An ego-centred approach for the evaluation of health inequalities in urban areas

• Published in: European journal of public health 2019-11, Vol.29 (Supplement_4)
• Main Authors: Sauzet, O, Zolitschka, K A, Spallek, J, Breckenkamp, J, Razum, O
• Format: Article
• Language: English
• Subjects: Birth weight; Demographics; Demography; Evaluation; Health disparities; Heterogeneity; Inequalities; Investment; Investments; Modelling; Neighborhoods; Planners; Policy making; Production; Public health; Social inequality; Socioeconomics; Urban areas; Urban environments
• ISSN: 1101-1262; 1464-360X
• DOI: 10.1093/eurpub/ckz186.243

Abstract Background Neighbourhood possesses attributes, structural, physical and social, for which pathways to health inequalities could be hypothesized. Hence, neighbourhood is a complex mixture of factors which cannot be simply defined by a delineation on a map, making common definitions of neighbourhood (e.g. administrative borders) problematic. We present a new concept for the evaluation of contextual health inequalities in an urban setting. Methods An ego-centred approach to neighbourhood effects on health allows to establish to what degree the health outcomes of a person are on average correlated to the health outcomes of his/her neighbours. This approach does not necessitate the definition of what a neighbourhood is, or of its boundaries. Using data from the BaBi birth cohort following up 958 mother-child pairs in Bielefeld/Germany we illustrate how the method provides information about the spatial structure of a possible association between unmeasured neighbourhood factors and birthweight. Spatially correlated birthweight indicates a neighbourhood effect on maternal health. Results A parametric model of the correlation structure gives two indicators: a distance after which health outcomes are no longer correlated (practical range), and the strength of correlation (RSV). We modelled birthweight directly and residuals after controlling for (spatially correlated) covariates. After adjusting for the mother’s demographics and neighbourhood characteristics, birthweights remained spatially correlated with RSV of 11% and a practical range of 128 m. Conclusions Modelling the spatial correlation of a health outcome provides a measure of the degree of health correlation, thus offering new evidence on the production of health inequalities while incorporating current modelling approaches. Moreover, it measures heterogeneity in a city. This could be used as an indicator for policy makers or town planners to identify areas in need of socioeconomic investment. Key messages Modelling the spatial correlation of health outcomes is an approach which enable to assess unmeasured neighbourhood effects. The health correlation neighbourhood approach helps to investigate the production of health inequalities and to identify urban areas in need of socioeconomic investment.