Soda, Salad, and Socioeconomic Status: Findings from the Seattle Obesity Study (SOS)

Overview

Journal SSM Popul Health

Date 2019 Jan 10

PMID 30623013

Citations 5

Authors

Adam Drewnowski

James Buszkiewicz

Anju Aggarwal

Affiliations

Soon will be listed here.

Abstract

Background: Documenting geographic disparities in dietary behaviors can help inform public health interventions at the local level.

Objective: To study and visualize socioeconomic gradient in soda and salad consumption using a geo-localized measure of socioeconomic status in contrast to more traditional measures.

Methods: Geo-localized dietary intake data came from the Seattle Obesity Study I, a population-based sample of King County adults (n=1099). Socio-demographic data and soda and salad consumption frequencies (times/week) were obtained by 20-min telephone survey. Food frequency questionnaire (FFQ) data were used to construct Healthy Eating Index (HEI) scores. Individual residential property values obtained from the King County tax assessor. Multivariable linear regressions examined socioeconomic gradient in the frequency of soda and salad consumption by residential property values, the primary independent variable, in comparison to annual household incomes and educational attainment, with adjustment for age, gender, and race/ethnicity. Geographic disparities in soda and salad consumption by property value metric were illustrated at the census block level using modeled predicted marginal means.

Results: Among all three socioeconomic indicators (income, education and residential property values), residential property values captured strongest gradient in soda and salad consumption. Higher quintiles of residential property values were associated with lower soda and higher salad consumption. Respondents living in the highest quintile of property values -1.04 fewer sodas per week (95% CI= -1.87, -0.21) and 0.89 more salads per week (95% CI= 0.36, 1.42), adjusting for sociodemographic covariates. Residential property values illustrated geographic disparities in soda and salad consumption at the census-block level.

Conclusion: Geo-localized disparities in food consumption patterns by neighborhood can inform current discourse on the socioeconomic determinants of health, while providing a useful tool for targeted interventions at the local level.

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References

Patterson R, Kristal A, Tinker L, Carter R, Bolton M, Agurs-Collins T . Measurement characteristics of the Women's Health Initiative food frequency questionnaire. Ann Epidemiol. 1999; 9(3):178-87. DOI: 10.1016/s1047-2797(98)00055-6. View

Neuhouser M, Kristal A, McLerran D, Patterson R, Atkinson J . Validity of short food frequency questionnaires used in cancer chemoprevention trials: results from the Prostate Cancer Prevention Trial. Cancer Epidemiol Biomarkers Prev. 2000; 8(8):721-5. View

Kristal A, Vizenor N, Patterson R, Neuhouser M, Shattuck A, McLerran D . Precision and bias of food frequency-based measures of fruit and vegetable intakes. Cancer Epidemiol Biomarkers Prev. 2000; 9(9):939-44. View

Willett W, Hunter D, Stampfer M, Colditz G, Manson J, Spiegelman D . Dietary fat and fiber in relation to risk of breast cancer. An 8-year follow-up. JAMA. 1992; 268(15):2037-44. View

Drewnowski A, Specter S . Poverty and obesity: the role of energy density and energy costs. Am J Clin Nutr. 2003; 79(1):6-16. DOI: 10.1093/ajcn/79.1.6. View

Lippman S, Goodman P, Klein E, Parnes H, Thompson Jr I, Kristal A . Designing the Selenium and Vitamin E Cancer Prevention Trial (SELECT). J Natl Cancer Inst. 2005; 97(2):94-102. DOI: 10.1093/jnci/dji009. View

Chaix B, Merlo J, Chauvin P . Comparison of a spatial approach with the multilevel approach for investigating place effects on health: the example of healthcare utilisation in France. J Epidemiol Community Health. 2005; 59(6):517-26. PMC: 1757051. DOI: 10.1136/jech.2004.025478. View

Ellaway A, Macintyre S, Bonnefoy X . Graffiti, greenery, and obesity in adults: secondary analysis of European cross sectional survey. BMJ. 2005; 331(7517):611-2. PMC: 1215553. DOI: 10.1136/bmj.38575.664549.F7. View

Duncan M, Spence J, Mummery W . Perceived environment and physical activity: a meta-analysis of selected environmental characteristics. Int J Behav Nutr Phys Act. 2005; 2:11. PMC: 1236952. DOI: 10.1186/1479-5868-2-11. View

10.

Boehmer T, Hoehner C, Deshpande A, Brennan Ramirez L, Brownson R . Perceived and observed neighborhood indicators of obesity among urban adults. Int J Obes (Lond). 2007; 31(6):968-77. DOI: 10.1038/sj.ijo.0803531. View

11.

Monsivais P, Drewnowski A . The rising cost of low-energy-density foods. J Am Diet Assoc. 2007; 107(12):2071-6. DOI: 10.1016/j.jada.2007.09.009. View

12.

Burdette A, Hill T . An examination of processes linking perceived neighborhood disorder and obesity. Soc Sci Med. 2008; 67(1):38-46. DOI: 10.1016/j.socscimed.2008.03.029. View

13.

Bell J, Wilson J, Liu G . Neighborhood greenness and 2-year changes in body mass index of children and youth. Am J Prev Med. 2008; 35(6):547-53. PMC: 2649717. DOI: 10.1016/j.amepre.2008.07.006. View

14.

Dunton G, Kaplan J, Wolch J, Jerrett M, Reynolds K . Physical environmental correlates of childhood obesity: a systematic review. Obes Rev. 2009; 10(4):393-402. PMC: 3833101. DOI: 10.1111/j.1467-789X.2009.00572.x. View

15.

Monsivais P, Drewnowski A . Lower-energy-density diets are associated with higher monetary costs per kilocalorie and are consumed by women of higher socioeconomic status. J Am Diet Assoc. 2009; 109(5):814-22. PMC: 2907149. DOI: 10.1016/j.jada.2009.02.002. View

16.

Masset G, Monsivais P, Maillot M, Darmon N, Drewnowski A . Diet optimization methods can help translate dietary guidelines into a cancer prevention food plan. J Nutr. 2009; 139(8):1541-8. DOI: 10.3945/jn.109.104398. View

17.

Chaix B, Merlo J, Evans D, Leal C, Havard S . Neighbourhoods in eco-epidemiologic research: delimiting personal exposure areas. A response to Riva, Gauvin, Apparicio and Brodeur. Soc Sci Med. 2009; 69(9):1306-10. DOI: 10.1016/j.socscimed.2009.07.018. View

18.

Auchincloss A, Diez Roux A, Mujahid M, Shen M, Bertoni A, Carnethon M . Neighborhood resources for physical activity and healthy foods and incidence of type 2 diabetes mellitus: the Multi-Ethnic study of Atherosclerosis. Arch Intern Med. 2009; 169(18):1698-704. PMC: 2828356. DOI: 10.1001/archinternmed.2009.302. View

19.

Renalds A, Smith T, Hale P . A systematic review of built environment and health. Fam Community Health. 2009; 33(1):68-78. DOI: 10.1097/FCH.0b013e3181c4e2e5. View

20.

Andreyeva T, Long M, Brownell K . The impact of food prices on consumption: a systematic review of research on the price elasticity of demand for food. Am J Public Health. 2009; 100(2):216-22. PMC: 2804646. DOI: 10.2105/AJPH.2008.151415. View