Innovative methodology for the comprehensive assessment of population health: integrating classical and trend-based approaches

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Introduction

The formation of sound public policy and the setting of priorities in the field of healthcare require a comprehensive analysis of the health status of the population [1]. Effective public health management requires taking into account not only official statistics reflecting the level and long-term dynamics of medical and demographic, medical and social, economic and environmental indicators separately but their integrated evaluation as well.  The latter should be based on scientifically sound mathematical models that provide a comprehensive dynamic-spatial and prognostic characterization of public health [2].

The DALY assessment methodology recommended by the World Health Organization is often inapplicable in certain countries, particularly in the Republic of Kazakhstan. This is due to problems with the reliability or lack of data on certain parameters, insufficient statistical and special studies, as well as difficulties in accounting for macroeconomic indicators [3].

Various approaches to the integrated assessment of public health based on selective analysis of determining factors are presented in national studies [4; 5]. However, the use of isolated indicators does not allow for a comprehensive assessment, and the lack of a system for monitoring integral parameters makes it difficult to take prompt management decisions.

An important aspect of developing comprehensive assessment methods for countries with large territories is the comparative ranking of regions [6]. The specifics of the industry's functioning and territorial organization are determined by a significant number of multi-level factors [7], which leads to regional differences: differentiation of territories according to the nature of medical-demographic, medical-social, economic, and environmental processes. In the long term, this leads to increased spatial gaps and inequality in the provision of health services to the population¹.

Thus, there is a clear need to develop improved methodologies, including scientific justification of a system of integrated indicators, as well as the creation of objective standardized approaches that take into account the diversity of factors and their regional specificity.

Materials and Methods

A retrospective analysis was conducted of the dynamics and absolute values of medical-demographic, medical-social, socio-economic, and environmental indicators that determine the state of public health in the Republic of Kazakhstan for the period from 2010 to 2022. The empirical basis of the study consisted of official statistical data provided by the Statistics Committee of the Ministry of National Economy of the Republic of Kazakhstan, as well as departmental publications of the Ministry of Health of the Republic of Kazakhstan – the collections "Health of the Population of the Republic of Kazakhstan and the Activities of Healthcare Organizations" and "Regions of Kazakhstan". Regional characteristics influence the factors that determine differences in healthcare development. This leads to differentiation between regions in terms of medical-demographic, medical-social, socio-economic, and environmental processes, exacerbating spatial gaps and the inadequacy of services [8]. In this regard, the data array for 19 regions was subjected to cluster analysis, which made it possible to divide the set of studied determinants of public health into regional clusters similar in terms of their level and dynamics of development.

Given the limitations of existing approaches to public health assessment, a new methodology for integrated regional assessment has been proposed, based on a comprehensive analysis of medical-demographic, medical-social, economic, and environmental indicators [9]. The developed algorithm involves calculating three summary indicators that combine a number of specific coefficients: health status rating (reflects the current level of public health for a specific determining factor based on retrospective data analysis), development rating (characterizes the dynamics of change in the determining factor), and integral rating (a comprehensive indicator that takes into account both the current position of the region and the rate of positive change). The ratings of the regions are converted to a 100-point scale for standardization. The methodology made it possible to classify regions as typical (25–75 points), leaders (more than 75points), and laggards (less than 25 points) using interquartile intervals that do not require normal distribution. The methodology was tested based on an analysis of the determining factors of public health over 13 years using data from statistical compilations of the Republic of Kazakhstan with the help of the developed computer software "ROZA-1.0"² [10].

Results and Discussion

Cluster analysis of classic determinants of public health revealed significant differences between regions of the country. According to long-term dynamics and a calculated 5-year forecast, both clusters are expected to see population growth by 2027, but the population level throughout the observation period in the second cluster is 1.8–2.2 times lower than in the first (Fig. 1).

 

Fig. 1. Population dynamics of the first and second regional clusters of the Republic of Kazakhstan

 

An analysis of birth rate dynamics in the Republic of Kazakhstan revealed statistically significant differences between two regional clusters, as clearly shown in Fig. 2.

 

Fig. 2. Dynamics of the total fertility rate in regional clusters of the Republic of Kazakhstan (2010–2022, per 1,000 population)

 

As a result of analyzing the spatial distribution of mortality rates in the Republic of Kazakhstan using the method of cluster analysis, three statistically significant groups of regions were identified (Fig. 3). Regions classified in the first cluster show extremely low standardized mortality rates ranging from 4.2 to 6.2 cases per 1,000 population. On the contrary, regions in the third cluster are characterized by maximum indicators (from 9.9 to 11.9 cases per 1,000 population), corresponding to the median values established by the World Health Organization for countries with average mortality rates.

 

Fig. 3. Dynamics of the overall mortality rate in regional cluster groups of the Republic of Kazakhstan (2010–2022, per 1,000 population)

 

As a result of cluster analysis of life expectancy indicators, three statistically significant groups of regions were identified (Fig. 4, a). Clustering was performed based on standardized data, taking into account the age structure of the population and key medical and demographic characteristics. The identified clusters demonstrate significant spatial differentiation of this indicator across the territory of the Republic of Kazakhstan.

 

Fig. 4. Dynamics of indicators in regional clusters of the Republic of Kazakhstan: a – life expectancy of the population; b – primary morbidity of the population; c – provision of hospital beds for the population

 

Analysis of primary incidence data revealed marked regional differentiation (Fig. 4, b). Astatistically significant excess was recorded in the regions of the first cluster (626.4 ‰) compared to the regions of the second cluster (482.1‰). The differences identified indicate significant spatial heterogeneity of patterns across the territory of the Republic of Kazakhstan.

Indicators of resource provision of medical care showed statistically significant inter-cluster differences both in absolute values and in the direction of dynamic changes. The regions in the first cluster showed a steady negative trend in the number of doctors (Fig. 4, c), which indicates a systemic deterioration in human resources in these administrative-territorial entities.

When analyzing the number of people employed in the economy, three regional clusters with significant differences in the level and rate of growth were identified (Fig. 5, a).

 

Fig. 5. Dynamics of indicators in regional clusters of the Republic of Kazakhstan: a – employment rate in the economy; b – share of unemployed persons; c – average wage level; d – level of housing provision per capita

 

Cluster analysis based on the unemployment rate allowed us to identify two statistically significant groups of regions with average long-term values of 5.3% and 5.0% of the total labor force, respectively (Fig. 5, b). At the same time, the median wage in the regions of the first cluster was almost twice as high as the corresponding indicators for the second and third clusters (Fig. 5, c).

The application of cluster analysis based on the indicator of housing provision per capita made it possible to identify three statistically significant groups of regions with average long-term values of 15.7 m², 18.9 m², and 21.4 m², respectively (Fig. 5, d).

The assessment of regional gross domestic product also revealed significant inter-cluster differentiation (Fig. 6). There was a steady positive trend in industrial emissions into the atmosphere, mainly due to the activities of enterprises in the first and second clusters of the four identified groups (Fig. 7).

 

Fig. 6. Dynamics of regional gross domestic product per capita in the regions of the Republic of Kazakhstan

 

Fig. 7. Dynamics of industrial emissions in regional clusters of the Republic of Kazakhstan

 

The cluster analysis data presented, which includes all the indicators taken into account in the study, does not provide a clear result due to the complexity of interpreting the resulting pattern of indicator grouping and interaction (Fig. 8). Within the framework of the concept of sustainable development, it is important to have a conclusion about the current state of the indicators, temporary trends, and, based on them, to carry out an integrated assessment with the ranking of regions.

 

Fig. 8. Matrix visualization of clusters formed by regions, taking into account factors that shape public health: A – Shymkent; B – Astana; C – Almaty; D – Turkestan; E – Mangistau; F – Kyzylorda; G – Zhambyl; H – Atyrau; I – Almaty; J – Aktobe; K – Zhetysu; M – North Kazakhstan; N – West Kazakhstan; O – East Kazakhstan; P – Kostanay; R – Pavlodar; S – Karaganda; T – Akmola; U – Akbay; PN – population; BR– birth rate; MR – mortality rate; EL – life expectancy; PI – primary incidence; HBA – hospital bed availability; E – employment rate; UN – unemployment rate; W – wages; HSA – housing stock availability; RGP – regional gross product; IE – industrial emissions

 

The application of the developed methodological approach for the integrated assessment of public health based on the specified indicators demonstrates the multidimensional nature of the data obtained. This allows for a comprehensive analysis of the well-being of territories, taking into account not only the absolute values of individual indicators, but also the direction of their dynamics, which determined the level achieved (Fig. 9).

 

Fig. 9. Ratings of Kazakhstan's regions: by health status (a), by development dynamics (b), integrated assessment (c)

 

The application of the author's comprehensive assessment methodology made it possible to identify territories with critical values of medical, demographic, and socio-economic indicators. The results obtained provide a scientific basis for differentiated planning of measures to optimize the healthcare system at the regional level, ensuring the targeted allocation of resources and the development of targeted intervention programs in both operational and strategic aspects of management. The spatial patterns identified allow to rank territories by priority of intervention based on empirically confirmed criteria.

 Conclusions

1. An innovative methodology for the integrated assessment of public health has been developed and tested, combining classical and trend-based approaches. The methodology includes the calculation of three summary indicators (health status rating, development rating, and integral rating), standardized on a 100-point scale, and allows for multidimensional analysis taking into account both current indicator values and their dynamics.
2. Significant regional differences in key medical, demographic, and socioeconomic indicators have been identified in the Republic of Kazakhstan. Cluster analysis confirmed significant differences between regions in areas such as fertility, mortality, life expectancy, morbidity, availability of medical personnel and resources, and economic indicators.
3. The results obtained allow regions to be ranked according to the priority of interventions and provide a scientific basis for the development of differentiated management decisions in the field of healthcare. The methodology enables the targeted allocation of resources and the development of targeted programs to optimize the healthcare system at the tactical and strategic levels.

Funding. The study had no external funding.

Conflict of interest. The authors declare no conflict of interest.

Author contributions:

Begun D.N., Borshchuk E.L. – research concept and design.

Omarova D.S. – data collection.

Begun D.N., Bulycheva E.V. – analysis and interpretation of results.

Omarova D.S. – literature review.

Bulycheva E.V., Begun D.N. – manuscript preparation.

Borshchuk E.L. – manuscript editing.

All authors reviewed the results of the work and approved the final version of the article.

Study limitations. The study complies with the standards of the Declaration of Helsinki and has been approved by the Ethics Committee of the Ye.A. Vagner Perm State Medical University, protocol No. 6 dated September 10, 2025.

¹ N.N. Kiseleva. Sustainable development of the region's socio-economic system: research methodology, models, management: dissertation ... Doctor of Medical Sciences. Rostov-on-Don, 2008; 168.

² A.N. Dusaymbaeva, D.S. Omarova, D.N. Begun, E.L.Borshchuk ROZA-v.1.0. Certificate of state registration of computer software No. 2022661253. Patent holder: Federal State Budgetary Educational Institution of Higher Education Orenburg State Medical University of the Ministry of Health of Russia. 2022. Bulletin No. 6.

About the authors

D. N. Begun

Orenburg State Medical University

Email: be@orgma.ru
ORCID iD: 0000-0002-8920-6675

DSc (Medicine), Associate Professor, Head of the Department of Nursing

Russian Federation, Orenburg

E. L. Borshchuk

Orenburg State Medical University

Email: be@orgma.ru
ORCID iD: 0000-0002-3617-5908

DSc (Medicine), Professor, Head of the Department of Public Health and Healthcare №1

Russian Federation, Orenburg

E. V. Bulycheva

Orenburg State Medical University

Email: be@orgma.ru
ORCID iD: 0000-0002-8215-8674

PhD (Medicine), Associate Professor, Associate Professor of the Department of Nursing

Russian Federation, Orenburg

D. S. Omarova

Orenburg State Medical University

Author for correspondence.
Email: be@orgma.ru
ORCID iD: 0000-0002-9431-1998

Postgraduate Student of the Department of Nursing

Russian Federation, Orenburg

References

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