they predict onset of the full disorder; 2 or more weeks of sad mood in the year before full-blown depression is a better predictor (relative odds, 7.0) than weight loss or gain (relative odds, 3.0). The formula for population attributable risk was applied to the precursor relative risks and prevalences to estimate the potential success of interventions for specific precursors in preventing the disorder. The precursor attributable risks indicate that sleep problems would identify 47% of the new cases of major depression occurring in the following year, and the question 'Are you a nervous person?' would identify 60% of persons with onset of panic disorder in the following year. Conclusions: This conceptual framework links the early natural history of disorders with the search for syndromes. Knowledge of precursor prevalence and attributable risk, combined with other host characteristics and environmental risk factors, can be used in screening and prevention.
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American Journal of PsychiatryVolume
152Issue
7Pagination
967 - 972Publisher DOI
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0002-953XPublication classification
C1.1 Refereed article in a scholarly journalUsage metrics
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