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Risk of mental disorders, their changes and somatic consideration in rural Hungary
Kornél Sipos1, Michael Bodo, Zsolt May
1Faculty of Physical Education and Sport Sciences, Semmelweis University, Budapest, Hungary. sipos@mail.hupe.hu
Objective:
Although the primary purpose of the study reported here was to identify stroke risk factors among the residents of a village in eastern Hungary, the study also included a multi-faceted survey conducted in 1992-94 to ascertain the somatic, mental and socio-economic conditions of the residents. Here we report data from the survey on prevalence of mental disorders (a cross-sectional descriptive study).
Method:
The screenings included the following tests administered to 535 subjects: anxiety, depression, dementia, neurosis were measured; recent medical records were compared to survey data for 330 of the same subjects.
Findings:
The summary of prevalence of mental disorders measured in this study was as follows: anxiety 34.7% (severe), dementia 44.68% (mild), depression 66% (mild), 15.94% (medium), 7.88% (severe), neurosis 66.73% (mild, medium, and severe). Medical records maintained by village physicians since 1960 differed from the results obtained in the present study. A treatment gap was observed between mental health treatment for neurosis, as indicated by medical records, and the diagnostic prevalence of neurosis as measured by the survey instruments: there were three times as many people diagnosed as neurotic in the survey as had been noted in village medical records. Additionally, the unique position of cerebrovascular alteration was established between the mental and somatic factors.
Conclusion:
The study demonstrates the successful simultaneous collection of a wide spectrum of data on somatic conditions, mental disorders, and socio-economic status of the subjects. The study showed that 1. patient-centered medical care can simultaneously address both somatic and mental factors; 2. it is possible to decrease the treatment gap in mental health; 3. there is value in systematic collection of data in order to optimize the planning of prevention, health care costs and decision making.
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