Land-Use Regression of Long-Term Transportation Data on Metabolic Syndrome Risk Factors in Low-Income Communities. Issue 11 (November 2021)

• Record Type: Journal Article
• Authors: Aguilera, Juan; Jeon, Soyoung; Chavez, Mayra; Ibarra-Mejia, Gabriel; Ferreira-Pinto, Joao; Whigham, Leah D.; Li, Wen-Whai
• Is Part Of: Transportation research record. Volume 2675:Issue 11(2021)
• Page Start: 955
• Page End: 969
• Publication Date: 2021-11
• Subjects: Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.1177/03611981211021853
• Languages: English
• ISSNs: 0361-1981

Abstract

Traffic-related air pollution has been associated with adverse cardiovascular health effects in near-road residents. Transportation parameters are important surrogate variables to determine spatial variation of air pollution and consequential health outcomes. We used land-use regression models to explore associations between cardiovascular (metabolic syndrome [MetS]) health outcomes collected from a sample of low-income participants ( N = 4, 959) and transportation parameters within a defined impact zone of a participant's residence. We hypothesize cardiovascular risk factors are associated with spatially distributed transportation parameters and land-use data. MetS risk factors (waist circumference, blood pressure, triglycerides, HDL-cholesterol, and glucose) were obtained from 4, 945 participants between 2014 and 2020 across the city of El Paso, Texas. Traffic-related and land-use variables were acquired from the El Paso MPO and the U.S. Census Bureau within two impact zones of 500 m and 1, 000 m radius, centered at each participant resident's home latitude and longitude coordinates using GIS. The increase in street length within 500 m radius was found to associate with increases in BMI, waist circumference, triglycerides, and glucose ( p < 0.05). Glucose showed positive relationships with inverse distance to the nearest international ports of entry ( p = 0.02). Also, as the total length of the street increases, the likelihood of a high waist ( p < 0.01), high triglycerides ( p = 0.03), and low HDL-cholesterol ( p < 0.01) also increased. Based on a multivariable regression model, a probability surface map was prepared to show the spatial distribution of likelihood for acquiring MetS in El Paso, TX.