Obesity Politics/Policy

Vision + Action = Faithful Execution: Why Government Daydreams and How to Stop the Cascade of Breakdowns That Now Haunts It

Vision + Action = Faithful Execution: Why Government Daydreams and How to Stop the Cascade of Breakdowns That Now Haunts It
Light, P. C. (2016). Vision + Action = Faithful Execution: Why Government Daydreams and How to Stop the Cascade of Breakdowns That Now Haunts It. PS-POLITICAL SCIENCE & POLITICS, 49(1), 5-20. Chicago

Light, Paul
12/01/2015

Performance Standards for Restaurants: A New Approach to Addressing the Obesity Epidemic.

Performance Standards for Restaurants: A New Approach to Addressing the Obesity Epidemic.
Cohen D, Bhatia R, Story MT, Sugarman SD, Economos CD, Whitsel LP, Williams JD, Elbel B, Harris J, Kappagoda M, Champagne CM, Shields K, Lesser LI, Fox T, Becker N. Performance Standards for Restaurants: A New Approach to Addressing the Obesity Epidemic. Santa Monica, CA: RAND Corporation; 2013.

Cohen D, Bhatia R, Story MT, Sugarman SD, Economos CD, Whitsel LP, Williams JD, Elbel B, Harris J, Kappagoda M, Champagne CM, Shields K, Lesser LI, Fox T, Becker N.
09/10/2015

Calorie Labeling and Consumer Estimation of Calories Purchased.

Calorie Labeling and Consumer Estimation of Calories Purchased.
Taksler GB, Elbel B. Calorie Labeling and Consumer Estimation of Calories Purchased. International Journal of Behavioral Nutrition and Physical Activity. 2014; 11: 91.

Taksler GB, Elbel B.
09/10/2015

BACKGROUND:

Studies rarely find fewer calories purchased following calorie labeling implementation. However, few studies consider whether estimates of the number of calories purchased improved following calorie labeling legislation.

FINDINGS:

Researchers surveyed customers and collected purchase receipts at fast food restaurants in the United States cities of Philadelphia (which implemented calorie labeling policies) and Baltimore (a matched comparison city) in December 2009 (pre-implementation) and June 2010 (post-implementation). A difference-in-difference design was used to examine the difference between estimated and actual calories purchased, and the odds of underestimating calories.Participants in both cities, both pre- and post-calorie labeling, tended to underestimate calories purchased, by an average 216-409 calories. Adjusted difference-in-differences in estimated-actual calories were significant for individuals who ordered small meals and those with some college education (accuracy in Philadelphia improved by 78 and 231 calories, respectively, relative to Baltimore, p = 0.03-0.04). However, categorical accuracy was similar; the adjusted odds ratio [AOR] for underestimation by >100 calories was 0.90 (p = 0.48) in difference-in-difference models. Accuracy was most improved for subjects with a BA or higher education (AOR = 0.25, p < 0.001) and for individuals ordering small meals (AOR = 0.54, p = 0.001). Accuracy worsened for females (AOR = 1.38, p < 0.001) and for individuals ordering large meals (AOR = 1.27, p = 0.028).

CONCLUSIONS:

We concluded that the odds of underestimating calories varied by subgroup, suggesting that at some level, consumers may incorporate labeling information.

Corner store purchases in a low-income urban community in NYC.

Corner store purchases in a low-income urban community in NYC.
Kiszko K, Cantor J, Abrams C, Ruddock C, Moltzen K, Devia C, McFarline B, Singh H, Elbel B. Corner store purchases in a low-income urban community in NYC. Journal of Community Health. In press.

Kiszko K, Cantor J, Abrams C, Ruddock C, Moltzen K, Devia C, McFarline B, Singh H, Elbel B.
09/10/2015

We assessed purchases made, motivations for shopping, and frequency of shopping at four New York City corner stores (bodegas). Surveys and purchase inventories (n = 779) were collected from consumers at four bodegas in Bronx, NY. We use Chi square tests to compare types of consumers, items purchased and characteristics of purchases based on how frequently the consumer shops at the specific store and the time of day the purchase was made. Most consumers shopped at the bodega because it was close to their home (52 %). The majority (68 %) reported shopping at the bodega at least once per day. The five most commonly purchased items were sugary beverages, (29.27 %), sugary snacks (22.34 %), coffee, (13.99 %), sandwiches, (13.09 %) and non-baked potato chips (12.2 %). Nearly 60 % of bodega customers reported their purchase to be healthy. Most of the participants shopped at the bodega frequently, valued its convenient location, and purchased unhealthy items. Work is needed to discover ways to encourage healthier choices at these stores.

Determining Chronic Disease Prevalence in Local Populations Using Emergency Department Surveillance.

Determining Chronic Disease Prevalence in Local Populations Using Emergency Department Surveillance.
13. Lee DC, Long JA, Wall SP, Braithwaite RS, Elbel B. Determining Chronic Disease Prevalence in Local Populations Using Emergency Department Surveillance. American Journal of Public Health. In press.

Lee DC, Long JA, Wall SP, Braithwaite RS, Elbel B.
09/10/2015

Development and Evaluation of the US Healthy Food Diversity Index.

Development and Evaluation of the US Healthy Food Diversity Index.
Vadiveloo M, Dixon LB, Mijanovich T, Elbel B, Parekh N. Development and Evaluation of the US Healthy Food Diversity Index. British Journal of Nutrition. 2014; 112(9): 1562-1574.

Vadiveloo M, Dixon LB, Mijanovich T, Elbel B, Parekh N.
09/10/2015

Varied diets are diverse with respect to diet quality, and existing dietary variety indices do not capture this heterogeneity. We developed and evaluated the multidimensional US Healthy Food Diversity (HFD) index, which measures dietary variety, dietary quality and proportionality according to the 2010 Dietary Guidelines for Americans (DGA). In the present study, two 24 h dietary recalls from the 2003-6 National Health and Nutrition Examination Survey (NHANES) were used to estimate the intake of twenty-six food groups and health weights for each food group were informed by the 2010 DGA. The US HFD index can range between 0 (poor) and 1 - 1/n, where n is the number of foods; the score is maximised by consuming a variety of foods in proportions recommended by the 2010 DGA. Energy-adjusted Pearson's correlations were computed between the US HFD index and each food group and the probability of adequacy for fifteen nutrients. Linear regression was run to test whether the index differentiated between subpopulations with differences in dietary quality commonly reported in the literature. The observed mean index score was 0·36, indicating that participants did not consume a variety of healthful foods. The index positively correlated with nutrient-dense foods including whole grains, fruits, orange vegetables and low-fat dairy (r 0·12 to 0·64) and negatively correlated with added sugars and lean meats (r - 0·14 to - 0·23). The index also positively correlated with the mean probability of nutrient adequacy (r 0·41; P< 0·0001) and identified non-smokers, women and older adults as subpopulations with better dietary qualities. The US HFD index may be used to inform national dietary guidance and investigate whether healthful dietary variety promotes weight control.

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