Comparison of major epidemiological surveys using the diagnostic interview schedule
- 1 January 1994
- journal article
- research article
- Published by Taylor & Francis in International Review of Psychiatry
- Vol. 6 (4) , 309-327
- https://doi.org/10.3109/09540269409023270
Abstract
The National Institutes of Mental Health Diagnostic Interview Schedule (DIS) was developed to assess members of the general public for presence of psychiatric symptoms in the landmark Epidemiological Catchment Area (ECA) study in the United States. The success of this undertaking combined with the acceptance by researchers throughout the world of the Diagnostic and Statistical Manual of Mental Disorders, Third Edition (DSM-III) led to the replication of the ECA in various countries. This report summarizes the comparative results of six of these studies conducted in: Edmonton, Canada; Seoul and rural Korea; Christchurch, New Zealand; Puerto Rico; Taiwan; and the United States. The data presented includes comparison of: (1) rates of psychiatric disorders in selected urban areas from all six studies; (2) rates of psychiatric disorders in rural populations of the four studies with rural samples; (3) intra-cultural rates of psychiatric disorders in Chinese, Hispanic and American settings; (4) rates of psychiatric disorders between genders in the six different countries; (5) urban/rural predominance of psychiatric disorders in the four countries with urban and rural samples; and (6) the ratio of one-year versus lifetime prevalence of psychiatric disorders in all six countries. In general, the Asian sites had lower rates of most disorders. The marked differences in prevalence rates of psychiatric disorders among countries may be due to methodological or cultural factors. Perhaps just as important, one major ‘risk factor’ for certain psychiatric conditions—gender—showed remarkably consistent results across sites. These findings are discussed. Overall, the great cultural variations and similarities invite more specific scientific designs to explore the rich data sets.Keywords
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