Temporal Trends and Geographic Associations of Circulatory Disease Hospitalizations Among Young Adults in Almaty and the Turkestan Region, 2018–2024

Introduction

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality globally, largely attributable to preventable and modifiable risk factors. While age-standardized mortality and disability-adjusted life years (DALYs) from CVD have declined over recent decades, emerging evidence indicates a divergent pattern among younger populations. Global estimates from 1990 to 2019 demonstrate that, despite improvements in survival, both the incidence and prevalence of CVD among youths and young adults have increased, with disproportionate rises observed in low- and middle-income settings.¹ Notable sex-specific disparities have also been documented, with faster increases in prevalence among young women and greater DALY and mortality burdens among young men. These trends are predominantly driven by elevated blood pressure, adiposity, dyslipidemia, and clustering of lifestyle-related risk factors. Moreover, recent studies suggest that the growing burden of cardiometabolic risk, alongside increasing rates of substance use and the persistent incidence of CVD among aged individuals 18–50 years, may presage a substantial future rise in ischemic heart disease, heart failure, atrial fibrillation, and sudden cardiac death as this cohort advances in age.² Collectively, these observations emphasize the need for strengthened, youth-centred preventive strategies and health systems capable of detecting risk accumulation early.

Kazakhstan has aligned national health policy with principles of universal health coverage, prioritizing equitable access to primary health care (PHC) and expanding prevention-oriented services. Recent reforms have enhanced PHC financing and introduced standardized population-based screening protocols specifying target groups, procedures, and periodicity.^3,4 The updated framework mandates a multistage screening process encompassing initial risk assessment, diagnostic confirmation, and structured follow-up, with compulsory documentation within electronic medical information systems—thereby facilitating continuity of care and systematic risk surveillance.⁵

Despite these system-level improvements, important challenges persist. Workforce capacity, particularly in nursing, remains an area of ongoing development. Kazakhstan’s transition away from the Soviet nurse-training model has advanced over the past two decades, yet training and competency gaps remain.⁶ The introduction of nurse-led consultations represents a significant innovation within PHC, enabling nurses to conduct routine assessments, provide lifestyle counseling, deliver patient education, and coordinate diagnostic investigations. Evidence from early implementation shows improved service efficiency, reduced physician workload, and enhanced access to timely preventive care.⁷

Previous research suggests that the prevalence of CVD among young people in Kazakhstan may reach 22.4%, driven largely by modifiable behavioral and metabolic risks.⁸ However, empirical data on hospitalization patterns in this demographic remain limited. In this context, hospitalization rates may be influenced by a combination of demographic (age), geographic (urban–rural setting), and clinical (diagnostic group) factors, which operate as structural and health-system-related correlates rather than direct causal determinants.

Therefore, the aim of this study was to examine hospitalization patterns due to CVD among adults aged 18–44 years in Almaty and the Turkestan region by: (1) assessing temporal trends from 2018 to 2024, (2) comparing geographic differences between urban and rural populations, and (3) analyzing the distribution of urgent versus planned hospitalizations. This approach allows characterization of the burden of acute cardiovascular events within the context of Kazakhstan’s unified national health system while focusing on statistically observable associations rather than causal effects.

Methods

Study Design and Population

This study was designed as a retrospective registry-based observational study using aggregated administrative hospitalization data. We analyzed hospitalizations for circulatory system diseases among young adults aged 18–44 years in Almaty city, Turkestan city, and rural areas of the Turkestan region for the period 2018–2024. The data were obtained from the National Research Center for Health Development (NRCHD) of the Republic of Kazakhstan through an official institutional request. NRCHD is the authorized national body responsible for centralized collection, storage, and reporting of healthcare statistics and registry-based hospitalization data in Kazakhstan.

The dataset provided to the authors consisted of de-identified aggregated hospitalization records stratified by year, sex, age, ICD-10 diagnostic category, and geographic area. The source registry included hospitalizations classified under circulatory system diseases (ICD-10 codes I00–I99). The extracted data did not permit independent verification of whether only primary discharge diagnoses were included or whether secondary diagnoses also contributed to case totals; therefore, the analysis was conducted using the registry definitions as provided by NRCHD. Data entry, ICD coding, and routine validation were performed within the national healthcare reporting system. Because the authors received only aggregated data, no independent audit of individual coding accuracy was possible.

Study Population and Variables

The study population included hospitalized young adults aged 18–44 years with circulatory system diseases recorded during 2018–2024 in the selected administrative areas. The main outcome variable was the number of CVD hospitalizations for each stratum defined by year, sex, age, ICD-10 diagnostic group, and geographic area. Annual hospitalization rates per 100,000 population were also calculated descriptively using official population denominators for each administrative unit and year.Predictor variables in the regression analysis included: (1) year (categorical; reference category = 2018); (2) age (continuous, in years); (3) sex (male, female); (4) diagnostic group, categorized into ICD-10 subgroups (I00–I02, I05–I09, I10–I13, I15, I20, I21, I22, I24, I25, I26–I28, I30–I52, I60–I69, and other I-group diseases); and (5) place of residence (Almaty city, Turkestan city, rural Turkestan region). Categorical predictors were dummy-coded for regression modeling.

Hospitalizations were additionally classified as urgent or planned. This variable was used for descriptive analysis only and was not included in the primary regression model.

Statistical Analysis

Because the dependent variable represented count data, a Poisson regression model was initially considered. The expected hospitalization count for observation i was modeled as:

Where log(λ_i) denotes the expected count for observation i, and log(population_i) was included as an offset term to account for differences in population size between geographic areas and calendar years. Thus, exponentiated coefficients were interpreted as incidence rate ratios (IRRs) reflecting relative changes in hospitalization rates rather than simple case counts.

Assessment of the Poisson model demonstrated substantial overdispersion (dispersion statistic = 6.47), indicating that the variance exceeded the mean. Therefore, a negative binomial regression model was fitted as the primary analytic model. This model introduces an additional dispersion parameter and provides more reliable standard errors and confidence intervals under overdispersed count data conditions. In the final model, the estimated dispersion parameter was θ=1.073.

Year was modeled as a categorical variable rather than a continuous term in order to capture potentially non-linear temporal changes in hospitalization rates across the study period. This approach was considered especially appropriate given possible year-to-year fluctuations related to healthcare access, reporting patterns, and pandemic-era disruptions.

Model coefficients were estimated by maximum likelihood and reported as IRRs with corresponding p-values. Model fit was assessed using residual deviance, null deviance, Akaike information criterion (AIC), and log-likelihood. The residual deviance of the final model was substantially lower than the null deviance, indicating improved explanatory performance. Observations with missing data (n = 289) were excluded prior to modeling. No patient-level imputation was performed because the source dataset was aggregated.

Sensitivity analyses were limited due to the aggregated structure of the dataset; however, model robustness was assessed through comparison of Poisson and negative binomial models and evaluation of goodness-of-fit indicators.

Methodological Limitations

The available registry dataset did not include individual-level information on comorbidities, socioeconomic factors, health behaviors, outpatient management, or medication use; therefore, these determinants could not be evaluated in the regression model. In addition, because only aggregated administrative data were available, the authors could not independently validate ICD coding procedures or distinguish primary from secondary diagnoses within the hospitalization counts. These constraints should be considered when interpreting the results.

Ethical Approval

The study was approved by the Local Ethics Committee of the Khoja Ahmet Yassawi International Kazakh Turkish University, Turkistan, Kazakhstan (№87, 14–11-2024), Almaty, Kazakhstan (in accordance with the Declaration of Helsinki, the study involved no direct patient contact and was based on anonymized registry-level data; accordingly, the requirement for informed consent was waived by the Ethics Committee).

Results

Hospitalization per 100,000 Population (18–44 Years), CVD

In Almaty city, hospitalization rates for circulatory system diseases decreased from 77.3 per 100,000 population in 2018 to 33.4 in 2022, followed by a marked increase to 102.3 in 2024. A similar pattern was observed across sex-specific subgroups, although the magnitude of change appeared substantial. Male hospitalization rates decreased from 105.8 to 6.4 between 2018 and 2022 and subsequently increased to 129.1 in 2024, while female rates decreased from 53.6 to 2.2 and later rose to 78.6.

These pronounced reductions during 2020–2022 likely reflect the indirect effects of the COVID-19 pandemic, including reduced healthcare utilization, temporary suspension of elective admissions, and changes in hospital admission practices, rather than a true decrease in disease incidence. The subsequent rebound in 2023–2024 supports this interpretation.

In contrast, the Turkestan region demonstrated more moderate declines. In Turkestan city, hospitalization rates decreased from 2,360 to 716 per 100,000 population, while in rural areas the reduction was from 1,161 to 733. The smaller magnitude of decline compared to Almaty suggests regional differences in healthcare access, system organization, and reporting practices. The substantially higher hospitalization rates observed in Turkestan compared to Almaty may also reflect differences in referral patterns, a higher burden of cerebrovascular disease, and possible variations in admission thresholds and reporting practices.

Across all regions, cerebrovascular diseases, hypertensive disorders, and other heart diseases remained the leading contributors to hospitalization burden (Table 1 and supplementary material 1).

Table 1 Treated Cases per 100,000 Population (18–44 Age)

Type of Hospitalization (18–44 Years)

Across the entire study period, urgent hospitalizations predominated in both Almaty and the Turkestan region. In Turkestan city, urgent admissions accounted for 76–93% of all cases annually, while in rural areas this proportion ranged from 70% to 85%. Almaty demonstrated a similar pattern, with urgent admissions representing 60–65% of cases in 2018 and approximately 50% in 2024.

The predominance of urgent admissions suggests that circulatory diseases in young adults are frequently diagnosed at advanced or acute stages, particularly in regions with limited access to preventive care. This pattern was especially pronounced for cerebrovascular and hypertensive conditions (Table 2).

Table 2 Hospital Admissions for CVD in Turkestan Region and Almaty by Care Type

Regression Analysis

The negative binomial regression model demonstrated significant temporal, demographic, diagnostic, and geographic effects on hospitalization rates. The model included a population offset term (log(population)), and therefore incidence rate ratios (IRRs) should be interpreted as relative changes in hospitalization rates rather than absolute case counts.

Compared with 2018 (reference year), incidence rate ratios (IRRs) indicated substantial reductions in hospitalization rates during 2020–2024, with the largest declines observed in 2022 (IRR = 0.552; −45%), followed by 2020 (IRR = 0.576; −42%) and 2021 (IRR = 0.636; −36%). These findings are consistent with pandemic-related disruptions in healthcare utilization.

Age was a significant predictor (IRR = 1.066; p < 2×10⁻¹⁶), indicating a 6.5% increase in hospitalization rate per additional year of age (Table 3).

Table 3 Negative Binomial Regression Results (IRR, 95% CI, p-values)

Large IRR values observed for certain diagnostic groups (eg., IRR ≈ 30) reflect relative differences between ICD categories with low baseline frequencies, rather than absolute increases in risk. These values should therefore be interpreted with caution as category-specific effects rather than individual-level risk estimates.

Geographic variation was substantial: rural areas of the Turkestan region exhibited nearly threefold higher hospitalization rates (IRR = 2.97; p < 2×10⁻¹⁶), while Almaty showed significantly lower rates (IRR = 0.382; p < 2×10⁻¹⁶). Pairwise comparisons confirmed persistent regional disparities across all years (Tables 3 and 4).

Table 4 Pairwise City Comparisons (IRR)—All Years and Both Sexes

Sex Was Not a Significant Predictor (IRR = 1.039; p = 0.15).

Overall, the model demonstrated good fit (residual deviance = 8521.7 vs. null deviance = 18,937.4), indicating strong explanatory performance. Notably, the findings highlight a substantial burden of circulatory diseases in the 18–44 age group, which is traditionally considered low-risk, underscoring the emerging public health importance of early-onset cardiovascular disease in this population.

Discussion

This study identified marked regional variation in hospitalization rates for circulatory system diseases among young adults, with substantially higher rates observed in Turkestan city and rural Turkestan than in Almaty across the study period. In the adjusted analysis, rural Turkestan remained associated with significantly higher hospitalization rates, while sex was not a significant predictor after adjustment. These findings indicate that geography was more strongly associated with hospitalization patterns than sex in this dataset. However, because the study was based on aggregated administrative data, these regional differences should be interpreted as statistical associations rather than direct evidence of healthcare inequity or differential disease causation. Possible explanations may include variation in healthcare access, referral pathways, admission thresholds, coding practices, and underlying disease structure across regions. This interpretation is broadly consistent with previous literature showing that rural populations may experience higher cardiometabolic burden and different patterns of healthcare utilization, although these mechanisms could not be directly tested in the present study.^9–11

Another important finding was the predominance of urgent hospitalizations, which accounted for 60% to 93% of admissions depending on region and year. This pattern suggests that a large share of CVD-related hospitalizations in young adults occurred in acute care settings rather than through planned admissions. Although this may be consistent with delayed presentation, insufficient outpatient control, or limited preventive care, the current dataset does not allow direct evaluation of these pathways. The especially high proportion of urgent admissions for cerebrovascular and hypertensive diseases may indicate that these diagnostic categories contribute disproportionately to acute hospitalization burden in this age group. From a public health perspective, this finding supports the need for closer examination of ambulatory care pathways and earlier identification of high-risk young adults, but the present analysis cannot establish why urgent admissions were so common. These observations are consistent with evidence demonstrating that cumulative exposure to metabolic risk factors, particularly hypertension, substantially increases cardiovascular risk even in younger populations.^12–14

Age was significantly associated with higher hospitalization rates even within the relatively narrow range of 18–44 years, suggesting that cardiovascular risk accumulates measurably during early adulthood. At the same time, several diagnostic categories showed very large effect sizes in the regression model, particularly chronic rheumatic heart disease, other heart diseases, and other circulatory diseases. These estimates should be interpreted cautiously. They reflect relative differences between diagnostic categories in the fitted model and may be amplified by low baseline frequencies in the reference category. Therefore, these large IRRs should not be interpreted as direct measures of absolute risk at the individual level. Nevertheless, the pattern suggests that certain diagnostic categories may account for a disproportionate share of hospitalization burden among young adults and may warrant closer epidemiological attention.

The temporal pattern observed in Almaty also deserves consideration. Hospitalization rates declined sharply during 2020–2022 and then rebounded in 2023–2024. This pattern may be consistent with indirect effects of the COVID-19 pandemic, including reduced healthcare utilization, temporary disruption of elective and non-urgent services, changing admission thresholds, and delayed care-seeking behavior. However, because the study does not contain data on healthcare-seeking patterns or service availability, these explanations remain inferential. The magnitude of smaller decline observed in Turkestan suggests that temporal changes were not uniform across regions and may have depended on local healthcare organization, case mix, or reporting practices. Accordingly, these fluctuations should not be interpreted as direct changes in underlying disease incidence.

The public health relevance of these findings lies in the fact that they concern adults aged 18–44 years, a group often considered at relatively low short-term cardiovascular risk. The observed burden of circulatory disease hospitalization in this population indicates that serious cardiovascular morbidity is already present in early adulthood in this setting. This is particularly important because previous studies from Central Asia have documented the contribution of modifiable risk factors, including hypertension, obesity, smoking, and dyslipidemia, to cardiovascular disease patterns in the region.¹⁵ Future studies that integrate hospitalization data with individual-level clinical and behavioral information may help determine whether these factors contribute to the regional patterns observed here and may clarify the mechanisms underlying the burden of CVD among young adults.

Strengths and Limitations

This study has several strengths. It is based on a multi-year, population-level dataset covering seven consecutive years, which allows for the assessment of temporal patterns in cardiovascular disease (CVD) hospitalizations among young adults. The inclusion of both urban and rural settings—Almaty, Turkestan city, and rural Turkestan, enables comparison of hospitalization patterns across regions within a unified national health system. In addition, the use of negative binomial regression was appropriate for overdispersed count data and allowed estimation of adjusted associations across demographic, temporal, diagnostic, and geographic factors. Finally, the focus on adults aged 18–44 years provides valuable insight into CVD burden in a population group that remains relatively underrepresented in epidemiological research.

At the same time, these findings should be interpreted in light of several important limitations. First, the analysis relies on administrative hospitalization data and therefore captures only cases reaching inpatient care, which may underestimate the true burden of CVD in the community. Second, the dataset does not include individual-level information on behavioral, socioeconomic, or clinical risk factors (eg., smoking, body mass index, blood pressure control, income, or insurance status), limiting the ability to explore causal mechanisms. Third, diagnostic coding practices may vary across facilities and regions, introducing potential misclassification. Fourth, healthcare utilization patterns during 2020–2022 were likely influenced by the COVID-19 pandemic, which complicates the interpretation of temporal trends. Fifth, the study does not include outpatient or non-hospitalized cases, restricting assessment of earlier stages of disease. Finally, the analysis was limited to selected regions and may not fully represent national patterns.

Implications for Future Research

Future research should aim to integrate hospitalization data with individual-level clinical and behavioral information to better understand the mechanisms underlying cardiovascular disease in young adults. Linking inpatient data with primary care, outpatient, and screening records would allow a more comprehensive evaluation of disease progression and early detection. Further investigation of socioeconomic factors, access to care, and healthcare system organization is needed to better interpret regional differences. In addition, continued monitoring of post-pandemic trends will be important to determine whether the observed decline and rebound in hospitalization rates represent temporary disruptions or longer-term changes in disease patterns. Such work may help inform targeted prevention strategies aimed at reducing early-onset cardiovascular disease and geographic disparities.

Conclusion

This study identified significant regional variation in hospitalization rates for circulatory system diseases among young adults aged 18–44 years, with higher rates observed in Turkestan—particularly in rural areas—compared with Almaty. A large proportion of hospitalizations occurred as urgent admissions, and hospitalization rates showed a marked decline during 2020–2022 followed by a rebound in 2023–2024. Age was also associated with increasing hospitalization rates within this population, and certain diagnostic categories contributed disproportionately to the overall burden. These findings should be interpreted cautiously, as they are based on aggregated administrative data and reflect statistical associations rather than causal relationships. The absence of individual-level clinical, behavioral, and socioeconomic information limits the ability to assess underlying mechanisms, and the analysis was restricted to selected regions. In addition, the observed temporal patterns may be related to changes in healthcare utilization during the COVID-19 pandemic rather than true changes in disease incidence. Despite these limitations, the results highlight a measurable burden of cardiovascular disease in a relatively young population. From a public health perspective, the findings suggest the importance of strengthening primary care, improving early detection, and enhancing preventive strategies, particularly in regions with higher hospitalization rates. Continued monitoring of hospitalization trends over a longer period is needed to determine whether the observed fluctuations represent temporary disruptions or longer-term changes. Further research integrating individual-level data is required to better understand the drivers of these patterns and to support the development of targeted interventions.