Jessie Gruner1, Robin S DeWeese1, Cori Lorts1, Michael J Yedidia1, Punam Ohri-Vachaspati1. 1. Jessie Gruner, Robin S. DeWeese, Cori Lorts, and Punam Ohri-Vachaspati are with the School of Nutrition and Health Promotion, Arizona State University, Phoenix. Michael J. Yedidia is with the Rutgers Center for State Health Policy, New Brunswick, NJ.
Abstract
OBJECTIVES: To determine the proportion of restaurants that will be required to post calorie information under the Food and Drug Administration's menu-labeling regulations in 4 New Jersey cities. METHODS: We classified geocoded 2014 data on 1753 restaurant outlets in accordance with the Food and Drug Administration's guidelines, which will require restaurants with 20 or more locations nationwide to post calorie information. We used multivariate logistic regression analyses to assess the association between menu-labeling requirements and census tract characteristics. RESULTS: Only 17.6% of restaurants will be affected by menu labeling; restaurants in higher-income tracts have higher odds than do restaurants in lower-income tracts (odds ratio [OR] = 1.55; P = .02). Restaurants in non-Hispanic Black (OR = 1.62; P = .02) and mixed race/ethnicity (OR = 1.44; P = .05) tracts have higher odds than do restaurants in non-Hispanic White tracts of being affected. CONCLUSIONS: Additional strategies are needed to help consumers make healthy choices at restaurants not affected by the menu-labeling law. These findings have implications for designing implementation strategies for the law and for evaluating its impact.
OBJECTIVES: To determine the proportion of restaurants that will be required to post calorie information under the Food and Drug Administration's menu-labeling regulations in 4 New Jersey cities. METHODS: We classified geocoded 2014 data on 1753 restaurant outlets in accordance with the Food and Drug Administration's guidelines, which will require restaurants with 20 or more locations nationwide to post calorie information. We used multivariate logistic regression analyses to assess the association between menu-labeling requirements and census tract characteristics. RESULTS: Only 17.6% of restaurants will be affected by menu labeling; restaurants in higher-income tracts have higher odds than do restaurants in lower-income tracts (odds ratio [OR] = 1.55; P = .02). Restaurants in non-Hispanic Black (OR = 1.62; P = .02) and mixed race/ethnicity (OR = 1.44; P = .05) tracts have higher odds than do restaurants in non-Hispanic White tracts of being affected. CONCLUSIONS: Additional strategies are needed to help consumers make healthy choices at restaurants not affected by the menu-labeling law. These findings have implications for designing implementation strategies for the law and for evaluating its impact.
Authors: Stephanie Anzman-Frasca; Megan P Mueller; Sarah Sliwa; Peter R Dolan; Linda Harelick; Susan B Roberts; Kyle Washburn; Christina D Economos Journal: Obesity (Silver Spring) Date: 2015-05 Impact factor: 5.002
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