Impact of Changes in Chain Restaurant Calories over Time on Obesity Risk.

BACKGROUND: Prior research on the restaurant environment and obesity risk is limited by cross-sectional data and a focus on specific geographic areas. OBJECTIVE: To measure the impact of changes in chain restaurant calories over time on body mass index (BMI). DESIGN: We used a first-difference model to examine whether changes from 2012 to 2015 in chain restaurant calories per capita were associated with percent changes in BMI. We also examined differences by race and county income, restaurant type, and initial body weight categories. SETTING: USA (207 counties across 39 states). PARTICIPANTS: 447,873 adult patients who visited an athenahealth medical provider in 2012 and 2015 where BMI was measured. MAIN OUTCOMES MEASURED: Percent change in objectively measured BMI from 2012 to 2015. RESULTS: Across all patients, changes in chain restaurant calories per capita were not associated with percent changes in BMI. For Black or Hispanic adults, a 10% increase in exposure to chain restaurant calories per capita was associated with a 0.16 percentage-point increase in BMI (95% CI 0.03, 0.30). This translates into a predicted weight increase of 0.89 pounds (or a 0.53% BMI increase) for an average weight woman at the 90th percentile of increases in the restaurant environment from 2012 to 2015 versus an increase 0.39 pounds (or 0.23% BMI increase) at the 10th percentile. Greater increases in exposure to chain restaurant calories also significantly increased BMI for Black or Hispanic adults receiving healthcare services in lower-income counties (0.26, 95% CI 0.04, 0.49) and with overweight/obesity (0.16, 95% CI 0.04, 0.29). LIMITATIONS: Generalizability to non-chain restaurants is unknown and the sample of athenahealth patients is relatively homogenous. CONCLUSIONS: Increased exposure to chain restaurant calories per capita was associated with increased weight gain among Black or Hispanic adults.

Association of Chain Restaurant Advertising Spending With Obesity in US Adults.

Importance: Restaurants spend billions of dollars on marketing. However, little is known about the association between restaurant marketing and obesity risk in adults. Objective: To examine associations between changes in per capita county-level restaurant advertising spending over time and changes in objectively measured body mass index (BMI) for adult patients. Design, Setting, and Participants: This cohort study used regression models with county fixed effects to examine associations between changes in per capita county-level (370 counties across 44 states) restaurant advertising spending over time with changes in objectively measured body mass index (BMI) for US adult patients from 2013 to 2016. Different media types and restaurant types were analyzed together and separately. The cohort was derived from deidentified patient data obtained from athenahealth. The final analytic sample included 5 987 213 patients, and the analysis was conducted from March 2018 to November 2019. Exposure: Per capita county-level chain restaurant advertising spending. Main Outcomes and Measures: Individual-level mean BMI during the quarter. Results: The included individuals were generally older (37.1% older than 60 years), female (56.8%), and commercially insured (53.5%). For the full population of 29 285 920 person-quarters, there was no association between changes in all restaurant advertising per capita (all media types, all restaurants) and changes in BMI. However, restaurant advertising spending was positively associated with weight gain for patients in low-income counties but not in high-income counties. A $1 increase in quarterly advertising per capita across all media and restaurant types was associated with a 0.053-unit increase in BMI (95% CI, 0.001-0.102) for patients in low-income counties, corresponding to a 0.12% decrease in BMI at the 10th percentile of changes in county advertising spending vs a 0.12% increase in BMI at the 90th percentile. Conclusions and Relevance: The results of this study suggest that restaurant advertising is associated with modest weight gain among adult patients in low-income counties. To date, there has been no public policy action or private sector action to limit adult exposure to unhealthy restaurant advertising. Efforts to decrease restaurant advertising in low-income communities should be intensified and rigorously evaluated to understand their potential for increasing health equity.

The Voices for Healthy Kids and State Legislation to Prevent Childhood Obesity: An Update.

INTRODUCTION: The purpose of this study is to examine general time trends in childhood obesity legislative activity in all 50 states (overall and by health equity focus) and whether the Voices for Healthy Kids Campaign (Voices) was associated with increased legislative activity. METHODS: LexisNexis State Capital was used to identify bills related to childhood obesity from 2012 to 2016. Linear and linear probability models were used to assess general time trends and regression-based difference-in-difference models to assess whether time trends differed for states that received a Voices grant. The data were analyzed in 2017. RESULTS: A total of 989 bills were introduced (Year 1=304, Year 2=364; Year 3=321), and a total of 93 bills were enacted (Year 1=34, Year 2=24, Year 3=35) after baseline. The mean number of bills introduced (baseline=4.3, Year 1=6.6, Year 2=7.3, Year 3=7.0, p=0.007), and the average state enactment rate (baseline=11%, Year 1=16%, Year 2=8%, Year 3=27%, p-trend=0.02) increased significantly. States with Voices grantees introduced 2.1 more bills than non-grantee states (p=0.04). The estimated difference over time in bill enactment and health equity focus did not differ by Voices grantee status. CONCLUSIONS: Childhood obesity bill introduction and enactment increased between 2013 and 2016. The evidence-based advocacy supported by Voices appears to be significantly associated with greater increases in state-level bill introduction, but not enactment of legislation to address childhood obesity.