Email this article Statistic-mapping modeling of rheumatic diseases prevalence among population of different regions of Russia
- Authors: Erdes S.1, Folomeeva ОМ1, Dubinina TV1, Zhukova ОV1
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Affiliations:
- Issue: Vol 79, No 5 (2004)
- Pages: 40-45
- Section: Editorial
- URL: https://ter-arkhiv.ru/0040-3660/article/view/29826
- ID: 29826
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Abstract
Aim. To assess feasibility of the method of mapping modeling for spacial featuring of rheumatic diseases (RD) morbidity statistics for population of Russia.
Material and methods. Population morbidity statistics for 78 administrative units of the Russian Federation (1993-2000) were processed. Statistic-mapping modeling employed weighted mean interpolation i.e. mathematic prediction of the sign value depending on its magnitude in basic points. Results. Mapped images of RD prevalence, prevalence/primary morbidity values and number of documented cases/number of rheumatologists demonstrate special distribution of the signs studied about the RF territory. The estimation of mean, minimal, maximal values and dispersion of each sign established dynamic trends in the markers for the period under study(increased area with RD prevalence above 95/1000 from 15.9 to 44.5%; increased mean ratio prevalence/morbidity from 2.8 to 3.2 and decreased ratio of patients/rheumatologists from 10882 to 9031). Regions with minimal and maximal values are shown.
Conclusion. Statistic-mapping modeling demonstrates spacial distribution of the markers, reveals the relations between them.
Material and methods. Population morbidity statistics for 78 administrative units of the Russian Federation (1993-2000) were processed. Statistic-mapping modeling employed weighted mean interpolation i.e. mathematic prediction of the sign value depending on its magnitude in basic points. Results. Mapped images of RD prevalence, prevalence/primary morbidity values and number of documented cases/number of rheumatologists demonstrate special distribution of the signs studied about the RF territory. The estimation of mean, minimal, maximal values and dispersion of each sign established dynamic trends in the markers for the period under study(increased area with RD prevalence above 95/1000 from 15.9 to 44.5%; increased mean ratio prevalence/morbidity from 2.8 to 3.2 and decreased ratio of patients/rheumatologists from 10882 to 9031). Regions with minimal and maximal values are shown.
Conclusion. Statistic-mapping modeling demonstrates spacial distribution of the markers, reveals the relations between them.
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References
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