We are pleased to announce that Food Research International has been accepted in MEDLINE as of March 7th, 2017.
Food Research International provides a forum for the rapid dissemination of significant novel and high impact research in food science, technology, engineering and nutrition. The journal only publishes novel, high quality and high impact review papers, original research papers and letters to the editors, in the various disciplines encompassing the science and technology of food. It is journal policy to publish special issues on topical and emergent subjects of food research or food research-related areas. Special issues of selected, peer-reviewed papers from scientific meetings, workshops, conferences on the science, technology and engineering of foods will be also published.
Food Research International is the successor to the Canadian Institute of Food Science and Technology Journal. Building on the quality and strengths of its predecessor, Food Research International has been developed to create a truly international forum for the communication of research in food science.
Topics covered by the journal include:
- food chemistry
- food microbiology and safety
- food toxicology
- materials science of foods
- food engineering
- physical properties of foods
- sensory science
- food quality
- health and nutrition
- food biophysics analysis of foods
- food nanotechnology
- emerging technologies
- environmental and sustainability aspects of food processing.
Subjects that will not be considered for publication in Food Research International, and will be rejected as being outside of scope, include :
- Studies testing different formulations and ingredients leading to the choice of the best formulation or ingredient to be used in the manufacture of a specified food;
- Optimization studies aiming to determine processing conditions and/or raw materials that increase the yield of a production process or improve nutritional and sensorial qualities;
- Studies describing the production of ingredients and only their characterization without a strong mechanistic emphasis;
- Studies describing the biological activity of foods lacking identification of the compounds responsible for the reported activity will not be published. This is also valid for any other chemical compounds such as phytochemicals and minor components of foods. Compounds of interest need to be characterized at least by mass spectrometry-based methods.
- Studies on antimicrobial compounds that do not consider a validation step in foods, lacking full data on chemical composition indicating the compounds responsible for the inhibitory activity and, when appropriate, the use of molecular biology approaches to support the findings;
- Development of analytical methods not comprising a validation step in situ that represent the range of conditions faced during their application will not be considered;
- Surveys of chemical, nutritional, physical and microbiological hazards will not be considered. Only papers presenting a significant data set, wide coverage, novel and supported by adequate chemical or microbiological techniques will be considered;
- Pharmacology and nutritional studies papers focusing in hosts rather than in foods or effects of processing in major and minor components of foods.
- Pharmacology and nutritional studies that do not contain bioavailability or biofunctionality.
- Engineering studies lacking of mathematical verification or validation in situ, when appropriate;
- Fragmented studies, of low scientific quality, or poorly written.
- Studies with no food component.
1Other studies have examined the effects of price reductions, increases in availability, and promotion of low-fat foods in secondary schools on sales and purchases of these foods (French et al 2004, 2001, 1997a, 1997b, Jeffery et al 1994) as well as their consumption (Perry et al 2004) within experimental settings and found positive effects.
2Kubik et al (2003) find that a la carte availability in school is negatively associated with overall intake of fruits and vegetables and positively associated with total and saturated fat intake among 7th graders attending 16 Minneapolis-St Paul schools. Using the same data, Kubik et al (2005) show that using competitive foods as rewards and incentives is positively associated with BMI.
3Also, using the ECLS-K, Fernandes (2008) found small positive associations between soda availability in schools and both in-school and overall soda consumption of fifth graders.
4Their results for the other school policies, pouring rights contracts, and food and beverage advertisements are smaller and less precise.
5For example, California’s first nutrition policy (SB 677) implemented beverage standards for elementary and middle schools, not high schools.
6All sample sizes have been rounded to the nearest 10 per the ECLS-K’s restricted-use data agreement.
7Obesity is defined as BMI greater than the 95th percentile for age and gender on the Center for Disease Control growth charts.
8Sweets include candy, ice cream, cookies, brownies or other sweets; salty snack foods include potato chips, corn chips, Cheetos, pretzels, popcorn, crackers or other salty snacks, and sweetened beverages include soda pop, sports drinks or fruit drinks that are not 100 percent juice.
9To validate the ECLS-K estimates, we examined the Third School Nutrition and Dietary Assessment Study (SNDA-III), which collected 24-hour dietary recall from 2,300 children attending a nationally representative sample of public schools in 2005. Similar to the ECLS-K, eighty percent of elementary school children reported no competitive food purchases. Among children who made a purchase, the median daily caloric intake from these foods was 185 calories. The SNDA estimate is higher than our ECLS-K estimates (62 calories reported in Section 5) because it includes healthy foods purchased from competitive food venues: for example, milk was by far the most popular item purchased from competitive food venues and yogurt also ranked highly.
10The “potatoes” category excluded French fries, fried potatoes, and potato chips.
11The questionnaire separately asked about availability of high- and low-fat options for baked foods, salty snacks, and ice cream/frozen yogurt/sherbert. We include both the low- and high-fat options in our measure, however, in sensitivity analyses, we used only the high-fat versions to construct our school-administrator based measure of junk food availability and found results to be similar.
12We rely mainly on the first measure of junk food availability because it is the most specific with respect to the quality of foods and because school-level policies regarding junk food availability are frequently set by school principals and staff (Gordon et al 2007a). We prefer this measure over the simple dichotomy of having any (unregulated) competitive food outlets because the outlet-based measure does not differentiate the type of foods sold (e.g. milk vs. soda). We also prefer it over the child-report because children who do not consume junk foods are less likely to accurately report availability and because children reported only the availability of any sweets, salty snacks, or sweetened beverages, but did not differentiate specific items (e.g. low-fat vs. high-fat).
13The value of reduced form regressions has been highlighted by Angrist and Krueger (2001) and, more recently, Chernozhukov and Hansen (2008) formally show that the test for instrument irrelevance in the reduced form regression can be viewed as a weak-instrument-robust test of the hypothesis that the coefficient on the endogenous variable in the structural equation is zero.
14This literature examines peer effects on a wide range of outcomes including substance use (Lundborg 2006; Eisenberg 2004; Case and Katz 1991; Gaviria and Raphael 2001), crime (Case and Katz 1991; Glaeser, Sacerdote, and Scheinkman 1996; Regnerus 2002), teenage pregnancy (Crane 1991; Evans, Oates and Schwab 1992), discipline (Cook et al 2008), academic achievement (Hanushek et al 2003; Cook et al 2008), adolescent food choices (Perry, Kelder, Komro 1993; Cullen et al 2001; French et al 2004) and weight (Trogdon, Nonnemaker and Pais 2008).
15However, Clark and Loheac (2007) estimate how substance use behavior of students within the same school who are one year older influences adolescent substance use and find a positive relationship.
16One exception is Eisenberg (2004) who finds that 7th and 8th graders who attend schools with older peers are no more likely to use substances relative to those who attend schools with younger peers.
17We also examined unadjusted differences in children’s individual, family and school characteristics during the 5th grade (see Appendix Table A3). There were slight differences for some of the covariates. However, there was no overall pattern in the socioeconomic factors that would threaten the validity of the IV approach: that is, some differences imply better BMI outcomes for one group and others worse. For example, in our sample, elementary school students are more likely to be Hispanic and Asian while combined school students are more likely to be white. There are no differences in the share that are Black. Similarly, there is no consistent pattern in maternal education. Elementary school students are more likely to have poorly and highly educated mothers (less than high school, more than Bachelors).
18To check whether these null findings are merely due to lack of power instead of absence of peer effects, we estimated the same models using social-behavioral outcomes and test scores as dependent variables because the literature finds evidence of peer effects on these outcomes. We were able to identify statistically significant peer effects on social-behavioral outcomes (but not test scores), which suggests that lack of power is an unlikely explanation for the finding of null peer effects on BMI and related outcomes.
19In all models, we estimate robust standard errors clustered at the school level.
20In alternate analyses, we used continuous measures of the highest and lowest grades in the school as instruments. In these over-identified models, both instruments had a strong positive association with junk food availability (i.e. increases in the highest and lowest grades available at the school were strongly predictive of junk food availability). This approach yielded qualitatively similar results as the exactly-identified models (available upon request).
21The IV regressions were also estimated without baseline BMI. The point estimates, first-stage F-statistics, and Hausman tests yield similar results (available upon request).
22A concern with our IV specification estimated via two-stage least-squares is that our first stage models do not account for the dichotomous nature of the treatment variable (Maddala 1983). Estimates from binary treatment effect IV models confirm that the effects of junk food availability on BMI are neither substantive nor significant (available upon request).
23We also conducted additional sensitivity analyses not reported here. First, given that we do not know the exposure to junk food in previous grades and given concerns that genetic susceptibility may not have a constant proportional effect on BMI at every point in the life cycle, we controlled for 1st or 3rd grade BMI instead of BMI in Kindergarten and obtained similar results. Second, inclusion of controls for school meal participation did not change our findings. Third, we used BMI z-scores as the dependent variable to accurately control for age and gender influences on BMI and obtained qualitatively similar results. Fourth, we estimated quantile regressions to test whether the effects of junk food availability varied across the BMI distribution, but found no evidence for heterogeneous effects. Finally, we also re-estimated our BMI and obesity models separately for each gender. The results for junk food availability mirrored those for the full sample. The OLS, IV, and RF models show no significant effects of junk food availability for either boys or girls. Still we may be concerned about differential peer effects, for example, if girls are influenced by older peers’ concerns about body image, which would bias our IV estimates downward. Restricting the sample to those boys and girls attending schools without junk food availability, the coefficients from the reduced form were nearly identical to those based on the full sample of boys and girls, which suggests that peer effects are not an issue even when regressions are gender-specific.
24Estimates based only on the sample of private schools yield small and statistically insignificant effects of competitive food availability on BMI in both OLS and IV specifications, although the F-statistics for the instrument in the first stage were smaller (Results available upon request).
25Hausman tests cannot reject the consistency of fully-specified OLS estimates in any of our sensitivity checks.
26Although not shown, the IV (Wald) estimates are easily calculated by dividing the reduced form estimates in Table 10–Table 12 by 0.2 (first stage estimate from Table 2). The IV coefficients are never significant in part due to the larger standard errors in the regressions of reported eating behaviors and physical activity.
27We dichotomize the in-school purchase variables and estimate linear probability models since much of the variation in junk food purchases at school occurs on the extensive margin.
28The median number of times an item is purchased in school among children who purchase at least once is 1.5 times (1–2 times per week). We assume that salty snacks add 140 calories (typical calories from a bag of potato chips), sweets add 200 calories (typically calories from a candy bar), and soda adds 150 calories. Given the limitations of the consumption data in the ECLS-K, we caution the reader to treat these caloric intake calculations as approximations.
29Discretionary calories are the difference between an individual’s total energy requirement and the energy necessary to meet nutrient requirements. According to Dietary Guidelines for Americans, the discretionary allowance for a 2000 calorie diet is 267 calories. See: http://www.health.gov/dietaryguidelines/dga2005/document/html/chapter2.htm#table3 accessed August 22, 2008.
30The total consumption variables are not dichotomized because there is sufficient variation on the intensive margin.
31Negative binomial models with a binary treatment variable to account for the count-data distribution of the total consumption variable and the binary nature of junk food availability produced qualitatively similar results. (Results available upon request).
32Given the limitations of the ECLS-K’s consumption variables, we again examined the SNDA-III data and found no evidence that combined school attendance increases total caloric intake.
33“Schools expect budget cuts as economy sours: State problems, decline in property values eat away at district funds”. Available at: http://www.msnbc.msn.com/id/23116409/ (Accessed February 10, 2009).