Hidden demographic burden and cluster-oriented planning as an approach to geriatric care development in Russian regions

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Abstract

BACKGROUND: Demographic aging in the Russian Federation shows marked regional variation. The existing geriatric care system uses the administrative age criterion, which creates a mismatch with biological aging and leads to the phenomenon of “hidden demographic burden.” A uniform approach to geriatric care planning ignores regional differences.

AIM: To provide a scientific basis for transitioning to cluster-oriented geriatric care planning by validating the concept of “hidden demographic burden.”

METHODS: The study had two stages. Stage 1 was a cohort study of demographic trends in Saint Petersburg (2019–2024) with scenario forecasting (2025–2030). Stage 2 was a multicenter cross-sectional study with cluster analysis of 12 Russian regions. We also analyzed population-based frailty screening data (n = 378,631). Bootstrap stability analysis, cross-validation, and sensitivity analysis were performed.

RESULTS: In 2024, for the first time in Saint Petersburg, the biological (60+) group exceeded the administrative group by 31.6 thousand people. Population screening showed an exponential rise in frailty prevalence with age. K-means clustering identified three regional clusters: “Depressive” (n = 8), “Urbanized” (n = 3), and “Special Conditions” (n = 1). The silhouette coefficient was 0.41, indicating a satisfactory structure. Bootstrap analysis confirmed high stability for Cluster 1 (Jaccard = 0.89) and good stability for Cluster 2 (Jaccard = 0.76).

CONCLUSION: These findings support the “hidden demographic burden” concept and justify a shift to cluster-oriented geriatric care planning. A key limitation is the small sample size for cluster analysis, which requires validation of the typology on a larger sample.

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About the authors

Ekaterina S. Lapteva

North-Western State Medical University named after I.I. Mechnikov

Author for correspondence.
Email: Ekaterina.Lapteva@szgmu.ru
ORCID iD: 0000-0002-0857-1786
SPIN-code: 3168-2568

MD, Cand. Sci. (Medicine), Assistant Professor

Russian Federation, Saint Petersburg

Alexander L. Ariev

North-Western State Medical University named after I.I. Mechnikov

Email: alex.l.ariev@gmail.com
ORCID iD: 0000-0001-8754-2870
SPIN-code: 6545-5911

MD, Dr. Sci. (Medicine), Professor

Russian Federation, Saint Petersburg

Otari G. Khurtsilava

North-Western State Medical University named after I.I. Mechnikov

Email: rektorat@szgmu.ru
ORCID iD: 0000-0002-7199-671X

MD, Dr. Sci. (Medicine), Professor

Russian Federation, Saint Petersburg

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Supplementary files

Supplementary Files
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1. JATS XML
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2. Supplement 1. Raw data for 12 regions (table with all variables used for clustering).
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3. Supplement 2. Leave-one-out cross-validation of the predictive model (mean absolute percentage error 0.31%; comparison of alternative models).
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4. Supplement 3. Bootstrap analysis of cluster solution stability (Jaccard similarity, 1000 iterations).
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5. Supplement 4. Sensitivity analysis of the cluster solution and predictive model (region exclusion, variable exclusion, alternative clustering methods).
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