Spatial scale effects on associations between built environment and cognitive function: Multi-Ethnic Study of Atherosclerosis

Abstract

Built environments have the potential to favorably support cognitive function. Despite growing work on this topic, most of the work has ignored variation in the spatial scale of the effect. The issue with spatial scale effects is that the size and shape of the areal unit within which built environment characteristics are measured naturally influence the built environment exposure metric and thus the estimated associations with health. We used spatial distributed lag modeling (DLM) to estimate how associations between built environment exposures (walkable destinations [WD], social destinations [SD]) and change in cognition varied across distance of these destinations from participants' residences. Cognition was assessed as maintained/improved processing speed (PS) and global cognition (GC). Person-level data from Exam 5 (2010-2012) and Exam 6 (2016-2018) of the Multi-Ethnic Study of Atherosclerosis was used (N = 1380, mean age 67). Built environment data were derived from the National Establishment Time Series. Higher availability of walkable and social destinations at closer distance from participants' residence was associated with maintained/improved PS. The adjusted associations between maintained/improved PS and destinations waned with increasing distance from the residence; associations were evident until approximately 1.9-km for WD and 1.5-km for SD. Associations were most apparent for participants living in areas with high population density. We found little evidence for associations between change in GC and built environment at any distance. These results highlight the importance of identifying appropriate spatial scale to understand the mechanisms for built environment-cognition associations.

Keywords: Built environment; Cognitive function; Distributed lag modeling; Healthy aging; Spatial scale effects.

Figure 1.

Distributed lag modeling results for cognitive change (processing speed) and built environment destinations in ring-shaped network areas up to 5-km from residence. Note: y-axis represents log odds. Each model adjusted age, gender, race/ethnicity, education, annual household income, employment status, BMI, arthritis flareup in the last 2 weeks, self-rated health, APOE ε4 carriers, depression, diabetes, emphysema/chronic obstructive pulmonary disease, hypertension, smoking, neighborhood population density, and neighborhood socioeconomics. N = 1380.