Abstract

PURPOSE. To conduct a descriptive epidemiological study of glaucoma in the Far Eastern Federal District covering the years 2012 to 2019.METHODS. The study uses data of the Federal Research Institute for Health Organization and Informatics (FRIHOI) covering the 2012–2019 time period, as well as data from the register of the Unified Interdepartmental Information and Statistical System (UIISS) and the Federal State Statistics Service (FSSS). Statistical data processing was carried out using Microsoft Excel 2019. Diagrams and a cartogram were built to visualize the obtained data. The reliability of the trend line was determined by the value of approximation. A trend is a tendency of changes in the studied time series. In this work, we used a linear approximation — a straight line that best describes the time course of incidence and prevalence. The significance of linear regression was checked using the F-test to determine the quality of the regression model. The coefficient of determination was also used to indicate the dependence of the variability of prevalence on time. A linear regression model was used to predict the prevalence of glaucoma in the Russian Federation and the Far Eastern Federal District; 91% of the total variability of prevalence in the Russian Federation is explained by a change in the time parameter, while 86% in the Far Eastern Federal District indicates a high accuracy of the selection of trend equations.RESULTS. According to the study, in the 2012–2019 years there was a significant increase in the incidence of glaucoma in the Primorsky Krai (PK) amounting to 8%. Over the observed period, a significant increase in the prevalence of glaucoma is noted in the Republic of Buryatia (6.9%), and in the Magadan Region (5%). At the same time, the highest incidence and prevalence of glaucoma was noted in the Republic of Sakha (Yakutia) — 105.4 cases and 1551.6 cases per 100 000 population. The expected prevalence of glaucoma in the Russian Federation (RF) in 2020 is 895–999.7 per 100 000 population, in 2021 — 908–1020.2; in the Far Eastern Federal District (FEFD) in 2020 — 783.7–961.3 per 100 000 population, in 2021 — 799.5–989.8. The largest proportion of glaucoma was found among the population of the Magadan Region (16%) and Yakutia (13.8%), the smallest in the Amur Region (5%) and the Chukotka Autonomous Okrug (5.7%).CONCLUSION. The dynamics of glaucoma incidence in the Far Eastern Federal District is uneven, which corresponds to the epidemiological situation in the Russian Federation as a whole. But the prevalence and proportion of glaucoma in the structure of diseases of the eye and adnexa in the FEFD are characterized by negative dynamics in comparison with country-wide. At the same time, even within the regions of the FEFD, the incidence and prevalence of glaucoma is mosaic, which predisposes to studying the influence of factors on glaucoma incidence.

Highlights

  • Statistical data processing was carried out using Microsoft Excel 2019

  • Что значительный прирост превалентности глаукомы наблюдается в Республике Бурятия (6,9%) и Магаданской области (5%), умеренно выраженный прирост — в Сахалинской области (3,5%), Приморском крае (3%), Камчатском крае (2,8%), Хабаровском крае (2,6%)

  • Самый высокий удельный вес глаукомы в структуре заболеваний глаза и его придаточного аппарата отмечен среди населения Магаданской области (16%) и Якутии (13,8%), наименьший — в Амурской области (5%) и Чукотском автономном округе (5,7%)

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Summary

Introduction

Statistical data processing was carried out using Microsoft Excel 2019. Diagrams and a cartogram were built to visualize the obtained data. The reliability of the trend line was determined by the value of approximation. We used a linear approximation — a straight line that best describes the time course of incidence and prevalence. The coefficient of determination was used to indicate the dependence of the variability of prevalence on time. A linear regression model was used to predict the prevalence of glaucoma in the Russian Federation and the Far Eastern Federal District; 91% of the total variability of prevalence in the Russian Federation is explained by a change in the time parameter, while 86% in the Far Eastern Federal District indicates a high accuracy of the selection of trend equations

Methods
Results
Conclusion
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