Keyword: Chronic Diseases

ABSTRACT: Goals. To assess demographic trends in Uzbekistan (2010–2024) and evaluate the need for geriatric care with a moderate increase in the proportion of persons 65 +, comparing them with health resources.
Materials and Methods. Retrospective analysis of official data from the Statistics Agency of the Republic of Uzbekistan (2010-2024): population, share of 65 +, birth rate, mortality, life expectancy, health care resources (beds, doctors, outpatient network). The forecast for 2025 was performed by linear extrapolation (total population: y = 679.65x + 27.974, R² = 0.99; age 65+: y = 74.05x + 1.140, R² = 0.98; 95% CI for 65+ = 2.18–2.28 million). The standard for the need for geriatricians was estimated according to the Russian benchmark of 1 geriatrician/20,000 elderly.
Results. The population increased from 28.00 million (2010) to 37.80 million (2024); forecast for 2025 - 38.17 million. The share of 65 + increased from 3.9% (2013) to 5.6% (2024); forecast for 2025 - 5.8% (~ 2.23 million). Able-bodied - 56.2% (2024). In 2023, the provision was 47.4 beds and 28.7 doctors per 10,000 population; outpatient organizations increased to 8011. Circulatory system diseases - 57.6% of deaths (2024). According to the standard, about 112 geriatricians are required with an estimated current availability of 0-100 specialists.
Conclusions. Against the background of the low proportion of 65 + and expanding general health resources, the creation of a separate branched geriatric service is not a priority. Rationally - integration of geriatric consultants into large centers (Tashkent, Samarkand, Fergana, etc.) for the management of multimorbidity and geriatric syndromes, while strengthening the prevention of chronic non-communicable diseases in the predominant young population. Monitoring of age-specific mortality/morbidity rates and validated indicators of the functional status of the elderly is needed.

ABSTRACT: Chronic diseases remain a leading cause of global mortality, underscoring the need for developing reliable models that predict mortality prediction to guide individualized treatments and optimize resource allocation. This methodological note presents a reproducible framework for predicting one-year mortality in chronic disease patients using large-scale administrative healthcare data. The approach employs retrospective cohort design, year-specific subcohorts, and a stratified 5-fold cross-validation using a broad range of machine learning models. Performance is assessed with multiple metrics, including AUC, sensitivity, specificity, and balanced accuracy, to account for class imbalance. Model interpretability is enhanced through SHapley Additive exPlanations (SHAP), enabling identification of key mortality predictors and their directional impact. The proposed framework is general and can be applied to different chronic diseases. It has already been successfully demonstrated in nationwide cohorts of patients with diabetes mellitus and chronic viral hepatitis in Kazakhstan, achieving AUC values of 0.74–0.80, comparable to international benchmarks despite relying on administrative data alone. The method is scalable and adaptable, allowing integration of laboratory and clinical data with feature selection to address high-dimensionality challenges. Its generalizability and clinical relevance, however, should be validated in practice using enriched datasets across additional chronic diseases and diverse populations.