Background/Objectives: Artificial intelligence (AI) tools for periodontal diagnosis are proliferating, yet little is known about what clinicians and patients in Central Asia expect from such systems. We elicited requirements for an AI-enabled periodontal decision-support system from three stakeholder groups in Kazakhstan and examined which patient-reported risk factors are associated with self-reported signs of periodontal disease. Methods: A cross-sectional online survey (July–September 2026) enrolled 33 dentists, 91 primary health care (PHC) physicians and medical specialists, and 455 patients with periodontal disease. Proportions were reported with Wilson 95% confidence intervals (CI); groups were compared with Fisher’s exact, χ², Mann–Whitney and Kruskal–Wallis tests with effect sizes; multivariable logistic regression identified factors associated with self-reported periodontal signs. Results: Dentists prioritised automation of history-taking (81.8%) and visual gingival assessment (72.7%), and named early gingivitis diagnosis as the stage where AI is most needed (69.7%). Only human-controlled autonomy levels were chosen; the most frequently cited barrier was the absence of a legal framework and liability insurance (60.6%), and dentists with >5 years of experience more often demanded mandatory verification or a prohibition of full autonomy (exact p = 0.045; Cramér’s V = 0.54). PHC physicians considered early gingivitis screening their most effective contribution (80.2%) but cited insufficient competence as the main barrier (53.8%). Self-reported periodontal signs were present in 49.0% of patients and were independently associated with high stress (adjusted odds ratio 2.11; 95% CI 1.14–3.89) and a positive family history (1.61; 1.02–2.55); six symptom items did not form a reliable scale (Cronbach’s α = 0.56). Conclusions: Stakeholders in Kazakhstan favour a low-autonomy, clinician-verified AI system focused on early gingivitis detection, interprofessional data sharing and clearly regulated accountability. These requirements provide an evidence base for the design and governance of periodontal AI in emerging health systems.

Background/Objectives: Kazakhstan experienced one of the largest measles epidemics in the WHO European Region in 2023–2024 and a pertussis resurgence in 2024, while documented parental vaccine refusals kept accumulating. Subnational, register-based evidence on refusal is lacking for Central Asia. We assessed trends, geography, structure and data quality of registered refusals and their ecological links with coverage and disease incidence. Methods: Nationwide ecological study of the official refusal register (20 regions; stocks on 1 January 2024–2026), surveillance reports and population data. We estimated prevalence per 100,000 with exact Poisson 95% confidence intervals (CIs) and rate ratios (RRs), and applied χ² tests, Ward clustering, Moran’s I with local indicators of spatial association, Spearman correlation, negative binomial regression and Bland–Altman analysis. Results: Registered refusals rose from 39,792 to 66,240 (198.6 to 323.2 per 100,000; RR 1.63, 95% CI 1.61–1.65). Prevalence varied 23-fold between regions (39.8 in Turkestan to 932.4 in Atyrau) and increased significantly in 16 of 20 regions (Kostanay, RR 2.79). Personal beliefs dominated (62.1%); religious refusals (15.5%) clustered in the west and south (Moran’s I = 0.39, p = 0.011); media-attributed refusals grew by 39.8%. In 2024, refusal prevalence was associated with measles incidence (ρ = 0.55; incidence rate ratio 1.18 per 1/1000, 95% CI 1.03–1.34), and refusal explained 54.7% of pertussis cases. The register recorded 3.35–3.84 times more bacille Calmette–Guérin (BCG) refusals than routine coverage reports, and 46 of 180 cumulative vaccine-specific counts decreased. Conclusions: Vaccine refusal in Kazakhstan is growing, regionally heterogeneous in magnitude and motive, and tracked measles burden during the epidemic. Reason-tailored regional strategies and a validated, individual-level electronic refusal register are needed.

About 1 billion cases of seasonal influenza are registered annually in the world, including 3-5 million cases of severe form of the disease. Every year up to 650.000 people die from SARS/influenza. The purpose of the study: to make a forecast of possible activation of SARS/influenza viruses on the territory of the Republic of Kazakhstan in the epidemic season 2023-2024. Methods. The analysis of statistical data of subtypes of the virus type A(H1N1)pdm09 and A(H3N2) for the epidemic season 2022-2023 was carried out. Based on the analysis of the previous season, a forecast of possible activation of SARS/influenza viruses in the territory of the Republic of Kazakhstan in the epidemic season 2023-2024 was made. Results. According to the results of laboratory monitoring of SARS/influenza in the epidemic season of 2022-2023, the circulation of all three types of influenza viruses was noted, but their share in the etiological characteristics of the season was different. Since the beginning of the epidemic season influenza B viruses have been dominant - its share in the structure of positive cases reached maximum values from 40 to 49 weeks, the total share for the epidemic season was 66.2% (n=1613). The activation of influenza A(H1N1)pdm09 viruses was noted in the second half of the epidemic season from week 46, this type of virus accounted for 32.5% (n=792) of positive cases during the epidemic season. The circulation of influenza virus subtype A (H3N2) was noted by isolated cases everywhere, during the epidemic season the proportion of positive cases for them was only 1.3% (n=30). Conclusions. In the upcoming autumn-winter season of 2023-2024 parallel circulation of subtypes of type A(H1N1)pdm09 and A(H3N2) virus with the addition of virus B, and with a higher probability the dominance of influenza A(H1N1)pdm09 virus among other influenza viruses is predicted.

For the first time the physical development of children living in non-ferrous metallurgy enterprises (zinc, lead and titanium-magnesium simultaneously) and in urban areas without similar emission sources were compared, where development indexes and indices were evaluated.Research. To assess the physical development of children living in the sanitary protection zones of the Northern and Northeastern industrial zones.Methods. In the article the physical development of children living on the territory of sanitary protective zones of Northern and North-Eastern industrial centers of Ust-Kamenogorsk city is estimated. When comparing with the clean control areas it was found that the children of primary and secondary school age are characterized by accelerated growth rate, low body weight and weak type of body build.Results. Compared to the control and clean territories, the body mass deficit in the younger age is up to 83.0%, in the high school age - up to 71.2%. "Very weak" type of body build prevails in elementary school students living in the areas of JSC "Uskemen Titanium-Magnesium Plant" (63,0%), JSC "Kazmyrish" and JSC "Ulba Metallurgical Plant" (62,0%).Conclusions. Children living in the studied areas are characterized by an acceleration of the growth rate of body length compared with the control area. The estimation according to centile tables showed that children are characterized by a higher level of body weight in comparison with the control area.Keywords: sanitary protection zone, physical development, schoolchildren, environmental load.

Background/Objectives: Nationwide evidence on gallbladder disease in Central Asia remains limited. We assessed trends, care-related associations and projected burden of cholecystitis and other gallbladder diseases (ICD-10 K80–K82) in Kazakhstan. Methods: This retrospective ecological time-series study used official national statistics for 2012–2024. Rates were crude because complete age-specific annual series were unavailable. Stratified comparisons were descriptive, and reported associations represent population-level patterns rather than individual risk factors. The final analytical dataset comprised thirteen complete annual observations for each modelled indicator. Joinpoint regression identified changes in incidence trends. Negative binomial, quasi-binomial and linear models evaluated temporal associations and the 2020 pandemic disruption. Case growth was decomposed into population-size and incidence-rate components. ARIMA and explicit scenarios projected annual cases to 2039. Results: Registered incidence increased from 343.0 to 406.2 per 100,000, peaking in 2017. Annual changes were +10.6% during 2012–2017 and −4.2% during 2017–2024; the average annual percent change was +1.7% (95% confidence interval 0.2–3.2). Incidence in 2020 was lower than expected from the adjusted trend (incidence rate ratio 0.857, 95% confidence interval 0.749–0.981). Population growth accounted for 51.8% of the 24,401-case increase. Emergency-treatment rates increased by 1.6% annually, while the odds of late presentation declined by 6.2% annually and hospital stay shortened by 0.21 days annually. Central projections indicated approximately 95,000–124,000 annual cases by 2039, with wide model-based prediction intervals. Conclusions: Increasing absolute caseloads despite recent declines in registered incidence support scenario-based capacity planning. Shorter hospital stays and fewer late presentations suggest improvements in care indicators, although individual-level explanations require further investigation.

Background/Objectives: Clinicians’ expectations of artificial intelligence (AI) for periodontal care in Central Asia are largely unknown. We surveyed dentists and physicians in Kazakhstan on preliminary requirements for a future decision-support system and explored patients’ self-reported periodontal signs. Methods: Cross-sectional online surveys (July–September 2026; non-probability recruitment) included 33 dentists, 91 primary health care physicians and medical specialists, and 455 adult patients whose periodontal eligibility was not clinically verified; the patient questionnaire did not address AI. Analyses used Wilson 95% confidence intervals (CI), exact tests with Holm adjustment and exploratory logistic regression. Results: Dentists prioritised history-taking (81.8%) and early gingivitis diagnosis (69.7%). Autonomy choices were spread across five clinician-controlled configurations (low autonomy most frequent, 30.3%); no autonomous option was offered. Lack of a legal framework and liability insurance was the most cited barrier (60.6%). Physicians and specialists selected early gingivitis screening (80.2%) but cited insufficient competence (53.8%). Self-reported signs occurred in 49.0% of patients. Associations with high stress (adjusted odds ratio 2.11; 95% CI 1.14–3.89) and family history (1.61; 1.02–2.55) lost significance after excluding 204 responses collected via an inter-university platform. Conclusions: Clinician responses suggest preliminary requirements; patient associations remain exploratory. Instrument and clinical outcome validation should precede system design.