Literature DB >> 31253484

Interaction between neighborhood walkability and traffic-related air pollution on hypertension and diabetes: The CANHEART cohort.

Nicholas A Howell1, Jack V Tu2, Rahim Moineddin3, Hong Chen4, Anna Chu5, Perry Hystad6, Gillian L Booth7.   

Abstract

BACKGROUND: Living in unwalkable neighborhoods has been associated with heightened risk for diabetes and hypertension. However, highly walkable environments may have higher concentrations of traffic-related air pollution, which may contribute to increased cardiovascular disease risk. We therefore aimed to assess how walkability and traffic-related air pollution jointly affect risk for hypertension and diabetes.
METHODS: We used a cross-sectional, population-based sample of individuals aged 40-74 years residing in selected large urban centres in Ontario, Canada on January 1, 2008, assembled from administrative databases. Walkability and traffic-related air pollution (NO2) were assessed using validated tools and linked to individuals based on neighborhood of residence. Logistic regression was used to estimate adjusted associations between exposures and diagnoses of hypertension or diabetes accounting for potential confounders.
RESULTS: Overall, 2,496,458 individuals were included in our analyses. Low walkability was associated with higher odds of hypertension (lowest vs. highest quintile OR = 1.34, 95% CI: 1.32, 1.37) and diabetes (lowest vs. highest quintile OR = 1.25, 95% CI: 1.22, 1.29), while NO2 exhibited similar trends (hypertension: OR = 1.09 per 10 p.p.b., 95% CI: 1.08, 1.10; diabetes: OR = 1.16, 95% CI: 1.14, 1.17). Significant interactions were identified between walkability and NO2 on risk for hypertension (p < 0.0001 and diabetes (p < 0.0001). At higher levels of pollution (40 p.p.b.), differences in the probability of hypertension (lowest vs. highest walkability quintile: 0.26 vs. 0.25) or diabetes (lowest vs. highest walkability quintile: 0.15 vs. 0.15) between highly walkable and unwalkable neighborhoods were diminished, compared to differences observed at lower levels of pollution (5 p.p.b.) (hypertension, lowest vs. highest walkability quintile: 0.21 vs. 0.13; diabetes, lowest vs. highest walkability quintile: 0.09 vs. 0.06).
CONCLUSIONS: Walkability and traffic-related air pollution interact to jointly predict risk for hypertension and diabetes. Although walkable neighborhoods appear to have beneficial effects, they may accentuate the harmful effects of air pollution on cardiovascular risk factors. Crown
Copyright © 2019. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Air pollution; Diabetes; Hypertension; NO(2); Walkability

Mesh:

Year:  2019        PMID: 31253484     DOI: 10.1016/j.envint.2019.04.070

Source DB:  PubMed          Journal:  Environ Int        ISSN: 0160-4120            Impact factor:   9.621


  12 in total

1.  Walkability measures to predict the likelihood of walking in a place: A classification and regression tree analysis.

Authors:  Ronit R Dalmat; Stephen J Mooney; Philip M Hurvitz; Chuan Zhou; Anne V Moudon; Brian E Saelens
Journal:  Health Place       Date:  2021-10-23       Impact factor: 4.078

2.  Assessing Measurement Invariance of a Land Use Environment Construct Across Levels of Urbanicity.

Authors:  Melissa A Meeker; Brian S Schwartz; Karen Bandeen-Roche; Annemarie G Hirsch; S Shanika A De Silva; Tara P McAlexander; Nyesha C Black; Leslie A McClure
Journal:  Geohealth       Date:  2022-10-01

3.  Neighborhood walkability and pre-diabetes incidence in a multiethnic population.

Authors:  Ghazal S Fazli; Rahim Moineddin; Anna Chu; Arlene S Bierman; Gillian L Booth
Journal:  BMJ Open Diabetes Res Care       Date:  2020-06

4.  The probability of diabetes and hypertension by levels of neighborhood walkability and traffic-related air pollution across 15 municipalities in Southern Ontario, Canada: A dataset derived from 2,496,458 community dwelling-adults.

Authors:  Nicholas A Howell; Jack V Tu; Rahim Moineddin; Hong Chen; Anna Chu; Perry Hystad; Gillian L Booth
Journal:  Data Brief       Date:  2019-08-28

5.  Application of Big Data to Support Evidence-Based Public Health Policy Decision-Making for Hearing.

Authors:  Gabrielle H Saunders; Jeppe H Christensen; Johanna Gutenberg; Niels H Pontoppidan; Andrew Smith; George Spanoudakis; Doris-Eva Bamiou
Journal:  Ear Hear       Date:  2020 Sep/Oct       Impact factor: 3.562

6.  Association between neighbourhood deprivation and hypertension in a US-wide Cohort.

Authors:  Jing Xu; Kaitlyn G Lawrence; Katie M O'Brien; Chandra L Jackson; Dale P Sandler
Journal:  J Epidemiol Community Health       Date:  2021-11-17       Impact factor: 3.710

7.  Associations of Urban Environment Features with Hypertension and Blood Pressure across 230 Latin American Cities.

Authors:  Ione Avila-Palencia; Daniel A Rodríguez; J Jaime Miranda; Kari Moore; Nelson Gouveia; Mika R Moran; Waleska T Caiaffa; Ana V Diez Roux
Journal:  Environ Health Perspect       Date:  2022-02-15       Impact factor: 9.031

8.  Examine the association between key determinants identified by the chronic disease indicator framework and multimorbidity by rural and urban settings.

Authors:  John S Moin; Richard H Glazier; Kerry Kuluski; Alex Kiss; Ross E G Upshur
Journal:  J Multimorb Comorb       Date:  2021-06-30

9.  Association Between Neighborhood Walkability and Predicted 10-Year Cardiovascular Disease Risk: The CANHEART (Cardiovascular Health in Ambulatory Care Research Team) Cohort.

Authors:  Nicholas A Howell; Jack V Tu; Rahim Moineddin; Anna Chu; Gillian L Booth
Journal:  J Am Heart Assoc       Date:  2019-10-31       Impact factor: 5.501

10.  Attributes of the food and physical activity built environments from the Southern Cone of Latin America.

Authors:  Laura E Gutierrez; Natalia Elorriaga; Luz Gibbons; Santiago Melendi; Martín Chaparro; Matías Calandrelli; Fernando Lanas; Nora Mores; Jacqueline Ponzo; Rosana Poggio; Mabel Berrueta; Vilma Irazola
Journal:  Sci Data       Date:  2021-11-01       Impact factor: 6.444

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