Nerovnosti ve zdraví vznikají působením množství faktorů, což vyžaduje komplexní hodnocení. Holistický přístup využívá víceúrovňový systém kategorií, včetně individuálního subjektivního vnímání zdraví spojeného s kvalitou života. Cílem výzkumu je vyhodnotit prostorové diferenciace oblastí populačního zdraví v Česku. V jednotlivých okresech (77 regionů) jsou analyzované 3 oblasti: A – determinanty nerovností ve zdraví (36 ukazatelů), B – zdravotní stav (24 ukazatelů) a C – subjektivní kvalita života (40 ukazatelů). Data jsou vyhodnocena metodou váženého součtu (WSA), výsledkem je index, který nabývá hodnoty od 0 do 1; čím vyšší hodnota, tím příznivější situace. Kartogramy jsou zpracované metodou lokální prostorové autokorelace (LISA). Index determinant nerovností ve zdraví se v čase (srovnání roku 2001 a 2021) zlepšil v 65 okresech, zatímco index zdravotního stavu se zlepšil jen ve 21 okresech. Nejvyššího indexu determinant nerovností ve zdraví dosahují okresy Brno-město, Praha, České Budějovice, Praha-východ, Praha-západ, v indexu zdravotního stavu se přidávají Hradec Králové a Jihlava. Subjektivní kvalita života koresponduje s determinanty nerovností ve zdraví a zdravotním stavem pouze částečně. Při posuzování prostorových nerovností ve zdraví je nutné respektovat specifika Česka v kontextu vnitřní a vnější periferie, ale i periferie venkovské a městské.

Health inequalities are caused by a multitude of factors that require a comprehensive assessment, including individual subjective perceptions of health linked to the quality of life. The paper aims to evaluate the spatial differentiation of health in Czechia. The analysis covers three areas at the district level: a) determinants of health inequalities, b) health status, and c) subjective quality of life. The weight sum approach (WSA) method is used to evaluate the data, resulting in an index that takes values from 0 to 1; the higher the value, the more favourable the situation. The cartograms are processed using the local indicator of spatial association (LISA) method. The index of health inequalities determinants improved between 2001 and 2021 in 65 districts, while the index of health status improved in only 21 districts. The districts with the highest values for the health inequalities index are Brno-City, Prague, České Budějovice, Prague-East, Prague-West, with Hradec Králové and Jihlava joining in the health status index Subjective quality of life corresponds only partially with the determinants of health inequalities and health status. When evaluating health disparities in health, it is important to consider the specificities of the Czech context, including the inner and outer periphery, as well as the rural and urban periphery.

• MeSH
• kvalita života MeSH
• lidé MeSH
• sociální determinanty zdraví MeSH
• socioekonomické nerovnosti ve zdraví * MeSH
• výzkum MeSH
• zdravotní stav populace * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• grafy a diagramy MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Česká republika MeSH

BACKGROUND: Health inequities exist within and between societies at different hierarchical levels. Despite overall improvements in health status in European Union countries, disparities persist among socially, economically, and societally disadvantaged individuals. This study aims to develop a holistic model of health determinants, examining the complex relationship between various determinants of health inequalities and their association with health condition. METHODS: Health inequalities and conditions were assessed at the territorial level of Local Administrative Units (LAU1) in the Czech Republic. A dataset of 57 indicators was created, categorized into seven determinants of health and one health condition category. The necessary data were obtained from publicly available databases. Comparisons were made between 2001-2003 and 2016-2019. Various methods were employed, including composite indicator creation, correlation analysis, the Wilcoxon test, aggregate index calculation, cluster analysis, and data visualization using the LISA method. RESULTS: The correlation matrix revealed strong relationships between health inequality categories in both periods. The most significant associations were observed between Economic status and social protection and Education in the first period. However, dependencies weakened in the later period, approaching values of approximately 0.50. The Wilcoxon test confirmed variations in determinant values over time, except for three specific determinants. Data visualization identified persistently adverse or worsening health inequalities in specific LAU1, focusing on categories such as Economic status and social protection, Education, Demographic situation, Environmental status, Individual living status, and Road safety and crime. The health condition indices showed no significant change over time, while the aggregate index of health inequalities improved with widened differences. CONCLUSION: Spatial inequalities in health persist in the Czech Republic, influenced by economic, social, demographic, and environmental factors, as well as local healthcare accessibility. Both inner and outer peripheries exhibit poor health outcomes, challenging the assumption that urban areas fare better. The combination of poverty and vulnerabilities exacerbates these inequalities. Despite the low rates of social exclusion and poverty, regional health inequalities persist in the long term. Effectively addressing health inequalities requires interdisciplinary collaboration and evidence-based policy interventions. Efforts should focus on creating supportive social and physical environments, strengthening the healthcare system, and fostering cooperation with non-medical disciplines.

• MeSH
• disparity zdravotního stavu * MeSH
• lidé MeSH
• veřejná politika MeSH
• zdravotní nespravedlnost MeSH
• zdravotní politika * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Česká republika MeSH