Use of geographic indicators of healthcare, environment and socioeconomic factors to characterize environmental health disparities

Abstract

BackgroundAn 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.MethodsEnvironmental 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.ResultsBased 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.ConclusionExamination 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.

The Influence of Socio-economic and Socio-demographic Factors in the Association Between Urban Green Space and Health

“…Nevertheless, infant mortality rates were not randomly distributed over the study area, showing that both greenness and deprivation may have an impact. Padilla et al (2016) could not identify a significant association between neonatal mortality and urban green-space exposure. Kihal-Talantikite et al (2013) and Padilla et al (2016) found a significant association between neonatal mortality risk and level of deprivation.…”

“…Other studies, however, could not find any relationship between health outcome and socio-economic or socio-demographic confounders (Calogiuri et al 2016;Nichani et al 2017) or showed that significant associations between green space and health outcome became non-significant after models were adjusted for confounders (Cusack et al 2017;Richardson et al 2017a). For example, Padilla et al (2016) showed that the significant association between green space and stress disappeared when models were adjusted for socioeconomic confounders. A case study city in France showed a significant relationship between infant and neonatal mortality risk and level of deprivation, but could not clearly explain the link to urban green space (Kihal-Talantikite et al 2013) (Fig.…”

Social Justice: Disparities in Average Earnings across Portuguese Municipalities

“…Poverty and the risk of poverty are a concern for the European Union (Goedemé 2013); however, the current economic and financial situations across the several member states are proving difficult for creating an effective policy for these issues, namely in countries where the recent crises have had consequences for the population. Many social indexes stress the importance of variables such as employment, education, and income (Padilla et al 2016).…”

Identificación Multiatributo de Tipologías de Viviendas Vulnerables a Inundaciones en Riohacha, La Guajira-Colombia

“…El ACM es uno de los métodos de reducción de dimensionalidad más popular que permite tener en cuenta la co-linealidad de las variables, evitando así la información redundante. Con el ACM se obtienen categorías homogéneas en su composición y heterogéneas entre ellas (Padilla et al, 2016). El ACM es particularmente apto en la búsqueda de tipologías respecto a variables cualitativas categórica; puede ser aplicado inclusive para variables continuas, siempre que se transformen en categóricas (Rodrigues et al, 2016).…”