Higher Sugar-Sweetened Beverage Retail Prices After Excise Taxes in Oakland and San Francisco.

Objectives. To examine how much sugar-sweetened beverage (SSB) excise taxes increased SSB retail prices in Oakland and San Francisco, California.Methods. We collected pretax (April-May 2017) and posttax (April-May 2018) retail prices of SSBs and non-SSBs from 155 stores in Oakland, San Francisco, and comparison cities. We analyzed data using difference-in-differences high-dimensional fixed-effects regressions, weighted by regional beverage sales.Results. Across all beverage sizes, the weighted average price of SSBs increased by 0.92 cents per ounce (95% confidence interval [CI] = 0.28, 1.56) in Oakland and 1.00 cents per ounce (95% CI = 0.35, 1.65) in San Francisco, compared with prices in untaxed cities. The tax did not significantly alter prices of water, 100% juice, or milk of any size examined. Diet soda only, among non-SSBs, exhibited a higher price increase for some sizes in taxed cities.Conclusions. Within 4 to 10 months of implementation, Oakland's and San Francisco's SSB excise taxes significantly increased SSB retail prices by approximately the amount of the taxes, a key mechanism for reducing consumption.

Influence of school environments on childhood obesity in California.

OBJECTIVE: To conduct a state-wide examination of public schools and the school neighborhood as potential targets for environmental public health tracking to address childhood obesity. METHODS: We examined the relationship of social and physical environmental attributes of the school environment (within school and neighborhood) and childhood obesity in California with machine learning (Random Forest) and multilevel methods. We used data compiled from the California Department of Education, the U.S. Geological Survey, ESRI's Business Analyst, the U.S. Census, and other public sources for ecologic level variables for various years and assessed their relative importance to obesity as determined from the statewide Physical Fitness Test 2003 through 2007 for grades 5, 7, and 9 (n = 5,265,265). RESULTS: In addition to individual-level race and gender, the following within and school neighborhood variables ranked as the most important model contributors based on the Random Forest analysis and were included in multilevel regressions clustered on the county. Violent crime, English learners, socioeconomic disadvantage, fewer physical education (PE) and fully credentialed teachers, and diversity index were positively associated with obesity while academic performance index, PE participation, mean educational attainment and per capita income were negatively associated with obesity. The most highly ranked built or physical environment variables were distance to the nearest highway and greenness, which were 10th and 11th most important, respectively. CONCLUSIONS: Many states in the U.S. do not have school-based surveillance programs that collect body mass index data. System-level determinants of obesity can be important for tracking and intervention. The results of these analyses suggest that the school social environment factors may be especially important. Disadvantaged and low academic performing schools have a higher risk for obesity. Supporting such schools in a targeted way may be an efficient way to intervene and could impact both health and academic outcomes. Some of the more important variables, such as having credentialed teachers and participating in PE, are modifiable risk factors.

Developing small-area predictions for smoking and obesity prevalence in the United States for use in Environmental Public Health Tracking.

BACKGROUND: Globally and in the United States, smoking and obesity are leading causes of death and disability. Reliable estimates of prevalence for these risk factors are often missing variables in public health surveillance programs. This may limit the capacity of public health surveillance to target interventions or to assess associations between other environmental risk factors (e.g., air pollution) and health because smoking and obesity are often important confounders. OBJECTIVES: To generate prevalence estimates of smoking and obesity rates over small areas for the United States (i.e., at the ZIP code and census tract levels). METHODS: We predicted smoking and obesity prevalence using a combined approach first using a lasso-based variable selection procedure followed by a two-level random effects regression with a Poisson link clustered on state and county. We used data from the Behavioral Risk Factor Surveillance System (BRFSS) from 1991 to 2010 to estimate the model. We used 10-fold cross-validated mean squared errors and the variance of the residuals to test our model. To downscale the estimates we combined the prediction equations with 1990 and 2000 U.S. Census data for each of the four five-year time periods in this time range at the ZIP code and census tract levels. Several sensitivity analyses were conducted using models that included only basic terms, that accounted for spatial autocorrelation, and used Generalized Linear Models that did not include random effects. RESULTS: The two-level random effects model produced improved estimates compared to the fixed effects-only models. Estimates were particularly improved for the two-thirds of the conterminous U.S. where BRFSS data were available to estimate the county level random effects. We downscaled the smoking and obesity rate predictions to derive ZIP code and census tract estimates. CONCLUSIONS: To our knowledge these smoking and obesity predictions are the first to be developed for the entire conterminous U.S. for census tracts and ZIP codes. Our estimates could have significant utility for public health surveillance.