Measuring neighbourhood social and economic change for urban health studies. (May 2020)

• Record Type: Journal Article
• Authors: Bilal, Usama; Franco, Manuel; Lau, Bryan; Celentano, David; Glass, Thomas
• Is Part Of: Urban studies. Volume 57:Number 6(2020:May)
• Page Start: 1301
• Page End: 1319
• Publication Date: 2020-05
• Subjects: analysis -- finite mixture model -- latent class -- longitudinal data -- neighbourhoods -- residential environments -- Spain; 分析 -- 有限混合模型 -- 潜在阶级 -- 纵向数据 -- 街区 -- 居住环境 -- 西班牙; Cities and towns -- Periodicals; City planning -- Periodicals; 307.1216
• DOI: 10.1177/0042098019880754
• Languages: English
• ISSNs: 0042-0980

Abstract

Neighbourhood change is a complex phenomenon. To study its consequences for health outcomes, we developed a measure of neighbourhood social and economic change for all census tracts ( n = 2272) in the entire city of Madrid (Spain) in two epochs (2005–2009 and 2009–2013). We used a finite mixture modelling approach with 16 indicators from several administrative sources. We found four types of neighbourhoods: Decreasing Socioeconomic Status (SES) areas with increased diversity and decreased socioeconomic status; New Housing/Gentrification areas with high residential mobility, new housing construction and with markers of gentrification in the crisis epoch; Increasing SES areas with increased socioeconomic status and decreased diversity; and Aging areas with an aging population, low residential mobility and no new construction. We describe the baseline predictors of these types of change, finding that there is a potential widening of socioeconomic gaps, as Increasing SES areas start with higher SES, and Decreasing SES areas start with lower SES. We found a change in the spatial distribution of these types between the first and second epochs, as New Housing/Gentrification areas became more common in the centre of the city. We discuss two potential applications of this type of model to the study of the consequences of residential environment changes for health determinants and health outcomes, with a particular emphasis on retail food environments and diabetes incidence.