Search

Environmental health inequalities (EHIs) refer to general differences in environmental health conditions. This research aimed at introducing the EHI indicators and providing general recommendations that could improve environmental health and reduce the inequalities observed.This research is the review of WHO reports in various regions. A set of 14 EHI indicators was developed and categorized into three inequality dimensions, including housing-related inequalities, group injury-related inequalities, and environment-related inequalities. The environment-related inequalities include four indicators such as lack of access to green/recreational areas, and noise exposure and second-hand smoke exposure at home or work. The housing-related inequalities include six indicators such as inadequate water supply, lack of a flush toilet, lack of a bath or shower, overcrowding, dampness in home, and inability to keep the home adequately warm. According to literature, EHIs exist in all countries, and most often affect disadvantaged population groups. The magnitude of inequalities and the distribution of inequalities varies between countries and also depend on the socioeconomic or demographic variables. The socioeconomic and demographic inequalities in exposure to environmental hazards exist everywhere and could be associated with factors that may affect the risk of being exposed such as income, education, employment, age, sex, race/ethnicity, and specific locations or settings. In addition to these differences in exposure, environmental health inequalities are also caused by social or demographic differences in vulnerability towards certain risks. Finally, further studies are recommended for initial assessment of EHIs in Iran.

Environmental health inequalities (EHIs) refer to general differences in environmental health conditions. This research aimed at introducing the EHI indicators and providing general recommendations that could improve environmental health and reduce the inequalities observed.This research is the review of WHO reports in various regions. A set of 14 EHI indicators was developed and categorized into three inequality dimensions, including housing-related inequalities, group injury-related inequalities, and environment-related inequalities. The environment-related inequalities include four indicators such as lack of access to green/recreational areas, and noise exposure and second-hand smoke exposure at home or work. The housing-related inequalities include six indicators such as inadequate water supply, lack of a flush toilet, lack of a bath or shower, overcrowding, dampness in home, and inability to keep the home adequately warm. According to literature, EHIs exist in all countries, and most often affect disadvantaged population groups. The magnitude of inequalities and the distribution of inequalities varies between countries and also depend on the socioeconomic or demographic variables. The socioeconomic and demographic inequalities in exposure to environmental hazards exist everywhere and could be associated with factors that may affect the risk of being exposed such as income, education, employment, age, sex, race/ethnicity, and specific locations or settings. In addition to these differences in exposure, environmental health inequalities are also caused by social or demographic differences in vulnerability towards certain risks. Finally, further studies are recommended for initial assessment of EHIs in Iran. [ABSTRACT FROM AUTHOR]

Unplanned regional socioeconomic development contributes to PM 2.5 pollution, which severely infringes on human well-being and even lives. Facing the spillover and migration of pollution, the cross-regional issue of environmental health inequalities implied in trade networks requires accurate recognition and research. In this research, a city-level PM 2.5 -related health impact complex network model (PHCNM) is developed. The Community Detection Algorithm (CDA) and the Stochastic Approach for Weighted Link-Structure Algorithm (SAWLSA) are optimized and applied to deconstruct the structures, hierarchies, and characteristics of PHCNM to assess the interactions of environmental health inequalities across regions and cities. At the urban layer, the empirical results suggest that the top 5 cities (i.e., Chongqing, Shanghai, Chengdu, Beijing, and Tianjin) have premature mortality of more than 25000 people each year, which is about 27% of the total. The demographic surge and PM 2.5 pollution deterioration are responsible for the worsening health burden of cities. Chengdu, Suzhou, Shanghai, and Hangzhou are identified as development-driven critical cities; Shijiazhuang, Tangshan, Cangzhou, and Handan are recognized as pollution-driven critical cities. Thus, city-specific management and treatment of PM 2.5 are possible depending on the characteristics of their environmental health inequalities. While at the regional layer, the urban agglomerations of Yangtze River Delta (YRD-UA) and Pearl River Delta (PRD-UA) transfer pollution and health threats through commercial interaction among cities, leaving the urban agglomerations of Beijing-Tianjin-Hebei (BTH-UA) and Chengdu-Chongqing (CC-UA) as victims of their environmental and health development. And the regional inequalities index elevates from 0.770 to 0.780, reflecting the easing of regional health inequalities. The research reveals that health inequalities significantly reside in communal or regional boundaries; therefore, the internalities and externalities of communal structures can offer insights into the regionalized and communalized development patterns of urban agglomerations. [Display omitted] • A city level PM 2.5 -related health impact complex network model is developed. • A directed and weighted link-structure algorithm is innovatively optimized. • Development-driven and pollution-driven crucial cities are recognized for targeted governance. • Cross-regional issue of environmental health inequalities embedded in trade is revealed. • Regionalized and communalized management of cities is proposed based on the internalities and externalities of communities. [ABSTRACT FROM AUTHOR]

Background: An environmental health inequality is a major public health concern in Europe. However just few studies take into account a large set of characteristics to analyze this problematic. The aim of this study was to identify and describe how socioeconomic, health accessibility and exposure factors accumulate and interact in small areas in a French urban context, to assess environmental health inequalities related to infant and neonatal mortality.Methods: Environmental indicators on deprivation index, proximity to high-traffic roads, green space, and healthcare accessibility were created using the Geographical Information System. Cases were collected from death certificates in the city hall of each municipality in the Nice metropolitan area. Using the parental addresses, cases were geocoded to their census block of residence. A classification using a Multiple Component Analysis following by a Hierarchical Clustering allow us to characterize the census blocks in terms of level of socioeconomic, environmental and accessibility to healthcare, which are very diverse definition by nature. Relation between infant and neonatal mortality rate and the three environmental patterns which categorize the census blocks after the classification was performed using a standard Poisson regression model for count data after checking the assumption of dispersion.Results: Based on geographic indicators, three environmental patterns were identified. We found environmental inequalities and social health inequalities in Nice metropolitan area. Moreover these inequalities are counterbalance by the close proximity of deprived census blocks to healthcare facilities related to mother and newborn. So therefore we demonstrate no environmental health inequalities related to infant and neonatal mortality.Conclusion: Examination of patterns of social, environmental and in relation with healthcare access is useful to identify census blocks with needs and their effects on health. Similar analyzes could be implemented and considered in other cities or related to other birth outcomes. [ABSTRACT FROM AUTHOR]

Environmental health inequalities (EHI), understood as differences in environmental health factors and in health outcomes caused by environmental conditions, are studied by a wide range of disciplines. This results in challenges to both synthesizing key knowledge domains of the field. This study aims to uncover the global research status and trends in EHI research, and to derive a conceptual framework for the underlying mechanisms of EHI. In total, 12,320 EHI publications were compiled from the Web of Science core collection from 1970 to 2020. Scientometric analysis was adopted to characterize the research activity, distribution, focus, and trends. Content analysis was conducted for the highlight work identified from network analysis. Keyword co-occurrence and cluster analysis were applied to identify the knowledge domain and develop the EHI framework. The results show that there has been a steady increase in numbers of EHI publications, active journals, and involved disciplines, countries, and institutions since the 2000s, with marked differences between countries in the number of published articles and active institutions. In the recent decade, environment-related disciplines have gained importance in addition to social and health sciences. This study proposes a framework to conceptualize the multi-facetted issues in EHI research referring to existing key concepts.

Abstract: This paper contributes to the environmental justice literature by analyzing contextually relevant and racial/ethnic group-specific variables in relation to air toxics cancer risks in a US–Mexico border metropolis at the census block group-level. Results indicate that Hispanics'' ethnic status interacts with class, gender and age status to amplify disproportionate risk. In contrast, results indicate that non-Hispanic whiteness attenuates cancer risk disparities associated with class, gender and age status. Findings suggest that a system of white-Anglo privilege shapes the way in which race/ethnicity articulates with other dimensions of inequality to create unequal cancer risks from air toxics. [Copyright &y& Elsevier]

The main objective of this study was to analyse the space-time epidemiological differences by sex during the 2009-2020 period in the total mortality recorded among residents in each of the 25 districts of the Genoa municipality, net of the age effect. The analysis was based on official statistical data relating to total mortality and on the resident population. An estimate of the expected deaths was made to calculate the sex-specific age-standardised mortality ratio (SMR). The temporal trends and age-standardized death rates (SDRs) with respect to those of the European population specific to sex and calendar year were identified for each district. Over the entire observation period, the SMR for males ranged from 124.4 (Cornigliano) to 82.0 (Albaro); for females, the values ranged between 133.4 (Cornigliano) and 85.6 (Nervi-Quinto-S. Ilario). Between 2019 and 2020, Genoa recorded an increase in SDR of 24.5%, more pronounced in males (+26.7%) than in females (+22.4%). This epidemiological methodology is replicable and allows to quickly identify spatial, temporal, sex, and age differences in the general mortality within a municipality.

Reducing environmental health inequalities has become a major focus of public health efforts in France, as evidenced by the French action plans for health and the environment. To evaluate environmental inequalities, routine monitoring networks provide a valuable source of data on environmental contamination, which can be used in integrated assessments, to identify overexposed populations and prioritize actions. However, available databases generally do not meet sufficient spatial representativeness to characterize population exposure, as they are usually not assembled for this specific purpose. The aim of this study was to develop geoprocessing procedures and statistical methods to build spatial environmental variables (water, air, soil, and food pollutant concentrations) at a fine resolution, and provide appropriate input for the exposure modelling. Those methods were designed to combine in situ monitoring data with correlated auxiliary information (for example, atmospheric emissions, population, and altitude), in order to better represent the variability of the environmental compartment quality. The MODUL'ERS multimedia exposure model developed by INERIS (French Institute for industrial Environment and Risks) was then used to assess the transfer of substances from the environment to humans, through inhalation and ingestion pathway characterization. We applied the methodology to a carcinogenic Polycyclic Aromatic Hydrocarbon substance, benzo[a]pyrene(B[a]P), to map spatialized exposure indicators, at the national scale. The largest environmental contribution corresponded to the ingestion pathway. Data processing algorithms and calculation of exposure will be integrated into the French coordinated integrated environment and health platform PLAINE (PLteforme intégrée d'Analyse des INégalités Environnementales) which has been developed to map and analyze environmental health inequalities.

Perceived annoyance due to traffic noise and lack of urban green space is mostly determined using data from self-administered questionnaires. However, there is still no clear evidence to what extent such perceived measures are related to objectively assessed environmental data and whether socioeconomic dimensions modify such relationships. In a cross-sectional study in Dortmund, Germany, georeferenced home addresses from parents with preschool aged children were used to analyse relations between exposures to objectively measured green space and traffic noise and subjective annoyance due to noise and lack of green space with the additional consideration of socioeconomic characteristics as effect modifiers. Higher perceived annoyance correlated with higher objectively measured traffic noise and lower objectively measured green, respectively. Stratified logistic regression models indicated a modifying role of socioeconomic characteristics. The strengths of associations between objectively measured environmental exposures and perceived annoyance differed by socioeconomic strata. Especially for noise, odds ratios were higher in low socioeconomic strata than in high socioeconomic strata. Therefore, using objective measures of the built environment as a proxy for individual perception should be made with caution as negative relations between objectively assessed built environments and health could be underestimated when considering individual socioeconomic position only as a confounder.