Problemy sotsial'noi gigieny, zdravookhraneniia i istorii meditsiny · 2026
[Diabetes prevalence in federal districts and Moscow: gaussian modeling, new trends, and regional variations (2026 vs 2017)].
Аннотация на английскомСтатьяНазвание и аннотация: Оригинал на русском
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Andreev D A
Аннотация
INTRODUCTION: The projected global increase in diabetes-related mortality by 2050 (6.7% for T1DM and 128.6% for T2DM) highlights the need for proactive prevalence assessments. Analysis at the Federal District level establishes an evidence-based foundation for further optimizing the management of healthcare sector resources.
OBJECTIVE: To cluster regional indicators and identify distribution trends in T1DM and T2DM prevalence across the Federal Districts and the city of Moscow, comparing 2017 data against the maturity state data as of January 1, 2026.
MATERIALS AND METHODS: Based on variables from the National Diabetes Register, distribution parameters for T1DM and T2DM prevalence variables were estimated, with regions clustered by Federal District. Statistical analysis was performed using MS Excel and Python, employing the Wilcoxon signed-rank test, univariate and bivariate (2D KDE) Gaussian kernel density estimation, and the box-and-whisker method to calculate medians, quartiles, and outliers. Data analysis and synthesis were supported by MS Copilot and Google tools.
RESULTS: The median prevalence per 100,000 population increased from 167.4 to 205.9 for T1DM and from 2,882.3 to 3,896.2 for T2DM. For T1DM, the maximum variability was observed in the North Caucasian Federal District (70.1-201.8), while the minimum interquartile range was found in the Siberian Federal District. For T2DM, the highest variance was characteristic of the Northwestern Federal District (2,567.5-5,949.5), alongside substantial ranges in the Siberian (1,660.9-5,056.9) and Southern Federal Districts (90.4-4236.7). A «low-base effect» was verified in regions with initially low T2DM indicators. Probability density analysis revealed an isolated drift for the city of Moscow: by 2026, the city entered the modal core for T1DM (200.7) but remained in the left tail of the overall distribution for T2DM (2,677.6, compared to the Central Federal District median of 4,272.4).
DISCUSSION: The identified trends enable the development of targeted approaches for endocrinological services across Federal Districts.
OBJECTIVE: To cluster regional indicators and identify distribution trends in T1DM and T2DM prevalence across the Federal Districts and the city of Moscow, comparing 2017 data against the maturity state data as of January 1, 2026.
MATERIALS AND METHODS: Based on variables from the National Diabetes Register, distribution parameters for T1DM and T2DM prevalence variables were estimated, with regions clustered by Federal District. Statistical analysis was performed using MS Excel and Python, employing the Wilcoxon signed-rank test, univariate and bivariate (2D KDE) Gaussian kernel density estimation, and the box-and-whisker method to calculate medians, quartiles, and outliers. Data analysis and synthesis were supported by MS Copilot and Google tools.
RESULTS: The median prevalence per 100,000 population increased from 167.4 to 205.9 for T1DM and from 2,882.3 to 3,896.2 for T2DM. For T1DM, the maximum variability was observed in the North Caucasian Federal District (70.1-201.8), while the minimum interquartile range was found in the Siberian Federal District. For T2DM, the highest variance was characteristic of the Northwestern Federal District (2,567.5-5,949.5), alongside substantial ranges in the Siberian (1,660.9-5,056.9) and Southern Federal Districts (90.4-4236.7). A «low-base effect» was verified in regions with initially low T2DM indicators. Probability density analysis revealed an isolated drift for the city of Moscow: by 2026, the city entered the modal core for T1DM (200.7) but remained in the left tail of the overall distribution for T2DM (2,677.6, compared to the Central Federal District median of 4,272.4).
DISCUSSION: The identified trends enable the development of targeted approaches for endocrinological services across Federal Districts.