Population Health in Uzbekistan: Emerging Public Health Trends and Widening Regional Disparities

2.1. Indicator Selection and Justification

Three indicators were selected based on their international comparability, availability in Uzbekistan’s national statistics, and relevance to core dimensions of population health:

1. Life Expectancy (LE) – captures overall population longevity.
2. Infant Mortality Rate (IMR) – a globally recognized proxy for health system performance.
3. Access to Healthcare Services (AHS) – reflects structural accessibility and service availability.

These indicators represent mortality, survival, and health system access-the minimum set required for a balanced and interpretable composite index. Similar three-component frameworks have been used in global health assessments and regional inequality studies [17, 18].

Equal weighting was applied because:

1. All three indicators represent distinct but equally important dimensions;
2. No empirical evidence supports differential weighting for Uzbekistan;
3. Equal weights are recommended when building first-generation indices to avoid subjective bias.

2.2. Data Sources

Data were obtained from the Statistical Agency under the President of Uzbekistan for all 14 administrative regions (2010–2023). Only official, methodologically consistent datasets were included.

2.3. Data Quality Checks and Cleaning

A structured data-cleaning procedure was applied:

2.4. PHI Construction

Indicators were normalized using min–max scaling to ensure comparability (Eq. 1):

IMR was reverse-scaled (lower = better health) (Eq. 2):

The Public Health Index for each region r and year t was calculated as (Eq. 3):

Regional averages (APHI) were computed across the 2010–2023 period.

2.5. Spatial Analysis

ArcGIS 10.8 was used to map PHI values and visualize regional disparities [19].

Key steps included:

1. Spatial data preparation: attaching PHI values to the regional shapefile (UZB_admin1).
2. Classification method: the maps used Natural Breaks (Jenks) to highlight real differences between high- and low-performing regions [20, 21].
3. Visualization: regions were categorized into five PHI classes to ensure interpretability and comparability [22].

This GIS-based approach allows clear spatial interpretation of health inequalities across Uzbekistan.

3.1. Overview of Indicators Used in PHI Calculation

The Public Health Index (PHI) was calculated for all 14 administrative regions of Uzbekistan for the period 2010–2023 using Life Expectancy (LE) Infant Mortality Rate (IMR), and Access to Healthcare Services (AHS). Tables 1-3 summarize the annual values of these indicators. All data were obtained from the Statistical Agency under the President of the Republic of Uzbekistan.

3.2. Regional Patterns in Component Indicators (2010–2023)

3.3. Public Health Index (PHI) Variability (2010–2023)

This study employs the Public Health Index (PHI) as a composite measure incorporating multiple health indicators to provide a comprehensive assessment of the population's health status (Fig. 2). The PHI is calculated using the following formula in Eq. (4):

Across the 14-year period:

• PHI ranged from 6.2 to 14.5, demonstrating substantial regional variability (Fig. 3).
• Regions generally fell into four classification groups: poor (<7), moderate (7–8.99), good (9–11.99), and excellent (≥12) (Table 4).

3.4. Average Public Health Index (APHI) by Region

Mean PHI values for 2010–2023 were computed to identify long-term public health levels. Table 5 presents the APHI values and classification categories.

In summary:

A 14-year analysis of PHI values across Uzbekistan's administrative units was conducted to determine regional health trends. The next phase involved grouping these indicators into qualitative classifications based on regional averages (Table 5). The Average Public Health Index (APHI) for each region was calculated as follows in Eq. (5):

APHI index for the Republic of Karakalpakistan (Eq. 6):

APHI index for Andijan region (Eq. 7):

APHI index for Bukhara region (Eq. 8):

APHI index for Fergana region (Eq. 9):

APHI index for Namangan region (Eq. 10):

APHI index for Navoi region (Eq. 11):

APHI index for Jizzakh region (Eq. 12):

APHI index for Syrdarya region (Eq. 13):

APHI index for Surkhandarya region (Eq. 14):

APHI index for Tashkent region (Eq. 15):

APHI index for Khorezm region (Eq. 16):

APHI index for Kashkadarya region (Eq. 17):

APHI index for Samarkand region (Eq. 18):

APHI index for Tashkent city (Eq. 19):

3.5. Spatial Clustering of APHI Values

The spatial representation of APHI (Fig. 4) in ArcGIS 10.8 shows clear geographic grouping patterns. Regions were assigned color-coded classes based on the APHI scale:

• Green (Excellent, APHI ≥ 12): Only two regions, Samarkand (13.00) and Fergana (13.87), fall into this category, indicating the most favorable public health conditions.
• Blue (Good, APHI 9 - 11.99): A substantial portion of the country falls within this range, including Navoi (11.84), Bukhara (11.11), Kashkadarya (11.72), Jizzakh (9.87), Surkhandarya (10.41), and Tashkent (10.00). These regions maintain relatively good health conditions, though they do not reach the highest level.
• Beige (Moderate, APHI 7 - 8.99): The Republic of Karakalpakstan (7.32), Khorezm (7.79), Namangan (8.57), Andijan (8.73), and Syrdarya (7.09) exhibit moderate health conditions, as reflected in their lower APHI values.
• Red (Poor, APHI < 7): Tashkent City (6.73) is the only region classified under this category, indicating significant public health challenges.

These spatial patterns provide a descriptive overview of regional public health conditions; interpretation of underlying causes is presented in the Discussion section.

4.1. Key Findings

This study identified clear regional disparities in public health across Uzbekistan between 2010 and 2023. Fergana and Samarkand showed the highest APHI values. indicating consistently favorable health conditions, while Tashkent City, Karakalpakstan, Syrdarya, and Khorezm exhibited the lowest long-term scores. Spatial clustering revealed a distinct east–west gradient: eastern and central regions tended to perform better. whereas western and northwestern regions remained persistently below the national average. The PHI also demonstrated a steady national improvement over time. driven mainly by declining infant mortality.

4.2. Interpretation of Regional Patterns

The observed differences align with region-specific demographic and environmental profiles reported in national and international studies.

Western and northwestern regions - including Karakalpakstan and Khorezm-record lower APHI values. consistent with documented environmental stressors such as Aral Sea–related dust pollution. high respiratory disease prevalence. and limited access to specialized medical care (UNEP, 2022; WHO, 2020).

Central and southern regions (Samarkand, Bukhara, Kashkadarya, Navoi) benefit from relatively stable demographic structures and broader healthcare coverage. reflected in higher LE and lower IMR.

Eastern regions show internal variability: Fergana performs strongly across all indicators. In contrast, Andijan and Namangan remain moderate, mirroring earlier findings on the uneven distribution of medical resources within the Fergana Valley.

Tashkent City has an extensive healthcare infrastructure and the lowest APHI due to high IMR and environmental pressures associated with traffic emissions, population density, and reduced green space availability-patterns consistent with recent urban health assessments in Central Asian capitals.

These interpretations are evidence-based, drawing on documented environmental and demographic characteristics rather than assumptions.

4.3. Comparison with Existing Literature

The findings align with earlier work indicating strong links between environmental quality, healthcare accessibility, and population health outcomes in Central Asia. Prior studies highlight elevated morbidity in western Uzbekistan linked to atmospheric pollution and water scarcity. The relatively higher performance of Samarkand and Fergana corresponds to research showing stronger primary healthcare networks and more favorable socio-economic profiles in these regions (MOH, 2021). International literature likewise supports the use of composite indices-such as PHI or HDI-based health measures-to capture multidimensional health differences across territories.

4.4. Strengths and Limitations

A key strength of this study is the use of a transparent composite index derived from three robust indicators with complete regional coverage over 14 years, allowing for consistent comparisons. The application of GIS visualization adds a spatial dimension that enhances interpretability.

However, the analysis is limited to indicators available in official datasets and does not incorporate additional determinants such as air-quality metrics, income distribution, or disease-specific morbidity rates. Equal weighting of indicators, while methodologically justified for transparency, may not fully capture the differential importance of LE, IMR, or AHS. Future research could test alternative weighting schemes or incorporate more granular environmental and socio-economic variables.

4.5. Policy Implications

The results point to several region-specific policy needs:

• Western regions (Karakalpakstan, Khorezm, Syrdarya): Policies should prioritize environmental remediation, expansion of primary healthcare facilities, and targeted maternal-child health programs, given persistently high IMR and lower LE.
• Tashkent City: Urban health interventions must focus on air-quality management, transport-related emissions, and green-infrastructure expansion as these environmental pressures align closely with the city’s low APHI.
• Moderate-performing eastern regions (Andijan, Namangan): Strengthening healthcare access and addressing internal resource disparities within the valley could reduce intra-regional inequality.
• High-performing regions (Fergana, Samarkand): Continued investment in preventive care and maintenance of current healthcare standards is essential to sustain gains.

The PHI provides a practical tool for monitoring regional progress and evaluating health policy effectiveness over time. Its use by national and regional authorities could support evidence-based planning aligned with the country’s public health reform agenda.