Persistent Spatial Health Inequalities in Czechia: A Two-Decade Analysis Using a Holistic Determinants Model

Persistent Spatial Health Inequalities in Czechia: A Two-Decade Analysis Using a Holistic Determinants Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Persistent Spatial Health Inequalities in Czechia: A Two-Decade Analysis Using a Holistic Determinants Model Dana Hübelová, Beatrice-Elena Chromková Manea, Jan Caha, Alice Kozumplíková This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7289799/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Health inequalities represent a persistent and pressing public health challenge. This study investigates the spatial patterns and temporal dynamics of health inequalities in Czechia over two decades (2001–2021), using a holistic model of health determinants. The analysis is conducted at the LAU 1 regional level and incorporates 57 indicators across seven categories of contextual determinants (A.1–A.7) and a composite index of population health outcomes (B). Composite indicators were developed using the Weighted Sum Approach and spatial relationships were explored using Moran’s Index and Local Indicators of Spatial Association (LISA). Statistical significance of temporal change was tested using the Wilcoxon Signed-Rank Test, and interregional inequality was measured with the Theil Index. Results indicate that while some determinants improved, particularly economic and social conditions (A.1), education (A.2), and individual living status (A.5), others remained stagnant or deteriorated. The composite determinant index (A.1–A.7) improved between 2001 and 2011 but stagnated thereafter. Spatial clustering of low values was repeatedly observed in both urban and rural peripheral regions, with increasing disparities in access to care (A.7) and environmental status (A.4). The findings suggest that health inequalities in Czechia are structurally embedded and remain stable over time, despite policy efforts. Regional disparities reflect a complex interplay of socioeconomic deprivation, institutional capacity, selective migration, and territorial development trajectories. This study highlights the need for more targeted, locally sensitive interventions and improved coordination between health and social policy. The methodological framework is scalable and can be used for ongoing monitoring and international comparison of health inequalities. Spatial health inequalities Holistic determinants Regional disparities Temporal analysis Social determinants Public health Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction Health equity is widely recognised as a fundamental pillar of a just society and has long held a prominent place in public health and social policy discourse. Despite this, substantial disparities in health outcomes continue to exist across different population groups, regions, and social strata. These health inequalities are considered unjust and avoidable differences (Marmot, 2010 ), resulting from a wide array of determinants, including socioeconomic, environmental, and behavioural factors (Schoon & Krumwiede, 2022 ). The relationship between health inequalities and broader socio-economic disparities is well documented (Sen et al., 2009; Kapilashrami & Hankivsky, 2018; Luiz et al., 2020). The social gradient in health has been identified as one of the most robust indicators of inequality in public health research (Yang et al., 2020 ). Furthermore, ample evidence shows that health inequalities are persistent across time and across societies (Mackenbach, 2012). These disparities are not limited to differences between countries but also occur within them, cutting across all layers of the social hierarchy (Ottersen et al., 2014; Cabrera-Barona et al., 2015; Agénor, 2020). Health inequalities also have a pronounced spatial dimension, evident between regions, between urban and rural areas, and even within neighbourhoods in individual cities (Lakes et al., 2013; Fayet et al., 2020; Floková et al., 2023). Health disparities have been reported in all European countries, with life expectancy gaps reaching 5–10 years, and differences in healthy life expectancy spanning 10–20 years (Mackenbach, 2006; Mackenbach et al., 2008). In the United States, the life expectancy difference between the wealthiest and poorest men reaches 14.6 years, and among women 10.1 years, with disparities deepening based on geographical location and immigrant population density (Chetty et al., 2016). Evidence also suggests that in many European countries and the U.S., these inequalities are widening (Singh & Siahpush, 2006 ; Mackenbach et al., 2015 ; Marmot, 2020 ). The link between poverty and poor health outcomes is well established (Hamlin, 1998; Foege, 2010). Greater income inequality tends to be associated with more pronounced health disparities (Hong & Ahn, 2011). A statistically significant relationship between cardiovascular mortality and GDP levels was demonstrated in a panel of 27 European countries between 2003 and 2014, following an inverted U-curve—initially rising with GDP growth before declining (Spiteri & von Brockdorff, 2019 ). Previous research in the Czech context has developed a systemic methodological framework incorporating social, economic, demographic, environmental, and individual health determinants (see e.g., Hübelová et al., 2021 ). This framework enabled the construction of an extensive dataset and the development of online cartographic visualisations (Health Index, 2020). It resulted in the first comprehensive model for assessing health inequalities under Czech conditions, based on the holistic public health determinants model proposed by Shi & Zhong (2014); see also the updated framework in Hübelová et al. ( 2023 ). This article builds on this foundation, aiming to enhance both the methodological and analytical approaches to health inequality research by identifying spatial patterns, regional disparities, and their development over time. The analysis focuses on the regional (LAU 1) level in Czechia, using data from three reference years: 2001, 2011, and 2021. The main objectives are to: Identify spatial patterns of health inequalities and determine regions where these disparities are concentrated. Compare regions with pronounced and less pronounced inequality patterns and assess differences in determinant categories. Measure the degree of interregional disparities, classify the types of inequalities, and trace their temporal evolution. While earlier studies provided a static classification of determinants, this work offers a more dynamic and differentiated perspective, capturing both temporal shifts and spatial variability, including intra-regional differences. The importance of holistic approaches (Schoon & Krumwiede, 2022 ; Hübelová et al., 2023 ), interdisciplinary methods (Browne et al., 2012), and systems-based frameworks (Shi & Zhong, 2014; Hernández et al., 2017; Hübelová et al., 2021 ) in studying health inequalities is well documented. These approaches account for the interplay of multiple determinants. The first comprehensive model was introduced in the Canadian report A New Perspective on the Health of Canadians (Lalonde, 1974), followed by the conceptual model of social determinants developed by Dahlgren & Whitehead ( 1991 ), which included four interrelated categories and has been applied in many contexts (Barton, 2005; Barton et al., 2010). Shi & Zhong (2014) integrated these frameworks into a three-level model encompassing behavioural, social, and environmental determinants, all interacting with one another. They emphasised that health is shaped not only by individual decisions but also by structural socio-economic and political conditions. In our analysis, we examined the influence of determinant categories A.1 through A.7 on health conditions (Category B). Category B includes life expectancy indicators by age and gender, which serve as proxies for mortality and indirectly reflect quality of life and broader socioeconomic conditions (Mackenbach, 2006; Mackenbach et al., 2008; Elo, 2009; Kaikkonen et al., 2009; Aittomäki et al., 2010), education (Leinsalu et al., 2003; Mackenbach et al., 2018 ; Costa, Freitas et al., 2019 ; Costa, Santana et al., 2019 ), and access to healthcare (Ho & Hendi, 2018). The category also incorporates data on causes of death (Lillini, Quaglia et al., 2012 ; Hübelová, Kozumplíková, & Walicová, 2020; Hübelová, Kozumplíková, Kosová et al., 2020; Ruiz et al., 2024 ) and indicators of reproductive health (Kyriopoulos et al., 2019). Analysis of regional disparities in Czechia indicates that the strongest associations between determinant categories and health conditions (Category B) are found in economic status and social protection (A.1) and education (A.2) (Hübelová et al., 2023 ). These findings align with international research (Mackenbach et al., 2018 ; Santana et al., 2017 ; Costa, Santana et al., 2019 ; Pornet et al., 2012 ; Arcaya et al., 2016; Manor et al., 2003 ; Lahelma et al., 2004 ; Petrelli Alessio et al., 2019; Aittomäki et al., 2010), confirming the critical role of these factors in shaping health outcomes. A cluster analysis of the composite index constructed from categories A.1–A.7 revealed both regional cores and peripheries. However, the definition of peripherality in the Czech context is complex due to its heterogeneous nature. The Czech periphery is not homogeneous—it comprises both urban and rural territories, with further distinctions between internal and external peripheries. This challenges the often-held assumption that urban areas consistently outperform rural ones in health outcomes (Lakes et al., 2013). A comparison of data from 2001–2003 and 2017–2019 shows a partial narrowing of disparities, yet the most pronounced health inequalities persist in external urban peripheries as well as in rural regions—regardless of whether they are internally or externally peripheral (Hübelová et al., 2023 ). Among the key drivers that exacerbate health inequalities are limited employment opportunities, underdeveloped transport and social infrastructure (Gløersen et al., 2012), selective migration that drains younger populations (Pileček et al., 2013), and unaffordable housing (Marsden et al., 1993). Although the overall share of socially excluded individuals remains relatively low in Czechia, significant micro-regional health disparities persist, as confirmed by multiple studies (e.g., Hübelová, Chromková Manea et al., 2021). The conceptual model used in this study is modular and scalable, allowing for spatial and temporal analysis of health inequalities (Hübelová et al., 2023 ). The contextual risk determinants are grouped into seven categories (A.1–A.7), and Table 1 provides a detailed overview of the sources used for each. Table 1. Overview of resources used for each category A1 Economic Status and Social Protection (Carstairs & Morris, 1989 ); (Davey Smith et al., 1998 ); (Elstad, 2001 ); (Manor et al., 2003 ); (Rey et al., 2009 ); (Pornet et al., 2012 ); (Santana et al., 2017 ); (Mackenbach et al., 2018 ); (Bosakova et al., 2019 ); (Costa, Freitas, et al., 2019 ); (Costa, Santana, et al., 2019 ); (Norström et al., 2019 ); (Lillini et al., 2019 ); (Spiteri & von Brockdorff, 2019 ); (Vu, 2020 ). A2 Education (Kickbusch, 2001 ); (Lahelma et al., 2004 ); (Solar & Irwin, 2010 ); (Mackenbach et al., 2018 ); (Petrelli et al., 2019 ); (Yang et al., 2020 ). A3 Demographic Situation (Ramos et al., 2016 ); (Srivarathan et al., 2019 ); (O’Connell et al., 2019 ); (Ruiz et al., 2024 ). A4 Environmental Status (James et al., 2016 ); (Crouse et al., 2017 ); (Roh et al., 2017 ); (Savoye et al., 2018 ); (Crouse et al., 2019 ); (Rojas-Rueda et al., 2019 ); (Sun et al., 2020 ). A5 Individual Living Status (Dahlgren & Whitehead, 1991 ); (Marmot, 2010 ); (OECD, 2020 ); (Eurofound, 2021 ). A6 Road Safety and Crime (Nolan, 2004 ); (Christie, 2018 ); (Touahmia, 2018 ). A7 Sources of Health and Social Care (Guagliardo, 2004 ); (Foster et al., 2008 ); (Tanke & Ikkersheim, 2012 ); (Chotvijit et al., 2018 ); (Gao et al., 2022 ); (Šídlo & Maláková, 2022 ). 2. Materials and methods Spatial differentiation in the determinants of health inequalities and population health status was analysed at the LAU 1 level (Local Administrative Units) in Czechia, comprising 76 units plus the capital city of Prague. A dataset of 57 indicators was compiled for each region, grouped into seven categories of health determinants (A.1 to A.7) and one category for health status (B), which included 24 indicators (see Appendix 1). Although LAU 1 units lack formal legal status in Czechia, they were selected due to the practical advantage of data availability. In contrast, higher-level territorial units (NUTS2 and NUTS3) are fragmented both geographically and economically, limiting their suitability for detailed spatial analysis. The study covers three reference years—2001, 2011, and 2021—corresponding to census years with complete datasets for all indicators. Data were sourced from publicly available databases, including the Czech Statistical Office (CZSO), the Institute of Health Information and Statistics (IHIS), the Ministry of Labour and Social Affairs (MoLSA), and the Czech Hydrometeorological Institute (CHMI). To facilitate interpretation, a composite index was calculated for each category (A.1 to A.7 and B), aggregating multiple indicators into a single score ranging from 0 to 1. Higher values denote more favourable outcomes. Composite indices were constructed using the Weighted Sum Approach (WSA), a method based on utility maximisation principles. For details, see Hübelová, Kuncová et al. ( 2021 )d belová et al. (2023). Descriptive statistics were used to summarise the resulting index values. For the spatial analysis, several analytical tools were employed. To assess spatial autocorrelation, Moran’s I was used (Moran, 1950; Anselin, 1995). This index measures the similarity of values among neighbouring regions. Its values range from − 1 (strong negative autocorrelation) to + 1 (strong positive autocorrelation), with values near 0 indicating random spatial distribution. To identify localised spatial patterns, the Local Indicators of Spatial Association (LISA) method was applied (Anselin, 1995). This method classifies spatial relationships into four types: High-High (HH): high values surrounded by high values (positive clusters) Low-Low (LL): low values surrounded by low values (negative clusters) High-Low (HL): high values surrounded by low values (spatial outliers) Low-High (LH): low values surrounded by high values (spatial outliers) Together, Moran’s I and LISA allowed for the identification of regional clusters and spatial outliers, highlighting concentrations of inequality. This spatial focus was necessary due to the large number of LAU 1 units and the three reference years, making region-by-region descriptions impractical. Assessing changes in LISA classifications over time is complex. In addition to tracking changes in local Moran’s I values, it is also necessary to consider shifts in overall spatial autocorrelation within the dataset. Changes were evaluated based on shifts in a region’s classification across LISA categories, as outlined in Table 2. Table 2. Typology of changes in LISA categories Category at time t Category at time t + 1 Evaluation of change High High No Change High Insignificant Decrease High Low Significant decrease Insignificant Insignificant No Change Insignificant Low Decrease Insignificant High Increase Low Low No Change Low‍ Insignificant Increase Low High Significant increase To identify statistically significant changes over time (2001–2011, 2011–2021, and 2001–2021), the Wilcoxon Signed-Rank Test for paired data was applied. This non-parametric test was used to detect whether index values in each category (A.1 to A.7), the composite index (A.1–A.7), and the health status index (B) increased, decreased, or remained stable. A significance level of 5% (p < 0.05) was used. The test was conducted in three forms: Two-tailed test: to identify any statistically significant change, regardless of direction One-tailed left-sided test: to detect whether newer values are significantly higher (improvement) One-tailed right-sided test: to detect whether newer values are significantly lower (deterioration) To quantify regional disparities in health determinants, the Theil Index was used. This index captures both absolute and relative deviations of regional values from the national mean (Gao et al., 2022 ). The Theil Index is non-negative, with zero indicating perfect equality and higher values representing greater inequality. It does not have a fixed upper bound, as its magnitude depends on the degree of concentration within the dataset. 3. Results The descriptive statistics for the years 2001, 2011, and 2021 enable an assessment of trends in selected determinants of health inequalities (A.1 to A.7) and in population health indicators (B) across regions in Czechia. A composite index (A.1–A.7), which summarises these individual dimensions into a single score, is also included in the evaluation. Most index categories showed positive development between 2001 and 2011. After 2011, however, some indicators began to stagnate or even decline. Economic and social conditions (A.1 Ec-soc) worsened between 2001 and 2011 but improved again by 2021. This development suggests some instability, although the 2021 values were slightly higher than those in 2001. Median values confirm this trend. The education index (A.2 Edu) showed steady, although relatively slow, improvement across all three time points. The demographic situation index (A.3 Demo) improved between 2001 and 2011, but a slight decline between 2011 and 2021 points to stagnation and a potentially worsening trend. Environmental conditions (A.4 Envi) improved from 2001 to 2011, but the index declined between 2011 and 2021. This decline may not necessarily reflect a deterioration in environmental quality—it could also be influenced by changes in monitoring or data interpretation methods. Individual-level indicators (A.5 Indiv) recorded significant growth, especially between 2011 and 2021. This is reflected in both the mean and median values. Safety (A.6) remained stable throughout the period, with no major changes in either the average or the median. Health and social care (A.7 Care) improved in 2011 but declined again in 2021. The drop is visible in both mean and median values, indicating reduced capacity in health and social care services. The Health Condition Index (B) changed only slightly over time and showed a modest improvement overall, suggesting general stability in health outcomes. The composite index (A.1–A.7) increased between 2001 and 2011 but remained virtually unchanged between 2011 and 2021 (see Fig. 1). The standard deviation for the composite index was 0.037 in 2001, 0.048 in 2011, and 0.042 in 2021. Although variability peaked in 2011, overall dispersion remained low, indicating a relatively stable distribution of values across regions. This suggests a concentration of values around the median rather than any major spatial shifts. For the Health Condition Index (B), the standard deviation was 0.109 in 2001, dropped to 0.061 in 2011, and rose again to 0.114 in 2021. While these fluctuations suggest some regional variation, the differences are not substantial. The increase from 2011 to 2021 may point to a slight rise in disparities (see Fig. 1). The boxplot illustrates the distribution of values for each index and complements the descriptive statistics. Prominent outliers—especially in A.2 Edu—appear as points beyond the box limits. The interquartile ranges for A.1, A.2, A.3, and A.5 narrow over time, indicating reduced variability and a gradual convergence of regional values. Conversely, A.4 and A.7 display increasing variability. For Health Condition B, the boxplot for 2011 shows reduced dispersion compared to 2001, but in 2021, variability increases again, with the appearance of extreme values. This may suggest growing regional disparities (see Fig. 2). 3.1 Composite Index of Health Inequality Determinants A.1–A.7 and Health Condition B Moran’s Index for the composite index of health inequality determinants (A.1–A.7) shows values of 0.26, 0.51, and 0.39 for the years 2001, 2011, and 2021, respectively. This indicates a progression from weak to moderate spatial autocorrelation in 2001, to moderately strong autocorrelation in 2011, followed by a partial weakening of spatial patterns in 2021. This trend may signal a dispersal of spatial clusters and a potential shift in regional concentration. LISA cluster maps identify stable clusters of regions with both high and low values of the composite index. Low-Low areas remain concentrated in the northwestern and northeastern border peripheries, while High-High areas are primarily located in the central regions of Bohemia and in southern regions, which become more prominent in 2021 (see Fig. 3–5). For Health Condition B, spatial autocorrelation in the years 2001, 2011, and 2021 is characterized by Moran’s Index values of 0.55, 0.44, and 0.54, respectively. These figures indicate stable clustering of similar values in space, with persistent regional disparities. Low-Low areas remain concentrated in the northwestern border periphery, while High-High areas are found in the southeastern regions and the Bohemian-Moravian border zone (in 2001). By 2011, additional High-High clusters appear in the capital city region and its surroundings, and by 2021, also in parts of eastern Bohemia (see Fig. 6–8). 3.2 Change in the Relative Position of LAU1 Regions Compared to the National Average For the composite index of health inequality determinants (A.1–A.7) and Health Condition B, maps were created to illustrate changes in the relative position of districts compared to the national average, based on comparisons between the years 2001 and 2011, and 2011 and 2021. Changes are categorized into three levels (low, average, high), where "increase" or "decrease" indicates a shift by one category, and "significant increase" or "significant decrease" denotes a jump by two categories. A comparison of changes in composite indicator A.1–A.7 between 2001 and 2011 shows that for most LAU1 regions, no significant shift occurred (gray clusters). Only a small number of LAU1 units (10 out of 77) experienced an increase, meaning an improvement, which includes both regions with initially high index values (e.g., the capital city and its metropolitan area) and those with below-average index values (e.g., the outer border periphery). The same applies to LAU1 regions that showed a decline. A comparison of the period 2011 to 2021 again indicates no change in the majority of LAU1 regions. However, compared to the previous decade, a greater number of regions showed improvement across the assessed levels, including some that had previously declined. These are primarily LAU1 units located in both inner and outer peripheries. A particularly notable case is the region of Ústí nad Labem, which moved from a significant decline to a significant improvement (from dark red on the left to dark blue on the right; see Fig. 9–10). In the Health Condition B category, a comparison of changes between 2001 and 2011 reveals that most LAU1 regions experienced no significant shift (gray clusters). Only a small number of LAU1 units (nine out of 77) showed a significant increase in Index B. As with the composite index A.1–A.7, these include both regions with previously high values (e.g., the capital city and its surrounding area) and those with below-average values (such as the outer border periphery and inner peripheral zones). The same pattern applies to LAU1 units that recorded a decline. The comparison between 2011 and 2021 again indicates that the majority of LAU1 regions remained unchanged and continue to be represented in gray. Compared to the previous period, the number of regions showing improvement across the assessed levels decreased, and some of them overlap with areas that had previously demonstrated growth (see Fig. 11–12). 3.3 Temporal Dynamics of Health Inequality Determinants Between 2001 and 2011, most health inequality determinants (A.1–A.5, A.7, B) showed statistically significant changes (Wilcoxon p < 0.05), indicating a period of considerable structural transformation. Only A.6 remained stable. From 2011 to 2021, the extent of change was more moderate, with significant shifts limited to A.1, A.3–A.5, A.7, and B. Variables A.2 and A.6 did not change significantly, and the composite index remained stable, suggesting overall stagnation in inequality structures in the latter period (see Table 3). Table 3. Statistical significance of changes in determinants (2001–2021) Variable Is there a statistically significant difference between the years 2001 and 2011? Is there a statistically significant difference between the years 2011 and 2021? A.1 Yes (p-value − 0.0002093) Yes (p-value − 0.0000000031) A.2 Yes (p-value − 0.02426) No (p-value − 0.06885) A.3 Yes o (p-value − 0.00000004298) Yes (p-value − 0.03127) A.4 Yes (p-value − 0.00003739) Yes (p-value − 0.01178) A.5 Yes (p-value − 0.04687) Yes (p-value − 0.000000000104) A.6 No (p-value − 0.3568) No (p-value − 0.9798) A.7 Yes (p-value − 0.0000000002437) Yes (p-value − 0.000000000007319) B Yes (p-value − 0.0002123) Yes (p-value − 0.03642) A.1–A.7 Yes (p-value − 0.00000004663) No (p-value − 0.5177) The directional Wilcoxon tests indicate that the categories A.1, A.2, A.5, and B significantly improved over time (left-sided test), while A.3, A.4, and A.7 displayed significant declines (right-sided test). The remaining categories, including A.6 and the composite index, exhibited no statistically significant trend in either direction, suggesting structural inertia (see Table 4). Table 4. Direction of trends in health inequality determinants (2001–2021) Indicator left-sided test (improvement over time) right-sided test (deterioration over time) description A.1 Yes No Improved A.2 Yes No Improved A.3 No Yes Improved A.4 No Yes Improved A.5 Yes No Improved A.6 No No Stable, no trend A.7 No Yes Improved B Yes No Improved Index A.1–A.7 No No Stable, no trend The Theil index results reveal persistent and pronounced regional disparities in certain determinants, particularly A.2 (Education), which maintained the highest inequality levels across all years. Between 2001 and 2011, inequalities declined in several categories (e.g., A.3, A.4, A.6, A.7), but increased in A.1 and A.5. The composite index rose slightly during this period. By 2021, a renewed increase in disparities was observed in A.4 (Environment), A.7 (Care), and B (Health Condition), while improvements occurred in A.1, A.3, and A.5. The composite index slightly decreased, indicating a partial reduction in regional inequality overal (see Table 5). Table 5. Theil index of interregional disparities A.1 Ec-soc A.2 Edu A.3 Demo A.4 Envi A.5 Indiv A.6 Safety A.7 Care B Health Condit INDEX A.1-A.7 2001 2.12 9.98 2.22 1.96 3.80 1.06 3.87 1.25 0.31 2011 3.01 9.09 0.84 0.37 5.65 0.53 2.75 0.45 0.42 2021 1.08 8.47 0.58 3.30 2.64 0.76 5.52 1.49 0.32 4. Discussion Spatial health inequalities in Czechia are shaped by a complex interplay of historical, economic, and social forces that influence the territorial organisation of society. Our findings confirm that these inequalities persist over time and reveal distinct spatial patterns at the LAU 1 level. Importantly, they are not merely the result of individual-level factors but are deeply embedded in broader structural and geographical contexts (Smętkowski, 2013 ; Capello & Caragliu, 2021 ; Vinci, 2021 ). Regional differences in Czechia also reflect varied historical and geographical trajectories (Macintyre et al., 2002 ). The spatial inequalities identified in our study mirror both the socioeconomic makeup of regions and long-term geographic processes, such as rural restructuring, territorial polarisation, and divergent institutional capacities. These patterns align with theoretical insights from Hampl ( 2007 ), Musil & Müller ( 2008 ), and Perlín et al. ( 2010 ), who describe spatial dynamics contributing to the reproduction of peripheral status. Building on this, Netrdová et al. ( 2025 ) introduced the concept of vulnerability amplification, which accounts for both compositional (socioeconomic characteristics) and contextual (e.g., institutional capacity, access to care, risk perception) factors. Although their research focused on covid-19, they demonstrated how regional health inequalities are shaped not only by individual characteristics but also by the interaction between individuals and their environment. This is particularly pronounced in regions marked by low education levels, high unemployment, and underdeveloped public infrastructure. Socioeconomic functioning in such regions is further influenced by macro-political and economic processes at national and international levels (Bambra et al., 2019 ). Our analysis highlights persistent concentrations of health inequalities, especially in socioeconomic status, education, and access to health and social care (Lillini et al., 2012 ). Selective migration and related demographic dynamics further exacerbate these disparities. The most severe inequalities are consistently found in peripheral regions—both urban and rural—reflecting a structural characteristic of the Czech context (Hübelová et al., 2023 ). Socioeconomic deprivation is strongly linked to higher incidences of diseases such as diabetes and cardiovascular conditions (Agardh et al., 2011 ; Tamayo et al., 2010 ), a connection supported by substantial evidence (Kerr et al., 2010 ; Lillini et al., 2012 ; Manrique-Garcia et al., 2011 ; Sommer et al., 2015 ). As demonstrated by our use of Moran’s I, spatial clustering of inequality has intensified, particularly in peripheries where socioeconomic and educational indicators remain low. Categories A.1 (economic and social conditions) and A.2 (education) showed the greatest disparities—consistent with previous research (Agardh et al., 2011 ; Mackenbach et al., 2018 ; Sommer et al., 2015 ). These findings reinforce the conclusion that socioeconomic factors and education play pivotal roles in shaping population health (Wu et al., 2004 ; Zhang & Kanbur, 2005 ; Marmot, 2010 ; Petrelli et al., 2019 ; Schoon & Krumwiede, 2022 ;). Education stands out as a particularly stable determinant of health. It affects health outcomes not only via income and employment (Solar & Irwin, 2010 ; Santana et al., 2017 ; Petrelli et al., 2019 ), but also through psychological and behavioural mechanisms (Egerter et al., 2009 ). Importantly, education is a non-reversible status that typically precedes the onset of major health issues, reducing the likelihood of reverse causation (Braveman & Gottlieb, 2014; Kröger et al., 2015 ). Individuals with lower educational attainment, occupational status, or income experience shorter life expectancy and higher disease prevalence (Mackenbach et al., 2018 ; Costa, Freitas, et al., 2019 ; Costa, Santana, et al., 2019 ). Social disadvantage is also linked to greater exposure to environmental risks (Šlachtová et al., 2016 ). The integration of spatial methods into public health research provides effective tools for identifying regional inequalities and supporting targeted interventions (Pearce et al., 2006 ; Cromley & McLafferty, 2011 ). In line with other studies(Lillini et al., 2019 ;Spiteri & von Brockdorff, 2019 ; Ruiz et al., 2024 ), our findings confirm the relationship between deprivation, spatial vulnerability (Netrdová et al., 2025 ), and adverse health outcomes, including mortality and reproductive health indicators. These patterns are influenced by regional economic structures and income distribution, as demonstrated in various contexts including Europe(Doran et al., 2004 ; Van Ourti et al., 2009 ), the United States (Lahiri et al., 2006 ), China (Wu et al., 2004 ; Zhang & Kanbur, 2005 ), and South Korea (Ahn et al., 2009 ). Despite policy efforts, the spatial distribution of health determinants and health outcomes in Czechia has remained relatively stable. This suggests that existing interventions have had limited success in mitigating regional disparities. The persistence of these patterns likely reflects both implementation gaps and enduring structural deficiencies—such as underdeveloped infrastructure and institutional fragmentation (Borrell et al., 2016 ; Netrdová et al., 2025 ). Even in high-income countries, reducing health inequalities remains a major challenge (Mackenbach et al., 2015 ). In some cases, disparities are even widening (Singh & Siahpush, 2006 ; Mackenbach et al., 2015 ; Marmot, 2020 ). Our findings support the argument that effective responses require locally tailored interventions that take into account the spatial distribution of inequality and the specific needs of affected populations. Strengths and Limitations This study benefits from the use of a consistent and systematically updated database of health determinants and indicators (Hübelová, Chromková Manea, et al., 2021; Hübelová, Kuncová, et al., 2021 ; Hübelová et al., 2023 ). Analytical methods were carefully selected to maximise the validity of causal interpretation, and all data were sourced from public databases, ensuring continuity and comparability over time. Nonetheless, we acknowledge that spatial patterns in mortality cannot be fully explained by social, economic, or environmental factors alone. While the composite index of health inequality determinants (A.1–A.7) increased between 2001 and 2011, it remained stagnant between 2011 and 2021. This raises the question of whether the index has lost sensitivity to subtle shifts in inequality trends. Future refinement may include adjusting indicator weights or incorporating additional dimensions—such as digital exclusion or access to green infrastructure. 5. Conclusion Regional health inequalities in Czechia persist and exhibit clear spatial patterns, particularly in relation to socioeconomic conditions, education, and access to health and social services (Lillini et al., 2012 ). These findings highlight the need for comprehensive, targeted policy responses that consider the unique needs of local populations (Mackenbach et al., 2015 ; Marmot, 2020 ). Our results underscore the role of socioeconomic deprivation as a key driver of entrenched spatial health inequalities. These disparities are especially prominent in both rural and urban peripheries, where low educational attainment and high unemployment intersect (Borrell et al., 2016 ). While the Czech case has its own specific features—such as a relatively expansive internal periphery—it also reflects broader regional patterns observed in other post-socialist countries. Similar processes are evident in Slovakia (e.g., peripheralisation of urban spaces), Poland (economic decline in marginal city districts), and Hungary (deindustrialisation of former urban cores). These shared features make the Czech experience analytically valuable for comparative research within the Visegrád region. This study enhances our understanding of the spatial and temporal dynamics of health inequalities and underscores the importance of consistent monitoring. The findings can inform public health and regional development policy, as well as service planning. Given the persistence of these inequalities, there is a pressing need to strengthen coordination between health and social systems and to ensure that regional strategies are responsive to the needs of vulnerable groups. Future research should focus on identifying high-risk subpopulations and designing interventions to improve health literacy and living conditions in socioeconomically disadvantaged areas. Such efforts could contribute to a more effective reduction of regional disparities and promote health equity across Czechia. Declarations Funding Declaration This research was funded by the Technology Agency of the Czech Republic in the Program ÉTA, grant number TL03000202 “Health Inequalities in the Czech Republic: Importance and Relationship of Health Determinants of Population in Territorial Disparities”. Data availability Data will be made available on request. Author Contribution D.H. contributed to the conceptualization, methodology, and supervision of the study, and was involved in writing the original draft and reviewing and editing the manuscript. B-E.CH.M. contributed to the conceptualization and methodology, and was responsible for writing the original draft and reviewing and editing the manuscript. J.C. performed the data curation, formal analysis, validation, and visualization, and contributed to the methodology. 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10:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7289799/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7289799/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89373837,"identity":"60c5dc9d-5710-43b9-ab47-c742c215ecec","added_by":"auto","created_at":"2025-08-19 10:39:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":191995,"visible":true,"origin":"","legend":"\u003cp\u003eMedian values of individual indices in 2001, 2011, and 2021; LAU1 Czech Republic\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7289799/v1/8abb91f877fc6a331e145844.png"},{"id":89373852,"identity":"b9b9fc3c-4d44-4801-873e-f7b328a62b50","added_by":"auto","created_at":"2025-08-19 10:39:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":90158,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of values in the observed categories (LAU1 Czech Republic, years 2001, 2011, and 2021)\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7289799/v1/dbf29617f69aa8271fb020bf.png"},{"id":89373846,"identity":"798e3602-26d6-4646-b242-70ac281e1898","added_by":"auto","created_at":"2025-08-19 10:39:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":346233,"visible":true,"origin":"","legend":"\u003cp\u003eVariable A.1–A.7 for the year 2001\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7289799/v1/98dc9e26c3ac09cd4ece17f8.png"},{"id":89373845,"identity":"3e89dc59-d7d7-41e5-b4cc-aad5a864f299","added_by":"auto","created_at":"2025-08-19 10:39:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":361522,"visible":true,"origin":"","legend":"\u003cp\u003eVariable A.1–A.7 for the year 2011\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7289799/v1/1466891fb2e1d10fc205c1ce.png"},{"id":89373849,"identity":"84b3f2fd-d44a-4762-9602-013d332cd8d3","added_by":"auto","created_at":"2025-08-19 10:39:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":366849,"visible":true,"origin":"","legend":"\u003cp\u003eVariable A.1–A.7 for the year 2021\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7289799/v1/39f40d66d040630287f3e0eb.png"},{"id":89374283,"identity":"3ede568e-dd63-4421-901e-69f76f27fc30","added_by":"auto","created_at":"2025-08-19 10:47:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":363201,"visible":true,"origin":"","legend":"\u003cp\u003eVariable B for the year 2001\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7289799/v1/7cf42fa720870041899f7865.png"},{"id":89374284,"identity":"211d759f-8177-4190-9f17-6c4ad8be5f7e","added_by":"auto","created_at":"2025-08-19 10:47:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":371355,"visible":true,"origin":"","legend":"\u003cp\u003eVariable B for the year 2011\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7289799/v1/6517628011a2a88a68babc48.png"},{"id":89373881,"identity":"2fe7a1e2-d4cc-4be7-8232-a3cd0c0c78f8","added_by":"auto","created_at":"2025-08-19 10:39:16","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":380922,"visible":true,"origin":"","legend":"\u003cp\u003eVariable B for the year 2021\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7289799/v1/80db1fa025633dab12b76bf4.png"},{"id":89373889,"identity":"7d07009d-ce3f-4433-9a12-8c6d05542063","added_by":"auto","created_at":"2025-08-19 10:39:17","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":404175,"visible":true,"origin":"","legend":"\u003cp\u003eChange in A.1–A.7 between the years 2001–2011\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7289799/v1/0cbae0378f811fb44b72eb43.png"},{"id":89373867,"identity":"3f093551-394e-471e-8e4f-e693cf90f788","added_by":"auto","created_at":"2025-08-19 10:39:16","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":390986,"visible":true,"origin":"","legend":"\u003cp\u003eChange in A.1–A.7 between the years 2011–2021\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-7289799/v1/16de27e6f4770fc54157536f.png"},{"id":89373874,"identity":"7a4a0e6e-ea32-4699-bef5-f77515bcef68","added_by":"auto","created_at":"2025-08-19 10:39:16","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":381119,"visible":true,"origin":"","legend":"\u003cp\u003eChange in B between the years 2001–2011\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-7289799/v1/c8af1e1744c4a23083b64f70.png"},{"id":89373877,"identity":"00087ae5-a8d8-4137-bdc5-fc8e4d3af608","added_by":"auto","created_at":"2025-08-19 10:39:16","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":453215,"visible":true,"origin":"","legend":"\u003cp\u003eChange in B between the years 2011–2021\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-7289799/v1/0a3cd931d205d1f27fd38063.png"},{"id":98629306,"identity":"ba8f6851-a5ae-4e4f-a441-2ae6385cf154","added_by":"auto","created_at":"2025-12-19 17:13:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5227636,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7289799/v1/773afb4f-a5f8-4abd-8c28-04abbcc84260.pdf"},{"id":89373836,"identity":"f73d3572-17d4-41e5-a546-d7bef83709bd","added_by":"auto","created_at":"2025-08-19 10:39:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22319,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.docx","url":"https://assets-eu.researchsquare.com/files/rs-7289799/v1/baff0bc5e87af32dd61bc21a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Persistent Spatial Health Inequalities in Czechia: A Two-Decade Analysis Using a Holistic Determinants Model","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHealth equity is widely recognised as a fundamental pillar of a just society and has long held a prominent place in public health and social policy discourse. Despite this, substantial disparities in health outcomes continue to exist across different population groups, regions, and social strata. These health inequalities are considered unjust and avoidable differences (Marmot, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), resulting from a wide array of determinants, including socioeconomic, environmental, and behavioural factors (Schoon \u0026amp; Krumwiede, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe relationship between health inequalities and broader socio-economic disparities is well documented (Sen et al., 2009; Kapilashrami \u0026amp; Hankivsky, 2018; Luiz et al., 2020). The social gradient in health has been identified as one of the most robust indicators of inequality in public health research (Yang et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, ample evidence shows that health inequalities are persistent across time and across societies (Mackenbach, 2012). These disparities are not limited to differences between countries but also occur within them, cutting across all layers of the social hierarchy (Ottersen et al., 2014; Cabrera-Barona et al., 2015; Ag\u0026eacute;nor, 2020). Health inequalities also have a pronounced spatial dimension, evident between regions, between urban and rural areas, and even within neighbourhoods in individual cities (Lakes et al., 2013; Fayet et al., 2020; Flokov\u0026aacute; et al., 2023).\u003c/p\u003e\u003cp\u003eHealth disparities have been reported in all European countries, with life expectancy gaps reaching 5\u0026ndash;10 years, and differences in healthy life expectancy spanning 10\u0026ndash;20 years (Mackenbach, 2006; Mackenbach et al., 2008). In the United States, the life expectancy difference between the wealthiest and poorest men reaches 14.6 years, and among women 10.1 years, with disparities deepening based on geographical location and immigrant population density (Chetty et al., 2016). Evidence also suggests that in many European countries and the U.S., these inequalities are widening (Singh \u0026amp; Siahpush, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Mackenbach et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Marmot, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe link between poverty and poor health outcomes is well established (Hamlin, 1998; Foege, 2010). Greater income inequality tends to be associated with more pronounced health disparities (Hong \u0026amp; Ahn, 2011). A statistically significant relationship between cardiovascular mortality and GDP levels was demonstrated in a panel of 27 European countries between 2003 and 2014, following an inverted U-curve\u0026mdash;initially rising with GDP growth before declining (Spiteri \u0026amp; von Brockdorff, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePrevious research in the Czech context has developed a systemic methodological framework incorporating social, economic, demographic, environmental, and individual health determinants (see e.g., H\u0026uuml;belov\u0026aacute; et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This framework enabled the construction of an extensive dataset and the development of online cartographic visualisations (Health Index, 2020). It resulted in the first comprehensive model for assessing health inequalities under Czech conditions, based on the holistic public health determinants model proposed by Shi \u0026amp; Zhong (2014); see also the updated framework in H\u0026uuml;belov\u0026aacute; et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis article builds on this foundation, aiming to enhance both the methodological and analytical approaches to health inequality research by identifying spatial patterns, regional disparities, and their development over time. The analysis focuses on the regional (LAU 1) level in Czechia, using data from three reference years: 2001, 2011, and 2021. The main objectives are to:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIdentify spatial patterns of health inequalities and determine regions where these disparities are concentrated.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eCompare regions with pronounced and less pronounced inequality patterns and assess differences in determinant categories.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eMeasure the degree of interregional disparities, classify the types of inequalities, and trace their temporal evolution.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eWhile earlier studies provided a static classification of determinants, this work offers a more dynamic and differentiated perspective, capturing both temporal shifts and spatial variability, including intra-regional differences.\u003c/p\u003e\u003cp\u003eThe importance of holistic approaches (Schoon \u0026amp; Krumwiede, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; H\u0026uuml;belov\u0026aacute; et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), interdisciplinary methods (Browne et al., 2012), and systems-based frameworks (Shi \u0026amp; Zhong, 2014; Hern\u0026aacute;ndez et al., 2017; H\u0026uuml;belov\u0026aacute; et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) in studying health inequalities is well documented. These approaches account for the interplay of multiple determinants. The first comprehensive model was introduced in the Canadian report \u003cem\u003eA New Perspective on the Health of Canadians\u003c/em\u003e (Lalonde, 1974), followed by the conceptual model of social determinants developed by Dahlgren \u0026amp; Whitehead (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), which included four interrelated categories and has been applied in many contexts (Barton, 2005; Barton et al., 2010). Shi \u0026amp; Zhong (2014) integrated these frameworks into a three-level model encompassing behavioural, social, and environmental determinants, all interacting with one another. They emphasised that health is shaped not only by individual decisions but also by structural socio-economic and political conditions.\u003c/p\u003e\u003cp\u003eIn our analysis, we examined the influence of determinant categories A.1 through A.7 on health conditions (Category B). Category B includes life expectancy indicators by age and gender, which serve as proxies for mortality and indirectly reflect quality of life and broader socioeconomic conditions (Mackenbach, 2006; Mackenbach et al., 2008; Elo, 2009; Kaikkonen et al., 2009; Aittom\u0026auml;ki et al., 2010), education (Leinsalu et al., 2003; Mackenbach et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Costa, Freitas et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Costa, Santana et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and access to healthcare (Ho \u0026amp; Hendi, 2018). The category also incorporates data on causes of death (Lillini, Quaglia et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; H\u0026uuml;belov\u0026aacute;, Kozumpl\u0026iacute;kov\u0026aacute;, \u0026amp; Walicov\u0026aacute;, 2020; H\u0026uuml;belov\u0026aacute;, Kozumpl\u0026iacute;kov\u0026aacute;, Kosov\u0026aacute; et al., 2020; Ruiz et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and indicators of reproductive health (Kyriopoulos et al., 2019).\u003c/p\u003e\u003cp\u003eAnalysis of regional disparities in Czechia indicates that the strongest associations between determinant categories and health conditions (Category B) are found in economic status and social protection (A.1) and education (A.2) (H\u0026uuml;belov\u0026aacute; et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These findings align with international research (Mackenbach et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Santana et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Costa, Santana et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pornet et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Arcaya et al., 2016; Manor et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Lahelma et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Petrelli Alessio et al., 2019; Aittom\u0026auml;ki et al., 2010), confirming the critical role of these factors in shaping health outcomes.\u003c/p\u003e\u003cp\u003eA cluster analysis of the composite index constructed from categories A.1\u0026ndash;A.7 revealed both regional cores and peripheries. However, the definition of peripherality in the Czech context is complex due to its heterogeneous nature. The Czech periphery is not homogeneous\u0026mdash;it comprises both urban and rural territories, with further distinctions between internal and external peripheries. This challenges the often-held assumption that urban areas consistently outperform rural ones in health outcomes (Lakes et al., 2013). A comparison of data from 2001\u0026ndash;2003 and 2017\u0026ndash;2019 shows a partial narrowing of disparities, yet the most pronounced health inequalities persist in external urban peripheries as well as in rural regions\u0026mdash;regardless of whether they are internally or externally peripheral (H\u0026uuml;belov\u0026aacute; et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAmong the key drivers that exacerbate health inequalities are limited employment opportunities, underdeveloped transport and social infrastructure (Gl\u0026oslash;ersen et al., 2012), selective migration that drains younger populations (Pileček et al., 2013), and unaffordable housing (Marsden et al., 1993). Although the overall share of socially excluded individuals remains relatively low in Czechia, significant micro-regional health disparities persist, as confirmed by multiple studies (e.g., H\u0026uuml;belov\u0026aacute;, Chromkov\u0026aacute; Manea et al., 2021).\u003c/p\u003e\u003cp\u003eThe conceptual model used in this study is modular and scalable, allowing for spatial and temporal analysis of health inequalities (H\u0026uuml;belov\u0026aacute; et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The contextual risk determinants are grouped into seven categories (A.1\u0026ndash;A.7), and Table\u0026nbsp;1 provides a detailed overview of the sources used for each.\u003c/p\u003e\u003cp\u003eTable 1. Overview of resources used for each category\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEconomic Status and Social Protection\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Carstairs \u0026amp; Morris, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1989\u003c/span\u003e); (Davey Smith et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1998\u003c/span\u003e); (Elstad, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2001\u003c/span\u003e); (Manor et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2003\u003c/span\u003e); (Rey et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2009\u003c/span\u003e); (Pornet et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e); (Santana et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); (Mackenbach et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); (Bosakova et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); (Costa, Freitas, et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); (Costa, Santana, et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); (Norstr\u0026ouml;m et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); (Lillini et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); (Spiteri \u0026amp; von Brockdorff, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); (Vu, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Kickbusch, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2001\u003c/span\u003e); (Lahelma et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2004\u003c/span\u003e); (Solar \u0026amp; Irwin, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e); (Mackenbach et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); (Petrelli et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); (Yang et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDemographic Situation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Ramos et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); (Srivarathan et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); (O\u0026rsquo;Connell et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); (Ruiz et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEnvironmental Status\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(James et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); (Crouse et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); (Roh et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); (Savoye et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); (Crouse et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); (Rojas-Rueda et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); (Sun et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndividual Living Status\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Dahlgren \u0026amp; Whitehead, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1991\u003c/span\u003e); (Marmot, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e); (OECD, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); (Eurofound, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRoad Safety and Crime\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Nolan, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2004\u003c/span\u003e); (Christie, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); (Touahmia, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSources of Health and Social Care\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Guagliardo, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2004\u003c/span\u003e); (Foster et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e); (Tanke \u0026amp; Ikkersheim, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2012\u003c/span\u003e); (Chotvijit et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); (Gao et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); (Š\u0026iacute;dlo \u0026amp; Mal\u0026aacute;kov\u0026aacute;, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003eSpatial differentiation in the determinants of health inequalities and population health status was analysed at the LAU 1 level (Local Administrative Units) in Czechia, comprising 76 units plus the capital city of Prague. A dataset of 57 indicators was compiled for each region, grouped into seven categories of health determinants (A.1 to A.7) and one category for health status (B), which included 24 indicators (see Appendix 1).\u003c/p\u003e\u003cp\u003eAlthough LAU 1 units lack formal legal status in Czechia, they were selected due to the practical advantage of data availability. In contrast, higher-level territorial units (NUTS2 and NUTS3) are fragmented both geographically and economically, limiting their suitability for detailed spatial analysis. The study covers three reference years\u0026mdash;2001, 2011, and 2021\u0026mdash;corresponding to census years with complete datasets for all indicators. Data were sourced from publicly available databases, including the Czech Statistical Office (CZSO), the Institute of Health Information and Statistics (IHIS), the Ministry of Labour and Social Affairs (MoLSA), and the Czech Hydrometeorological Institute (CHMI).\u003c/p\u003e\u003cp\u003eTo facilitate interpretation, a composite index was calculated for each category (A.1 to A.7 and B), aggregating multiple indicators into a single score ranging from 0 to 1. Higher values denote more favourable outcomes. Composite indices were constructed using the Weighted Sum Approach (WSA), a method based on utility maximisation principles. For details, see H\u0026uuml;belov\u0026aacute;, Kuncov\u0026aacute; et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)d belov\u0026aacute; et al. (2023). Descriptive statistics were used to summarise the resulting index values.\u003c/p\u003e\u003cp\u003eFor the spatial analysis, several analytical tools were employed. To assess spatial autocorrelation, Moran\u0026rsquo;s I was used (Moran, 1950; Anselin, 1995). This index measures the similarity of values among neighbouring regions. Its values range from \u0026minus;\u0026thinsp;1 (strong negative autocorrelation) to +\u0026thinsp;1 (strong positive autocorrelation), with values near 0 indicating random spatial distribution.\u003c/p\u003e\u003cp\u003eTo identify localised spatial patterns, the Local Indicators of Spatial Association (LISA) method was applied (Anselin, 1995). This method classifies spatial relationships into four types:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eHigh-High (HH): high values surrounded by high values (positive clusters)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eLow-Low (LL): low values surrounded by low values (negative clusters)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHigh-Low (HL): high values surrounded by low values (spatial outliers)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eLow-High (LH): low values surrounded by high values (spatial outliers)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eTogether, Moran\u0026rsquo;s I and LISA allowed for the identification of regional clusters and spatial outliers, highlighting concentrations of inequality. This spatial focus was necessary due to the large number of LAU 1 units and the three reference years, making region-by-region descriptions impractical.\u003c/p\u003e\u003cp\u003eAssessing changes in LISA classifications over time is complex. In addition to tracking changes in local Moran\u0026rsquo;s I values, it is also necessary to consider shifts in overall spatial autocorrelation within the dataset. Changes were evaluated based on shifts in a region\u0026rsquo;s classification across LISA categories, as outlined in Table\u0026nbsp;2.\u003c/p\u003e\u003cp\u003eTable 2. Typology of changes in LISA categories\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory at time t\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory at time t\u0026thinsp;+\u0026thinsp;1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEvaluation of change\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo Change\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInsignificant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDecrease\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSignificant decrease\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsignificant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInsignificant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo Change\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsignificant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDecrease\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsignificant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIncrease\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo Change\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u0026zwj;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInsignificant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIncrease\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSignificant increase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo identify statistically significant changes over time (2001\u0026ndash;2011, 2011\u0026ndash;2021, and 2001\u0026ndash;2021), the Wilcoxon Signed-Rank Test for paired data was applied. This non-parametric test was used to detect whether index values in each category (A.1 to A.7), the composite index (A.1\u0026ndash;A.7), and the health status index (B) increased, decreased, or remained stable. A significance level of 5% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was used.\u003c/p\u003e\u003cp\u003eThe test was conducted in three forms:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eTwo-tailed test: to identify any statistically significant change, regardless of direction\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eOne-tailed left-sided test: to detect whether newer values are significantly higher (improvement)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eOne-tailed right-sided test: to detect whether newer values are significantly lower (deterioration)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eTo quantify regional disparities in health determinants, the Theil Index was used. This index captures both absolute and relative deviations of regional values from the national mean\u003c/p\u003e\u003cp\u003e(Gao et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The Theil Index is non-negative, with zero indicating perfect equality and higher values representing greater inequality. It does not have a fixed upper bound, as its magnitude depends on the degree of concentration within the dataset.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe descriptive statistics for the years 2001, 2011, and 2021 enable an assessment of trends in selected determinants of health inequalities (A.1 to A.7) and in population health indicators (B) across regions in Czechia. A composite index (A.1\u0026ndash;A.7), which summarises these individual dimensions into a single score, is also included in the evaluation.\u003c/p\u003e\n\u003cp\u003eMost index categories showed positive development between 2001 and 2011. After 2011, however, some indicators began to stagnate or even decline. Economic and social conditions (A.1 Ec-soc) worsened between 2001 and 2011 but improved again by 2021. This development suggests some instability, although the 2021 values were slightly higher than those in 2001. Median values confirm this trend. The education index (A.2 Edu) showed steady, although relatively slow, improvement across all three time points. The demographic situation index (A.3 Demo) improved between 2001 and 2011, but a slight decline between 2011 and 2021 points to stagnation and a potentially worsening trend. Environmental conditions (A.4 Envi) improved from 2001 to 2011, but the index declined between 2011 and 2021. This decline may not necessarily reflect a deterioration in environmental quality\u0026mdash;it could also be influenced by changes in monitoring or data interpretation methods. Individual-level indicators (A.5 Indiv) recorded significant growth, especially between 2011 and 2021. This is reflected in both the mean and median values. Safety (A.6) remained stable throughout the period, with no major changes in either the average or the median. Health and social care (A.7 Care) improved in 2011 but declined again in 2021. The drop is visible in both mean and median values, indicating reduced capacity in health and social care services. The Health Condition Index (B) changed only slightly over time and showed a modest improvement overall, suggesting general stability in health outcomes. The composite index (A.1\u0026ndash;A.7) increased between 2001 and 2011 but remained virtually unchanged between 2011 and 2021 (see Fig. 1). The standard deviation for the composite index was 0.037 in 2001, 0.048 in 2011, and 0.042 in 2021. Although variability peaked in 2011, overall dispersion remained low, indicating a relatively stable distribution of values across regions. This suggests a concentration of values around the median rather than any major spatial shifts. For the Health Condition Index (B), the standard deviation was 0.109 in 2001, dropped to 0.061 in 2011, and rose again to 0.114 in 2021. While these fluctuations suggest some regional variation, the differences are not substantial. The increase from 2011 to 2021 may point to a slight rise in disparities (see Fig. 1).\u003c/p\u003e\n\u003cp\u003eThe boxplot illustrates the distribution of values for each index and complements the descriptive statistics. Prominent outliers\u0026mdash;especially in A.2 Edu\u0026mdash;appear as points beyond the box limits. The interquartile ranges for A.1, A.2, A.3, and A.5 narrow over time, indicating reduced variability and a gradual convergence of regional values. Conversely, A.4 and A.7 display increasing variability. For Health Condition B, the boxplot for 2011 shows reduced dispersion compared to 2001, but in 2021, variability increases again, with the appearance of extreme values. This may suggest growing regional disparities (see Fig. 2).\u003c/p\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cstrong\u003e3.1 Composite Index of Health Inequality Determinants A.1\u0026ndash;A.7 and Health Condition B\u003c/strong\u003e\u003c/h2\u003e\n \u003cp\u003eMoran\u0026rsquo;s Index for the composite index of health inequality determinants (A.1\u0026ndash;A.7) shows values of 0.26, 0.51, and 0.39 for the years 2001, 2011, and 2021, respectively. This indicates a progression from weak to moderate spatial autocorrelation in 2001, to moderately strong autocorrelation in 2011, followed by a partial weakening of spatial patterns in 2021. This trend may signal a dispersal of spatial clusters and a potential shift in regional concentration. LISA cluster maps identify stable clusters of regions with both high and low values of the composite index. Low-Low areas remain concentrated in the northwestern and northeastern border peripheries, while High-High areas are primarily located in the central regions of Bohemia and in southern regions, which become more prominent in 2021 (see Fig. 3\u0026ndash;5).\u003c/p\u003e\n \u003cp\u003eFor Health Condition B, spatial autocorrelation in the years 2001, 2011, and 2021 is characterized by Moran\u0026rsquo;s Index values of 0.55, 0.44, and 0.54, respectively. These figures indicate stable clustering of similar values in space, with persistent regional disparities. Low-Low areas remain concentrated in the northwestern border periphery, while High-High areas are found in the southeastern regions and the Bohemian-Moravian border zone (in 2001). By 2011, additional High-High clusters appear in the capital city region and its surroundings, and by 2021, also in parts of eastern Bohemia (see Fig. 6\u0026ndash;8).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Change in the Relative Position of LAU1 Regions Compared to the National Average\u003c/h2\u003e\n \u003cp\u003eFor the composite index of health inequality determinants (A.1\u0026ndash;A.7) and Health Condition B, maps were created to illustrate changes in the relative position of districts compared to the national average, based on comparisons between the years 2001 and 2011, and 2011 and 2021. Changes are categorized into three levels (low, average, high), where \u0026quot;increase\u0026quot; or \u0026quot;decrease\u0026quot; indicates a shift by one category, and \u0026quot;significant increase\u0026quot; or \u0026quot;significant decrease\u0026quot; denotes a jump by two categories.\u003c/p\u003e\n \u003cp\u003eA comparison of changes in composite indicator A.1\u0026ndash;A.7 between 2001 and 2011 shows that for most LAU1 regions, no significant shift occurred (gray clusters). Only a small number of LAU1 units (10 out of 77) experienced an increase, meaning an improvement, which includes both regions with initially high index values (e.g., the capital city and its metropolitan area) and those with below-average index values (e.g., the outer border periphery). The same applies to LAU1 regions that showed a decline.\u003c/p\u003e\n \u003cp\u003eA comparison of the period 2011 to 2021 again indicates no change in the majority of LAU1 regions. However, compared to the previous decade, a greater number of regions showed improvement across the assessed levels, including some that had previously declined. These are primarily LAU1 units located in both inner and outer peripheries. A particularly notable case is the region of \u0026Uacute;st\u0026iacute; nad Labem, which moved from a significant decline to a significant improvement (from dark red on the left to dark blue on the right; see Fig. 9\u0026ndash;10).\u003c/p\u003e\n \u003cp\u003eIn the Health Condition B category, a comparison of changes between 2001 and 2011 reveals that most LAU1 regions experienced no significant shift (gray clusters). Only a small number of LAU1 units (nine out of 77) showed a significant increase in Index B. As with the composite index A.1\u0026ndash;A.7, these include both regions with previously high values (e.g., the capital city and its surrounding area) and those with below-average values (such as the outer border periphery and inner peripheral zones). The same pattern applies to LAU1 units that recorded a decline.\u003c/p\u003e\n \u003cp\u003eThe comparison between 2011 and 2021 again indicates that the majority of LAU1 regions remained unchanged and continue to be represented in gray. Compared to the previous period, the number of regions showing improvement across the assessed levels decreased, and some of them overlap with areas that had previously demonstrated growth (see Fig. 11\u0026ndash;12).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Temporal Dynamics of Health Inequality Determinants\u003c/h2\u003e\n \u003cp\u003eBetween 2001 and 2011, most health inequality determinants (A.1\u0026ndash;A.5, A.7, B) showed statistically significant changes (Wilcoxon p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating a period of considerable structural transformation. Only A.6 remained stable. From 2011 to 2021, the extent of change was more moderate, with significant shifts limited to A.1, A.3\u0026ndash;A.5, A.7, and B. Variables A.2 and A.6 did not change significantly, and the composite index remained stable, suggesting overall stagnation in inequality structures in the latter period (see Table 3).\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003eTable 3. Statistical significance of changes in determinants (2001\u0026ndash;2021)\u003c/span\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tabc\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIs there a statistically significant difference between the years 2001 and 2011?\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIs there a statistically significant difference between the years 2011 and 2021?\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (p-value \u0026minus;\u0026thinsp;0.0002093)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (p-value \u0026minus;\u0026thinsp;0.0000000031)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (p-value \u0026minus;\u0026thinsp;0.02426)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo (p-value \u0026minus;\u0026thinsp;0.06885)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes o (p-value \u0026minus;\u0026thinsp;0.00000004298)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (p-value \u0026minus;\u0026thinsp;0.03127)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (p-value \u0026minus;\u0026thinsp;0.00003739)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (p-value \u0026minus;\u0026thinsp;0.01178)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (p-value \u0026minus;\u0026thinsp;0.04687)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (p-value \u0026minus;\u0026thinsp;0.000000000104)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo (p-value \u0026minus;\u0026thinsp;0.3568)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo (p-value \u0026minus;\u0026thinsp;0.9798)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (p-value \u0026minus;\u0026thinsp;0.0000000002437)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (p-value \u0026minus;\u0026thinsp;0.000000000007319)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (p-value \u0026minus;\u0026thinsp;0.0002123)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (p-value \u0026minus;\u0026thinsp;0.03642)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA.1\u0026ndash;A.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (p-value \u0026minus;\u0026thinsp;0.00000004663)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo (p-value \u0026minus;\u0026thinsp;0.5177)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eThe directional Wilcoxon tests indicate that the categories A.1, A.2, A.5, and B significantly improved over time (left-sided test), while A.3, A.4, and A.7 displayed significant declines (right-sided test). The remaining categories, including A.6 and the composite index, exhibited no statistically significant trend in either direction, suggesting structural inertia (see Table 4).\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003eTable 4. Direction of trends in health inequality determinants (2001\u0026ndash;2021)\u003c/span\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tabd\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndicator\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eleft-sided test (improvement over time)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eright-sided test (deterioration over time)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edescription\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImproved\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImproved\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImproved\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImproved\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImproved\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStable, no trend\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImproved\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImproved\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndex A.1\u0026ndash;A.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStable, no trend\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eThe Theil index results reveal persistent and pronounced regional disparities in certain determinants, particularly A.2 (Education), which maintained the highest inequality levels across all years. Between 2001 and 2011, inequalities declined in several categories (e.g., A.3, A.4, A.6, A.7), but increased in A.1 and A.5. The composite index rose slightly during this period.\u003c/p\u003e\n \u003cp\u003eBy 2021, a renewed increase in disparities was observed in A.4 (Environment), A.7 (Care), and B (Health Condition), while improvements occurred in A.1, A.3, and A.5. The composite index slightly decreased, indicating a partial reduction in regional inequality overal (see Table 5).\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003eTable 5. Theil index of interregional disparities\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tabe\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA.1\u003c/p\u003e\n \u003cp\u003eEc-soc\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA.2\u003c/p\u003e\n \u003cp\u003eEdu\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA.3 Demo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA.4 Envi\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA.5 Indiv\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA.6 Safety\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA.7 Care\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB Health Condit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eINDEX A.1-A.7\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eSpatial health inequalities in Czechia are shaped by a complex interplay of historical, economic, and social forces that influence the territorial organisation of society. Our findings confirm that these inequalities persist over time and reveal distinct spatial patterns at the LAU 1 level. Importantly, they are not merely the result of individual-level factors but are deeply embedded in broader structural and geographical contexts (Smętkowski, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Capello \u0026amp; Caragliu, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Vinci, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Regional differences in Czechia also reflect varied historical and geographical trajectories (Macintyre et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe spatial inequalities identified in our study mirror both the socioeconomic makeup of regions and long-term geographic processes, such as rural restructuring, territorial polarisation, and divergent institutional capacities. These patterns align with theoretical insights from Hampl (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), Musil \u0026amp; M\u0026uuml;ller (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and Perl\u0026iacute;n et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), who describe spatial dynamics contributing to the reproduction of peripheral status.\u003c/p\u003e\u003cp\u003eBuilding on this, Netrdov\u0026aacute; et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) introduced the concept of vulnerability amplification, which accounts for both compositional (socioeconomic characteristics) and contextual (e.g., institutional capacity, access to care, risk perception) factors. Although their research focused on covid-19, they demonstrated how regional health inequalities are shaped not only by individual characteristics but also by the interaction between individuals and their environment. This is particularly pronounced in regions marked by low education levels, high unemployment, and underdeveloped public infrastructure. Socioeconomic functioning in such regions is further influenced by macro-political and economic processes at national and international levels (Bambra et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur analysis highlights persistent concentrations of health inequalities, especially in socioeconomic status, education, and access to health and social care (Lillini et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Selective migration and related demographic dynamics further exacerbate these disparities. The most severe inequalities are consistently found in peripheral regions\u0026mdash;both urban and rural\u0026mdash;reflecting a structural characteristic of the Czech context (H\u0026uuml;belov\u0026aacute; et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Socioeconomic deprivation is strongly linked to higher incidences of diseases such as diabetes and cardiovascular conditions (Agardh et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tamayo et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), a connection supported by substantial evidence (Kerr et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lillini et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Manrique-Garcia et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sommer et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAs demonstrated by our use of Moran\u0026rsquo;s I, spatial clustering of inequality has intensified, particularly in peripheries where socioeconomic and educational indicators remain low. Categories A.1 (economic and social conditions) and A.2 (education) showed the greatest disparities\u0026mdash;consistent with previous research (Agardh et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mackenbach et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sommer et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These findings reinforce the conclusion that socioeconomic factors and education play pivotal roles in shaping population health (Wu et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Zhang \u0026amp; Kanbur, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Marmot, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Petrelli et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Schoon \u0026amp; Krumwiede, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e;).\u003c/p\u003e\u003cp\u003eEducation stands out as a particularly stable determinant of health. It affects health outcomes not only via income and employment (Solar \u0026amp; Irwin, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Santana et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Petrelli et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), but also through psychological and behavioural mechanisms (Egerter et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Importantly, education is a non-reversible status that typically precedes the onset of major health issues, reducing the likelihood of reverse causation (Braveman \u0026amp; Gottlieb, 2014; Kr\u0026ouml;ger et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Individuals with lower educational attainment, occupational status, or income experience shorter life expectancy and higher disease prevalence (Mackenbach et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Costa, Freitas, et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Costa, Santana, et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Social disadvantage is also linked to greater exposure to environmental risks (Šlachtov\u0026aacute; et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe integration of spatial methods into public health research provides effective tools for identifying regional inequalities and supporting targeted interventions (Pearce et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Cromley \u0026amp; McLafferty, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In line with other studies(Lillini et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e;Spiteri \u0026amp; von Brockdorff, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ruiz et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), our findings confirm the relationship between deprivation, spatial vulnerability (Netrdov\u0026aacute; et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and adverse health outcomes, including mortality and reproductive health indicators. These patterns are influenced by regional economic structures and income distribution, as demonstrated in various contexts including Europe(Doran et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Van Ourti et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), the United States (Lahiri et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), China (Wu et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Zhang \u0026amp; Kanbur, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), and South Korea (Ahn et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite policy efforts, the spatial distribution of health determinants and health outcomes in Czechia has remained relatively stable. This suggests that existing interventions have had limited success in mitigating regional disparities. The persistence of these patterns likely reflects both implementation gaps and enduring structural deficiencies\u0026mdash;such as underdeveloped infrastructure and institutional fragmentation (Borrell et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Netrdov\u0026aacute; et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Even in high-income countries, reducing health inequalities remains a major challenge (Mackenbach et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In some cases, disparities are even widening (Singh \u0026amp; Siahpush, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Mackenbach et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Marmot, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Our findings support the argument that effective responses require locally tailored interventions that take into account the spatial distribution of inequality and the specific needs of affected populations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStrengths and Limitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study benefits from the use of a consistent and systematically updated database of health determinants and indicators (H\u0026uuml;belov\u0026aacute;, Chromkov\u0026aacute; Manea, et al., 2021; H\u0026uuml;belov\u0026aacute;, Kuncov\u0026aacute;, et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; H\u0026uuml;belov\u0026aacute; et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Analytical methods were carefully selected to maximise the validity of causal interpretation, and all data were sourced from public databases, ensuring continuity and comparability over time.\u003c/p\u003e\u003cp\u003eNonetheless, we acknowledge that spatial patterns in mortality cannot be fully explained by social, economic, or environmental factors alone. While the composite index of health inequality determinants (A.1\u0026ndash;A.7) increased between 2001 and 2011, it remained stagnant between 2011 and 2021. This raises the question of whether the index has lost sensitivity to subtle shifts in inequality trends. Future refinement may include adjusting indicator weights or incorporating additional dimensions\u0026mdash;such as digital exclusion or access to green infrastructure.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eRegional health inequalities in Czechia persist and exhibit clear spatial patterns, particularly in relation to socioeconomic conditions, education, and access to health and social services (Lillini et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These findings highlight the need for comprehensive, targeted policy responses that consider the unique needs of local populations (Mackenbach et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Marmot, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur results underscore the role of socioeconomic deprivation as a key driver of entrenched spatial health inequalities. These disparities are especially prominent in both rural and urban peripheries, where low educational attainment and high unemployment intersect (Borrell et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). While the Czech case has its own specific features\u0026mdash;such as a relatively expansive internal periphery\u0026mdash;it also reflects broader regional patterns observed in other post-socialist countries. Similar processes are evident in Slovakia (e.g., peripheralisation of urban spaces), Poland (economic decline in marginal city districts), and Hungary (deindustrialisation of former urban cores). These shared features make the Czech experience analytically valuable for comparative research within the Visegr\u0026aacute;d region.\u003c/p\u003e\u003cp\u003eThis study enhances our understanding of the spatial and temporal dynamics of health inequalities and underscores the importance of consistent monitoring. The findings can inform public health and regional development policy, as well as service planning. Given the persistence of these inequalities, there is a pressing need to strengthen coordination between health and social systems and to ensure that regional strategies are responsive to the needs of vulnerable groups. Future research should focus on identifying high-risk subpopulations and designing interventions to improve health literacy and living conditions in socioeconomically disadvantaged areas. Such efforts could contribute to a more effective reduction of regional disparities and promote health equity across Czechia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Technology Agency of the Czech Republic in the Program \u0026Eacute;TA, grant number TL03000202 \u0026ldquo;Health Inequalities in the Czech Republic: Importance and Relationship of Health Determinants of Population in Territorial Disparities\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eD.H. contributed to the conceptualization, methodology, and supervision of the study, and was involved in writing the original draft and reviewing and editing the manuscript. B-E.CH.M. contributed to the conceptualization and methodology, and was responsible for writing the original draft and reviewing and editing the manuscript. J.C. performed the data curation, formal analysis, validation, and visualization, and contributed to the methodology. A.K. conducted the investigation, acquired resources, and was involved in writing the original draft, reviewing and editing the manuscript, and visualization. The final version of the manuscript was read and approved by all authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgardh, E., Allebeck, P., Hallqvist, J., Moradi, T., \u0026amp; Sidorchuk, A. (2011). Type 2 diabetes incidence and socio-economic position: a systematic review and meta-analysis. \u003cem\u003eInternational Journal of Epidemiology\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(3), 804\u0026ndash;818. https://doi.org/10.1093/ije/dyr029\u003c/li\u003e\n\u003cli\u003eAhn, B. 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Spatial inequality in education and health care in China. \u003cem\u003eChina Economic Review\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(2), 189\u0026ndash;204. https://doi.org/https://doi.org/10.1016/j.chieco.2005.02.002\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Spatial health inequalities, Holistic determinants, Regional disparities, Temporal analysis, Social determinants, Public health","lastPublishedDoi":"10.21203/rs.3.rs-7289799/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7289799/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHealth inequalities represent a persistent and pressing public health challenge. This study investigates the spatial patterns and temporal dynamics of health inequalities in Czechia over two decades (2001\u0026ndash;2021), using a holistic model of health determinants. The analysis is conducted at the LAU 1 regional level and incorporates 57 indicators across seven categories of contextual determinants (A.1\u0026ndash;A.7) and a composite index of population health outcomes (B). Composite indicators were developed using the Weighted Sum Approach and spatial relationships were explored using Moran\u0026rsquo;s Index and Local Indicators of Spatial Association (LISA). Statistical significance of temporal change was tested using the Wilcoxon Signed-Rank Test, and interregional inequality was measured with the Theil Index.\u003c/p\u003e\u003cp\u003eResults indicate that while some determinants improved, particularly economic and social conditions (A.1), education (A.2), and individual living status (A.5), others remained stagnant or deteriorated. The composite determinant index (A.1\u0026ndash;A.7) improved between 2001 and 2011 but stagnated thereafter. Spatial clustering of low values was repeatedly observed in both urban and rural peripheral regions, with increasing disparities in access to care (A.7) and environmental status (A.4). The findings suggest that health inequalities in Czechia are structurally embedded and remain stable over time, despite policy efforts. Regional disparities reflect a complex interplay of socioeconomic deprivation, institutional capacity, selective migration, and territorial development trajectories. This study highlights the need for more targeted, locally sensitive interventions and improved coordination between health and social policy. The methodological framework is scalable and can be used for ongoing monitoring and international comparison of health inequalities.\u003c/p\u003e","manuscriptTitle":"Persistent Spatial Health Inequalities in Czechia: A Two-Decade Analysis Using a Holistic Determinants Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 10:39:09","doi":"10.21203/rs.3.rs-7289799/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8fe771d4-778a-4fac-ab1e-807dba2c2b17","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-19T15:54:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-19 10:39:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7289799","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7289799","identity":"rs-7289799","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}