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Gao X, Engeda J, Moore LV, Auchincloss AH, Moore K, Mujahid MS. Longitudinal associations between objective and perceived healthy food environment and diet: The Multi-Ethnic Study of Atherosclerosis. Soc Sci Med 2022; 292:114542. [PMID: 34802783 PMCID: PMC8748383 DOI: 10.1016/j.socscimed.2021.114542] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 06/18/2021] [Accepted: 11/04/2021] [Indexed: 01/03/2023]
Abstract
INTRODUCTION Research examining the influence of neighborhood healthy food environment on diet has been mostly cross-sectional and has lacked robust characterization of the food environment. We examined longitudinal associations between features of the local food environment and healthy diet, and whether associations were modified by race/ethnicity. METHODS Data on 3634 adults aged 45-84 followed for 10 years were obtained from the Multi-Ethnic Study of Atherosclerosis. Diet quality was assessed using the Alternative Healthy Eating Index at Exam 1 (2000-2002) and Exam 5 (2010-2012). We assessed four measures of the local food environment using survey-based measures (e.g. perceptions of healthier food availability) and geographic information system (GIS)-based measures (e.g. distance to and density of healthier food stores) at Exam 1 and Exam 5. Random effects models adjusted for age, sex, education, moving status, per capita adjusted income, and neighborhood socioeconomic status, and used interaction terms to assess effect measure modification by race/ethnicity. RESULTS Net of confounders, one standard z-score higher average composite local food environment was associated with higher average AHEI diet score (β=1.39, 95% CI: 1.05, 1.73) over the follow-up period from Exam 1 to 5. This pattern of association was consistent across both GIS-based and survey-based measures of local food environment and was more pronounced among minoritized racial/ethnic groups. There was no association between changes in neighborhood environment and change in AHEI score, or effect measure modification by race/ethnicity. CONCLUSION Our findings suggest that neighborhood-level food environment is associated with better diet quality, especially among racially/ethnically minoritized populations.
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Affiliation(s)
- Xing Gao
- Division of Epidemiology, School of Public Health, University of California, Berkeley, Berkeley, CA, USA.
| | - Joseph Engeda
- Division of Epidemiology, School of Public Health, University of California, Berkeley, Berkeley, CA, USA; Social & Scientific Systems, Durham, NC, USA
| | - Latetia V Moore
- Division of Nutrition, Physical Activity, and Obesity, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Amy H Auchincloss
- Urban Health Collaborative, Dornsife School of Public Health, Drexel University, Philadelphia, PA, USA; Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, PA, USA
| | - Kari Moore
- Urban Health Collaborative, Dornsife School of Public Health, Drexel University, Philadelphia, PA, USA
| | - Mahasin S Mujahid
- Division of Epidemiology, School of Public Health, University of California, Berkeley, Berkeley, CA, USA.
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Jones KK, Zenk SN, Tarlov E, Powell LM, Matthews SA, Horoi I. A step-by-step approach to improve data quality when using commercial business lists to characterize retail food environments. BMC Res Notes 2017; 10:35. [PMID: 28061798 PMCID: PMC5219657 DOI: 10.1186/s13104-016-2355-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Accepted: 12/20/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Food environment characterization in health studies often requires data on the location of food stores and restaurants. While commercial business lists are commonly used as data sources for such studies, current literature provides little guidance on how to use validation study results to make decisions on which commercial business list to use and how to maximize the accuracy of those lists. Using data from a retrospective cohort study [Weight And Veterans' Environments Study (WAVES)], we (a) explain how validity and bias information from existing validation studies (count accuracy, classification accuracy, locational accuracy, as well as potential bias by neighborhood racial/ethnic composition, economic characteristics, and urbanicity) were used to determine which commercial business listing to purchase for retail food outlet data and (b) describe the methods used to maximize the quality of the data and results of this approach. METHODS We developed data improvement methods based on existing validation studies. These methods included purchasing records from commercial business lists (InfoUSA and Dun and Bradstreet) based on store/restaurant names as well as standard industrial classification (SIC) codes, reclassifying records by store type, improving geographic accuracy of records, and deduplicating records. We examined the impact of these procedures on food outlet counts in US census tracts. RESULTS After cleaning and deduplicating, our strategy resulted in a 17.5% reduction in the count of food stores that were valid from those purchased from InfoUSA and 5.6% reduction in valid counts of restaurants purchased from Dun and Bradstreet. Locational accuracy was improved for 7.5% of records by applying street addresses of subsequent years to records with post-office (PO) box addresses. In total, up to 83% of US census tracts annually experienced a change (either positive or negative) in the count of retail food outlets between the initial purchase and the final dataset. DISCUSSION Our study provides a step-by-step approach to purchase and process business list data obtained from commercial vendors. The approach can be followed by studies of any size, including those with datasets too large to process each record by hand and will promote consistency in characterization of the retail food environment across studies.
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Affiliation(s)
- Kelly K Jones
- Department of Health Systems Science, College of Nursing, University of Illinois at Chicago, 845 S. Damen Ave, Chicago, IL, 60612, USA.
| | - Shannon N Zenk
- Department of Health Systems Science, College of Nursing, University of Illinois at Chicago, 845 S. Damen Ave, Chicago, IL, 60612, USA
| | - Elizabeth Tarlov
- Department of Health Systems Science, College of Nursing, University of Illinois at Chicago, 845 S. Damen Ave, Chicago, IL, 60612, USA.,Center of Innovation for Complex Chronic Healthcare, Edward Hines, Jr. VA Hospital, Hines, IL, 60141, USA
| | - Lisa M Powell
- Health Policy and Administration Division, School of Public Health, University of Illinois at Chicago, 1603 W. Taylor St, Chicago, IL, 60612, USA
| | - Stephen A Matthews
- Department of Sociology and Criminology, The Pennsylvania State University, 206 Oswald Tower, University Park, PA, 16802, USA.,Department of Anthropology, The Pennsylvania State University, 410 Carpenter Building, University Park, PA, 16802, USA
| | - Irina Horoi
- Department of Economics, University of Illinois at Chicago, 601 S. Morgan St, Chicago, IL, 60607, USA
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Richardson AS, Meyer KA, Howard AG, Boone-Heinonen J, Popkin BM, Evenson KR, Shikany JM, Lewis CE, Gordon-Larsen P. Multiple pathways from the neighborhood food environment to increased body mass index through dietary behaviors: A structural equation-based analysis in the CARDIA study. Health Place 2015; 36:74-87. [PMID: 26454248 PMCID: PMC4791952 DOI: 10.1016/j.healthplace.2015.09.003] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2015] [Revised: 09/10/2015] [Accepted: 09/15/2015] [Indexed: 11/17/2022]
Abstract
OBJECTIVES To examine longitudinal pathways from multiple types of neighborhood restaurants and food stores to BMI, through dietary behaviors. METHODS We used data from participants (n=5114) in the United States-based Coronary Artery Risk Development in Young Adults study and a structural equation model to estimate longitudinal (1985-86 to 2005-06) pathways simultaneously from neighborhood fast food restaurants, sit-down restaurants, supermarkets, and convenience stores to BMI through dietary behaviors, controlling for socioeconomic status (SES) and physical activity. RESULTS Higher numbers of neighborhood fast food restaurants and lower numbers of sit-down restaurants were associated with higher consumption of an obesogenic fast food-type diet. The pathways from food stores to BMI through diet were inconsistent in magnitude and statistical significance. CONCLUSIONS Efforts to decrease the numbers of neighborhood fast food restaurants and to increase the numbers of sit-down restaurant options could influence diet behaviors. Availability of neighborhood fast food and sit-down restaurants may play comparatively stronger roles than food stores in shaping dietary behaviors and BMI.
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Affiliation(s)
| | - Katie A Meyer
- Department of Nutrition, Gillings School of Global Public Health & School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA
| | - Annie Green Howard
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA
| | - Janne Boone-Heinonen
- Department of Public Health and Preventive Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Barry M Popkin
- Department of Nutrition, Gillings School of Global Public Health & School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA; Carolina Population Center, 137 East Franklin Street, Campus Box 8120, Chapel Hill, NC 27516, USA
| | - Kelly R Evenson
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA; UNC Center for Health Promotion and Disease Prevention, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA
| | - James M Shikany
- Division of Preventive Medicine, School of Medicine, University of Alabama, Birmingham, AL, USA
| | - Cora E Lewis
- Division of Preventive Medicine, School of Medicine, University of Alabama, Birmingham, AL, USA
| | - Penny Gordon-Larsen
- Department of Nutrition, Gillings School of Global Public Health & School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA; Carolina Population Center, 137 East Franklin Street, Campus Box 8120, Chapel Hill, NC 27516, USA
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