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Association of State Laws Regarding Snacks in US Schools With Students' Consumption of Solid Fats and Added Sugars

Association of State Laws Regarding Snacks in US Schools With Students' Consumption of Solid Fats... Key Points Question Are state laws requiring IMPORTANCE The Smart Snacks in School standards (hereafter, Smart Snacks) were issued in 2013 schools to implement federal Smart with the aim of improving students’ dietary intake behaviors. Goals of Smart Snacks included Snacks in School standards associated reducing total energy intake, consumption of solid fats and added sugars, and sodium intake. Smart with student dietary consumption? Snacks standards were required to be implemented by the start of the 2014 to 2015 school year at all Findings In this cross-sectional study US schools participating in federal child nutrition programs. with a nationally representative sample of 1959 students in grades 1 through 12, OBJECTIVE To examine the association of state laws that specifically direct schools to implement students who attended schools in states Smart Snacks with student dietary consumption outcomes. with laws requiring the implementation of Smart Snacks in School standards DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study used nationally representative consumed an adjusted mean of 53.9 data collected in the 2014 to 2015 school year as part of the School Nutrition and Meal Cost Study. fewer kcal from solid fats and added Students in grades 1 through 12 (ages approximately 6-18 years) were randomly selected from 310 sugars per day than did students in public schools in 30 US states and the District of Columbia. Analytic weights were applied and all states with no such laws, a statistically percentages reported are weighted. Analyses were conducted from March 1, 2018, to December significant difference. 12, 2019. Meaning These findings suggest that EXPOSURES State laws requiring schools to implement Smart Snacks. state laws may support the implementation of federal standards, MAIN OUTCOMES AND MEASURES A 24-hour recall was used to assess student dietary intake as with significant implications for student daily kilocalories consumed as (a) total energy, (b) solid fats and added sugars combined, (c) solid dietary behaviors. fats, or (d) added sugars. Milligrams of daily sodium consumption were also computed. Supplemental content RESULTS Among 1959 students (mean [SD] age, 11.9 [3.5] years; 1014 [50.9%] boys), 420 students (22.5%) attended school in a state with Smart Snacks laws, and 528 students (26.1%) consumed Author affiliations and article information are listed at the end of this article. snacks obtained at school. In covariate-adjusted models, total energy intake did not vary based on state law. Adjusted mean daily kilocalories from solid fats and added sugars was significantly lower among students in states with laws (508.7 [95% CI, 463.0 to 554.4] kcal) than among students in states without laws (562.5 [95% CI, 534.3 to 590.8] kcal; difference, −53.9 [95% CI, −104.5 to −3.2] kcal; P = .04). Consumption of sodium did not differ by state law. Kilocalories from solid fats contributed more to the difference than kilocalories from added sugars (−37.7 [95% CI −62.8 to −12.6] kcal vs −16.2 [95% CI, −51.3 to 19.0] kcal). CONCLUSIONS AND RELEVANCE These findings suggest that students in states with laws requiring schools to implement Smart Snacks had better dietary intake than students in states without laws, consuming a mean of 53.9 fewer kilocalories from solid fats and added sugars per day, after adjusting for covariates. State-level policy mechanisms may support schools’ implementation of federal standards in ways that are associated with healthier diets among children and adolescents. JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 Open Access. This is an open access article distributed under the terms of the CC-BY License. JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 (Reprinted) January 15, 2020 1/12 JAMA Network Open | Nutrition, Obesity, and Exercise State Laws Regarding Snacks in US Schools Students' Consumption of Solid Fats and Added Sugars Introduction Dietary intake is the primary modifiable behavioral risk factor associated with morbidity and mortality among people in the United States, with unhealthful dietary habits associated with 1,2 cardiovascular disease, stroke, diabetes, and obesity. The Dietary Guidelines for Americans provide science-based advice to reduce these risks through optimal diets. The 2010 guidelines recommend that children, adolescents, and adults limit their intake of empty calories, which are commonly obtained by consumption of foods and beverages that contain solid fats and added sugars. Solid fats and added sugars are characterized as empty calories because they do not provide essential nutrients, they displace the consumption of other nutrient-dense foods and beverages, and they 3,5,6 increase overall energy intake. Schools are locations that have contributed substantially to the 7,8 consumption of empty calories by children and adolescents. Large-scale studies conducted in the early 2000s reported that sugar-sweetened beverages, other sugary foods, and snacks high in fat and sodium were common in schools across the United 9-11 States and significantly affected students’ consumption of empty calories. As directed by the Healthy, Hunger-Free Kids Act of 2010, the US Department of Agriculture (USDA) issued revised standards for school meals in 2012 and new standards for all foods and beverages sold in other 13 3 locations at schools in 2013. These standards aligned with the Dietary Guidelines for Americans and science-based recommendations. The latter, named the Smart Snacks in School standards (hereafter, Smart Snacks), aimed to increase the availability and consumption of healthful options such as fruit, vegetables, whole grain foods, and low-fat dairy and to reduce availability and consumption of high-calorie items with high amounts of fat, added sugar, and sodium. Smart Snacks was required to be implemented in schools by July 1, 2014. Prior to 2014, some states, districts, and schools had already addressed the nutritional quality of foods and beverages sold in schools outside of school meals. The number of states with policies addressing nutrition standards for such foods and beverages increased between the 2006 to 2007 school year and the 2014 to 2015 school year, by which point 50% of states had strong laws and 13% of states had weak laws that established some type of nutrition standards. A systematic review in 2014 found that such policies at the state or district level were associated with reduced availability of unhealthful foods and beverages sold outside of school meals (ie, in school stores or snack bars, vending machines, and à la carte lines). Although all schools participating in USDA child nutrition programs are required to comply with Smart Snacks, state laws may further facilitate compliance with national policy, particularly during the early phases of implementation. This is an important question for understanding the mechanisms of policy implementation, but it has not been addressed empirically, to our knowledge. At the start of the 2014 to 2015 school year, 9 states had laws requiring compliance with Smart Snacks. Given that national implementation of Smart Snacks was slow and many schools found it challenging to fully implement these policy changes, having a state law may have facilitated implementation of systemic changes in school food and beverage environments. This cross-sectional study examined associations of state laws requiring Smart Snacks implementation with student dietary outcomes in the 2014 to 2015 school year. The hypothesis was that such laws would be associated with better dietary outcomes among students attending schools in those states. Methods This study combined student-level data collected by the nationally representative School Nutrition and Meal Cost Study (SNMCS) with corresponding state laws collected and coded by the National Wellness Policy Study (NWPS). A brief description of SNMCS data collection is provided here, with extensive details available elsewhere. The SNMCS protocol and instruments were reviewed and approved by the US Office of Management and Budget. Student assent and parental informed JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 (Reprinted) January 15, 2020 2/12 JAMA Network Open | Nutrition, Obesity, and Exercise State Laws Regarding Snacks in US Schools Students' Consumption of Solid Fats and Added Sugars consent were obtained for the diet interview. Nonidentifiable data were provided to the authors by Mathematica Policy Research, which was contracted to conduct the SNMCS for the USDA. Per the Common Rule, the collection of data on state laws is not considered to be human subjects research, so ethics board approval was not required. The study is reported following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. Student-Level Data The SNMCS was commissioned by the USDA to assess practices in a nationally representative sample of public schools serving kindergarten through 12th grade during the 2014 to 2015 school year. The universe for sampling included public school food authorities and public noncharter schools participating in the National School Lunch Program. A stratified 2-stage sampling approach was used, with 3 groups of school food authorities selected using probability proportional to size sampling. Within 1 of those school food authority groups, a sample of 310 schools was recruited; thereafter, students were randomly selected and recruited from those schools for interviews. Dietary interviews were conducted with 2165 respondents in grades 1 through 12 (63.6% weighted response rate). Children in kindergarten and prekindergarten were excluded from the study because of concerns about their ability to provide accurate dietary recalls. Student Dietary Data Collection As detailed in the SNMCS report, collection of student dietary data used a 24-hour dietary recall with the USDA’s Dietary Intake Data System. The computer-assisted Automated Multiple-Pass Method was used to collect information about each student’s dietary intake. Students in middle school (typically grades 6-8, including students ages approximately 11-14 years) and high school (typically grades 9-12, including students ages approximately 14-18 years) completed the interview independently, and students in elementary school (typically grades 1-5, including students ages approximately 6-11 years) had parental assistance. The USDA’s Post-Interview Processing System, 19,20 combined with Survey Net, was used to code types and amounts of foods and beverages consumed, from which nutrient characteristics were computed. The analyses used information about total daily energy intake (in kilocalories), as well as daily energy intake from solid fats and added sugars and sodium intake. In the data coding process, SNMCS indicated whether students had consumed any snacks obtained at schools (eg, purchased from sales venues, given by teachers, or at parties; coded as yes or no). Student Demographic Characteristics The restricted-use data set supplied for this study included student demographic characteristics, which were used as contextual covariates in the analyses. Demographic variables included student sex (boys or girls), grade in school, and self- or parent-reported race/ethnicity. Additional 21-23 demographic data for students were based on the school or district at which they were enrolled. These included region, school urbanicity, school size, racial/ethnic composition of the district’s students, and socioeconomic composition. State-Level Data The NWPS is the largest nationwide evaluation of congressionally mandated school district wellness 24 15 policies and state laws for 50 states and the District of Columbia (collectively referred to as states). Codified state statutes and administrative regulations for each state were compiled using subscription-based services, Lexis Advance (LexisNexis) and WestlawNext (Thomson Reuters). Boolean keyword searches and reviews of the indices or tables of contents of the codified laws for each state were conducted by trained attorneys and legal researchers using the state law databases from each service. State laws were defined to include the codified laws as well as any state health or nutrition education standards incorporated by reference into the codified law. Laws were deemed JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 (Reprinted) January 15, 2020 3/12 JAMA Network Open | Nutrition, Obesity, and Exercise State Laws Regarding Snacks in US Schools Students' Consumption of Solid Fats and Added Sugars relevant if they were effective as of September 2, 2014, a proxy for the beginning of the 2014 to 2015 25,26 school year. The existence of state laws was verified against public sources. State laws were reviewed and verified by 2 members of the NWPS team (including E.P.-P.), then coded by 2 trained analysts (including E.P.-P.). Coding used a wellness policy coding scheme that was modified by NWPS to capture new Smart Snacks provisions. State laws were coded for whether they were definitively required; encouraged, suggested, or required with exceptions; or did not address compliance with Smart Snacks. Strong policy provisions definitively required 28,29 implementation and met Smart Snacks standards if they included language such as shall, must, will, require, comply, and enforce. Laws were coded at elementary school (grades 1-5), middle school (grades 6-8), and high school (grades 9-12) levels. Locations of sale included 4 venues: vending machines, school stores or snack bars, à la carte, and fundraisers. Analyses used a dichotomous variable indicating whether state law met Smart Snacks standards in all 4 venues. Statistical Analysis The SNMCS student-level data and state law data were linked using state geocodes by Mathematica Policy Research (Mathematica) and returned to the NWPS for analyses. Grade level–specific state law coding was linked to schools of the corresponding grade level. Analyses were conducted from March 1, 2018, to December 12, 2019, with Stata statistical software version 13 (StataCorp) using the svy command to account for the sampling design. Analytic weights were used; thus numbers of students are unadjusted, and all percentages given are weighted. Descriptive statistics for demographic variables were computed to examine characteristics of students. Descriptive statistics for student dietary intake were examined for all students and for the subset of students who consumed snacks at school. A series of multivariable linear regression models were computed to examine dietary outcomes of interest: (a) total daily energy intake in kilocalories, (b) kilocalories from solid fats and added sugars combined, (c) kilocalories from solid fats alone, (d) kilocalories from added sugars alone, and (e) sodium intake in milligrams. A multivariable logistic regression model was computed to examine differences between students who consumed snacks at school vs those who did not. After computing models for all students, regression models were recomputed for the subset of students who consumed snacks at school. Each model included state law as a key variable while controlling for contextual covariates of student, school, and district demographic characteristics. A 2-tailed a priori α level of .05 was used for significance tests, considering the coefficients for state law in the multivariable regression models. The adjusted margins for state law were examined, which represent the mean value of the outcome at each level while accounting for all other covariates. Results Among 2165 students who completed the 24-hour dietary recall, 206 students had missing data for some demographic variables, reducing the analytical sample to 1959 students (mean [SD] age, 11.9 [3.5] years; 1014 [50.9%] boys) from 290 schools (Table 1). Descriptive statistics presented here may differ from the SNMCS report because of this restriction on the analytic sample. Among 1959 students sampled, 420 students (22.5%) attended school in a state with Smart Snack laws, and 528 students (26.1%) reported consuming snacks obtained at school on the day of reporting. Students attended schools in 30 states and the District of Columbia. Among these states, 7 had a Smart Snacks law (Arkansas, District of Columbia, Florida, Georgia, Illinois, Mississippi, and Utah). Students consumed a mean total of 1982.2 (95% CI, 1919.2 to 2045.2) kcal per day (Table 2), but as expected there were significant differences by demographic characteristics, such as grade in school and sex (Table 3). Few school-level characteristics were associated with students’ daily energy intake. State laws regarding Smart Snacks were not significantly associated with total energy intake among the full sample or among students who consumed snacks at school. Students attending schools in states with laws consumed an adjusted mean of 508.7 (95% CI, 463.0 to 554.4) kcal from solid fats and added sugars, which comprised 25.7% of their total daily energy intake, whereas students in states JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 (Reprinted) January 15, 2020 4/12 JAMA Network Open | Nutrition, Obesity, and Exercise State Laws Regarding Snacks in US Schools Students' Consumption of Solid Fats and Added Sugars Table 1. Student-, School-, and District-Level Characteristics of Sample Students, No. (%) Characteristic (N = 1959) Student Level Grade 1 146 (8.7) 2 149 (9.6) 3 146 (10.0) 4 136 (8.7) 5 138 (7.9) 6 191 (7.2) 7 220 (7.5) 8 228 (7.2) 9 174 (9.5) 10 148 (8.1) 11 160 (9.2) 12 123 (6.5) Sex Boys 1014 (50.9) Girls 945 (49.1) Race/ethnicity Non-Hispanic white 953 (52.6) Non-Hispanic black 267 (13.3) Hispanic 551 (25.5) Other or multiracial 188 (8.7) School Level Urbanicity Urban 436 (23.5) Suburban 1019 (49.3) Rural 504 (27.2) Size, No. of students ≥1000 561 (31.0) 500-999 878 (45.1) <500 520 (23.9) District Level Racial/ethnic composition of students ≥66% White 650 (37.3) ≥50% Black 83 (4.6) ≥50% Hispanic 465 (17.5) Other 761 (40.6) District-level child poverty rate <20% 996 (67.2) ≥20% 963 (32.8) Region West 486 (17.5) Midwest 457 (24.6) South 826 (44.4) Northeast 190 (13.5) State law requires schools to meet Smart Snacks in all venues No 1539 (77.5) Yes 420 (22.5) No. is unweighted, percentage is survey-weighted. Approximate student ages by grade: grade 1, 6 to 7 years; grade 2, 7 to 8 years; grade 3, 8 to 9 years; grade 4, 9 to 10 years; grade 5, 10 to 11 years; grade 6, 11 to 12 years; grade 7, 12 to 13 years; grade 8, 13 to 14 years; grade 9, 14 to 15 years; grade 10, 15 to 16 years; grade 11, 16 to 17 years; and grade 12, 17 to 18 years. JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 (Reprinted) January 15, 2020 5/12 JAMA Network Open | Nutrition, Obesity, and Exercise State Laws Regarding Snacks in US Schools Students' Consumption of Solid Fats and Added Sugars without laws consumed an adjusted mean of 562.5 (95% CI, 534.3 to 590.8) kcal, which comprised 28.4% of their total daily energy intake, a difference of −53.9 (95% CI, −104.5 to −3.2) kcal (P = .04) (Table 4). Calories from solid fats contributed more to the difference than calories from added sugars (−37.7 [95 CI, −62.8 to −12.6] kcal vs −16.2 [95% CI, −51.3 to 19.0] kcal). The pattern of results among students who consumed snacks at school was similar, but the difference was not statistically significant. Additional analyses examined the outcomes of consumption of solid fats alone, added sugars alone, and sodium (Table 5; eTables 1, 2, and 3 in the Supplement). Among all students, students in states with laws compared with those in states without laws had lower adjusted mean total daily consumption of solid fats (257.6 [95% CI, 237.1 to 278.1] kcal vs 295.3 [95% CI, 280.5 to 310.2] kcal; P = .004), but not added sugars or sodium. Results were similar for students who consumed snacks at school. Students who consumed snacks at school in states with laws consumed an adjusted mean of 247.1 (95% CI, 208.2 to 286.0) kcal from solid fats, whereas those in states without laws consumed an adjusted mean of 300.6 (95% CI, 272.1 to 329.1) kcal from solid fats, a difference of −53.5 (95% CI, −97.0 to −10.0) kcal (P = .02). To consider whether laws were associated with students’ at-school consumption behaviors, we used multivariable logistic regression to estimate snack consumption (eTable 4 in the Supplement). Similar percentages of students consumed snacks at school in states with laws (adjusted prevalence, 23.3% [95% CI, 18.3% to 28.4%]) as did in states without laws (adjusted prevalence, 26.9% [95% CI, 23.8% to 30.0%]; P = .25). Discussion This cross-sectional study examining differences in the dietary intake of students in states subject to laws regarding Smart Snacks vs students in states without such laws found that some dietary outcomes differed by state law among the overall sample and among students who consumed snacks at school on the measurement day. Among all students, those in states with laws requiring schools to implement Smart Snacks consumed an adjusted mean of 53.9 fewer total kcal per day from solid fats and added sugars. More of this difference came from solid fats (37.7 kcal) than added sugars (16.2 kcal); when examined as separate outcomes, solid fat consumption differed significantly by state law, but consumption of added sugars did not. When considering the subsample of students who consumed snacks at school, while the reduction limited statistical power, a similar pattern was seen, although the difference was not statistically significant. Students who consumed snacks at school in states with laws consumed 53.5 fewer kcal from solid fats compared with students in states without laws. Differences in daily consumption of solid fats and added sugars combined and sodium were not statistically significant. Policy approaches to improving the school food environment are hypothesized to work by improving the nutritional composition of foods and beverages sold at school, and thereby changing students’ dietary intake behaviors. However, another way they may affect behavior is by reducing students’ snacking behaviors—that is, decreasing the frequency of purchasing and consuming any snacks at school. We did not find that state laws were associated with differences in the percentages of students who consumed snacks at school; however, for students who consumed snacks at school, Table 2. Mean Daily Energy Intake and Sodium Intake Mean (95% CI), kcal Students Who Consumed Snacks Intake All Students (N = 1959) Obtained at School (n = 528) Total daily energy 1982.2 (1919.2 to 2045.2) 1997.1 (1894.0 to 2100.2) Solid fats and added sugars 550.4 (524.5 to 576.4) 568.3 (526.0 to 610.7) Solid fats only 286.9 (273.9 to 299.8) 289.1 (262.6 to 315.6) Added sugars only 263.6 (246.6 to 280.6) 279.2 (257.4 to 301.1) Sodium, mg 3165.8 (3057.7 to 3273.9) 3163.0 (2977.6 to 3348.4) JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 (Reprinted) January 15, 2020 6/12 JAMA Network Open | Nutrition, Obesity, and Exercise State Laws Regarding Snacks in US Schools Students' Consumption of Solid Fats and Added Sugars Table 3. Multivariable Linear Regression Model Examining the Association of State-Level Smart Snacks Policy With Students’ Total Daily Energy Intake All Students (N = 1959) Students Who Consumed Snacks Obtained at School (n = 528) Coefficient Adjusted Mean Coefficient Adjusted Mean Variable (95% CI), kcal P Value (95% CI), kcal (95% CI), kcal P Value (95% CI), kcal State law requires Smart Snacks standards to be followed in all school venues No 0 [Reference] NA 1983.5 (1919.1 to 0 [Reference] NA 2032.0 (1922.7 to 2047.9) 2141.4) Yes −5.77 (−145.65 to .94 1977.7 (1841.6 to −162.64 (−334.30 to .06 1869.4 (1705.2 to 134.10) 2113.9) 9.03) 2033.6) Student grade, 17.49 (1.22 to .04 NA 10.98 (−14.49 to .39 NA per increased grade level 33.75) 36.46) Sex Boys 0 [Reference] NA 2144.4 (2050.3 to 0 [Reference] NA 2292.2 (2132.4 to 2238.4) 2452.0) Girls −330.10 (−437.06 to <.001 1814.3 (1750.2 to −542.50 (−723.25 to <.001 1749.7 (1655.5 to −223.14) 1878.4) −361.74) 1843.9) Race/ethnicity Non-Hispanic white 0 [Reference] NA 1970.2 (1880.6 to 0 [Reference] NA 2018.6 (1876.2 to 2059.9) 2160.9) Non-Hispanic black 67.31 (−100.08 to .43 2037.5 (1884.9 to 15.22 (−183.67 to .88 2033.8 (1886.1 to 234.69) 2190.2) 214.10) 2181.5) Hispanic 19.08 (−111.42 to .77 1989.3 (1891.6 to −20.64 (−347.57 to .90 1997.9 (1722.2 to 149.57) 2087.0) 306.29) 2273.6) Other or multiracial −20.94 (−179.65 to .79 1949.3 (1797.6 to −230.07 (−518.12 to .12 1788.5 (1546.6 to 137.76) 2101.0) 57.97) 2030.4) School urbanicity Urban 0 [Reference] NA 2048.0 (1871.4 to 0 [Reference] NA 2042.4 (1908.8 to 2224.5) 2176.0) Suburban −92.09 (−295.95 to .37 1955.9 (1883.1 to −26.47 (−177.72 to .73 2015.9 (1872.9 to 111.78) 2028.6) 124.79) 2159.0) Rural −74.80 (−265.69 to .44 1973.2 (1866.2 to −119.08 (−281.63 to .15 1923.3 (1791.3 to 116.08) 2080.1) 43.47) 2055.3) School size, No. of students ≥1000 0 [Reference] NA 2067.1 (1957.9 to 0 [Reference] NA 2094.4 (1907.3 to 2176.2) 2281.4) 500-999 −140.48 (−277.71 to .045 1926.6 (1845.8 to −222.21 (−428.85 to .04 1872.2 (1756.7 to −3.26) 2007.3) −15.58) 1987.6) <500 −90.05 (−209.71 to .14 1977.0 (1883.7 to −20.14 (−279.84 to .88 2074.2 (1901.5 to 29.61) 2070.3) 239.56) 2246.9) District-level racial/ethnic composition ≥66% White 0 [Reference] NA 2049.4 (1957.8 to 0 [Reference] NA 2056.3 (1901.4 to 2141.1) 2211.2) ≥50% Black −83.63 (−261.43 to .35 1965.8 (1801.7 to 10.25 (−351.80 to .96 2066.5 (1701.8 to 94.17) 2130.0) 372.30) 2431.2) ≥50% Hispanic −65.73 (−255.08 to .49 1983.7 (1826.0 to −27.85 (−228.60 to .78 2028.4 (1890.8 to 123.61) 2141.5) 172.90) 2166.1) Other −127.80 (−255.69 to .05 1921.6 (1834.0 to −146.14 (−326.36 to .11 1910.1 (1792.9 to 0.09) 2009.3) 34.08) 2027.4) District-level child poverty rate <20% 0 [Reference] NA 1986.2 (1893.3 to 0 [Reference] NA 1985.5 (1863.0 to 2079.0) 2108.0) ≥20% −12.05 (−137.72 to .85 1974.1 (1903.2 to 35.29 (−113.93 to .64 2020.8 (1892.6 to 113.61) 2045.0) 184.51) 2149.1) Region West 0 [Reference] NA 2025.8 (1933.3 to 0 [Reference] NA 1997.5 (1863.7 to 2118.3) 2131.2) Midwest 52.90 (−83.87 to .45 2078.7 (1978.3 to 79.09 (−132.98 to .46 2076.5 (1912.3 to 189.68) 2179.1) 291.15) 2240.8) South −39.35 (−190.25 to .61 1986.5 (1894.5 to 51.14 (−130.12 to .58 2048.6 (1909.3 to 111.55) 2078.4) 232.40) 2187.9) Northeast −291.07 (−498.30 to .006 1734.7 (1531.8 to −379.80 (−685.14 to .02 1617.7 (1318.6 to −83.85) 1937.7) −74.45) 1916.7) Abbreviation: NA, not applicable. JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 (Reprinted) January 15, 2020 7/12 JAMA Network Open | Nutrition, Obesity, and Exercise State Laws Regarding Snacks in US Schools Students' Consumption of Solid Fats and Added Sugars Table 4. Multivariable Linear Regression Model Examining the Association of State-Level Smart Snacks Policy With Students’ Daily Energy Intake From Solid Fats and Added Sugars All Students (N = 1959) Students Who Consumed Snacks Obtained at School (n = 528) Coefficient Adjusted Mean Coefficient Adjusted Mean Variable (95% CI), kcal P Value (95% CI), kcal (95% CI), kcal P Value (95% CI), kcal State law requires Smart Snacks standards to be followed in all school venues No 0 [Reference] NA 562.5 (534.3 to 0 [Reference] NA 582.0 (539.0 to 590.8) 625.0) Yes −53.87 (−104.52 to .04 508.7 (463.0 to −63.53 (−137.93 to .09 518.5 (444.2 to −3.22) 554.4) 10.86) 592.8) Student grade, 4.76 (−3.00 to .23 NA 3.79 (−8.46 to .54 NA per increased grade level 12.51) 16.04) Sex Boys 0 [Reference] NA 593.6 (559.6 to 0 [Reference] NA 650.6 (589.1 to 627.7) 712.0) Girls −87.90 (−123.58 to <.001 505.7 (478.0 to −151.11 (−216.69 to <.001 499.4 (458.8 to −52.23) 533.4) −85.53) 540.1) Race/ethnicity Non-Hispanic white 0 [Reference] NA 553.0 (522.2 to 0 [Reference] NA 574.0 (514.5 to 583.8) 633.5) Non-Hispanic black 37.28 (−33.58 to .30 590.2 (519.5 to 50.21 (−69.01 to .41 624.2 (521.3 to 108.14) 661.0) 169.44) 727.1) Hispanic −20.99 (−73.86 to .43 532.0 (485.7 to −29.44 (−158.78 to .65 544.5 (438.9 to 31.89) 578.3) 99.91) 650.1) Other or multiracial −24.38 (−94.69 to .49 528.6 (459.1 to −86.31 (−232.78 to .25 487.7 (370.1 to 45.93) 598.1) 60.15) 605.3) School urbanicity Urban 0 [Reference] NA 600.3 (546.4 to 0 [Reference] NA 647.4 (589.1 to 654.1) 705.6) Suburban −77.88 (−138.23 to .01 522.4 (493.0 to −102.92 (−163.38 to .001 544.4 (497.5 to −17.53) 551.8) −42.47) 591.4) Rural −42.00 (−108.76 to .22 558.3 (510.8 to −106.06 (−184.09 to .008 541.3 (468.0 to 24.76) 605.7) −28.04) 614.6) School size, No. of students ≥1000 0 [Reference] NA 554.5 (513.5 to 0 [Reference] NA 603.5 (529.0 to 595.5) 678.0) 500-999 −13.60 (−67.84 to .62 540.9 (503.7 to −82.62 (−164.31 to .047 520.9 (469.6 to 40.64) 578.2) −0.94) 572.1) <500 8.56 (−45.83 to .76 563.1 (522.1 to −2.79 (−115.49 to .96 600.7 (528.4 to 62.96) 604.1) 109.91) 673.0) District-level racial/ethnic composition ≥66% White 0 [Reference] NA 602.4 (562.5 to 0 [Reference] NA 611.9 (546.5 to 642.3) 677.4) ≥50% Black −62.17 (−149.31 to .16 540.2 (461.6 to −95.35 (−231.44 to .17 516.6 (391.6 to 24.98) 618.8) 40.74) 641.6) ≥50% Hispanic −90.19 (−171.99 to .03 512.2 (445.8 to −69.94 (−168.48 to .16 542.0 (464.2 to −8.40) 578.5) 28.60) 619.8) Other −82.01 (−134.12 to .002 520.4 (486.0 to −67.88 (−147.48 to .09 544.0 (491.8 to −29.90) 554.7) 11.72) 596.3) District-level child poverty rate <20% 0 [Reference] NA 551.5 (514.4 to 0 [Reference] NA 563.0 (505.8 to 588.6) 620.1) ≥20% −3.22 (−55.59 to .90 548.3 (515.4 to 16.46 (−57.58 to .66 579.4 (530.1 to 49.15) 581.1) 90.50) 628.8) Region West 0 [Reference] NA 558.9 (516.7 to 0 [Reference] NA 580.1 (528.2 to 601.2) 632.1) Midwest 19.40 (−35.13 to .48 578.3 (547.0 to 46.42 (−30.98 to .24 626.6 (570.8 to 73.93) 609.7) 123.83) 682.4) South 5.68 (−62.88 to .87 564.6 (522.0 to −3.39 (−83.91 to .93 576.7 (516.1 to 74.24) 607.3) 77.13) 637.4) Northeast −117.42 (−198.95 to .005 441.5 (363.0 to −187.83 (−300.95 to .001 392.3 (287.0 to −35.90) 520.0) −74.70) 497.6) Abbreviation: NA, not applicable. JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 (Reprinted) January 15, 2020 8/12 JAMA Network Open | Nutrition, Obesity, and Exercise State Laws Regarding Snacks in US Schools Students' Consumption of Solid Fats and Added Sugars dietary outcomes were significantly different. This suggests that the association of policy with dietary outcomes works by changing the nutritional composition of items sold in school without an associated change in snacking behaviors—in other words, students may keep snacking, but when the snacks sold are more healthful, the dietary patterns of those students may improve. The differences observed in students’ total dietary energy intake may seem modest, but a 54 kcal reduction in solid fats and added sugars is significant practically as well as statistically. Consumption of solid fats and added sugars adds between 500 to 1050 kcal to total daily energy intake for people in the United States, far higher than optimal levels: the 2015 to 2020 Dietary Guidelines reiterated the importance of limiting solid fats and added sugars consumption, recommending no more than 10% of kcal daily from sugar and no more than 10% from saturated fat for all age groups. Our findings, which used data from a nationally-representative sample of children and adolescents, suggest that more than 25% of daily energy intake was derived from solid fats and added sugars, with solid fats and added sugars consumption each over 10%. While the subset of students who consumed snacks at school also exceeded those recommendations, students in states with laws that address the nutritional standard of school snacks had significantly better dietary patterns. Studies have shown that over time, small changes in daily dietary intake can substantially improve health outcomes, including weight status and cardiovascular outcomes 3,4 associated with consumption of solid fats and added sugars. Limitations This study has several limitations. National-level policy interventions, such as Smart Snacks, and state laws to support implementation are expected to improve the nutritional characteristics of foods and beverages sold to students on campus. Mediation analyses to examine the hypothesized associations could not be conducted because we did not have intermediate analytic weights for schools, which would be necessary for multilevel analyses. Furthermore, data were not available on the nutrient profiles of snacks sold at each school. As a result, these analyses do not explicitly demonstrate that policy changed the types of foods and beverages sold at the school that each student attended. Such analyses should be conducted in the future. Furthermore, the cross-sectional nature of these data does not allow for examination of changes in students’ dietary patterns. It is expected that policy changes would result in measurable changes in school-level food and beverage nutritional quality, as well as student-level dietary outcomes. Such changes cannot be examined with cross-sectional data; thus, these associations may not be causal or directional—third variables may explain the associations, such as other characteristics of the states with laws. However, we accounted for student demographic characteristics and other contextual characteristics. Furthermore, the states with laws are located in various regions of the United States and do not include states that have a history of state-level intervention to change school food environments. For example, California has a state nutrition policy, but we did not code it as having a Smart Snacks law because the state law was less stringent than Smart Snacks on some topics. The association of nutrition standards with dietary Table 5. Covariate-Adjusted Student Dietary Outcomes by State-Level Smart Snacks Policy All Students (N = 1959) Students Who Consumed Snacks Obtained at School (n = 528) Adjusted Mean (95% CI) Adjusted Mean (95% CI) Variable In States Without a Law In States With a Law Difference P Value In States Without a Law In States With a Law Difference P Value Total daily energy intake, 1983.5 (1919.1 to 1977.7 (1841.6 to −5.8 .94 2032.0 (1922.7 to 1869.4 (1705.2 to −162.6 .06 kcal 2047.9) 2113.9) 2141.4) 2033.6) Solid fats and added 562.5 (534.3 to 508.7 (463.0 to −53.9 .04 582.0 (539.0 to 518.5 (444.2 to −63.5 .09 sugars, kcal 590.8) 554.4) 625.0) 592.8) Solid fats, kcal 295.3 (280.5 to 257.6 (237.1 to −37.7 .004 300.6 (272.1 to 247.1 (208.2 to −53.5 .02 310.2) 278.1) 329.1) 286.0) Added sugars, kcal 267.2 (248.4 to 251.1 (219.4 to −16.2 .36 281.4 (259.8 to 271.4 (224.1 to −10.0 .67 286.0) 282.7) 303.0) 318.7) Sodium, mg 3162.7 (3056.0 to 3176.3 (2927.8 to 13.5 .92 3223.3 (3006.6 to 2942.7 (2648.4 to −280.6 .12 3269.5) 3424.8) 3440.1) 3237.0) JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 (Reprinted) January 15, 2020 9/12 JAMA Network Open | Nutrition, Obesity, and Exercise State Laws Regarding Snacks in US Schools Students' Consumption of Solid Fats and Added Sugars outcomes might be larger if a broader definition were used. The exposure variable was a state-level Smart Snacks law because our goal was to examine the potential for state-level policy actions to facilitate and support national-level policy intervention. Although all schools participating in USDA child nutrition programs must comply with Smart Snacks, some states reinforced this requirement and were quicker to implement those standards. As we note elsewhere, states incorporated Smart Snacks in various ways. For example, state laws in Arkansas, Arizona, Florida, Iowa, and Mississippi adopted the full text of Smart Snacks or linked to the USDA website or another state-adopted policy that listed the full text of the standards. The District of Columbia, Georgia, Illinois, and Utah law instead provided a general reference to the federal rule without listing details for compliance. Additionally, several points are noteworthy regarding the outcome. Only 1 day of dietary recall data were used, so although the sample of students was representative, their dietary intake on the measurement day represents only that day and may not be representative of each student’s overall dietary pattern owing to intraindividual variation in dietary intake. Response biases may have affected dietary self-report, and parental assistance for younger children may have biased responses. Regressions did not include a student-level measure of economic status, such as free or reduced- priced meal eligibility; students from wealthier families are more likely to be able to afford snacks, so the results for the sample of 528 students who consumed snacks at schools may include more students from wealthier families than the sample overall. Additionally, we recognize that there is not agreement among nutritional scientists that all solid fats and added sugars should be limited because some foods high in added sugars, such as some cereals and grain products, can contribute micronutrients to people’s diets. Nevertheless, the Dietary Guidelines focus on limiting energy intake from solid fats and added sugars and consuming nutrient-dense foods, such as lean meat, low-fat dairy, and fruits and vegetables; these are evidence-based strategies with scientific support. Conclusions In summary, our findings suggest that policy interventions, such as the Smart Snacks standards and state laws that support the implementation of these changes in schools, may be promising interventions for improving the dietary habits of children and adolescents. Owing to the significant negative health consequences associated with suboptimal diets, interventions to improve the dietary habits of people in the United States may be of significant value for the nation’s health. ARTICLE INFORMATION Accepted for Publication: November 6, 2019. Published: January 15, 2020. doi:10.1001/jamanetworkopen.2019.18436 Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Turner L et al. JAMA Network Open. Corresponding Author: Lindsey Turner, PhD, College of Education, Boise State University, 1910 University Dr, Boise, ID 83725-1740 (lindseyturner1@boisestate.edu). Author Affiliations: College of Education, Boise State University, Boise, Idaho (Turner); Institute for Health Research and Policy, University of Illinois at Chicago, Chicago (Leider, Piekarz-Porter, Chriqui); Division of Health Policy and Administration, School of Public Health, University of Illinois at Chicago, Chicago (Chriqui). Author Contributions: Mr Leider and Dr Chriqui had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Turner, Chriqui. Acquisition, analysis, or interpretation of data: Turner, Leider, Piekarz-Porter. Drafting of the manuscript: Turner, Piekarz-Porter. Critical revision of the manuscript for important intellectual content: All authors. JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 (Reprinted) January 15, 2020 10/12 JAMA Network Open | Nutrition, Obesity, and Exercise State Laws Regarding Snacks in US Schools Students' Consumption of Solid Fats and Added Sugars Statistical analysis: Turner, Leider. Obtained funding: Turner, Chriqui. Administrative, technical, or material support: Turner, Piekarz-Porter. Supervision: Turner, Chriqui. Conflict of Interest Disclosures: Dr Chriqui reported receiving grants from the Robert Wood Johnson Foundation and National Cancer Institute outside the submitted work and serving as an unpaid advisor for several nonprofit or academic institutions on specific projects, including Voices for Healthy Kids for American Heart Association, the Alliance for a Healthier Generation, Action for Healthy Kids, and the Consortium to Lower Obesity in Chicago’s Children. No other disclosures were reported. Funding/Support: This work was funded by a US Department of Agriculture (USDA) cooperative agreement (USDA-FNS-OPS-SWP-15-IL-01). Role of the Funder/Sponsor: The USDA was involved in the design and conduct of the study. The USDA was not involved in the collection, management, analysis, and interpretation of the data; The USDA reviewed the manuscript to provide feedback but was not involved in the preparation or approval of the manuscript or decision to submit the manuscript for publication. Disclaimer: The contents of this publication do not necessarily reflect the views or policies of the USDA, nor does mention of trade names, commercial products, or organizations imply endorsement by the US government. Additional Contributions: Wanting Lin, JD, PhD, and Rebecca Schermbeck, MPH, MS, RD (University of Illinois at Chicago), assisted with coding the policy data. They were not compensated for their contribution. REFERENCES 1. Murray CJ, Atkinson C, Bhalla K, et al; US Burden of Disease Collaborators. The state of US health, 1990-2010: burden of diseases, injuries, and risk factors. JAMA. 2013;310(6):591-608. doi:10.1001/jama.2013.13805 2. Mozaffarian D. Dietary and policy priorities for cardiovascular disease, diabetes, and obesity: a comprehensive review. Circulation. 2016;133(2):187-225. doi:10.1161/CIRCULATIONAHA.115.018585 3. Dietary Guidelines Advisory Committee. Report of the Dietary Guidelines Advisory Committee on the Dietary Guidelines for Americans, 2010, to the Secretary of Agriculture and the Secretary of Health and Human Services. Washington, DC: US Department of Agriculture; 2010. 4. Nicklas TA, O’Neil CE. Development of the SoFAS (solid fats and added sugars) concept: the 2010 Dietary Guidelines for Americans. Adv Nutr. 2015;6(3):368S-375S. doi:10.3945/an.114.007021 5. Reedy J, Krebs-Smith SM. Dietary sources of energy, solid fats, and added sugars among children and adolescents in the United States. J Am Diet Assoc. 2010;110(10):1477-1484. doi:10.1016/j.jada.2010.07.010 6. Templeton S. Sugar intake from combined school lunch and competitive food consumption. J Am Diet Assoc. 2005;105(7):1066-1067. doi:10.1016/j.jada.2005.05.259 7. Poti JM, Slining MM, Popkin BM. Where are kids getting their empty calories: stores, schools, and fast-food restaurants each played an important role in empty calorie intake among US children during 2009-2010. J Acad Nutr Diet. 2014;114(6):908-917. doi:10.1016/j.jand.2013.08.012 8. Poti JM, Slining MM, Popkin BM. Solid fat and added sugar intake among US children: the role of stores, schools, and fast food, 1994-2010. Am J Prev Med. 2013;45(5):551-559. doi:10.1016/j.amepre.2013.06.013 9. Fox MK, Gordon A, Nogales R, Wilson A. Availability and consumption of competitive foods in US public schools. J Am Diet Assoc. 2009;109(2)(suppl):S57-S66. doi:10.1016/j.jada.2008.10.063 10. Briefel RR, Crepinsek MK, Cabili C, Wilson A, Gleason PM. School food environments and practices affect dietary behaviors of US public school children. J Am Diet Assoc. 2009;109(2)(suppl):S91-S107. doi:10.1016/j.jada. 2008.10.059 11. Turner L, Chaloupka FJ. Wide availability of high-calorie beverages in US elementary schools. Arch Pediatr Adolesc Med. 2011;165(3):223-228. doi:10.1001/archpediatrics.2010.215 12. Healthy, Hunger-Free Kids Act of 2010, 42 USC §1751 (2010). 13. Food and Nutrition Service, USDA. National School Lunch Program and School Breakfast Program: nutrition standards for all foods sold in school as required by the Healthy, Hunger-Free Kids Act of 2010: interim final rule. Fed Regist. 2013;78(125):39067-39120. 14. Institute of Medicine. School Meals: Healthy Building Blocks for Children. Washington, DC: National Academies Press; 2009. JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 (Reprinted) January 15, 2020 11/12 JAMA Network Open | Nutrition, Obesity, and Exercise State Laws Regarding Snacks in US Schools Students' Consumption of Solid Fats and Added Sugars 15. Piekarz-Porter E, Chriqui JF, Schermbeck RM, Leider J, Lin W; Bridging the Gap Program, National Wellness Policy Study. The Active Role States Have Played in Helping to Transform the School Wellness Environment Through Policy. Chicago, IL: University of Illinois at Chicago; 2017. 16. Chriqui JF, Pickel M, Story M. Influence of school competitive food and beverage policies on obesity, consumption, and availability: a systematic review. JAMA Pediatr. 2014;168(3):279-286. doi:10.1001/ jamapediatrics.2013.4457 17. Asada Y, Chriqui J, Chavez N, Odoms-Young A, Handler A. USDA snack policy implementation: best practices from the front lines, United States, 2013-2014. Prev Chronic Dis. 2016;13:E79. doi:10.5888/pcd13.160023 18. US Department of Agriculture, Food and Nutrition Service. School nutrition and meal cost study. https://www. fns.usda.gov/school-nutrition-and-meal-cost-study. Accessed May 30, 2019. 19. US Department of Agriculture, Food and Nutrition Service. Nutrition and meal cost study: data collection instruments. https://fns-prod.azureedge.net/sites/default/files/resource-files/SNMCS-DataCollection-Instruments.pdf. Accessed August 5, 2019. 20. Raper N, Perloff B, Ingwersen L, Steinfeldt L, Anand J. An overview of USDA’s Dietary Intake Data System. J Food Compos Anal. 2004;17:545-555. doi:10.1016/j.jfca.2004.02.013 21. US Department of Education, National Center for Education Statistics. Documentation to the NCES Common Core of Data Public Elementary/Secondary School Universe Survey: school year 2011-12. https://nces.ed.gov/ccd/ pdf/SC2011_1a_doc.pdf. Accessed April 15, 2019. 22. US Department of Education, National Center for Education Statistics. Documentation to the NCES Common Core of Data Public Elementary/Secondary School Universe Survey: school year 2013-14. https://nces.ed.gov/ccd/ pdf/2015147_2013-14_LEA_documentation_v1a.pdf. Accessed April 1, 2017. 23. US Census Bureau. Small Area Income and Poverty Estimates: 2011 highlights. https://www.census.gov/library/ publications/2012/demo/SAIPE-highlights-2011.html. Accessed June 1, 2017. 24. Piekarz-Porter E, Schermbeck RM, Leider J, Young SK, Chriqui JF; National Wellness Policy Study, Institute for Health Research and Policy. Working on Wellness: How Aligned Are District Wellness Policies With the Soon-To-Be Implemented Federal Wellness Policy Requirements? Chicago, IL: University of Illinois at Chicago; 2017. 25. National Cancer Institute. Classification of Laws Associated with School Students. https://class.cancer.gov/. Accessed June 1, 2019. 26. National Association of School Boards of Education. State School Health Policy Database on School Health. https://statepolicies.nasbe.org/health. Accessed June 1, 2017. 27. Schwartz MB, Lund AE, Grow HM, et al. A comprehensive coding system to measure the quality of school wellness policies. J Am Diet Assoc. 2009;109(7):1256-1262. doi:10.1016/j.jada.2009.04.008 28. US Department of Agriculture. Nutrition standards in the National School Lunch and School Breakfast Programs. https://www.gpo.gov/fdsys/pkg/FR-2012-01-26/pdf/2012-1010.pdf. Accessed June 1, 2019. 29. US Department of Agriculture. National School Lunch Program and School Breakfast Program: nutrition standards for all foods sold in school as required by the Healthy, Hunger-Free Kids Act of 2010. https://www. federalregister.gov/documents/2013/06/28/2013-15249/national-school-lunch-program-and-school- breakfast-program-nutrition-standards-for-all-foods-sold-in. Accessed June 1, 2019. 30. US Department of Health and Human Services, US Department of Agriculture. Dietary guidelines for Americans 2015-2020: eighth edition. https://health.gov/dietaryguidelines/2015/guidelines/. Accessed June 1, 2019. SUPPLEMENT. eTable 1. Multivariable Linear Regression Model Examining the Association of State-Level Smart Snacks Policy With Students’ Daily Energy Intake From Solid Fats in Kilocalories eTable 2. Multivariable Linear Regression Model Examining the Association of State-Level Smart Snacks Policy With Students’ Daily Energy Intake From Added Sugars in Kilocalories eTable 3. Multivariable Linear Regression Model Examining the Association of State-Level Smart Snacks Policy With Students’ Daily Intake of Sodium in Milligrams eTable 4. Multivariable Logistic Regression Model Examining the Association of State-Level Smart Snacks Policy With Student Consumption of Snacks Obtained at School Among 1959 Students JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 (Reprinted) January 15, 2020 12/12 Supplementary Online Content Turner L, Leider J, Piekarz-Porter E, Chriqui JF. Association of state laws regarding snacks in US schools with students' consumption of solid fats and added sugars. JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 eTable 1. Multivariable Linear Regression Model Examining the Association of State-Level Smart Snacks Policy With From Solid Fats in Kilocalories eTable 2. Multivariable Linear Regression Model Examining the Association of State-Level Smart Snacks Policy With From Added Sugars in Kilocalories eTable 3. Multivariable Linear Regression Model Examining the Association of State-Level Smart Snacks Policy With in Milligrams eTable 4. Multivariable Logistic Regression Model Examining the Association of State-Level Smart Snacks Policy With Student Consumption of Snacks Obtained at School Among 1959 Students This supplementary material has been provided by the authors to give readers additional information about their work. © 2020 Turner L et al. JAMA Network Open. eTable 1. Multivariable Linear Regression Model Examining the Association of State-Level Smart Snacks Daily Energy Intake From Solid Fats in Kilocalories All Students (n = 1959) Students who Consumed Snacks Purchased at School (n = 528) Variable Adjusted 95% CI Adjusted Coefficient (95% CI) P Mean Coefficient (95% CI) P Mean Value Value State Law Requires Smart Snacks Standards to Be Followed in All School Venues No 0 [Reference] -- 295.3 280.5, 310.2 0 [Reference] -- 300.6 272.1, 329.1 Yes -37.70 (-62.78, -12.62) .004 257.6 237.1, 278.1 -53.50 (-97.03, -9.97) .02 247.1 208.2, 286.0 Student Grade 2.89 (-1.44, 7.21) .19 -- 2.53 (-4.77, 9.83) .49 -- (continuous) Student Gender Male 0 [Reference] -- 312.7 295.7, 329.8 0 [Reference] -- 346.3 306.1, 386.6 Female -52.69 (-71.87, -33.50) <.001 260.1 245.4, 274.7 -105.18 (-147.42, -62.95) <.001 241.1 217.5, 264.7 Student Race/Ethnicity White, non-Hispanic 0 [Reference] -- 290.5 274.2, 306.7 0 [Reference] -- 299.0 258.9, 339.0 Black, non-Hispanic -0.88 (-39.88, 38.13) .96 289.6 252.0, 327.1 1.20 (-79.18, 81.58) .98 300.2 236.7, 363.6 Hispanic -12.68 (-43.87, 18.51) .42 277.8 251.2, 304.4 -22.30 (-103.22, 58.61) .59 276.7 209.9, 343.4 Other -3.03 (-44.80, 38.74) .89 287.4 246.6, 328.3 -55.31 (-136.96, 26.33) .18 243.7 182.5, 304.8 School Urbanicity Urban 0 [Reference] -- 303.5 272.4, 334.6 0 [Reference] -- 315.1 275.5, 354.6 Suburban -27.30 (-65.23, 10.63) .16 276.2 258.6, 293.8 -25.75 (-66.30, 14.81) .21 289.3 258.4, 320.3 Rural -11.71 (-47.52, 24.10) .52 291.8 269.2, 314.4 -49.27 (-99.02, 0.47) .052 265.8 223.1, 308.4 School Size Large (>1000 students) 0 [Reference] -- 286.3 264.5, 308.1 0 [Reference] -- 303.3 252.4, 354.1 Medium (500 to 999 -3.21 (-34.03, 27.61) .84 283.1 263.8, 302.4 -36.51 (-95.59, 22.57) .22 266.8 233.0, 300.5 students) Small (< 500 students) 8.26 (-20.81, 37.33) .57 294.6 270.5, 318.7 5.07 (-55.97, 66.12) .87 308.3 271.3, 345.3 Race/Ethnicity (District) 0 [Reference] -- 291.8 272.4, 311.3 0 [Reference] -- 281.8 237.3, 326.4 4.12 (-46.44, 54.68) .87 296.0 247.7, 344.3 -3.94 (-93.84, 85.96) .93 277.9 198.5, 357.3 -5.37 (-55.79, 45.06) .83 286.5 243.6, 329.4 25.27 (-38.73, 89.27) .44 307.1 260.9, 353.4 Other -10.47 (-36.30, 15.35) .42 281.4 264.6, 298.1 7.38 (-46.65, 61.41) .79 289.2 257.3, 321.2 Child Poverty Rate (District) <20% 0 [Reference] -- 289.0 269.5, 308.6 0 [Reference] -- 292.0 256.6, 327.4 -6.63 (-36.77, 23.51) .66 282.4 264.1, 300.7 -8.99 (-58.32, 40.34) .72 283.0 248.2, 317.9 Region West 0 [Reference] -- 298.4 274.1, 322.7 0 [Reference] -- 288.4 248.0, 328.7 Midwest 10.20 (-21.83, 42.23) .53 308.6 291.8, 325.4 41.15 (-13.56, 95.86) .14 329.5 293.0, 366.0 © 2020 Turner L et al. JAMA Network Open. South -11.23 (-52.63, 30.16) .59 287.2 263.7, 310.6 -1.92 (-59.55, 55.72) .95 286.4 246.8, 326.1 Northeast -67.30 (-110.47, -24.13) .003 231.1 193.2, 269.0 -75.12 (-150.74, 0.50) .052 213.2 148.3, 278.2 © 2020 Turner L et al. JAMA Network Open. eTable 2. Multivariable Linear Regression Model Examining the Association of State- Daily Energy Intake From Added Sugars in Kilocalories All Students Students who Consumed Snacks Obtained at School (n = 1959) (n = 528) Variable Coefficient (95% CI) P Adjusted Coefficient (95% CI) P Adjusted Value Mean 95% CI Value Mean 95% CI State Law Requires Smart Snacks Standards to Be Followed in All School Venues No 0 [Reference] -- 267.2 248.4, 286.0 0 [Reference] -- 281.4 259.8, 303.0 Yes -16.17 (-51.28, 18.95) .36 251.1 219.4, 282.7 -10.04 (-56.92, 36.85) .67 271.4 224.1, 318.7 Student Grade (continuous) 1.87 (-2.63, 6.37) .41 -- 1.25 (-6.73, 9.24) .76 -- Student Gender Male 0 [Reference] -- 280.9 258.3, 303.5 0 [Reference] -- 304.2 273.6, 334.8 Female -35.22 (-56.92, -13.52) .002 245.7 228.7, 262.6 -45.93 (-80.19, -11.67) .009 258.3 234.3, 282.3 Student Race/Ethnicity White, non-Hispanic 0 [Reference] -- 262.5 242.7, 282.4 0 [Reference] -- 275.0 246.3, 303.7 Black, non-Hispanic 38.16 (-1.24, 77.55) .06 300.7 261.4, 339.9 49.02 (0.05, 97.99) .050 324.0 275.5, 372.5 Hispanic -8.31 (-38.56, 21.93) .59 254.2 227.2, 281.2 -7.13 (-67.43, 53.16) .82 267.9 220.7, 315.0 Other -21.35 (-60.73, 18.03) .29 241.2 201.4, 280.9 -31.00 (-106.20, 44.20) .42 244.0 177.2, 310.8 School Urbanicity Urban 0 [Reference] -- 296.8 267.3, 326.2 0 [Reference] -- 332.3 297.0, 367.6 Suburban -50.57 (-81.13, -20.02) .001 246.2 227.4, 264.9 -77.17 (-116.39, -37.96) <.001 255.1 230.6, 279.6 Rural -30.28 (-71.16, 10.59) .15 266.5 233.9, 299.1 -56.79 (-107.13, -6.45) .03 275.5 233.2, 317.8 School Size Large (>1000 students) 0 [Reference] -- 268.2 238.8, 297.6 0 [Reference] -- 300.2 259.7, 340.8 Medium (500 to 999 -10.39 (-42.32, 21.54) .52 257.8 235.0, 280.6 -46.11 (-90.86, -1.36) .04 254.1 227.0, 281.3 students) Small (< 500 students) 0.30 (-39.92, 40.53) .99 268.5 244.4, 292.6 -7.86 (-84.18, 68.45) .84 292.4 244.1, 340.6 Race/Ethnicity (District) White 0 [Reference] -- 310.5 280.8, 340.2 0 [Reference] -- 330.1 297.1, 363.0 -66.29 (-116.43, -16.15) .01 244.2 201.8, 286.6 -91.41 (-161.69, -21.12) .01 238.7 172.8, 304.6 -84.82 (-130.14, -39.51) <.001 225.7 195.6, 255.8 -95.20 (-156.03, -34.37) .002 234.9 185.6, 284.2 Other -71.54 (-109.09, -33.98) <.001 239.0 217.0, 261.0 -75.26 (-117.77, -32.76) .001 254.8 223.7, 285.9 Child Poverty Rate (District) <20% 0 [Reference] -- 262.5 240.3, 284.7 0 [Reference] -- 270.9 242.4, 299.4 3.40 (-23.97, 30.77) .81 265.9 246.5, 285.3 25.45 (-15.46, 66.36) .22 296.4 266.6, 326.2 Region West 0 [Reference] -- 260.6 235.4, 285.7 0 [Reference] -- 291.8 259.9, 323.6 Midwest 9.21 (-25.59, 44.00) .60 269.8 245.9, 293.6 5.28 (-40.81, 51.36) .82 297.1 262.2, 331.9 South 16.91 (-17.99, 51.82) .34 277.5 251.8, 303.2 -1.47 (-47.08, 44.15) .95 290.3 260.2, 320.4 Northeast -50.13 (-102.23, 1.98) .06 210.4 161.3, 259.6 -112.70 (-168.44, -56.96) <.001 179.1 128.7, 229.4 © 2020 Turner L et al. JAMA Network Open. eTable 3. Multivariable Linear Regression Model Examining the Association of State-Level Smart Snacks Policy With Daily Intake of Sodium in Milligrams All Students Students who Consumed Snacks Obtained at School (n = 1959) (n = 528) Variable P Adjusted P Adjusted Coefficient (95% CI) Value Mean 95% CI Coefficient (95% CI) Value Mean 95% CI State Law Requires Smart Snacks Standards to Be Followed in All School Venues No 0 [Reference] -- 3162.7 3056.0, 0 [Reference] -- 3223.3 3006.6, 3269.5 3440.1 Yes 13.55 (-238.71, 265.81) .92 3176.3 2927.8, -280.60 (-634.20, 72.99) .12 2942.7 2648.4, 3424.8 3237.0 Student Grade 45.36 (19.14, 71.58) .001 -- 46.24 (-10.86, 103.33) .11 -- (continuous) Student Gender Male 0 [Reference] -- 3453.5 3297.1, 0 [Reference] -- 3636.9 3337.3, 3609.9 3936.6 Female -585.56 (-764.04, -407.09) <.001 2867.9 2754.8, -871.15 (-1206.22, -536.08) <.001 2765.8 2591.9, 2981.0 2939.7 Student Race/Ethnicity White, non-Hispanic 0 [Reference] -- 3106.9 2948.1, 0 [Reference] -- 3154.7 2883.3, 3265.6 3426.0 Black, non-Hispanic 102.52 (-186.59, 391.63) .48 3209.4 2968.9, 3.83 (-435.40, 443.05) .99 3158.5 2843.4, 3449.9 3473.5 Hispanic 121.85 (-113.02, 356.71) .31 3228.7 3058.2, -87.23 (-575.70, 401.23) .72 3067.4 2684.9, 3399.3 3449.9 Other 164.08 (-181.47, 509.62) .35 3271.0 2938.6, 350.48 (-629.38, 1330.34) .48 3505.1 2543.5, 3603.3 4466.8 School Urbanicity Urban 0 [Reference] -- 3213.8 2914.2, 0 [Reference] -- 3105.1 2836.5, 3513.3 3373.6 Suburban -102.26 (-453.36, 248.84) .57 3111.5 2980.1, 79.71 (-330.56, 489.99) .70 3184.8 2890.1, 3242.9 3479.4 Rural 9.06 (-343.47, 361.59) .96 3222.8 3010.2, 70.14 (-396.44, 536.73) .77 3175.2 2705.3, 3435.5 3645.1 School Size Large (>1000 students) 0 [Reference] -- 3368.0 3168.6, 0 [Reference] -- 3393.9 2954.5, 3567.3 3833.2 Medium (500 to 999 -303.83 (-553.41, -54.26) .02 3064.2 2925.2, -425.93 (-911.23, 59.38) .09 2967.9 2748.2, students) 3203.2 3187.7 Small (< 500 students) -272.92 (-521.95, -23.90) .03 3095.1 2917.6, -242.22 (-864.42, 379.99) .44 3151.6 2835.6, 3272.5 3467.7 Race/Ethnicity (District) © 2020 Turner L et al. JAMA Network Open. 0 [Reference] -- 3232.4 3067.2, 0 [Reference] -- 3278.1 2968.3, 3397.6 3587.8 246.10 (-59.99, 552.19) .11 3478.5 3211.0, 292.15 (-653.02, 1237.33) .54 3570.2 2624.7, 3746.0 4515.7 -89.25 (-395.08, 216.58) .56 3143.2 2923.3, -249.37 (-775.86, 277.13) .35 3028.7 2701.1, 3363.0 3356.2 Other -153.82 (-393.28, 85.65) .21 3078.6 2908.0, -225.96 (-645.90, 193.99) .29 3052.1 2770.1, 3249.1 3334.1 Child Poverty Rate (District) <20% 0 [Reference] -- 3193.8 3051.6, 0 [Reference] -- 3103.2 2909.9, 3336.0 3296.6 -85.41 (-271.30, 100.47) .36 3108.4 2978.1, 182.96 (-89.88, 455.80) .19 3286.2 3010.1, 3238.7 3562.2 Region West 0 [Reference] -- 3273.5 3036.8, 0 [Reference] -- 3396.9 2820.7, 3510.3 3973.1 Midwest 46.15 (-269.95, 362.24) .77 3319.7 3103.9, -227.81 (-815.22, 359.60) .44 3169.1 2929.0, 3535.4 3409.1 South -127.52 (-408.46, 153.42) .37 3146.0 3027.1, -179.26 (-838.28, 479.75) .59 3217.6 2951.5, 3264.9 3483.7 Northeast -464.44 (-794.67, -134.22) .006 2809.1 2532.9, -787.78 (-1458.21, -117.35) .02 2609.1 2066.6, 3085.3 3151.6 © 2020 Turner L et al. JAMA Network Open. eTable 4. Multivariable Logistic Regression Model Examining the Association of State-Level Smart Snacks Policy With Student Consumption of Snacks Obtained at School Among 1959 Students Adjusted Variable Odds Ratio (95% CI) P Value Prevalence 95% CI State Law Requires Smart Snacks Standards to Be Followed in All School Venues No 1 [Reference] -- 26.9 23.8, 30.0 Yes 0.82 (0.59, 1.15) .25 23.3 18.3, 28.4 Student Grade (continuous) 1.06 (1.02, 1.11) .007 -- Student Gender Male 1 [Reference] -- 23.3 19.7, 27.0 Female 1.35 (1.03, 1.75) .03 28.9 25.3, 32.6 Student Race/Ethnicity White, non-Hispanic 1 [Reference] -- 23.8 19.9, 27.8 Black, non-Hispanic 1.82 (1.22, 2.71) .004 36.0 28.4, 43.7 Hispanic 1.13 (0.80, 1.61) .48 26.1 21.1, 31.2 Other 1.08 (0.65, 1.77) .77 25.2 17.7, 32.6 School Urbanicity Urban 1 [Reference] -- 27.0 18.9, 35.0 Suburban 0.93 (0.56, 1.53) .77 25.6 21.7, 29.4 Rural 0.97 (0.60, 1.55) .89 26.3 22.5, 30.1 School Size Large (>1000 students) 1 [Reference] -- 27.4 22.3, 32.5 Medium (500 to 999 students) 0.90 (0.64, 1.28) .57 25.5 21.4, 29.6 Small (< 500 students) 0.90 (0.57, 1.42) .64 25.4 18.8, 31.9 Race/Ethnicity (District) 1 [Reference] -- 28.4 23.6, 33.1 0.94 (0.57, 1.56) .82 27.3 19.1, 35.4 0.98 (0.51, 1.89) .95 27.9 17.2, 38.7 Other 0.76 (0.53, 1.08) .12 23.2 19.3, 27.0 Child Poverty Rate (District) <20% 1 [Reference] -- 26.6 22.3, 30.8 0.93 (0.63, 1.37) .70 25.2 20.5, 29.8 Region West 1 [Reference] -- 25.5 16.6, 34.4 Midwest 1.05 (0.60, 1.86) .86 26.4 21.5, 31.4 South 1.08 (0.61, 1.92) .79 27.0 22.8, 31.1 Northeast 0.89 (0.44, 1.80) .74 23.3 14.8, 31.8 © 2020 Turner L et al. 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Association of State Laws Regarding Snacks in US Schools With Students' Consumption of Solid Fats and Added Sugars

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American Medical Association
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Copyright 2020 Turner L et al. JAMA Network Open.
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2574-3805
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10.1001/jamanetworkopen.2019.18436
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Abstract

Key Points Question Are state laws requiring IMPORTANCE The Smart Snacks in School standards (hereafter, Smart Snacks) were issued in 2013 schools to implement federal Smart with the aim of improving students’ dietary intake behaviors. Goals of Smart Snacks included Snacks in School standards associated reducing total energy intake, consumption of solid fats and added sugars, and sodium intake. Smart with student dietary consumption? Snacks standards were required to be implemented by the start of the 2014 to 2015 school year at all Findings In this cross-sectional study US schools participating in federal child nutrition programs. with a nationally representative sample of 1959 students in grades 1 through 12, OBJECTIVE To examine the association of state laws that specifically direct schools to implement students who attended schools in states Smart Snacks with student dietary consumption outcomes. with laws requiring the implementation of Smart Snacks in School standards DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study used nationally representative consumed an adjusted mean of 53.9 data collected in the 2014 to 2015 school year as part of the School Nutrition and Meal Cost Study. fewer kcal from solid fats and added Students in grades 1 through 12 (ages approximately 6-18 years) were randomly selected from 310 sugars per day than did students in public schools in 30 US states and the District of Columbia. Analytic weights were applied and all states with no such laws, a statistically percentages reported are weighted. Analyses were conducted from March 1, 2018, to December significant difference. 12, 2019. Meaning These findings suggest that EXPOSURES State laws requiring schools to implement Smart Snacks. state laws may support the implementation of federal standards, MAIN OUTCOMES AND MEASURES A 24-hour recall was used to assess student dietary intake as with significant implications for student daily kilocalories consumed as (a) total energy, (b) solid fats and added sugars combined, (c) solid dietary behaviors. fats, or (d) added sugars. Milligrams of daily sodium consumption were also computed. Supplemental content RESULTS Among 1959 students (mean [SD] age, 11.9 [3.5] years; 1014 [50.9%] boys), 420 students (22.5%) attended school in a state with Smart Snacks laws, and 528 students (26.1%) consumed Author affiliations and article information are listed at the end of this article. snacks obtained at school. In covariate-adjusted models, total energy intake did not vary based on state law. Adjusted mean daily kilocalories from solid fats and added sugars was significantly lower among students in states with laws (508.7 [95% CI, 463.0 to 554.4] kcal) than among students in states without laws (562.5 [95% CI, 534.3 to 590.8] kcal; difference, −53.9 [95% CI, −104.5 to −3.2] kcal; P = .04). Consumption of sodium did not differ by state law. Kilocalories from solid fats contributed more to the difference than kilocalories from added sugars (−37.7 [95% CI −62.8 to −12.6] kcal vs −16.2 [95% CI, −51.3 to 19.0] kcal). CONCLUSIONS AND RELEVANCE These findings suggest that students in states with laws requiring schools to implement Smart Snacks had better dietary intake than students in states without laws, consuming a mean of 53.9 fewer kilocalories from solid fats and added sugars per day, after adjusting for covariates. State-level policy mechanisms may support schools’ implementation of federal standards in ways that are associated with healthier diets among children and adolescents. JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 Open Access. This is an open access article distributed under the terms of the CC-BY License. JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 (Reprinted) January 15, 2020 1/12 JAMA Network Open | Nutrition, Obesity, and Exercise State Laws Regarding Snacks in US Schools Students' Consumption of Solid Fats and Added Sugars Introduction Dietary intake is the primary modifiable behavioral risk factor associated with morbidity and mortality among people in the United States, with unhealthful dietary habits associated with 1,2 cardiovascular disease, stroke, diabetes, and obesity. The Dietary Guidelines for Americans provide science-based advice to reduce these risks through optimal diets. The 2010 guidelines recommend that children, adolescents, and adults limit their intake of empty calories, which are commonly obtained by consumption of foods and beverages that contain solid fats and added sugars. Solid fats and added sugars are characterized as empty calories because they do not provide essential nutrients, they displace the consumption of other nutrient-dense foods and beverages, and they 3,5,6 increase overall energy intake. Schools are locations that have contributed substantially to the 7,8 consumption of empty calories by children and adolescents. Large-scale studies conducted in the early 2000s reported that sugar-sweetened beverages, other sugary foods, and snacks high in fat and sodium were common in schools across the United 9-11 States and significantly affected students’ consumption of empty calories. As directed by the Healthy, Hunger-Free Kids Act of 2010, the US Department of Agriculture (USDA) issued revised standards for school meals in 2012 and new standards for all foods and beverages sold in other 13 3 locations at schools in 2013. These standards aligned with the Dietary Guidelines for Americans and science-based recommendations. The latter, named the Smart Snacks in School standards (hereafter, Smart Snacks), aimed to increase the availability and consumption of healthful options such as fruit, vegetables, whole grain foods, and low-fat dairy and to reduce availability and consumption of high-calorie items with high amounts of fat, added sugar, and sodium. Smart Snacks was required to be implemented in schools by July 1, 2014. Prior to 2014, some states, districts, and schools had already addressed the nutritional quality of foods and beverages sold in schools outside of school meals. The number of states with policies addressing nutrition standards for such foods and beverages increased between the 2006 to 2007 school year and the 2014 to 2015 school year, by which point 50% of states had strong laws and 13% of states had weak laws that established some type of nutrition standards. A systematic review in 2014 found that such policies at the state or district level were associated with reduced availability of unhealthful foods and beverages sold outside of school meals (ie, in school stores or snack bars, vending machines, and à la carte lines). Although all schools participating in USDA child nutrition programs are required to comply with Smart Snacks, state laws may further facilitate compliance with national policy, particularly during the early phases of implementation. This is an important question for understanding the mechanisms of policy implementation, but it has not been addressed empirically, to our knowledge. At the start of the 2014 to 2015 school year, 9 states had laws requiring compliance with Smart Snacks. Given that national implementation of Smart Snacks was slow and many schools found it challenging to fully implement these policy changes, having a state law may have facilitated implementation of systemic changes in school food and beverage environments. This cross-sectional study examined associations of state laws requiring Smart Snacks implementation with student dietary outcomes in the 2014 to 2015 school year. The hypothesis was that such laws would be associated with better dietary outcomes among students attending schools in those states. Methods This study combined student-level data collected by the nationally representative School Nutrition and Meal Cost Study (SNMCS) with corresponding state laws collected and coded by the National Wellness Policy Study (NWPS). A brief description of SNMCS data collection is provided here, with extensive details available elsewhere. The SNMCS protocol and instruments were reviewed and approved by the US Office of Management and Budget. Student assent and parental informed JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 (Reprinted) January 15, 2020 2/12 JAMA Network Open | Nutrition, Obesity, and Exercise State Laws Regarding Snacks in US Schools Students' Consumption of Solid Fats and Added Sugars consent were obtained for the diet interview. Nonidentifiable data were provided to the authors by Mathematica Policy Research, which was contracted to conduct the SNMCS for the USDA. Per the Common Rule, the collection of data on state laws is not considered to be human subjects research, so ethics board approval was not required. The study is reported following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. Student-Level Data The SNMCS was commissioned by the USDA to assess practices in a nationally representative sample of public schools serving kindergarten through 12th grade during the 2014 to 2015 school year. The universe for sampling included public school food authorities and public noncharter schools participating in the National School Lunch Program. A stratified 2-stage sampling approach was used, with 3 groups of school food authorities selected using probability proportional to size sampling. Within 1 of those school food authority groups, a sample of 310 schools was recruited; thereafter, students were randomly selected and recruited from those schools for interviews. Dietary interviews were conducted with 2165 respondents in grades 1 through 12 (63.6% weighted response rate). Children in kindergarten and prekindergarten were excluded from the study because of concerns about their ability to provide accurate dietary recalls. Student Dietary Data Collection As detailed in the SNMCS report, collection of student dietary data used a 24-hour dietary recall with the USDA’s Dietary Intake Data System. The computer-assisted Automated Multiple-Pass Method was used to collect information about each student’s dietary intake. Students in middle school (typically grades 6-8, including students ages approximately 11-14 years) and high school (typically grades 9-12, including students ages approximately 14-18 years) completed the interview independently, and students in elementary school (typically grades 1-5, including students ages approximately 6-11 years) had parental assistance. The USDA’s Post-Interview Processing System, 19,20 combined with Survey Net, was used to code types and amounts of foods and beverages consumed, from which nutrient characteristics were computed. The analyses used information about total daily energy intake (in kilocalories), as well as daily energy intake from solid fats and added sugars and sodium intake. In the data coding process, SNMCS indicated whether students had consumed any snacks obtained at schools (eg, purchased from sales venues, given by teachers, or at parties; coded as yes or no). Student Demographic Characteristics The restricted-use data set supplied for this study included student demographic characteristics, which were used as contextual covariates in the analyses. Demographic variables included student sex (boys or girls), grade in school, and self- or parent-reported race/ethnicity. Additional 21-23 demographic data for students were based on the school or district at which they were enrolled. These included region, school urbanicity, school size, racial/ethnic composition of the district’s students, and socioeconomic composition. State-Level Data The NWPS is the largest nationwide evaluation of congressionally mandated school district wellness 24 15 policies and state laws for 50 states and the District of Columbia (collectively referred to as states). Codified state statutes and administrative regulations for each state were compiled using subscription-based services, Lexis Advance (LexisNexis) and WestlawNext (Thomson Reuters). Boolean keyword searches and reviews of the indices or tables of contents of the codified laws for each state were conducted by trained attorneys and legal researchers using the state law databases from each service. State laws were defined to include the codified laws as well as any state health or nutrition education standards incorporated by reference into the codified law. Laws were deemed JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 (Reprinted) January 15, 2020 3/12 JAMA Network Open | Nutrition, Obesity, and Exercise State Laws Regarding Snacks in US Schools Students' Consumption of Solid Fats and Added Sugars relevant if they were effective as of September 2, 2014, a proxy for the beginning of the 2014 to 2015 25,26 school year. The existence of state laws was verified against public sources. State laws were reviewed and verified by 2 members of the NWPS team (including E.P.-P.), then coded by 2 trained analysts (including E.P.-P.). Coding used a wellness policy coding scheme that was modified by NWPS to capture new Smart Snacks provisions. State laws were coded for whether they were definitively required; encouraged, suggested, or required with exceptions; or did not address compliance with Smart Snacks. Strong policy provisions definitively required 28,29 implementation and met Smart Snacks standards if they included language such as shall, must, will, require, comply, and enforce. Laws were coded at elementary school (grades 1-5), middle school (grades 6-8), and high school (grades 9-12) levels. Locations of sale included 4 venues: vending machines, school stores or snack bars, à la carte, and fundraisers. Analyses used a dichotomous variable indicating whether state law met Smart Snacks standards in all 4 venues. Statistical Analysis The SNMCS student-level data and state law data were linked using state geocodes by Mathematica Policy Research (Mathematica) and returned to the NWPS for analyses. Grade level–specific state law coding was linked to schools of the corresponding grade level. Analyses were conducted from March 1, 2018, to December 12, 2019, with Stata statistical software version 13 (StataCorp) using the svy command to account for the sampling design. Analytic weights were used; thus numbers of students are unadjusted, and all percentages given are weighted. Descriptive statistics for demographic variables were computed to examine characteristics of students. Descriptive statistics for student dietary intake were examined for all students and for the subset of students who consumed snacks at school. A series of multivariable linear regression models were computed to examine dietary outcomes of interest: (a) total daily energy intake in kilocalories, (b) kilocalories from solid fats and added sugars combined, (c) kilocalories from solid fats alone, (d) kilocalories from added sugars alone, and (e) sodium intake in milligrams. A multivariable logistic regression model was computed to examine differences between students who consumed snacks at school vs those who did not. After computing models for all students, regression models were recomputed for the subset of students who consumed snacks at school. Each model included state law as a key variable while controlling for contextual covariates of student, school, and district demographic characteristics. A 2-tailed a priori α level of .05 was used for significance tests, considering the coefficients for state law in the multivariable regression models. The adjusted margins for state law were examined, which represent the mean value of the outcome at each level while accounting for all other covariates. Results Among 2165 students who completed the 24-hour dietary recall, 206 students had missing data for some demographic variables, reducing the analytical sample to 1959 students (mean [SD] age, 11.9 [3.5] years; 1014 [50.9%] boys) from 290 schools (Table 1). Descriptive statistics presented here may differ from the SNMCS report because of this restriction on the analytic sample. Among 1959 students sampled, 420 students (22.5%) attended school in a state with Smart Snack laws, and 528 students (26.1%) reported consuming snacks obtained at school on the day of reporting. Students attended schools in 30 states and the District of Columbia. Among these states, 7 had a Smart Snacks law (Arkansas, District of Columbia, Florida, Georgia, Illinois, Mississippi, and Utah). Students consumed a mean total of 1982.2 (95% CI, 1919.2 to 2045.2) kcal per day (Table 2), but as expected there were significant differences by demographic characteristics, such as grade in school and sex (Table 3). Few school-level characteristics were associated with students’ daily energy intake. State laws regarding Smart Snacks were not significantly associated with total energy intake among the full sample or among students who consumed snacks at school. Students attending schools in states with laws consumed an adjusted mean of 508.7 (95% CI, 463.0 to 554.4) kcal from solid fats and added sugars, which comprised 25.7% of their total daily energy intake, whereas students in states JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 (Reprinted) January 15, 2020 4/12 JAMA Network Open | Nutrition, Obesity, and Exercise State Laws Regarding Snacks in US Schools Students' Consumption of Solid Fats and Added Sugars Table 1. Student-, School-, and District-Level Characteristics of Sample Students, No. (%) Characteristic (N = 1959) Student Level Grade 1 146 (8.7) 2 149 (9.6) 3 146 (10.0) 4 136 (8.7) 5 138 (7.9) 6 191 (7.2) 7 220 (7.5) 8 228 (7.2) 9 174 (9.5) 10 148 (8.1) 11 160 (9.2) 12 123 (6.5) Sex Boys 1014 (50.9) Girls 945 (49.1) Race/ethnicity Non-Hispanic white 953 (52.6) Non-Hispanic black 267 (13.3) Hispanic 551 (25.5) Other or multiracial 188 (8.7) School Level Urbanicity Urban 436 (23.5) Suburban 1019 (49.3) Rural 504 (27.2) Size, No. of students ≥1000 561 (31.0) 500-999 878 (45.1) <500 520 (23.9) District Level Racial/ethnic composition of students ≥66% White 650 (37.3) ≥50% Black 83 (4.6) ≥50% Hispanic 465 (17.5) Other 761 (40.6) District-level child poverty rate <20% 996 (67.2) ≥20% 963 (32.8) Region West 486 (17.5) Midwest 457 (24.6) South 826 (44.4) Northeast 190 (13.5) State law requires schools to meet Smart Snacks in all venues No 1539 (77.5) Yes 420 (22.5) No. is unweighted, percentage is survey-weighted. Approximate student ages by grade: grade 1, 6 to 7 years; grade 2, 7 to 8 years; grade 3, 8 to 9 years; grade 4, 9 to 10 years; grade 5, 10 to 11 years; grade 6, 11 to 12 years; grade 7, 12 to 13 years; grade 8, 13 to 14 years; grade 9, 14 to 15 years; grade 10, 15 to 16 years; grade 11, 16 to 17 years; and grade 12, 17 to 18 years. JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 (Reprinted) January 15, 2020 5/12 JAMA Network Open | Nutrition, Obesity, and Exercise State Laws Regarding Snacks in US Schools Students' Consumption of Solid Fats and Added Sugars without laws consumed an adjusted mean of 562.5 (95% CI, 534.3 to 590.8) kcal, which comprised 28.4% of their total daily energy intake, a difference of −53.9 (95% CI, −104.5 to −3.2) kcal (P = .04) (Table 4). Calories from solid fats contributed more to the difference than calories from added sugars (−37.7 [95 CI, −62.8 to −12.6] kcal vs −16.2 [95% CI, −51.3 to 19.0] kcal). The pattern of results among students who consumed snacks at school was similar, but the difference was not statistically significant. Additional analyses examined the outcomes of consumption of solid fats alone, added sugars alone, and sodium (Table 5; eTables 1, 2, and 3 in the Supplement). Among all students, students in states with laws compared with those in states without laws had lower adjusted mean total daily consumption of solid fats (257.6 [95% CI, 237.1 to 278.1] kcal vs 295.3 [95% CI, 280.5 to 310.2] kcal; P = .004), but not added sugars or sodium. Results were similar for students who consumed snacks at school. Students who consumed snacks at school in states with laws consumed an adjusted mean of 247.1 (95% CI, 208.2 to 286.0) kcal from solid fats, whereas those in states without laws consumed an adjusted mean of 300.6 (95% CI, 272.1 to 329.1) kcal from solid fats, a difference of −53.5 (95% CI, −97.0 to −10.0) kcal (P = .02). To consider whether laws were associated with students’ at-school consumption behaviors, we used multivariable logistic regression to estimate snack consumption (eTable 4 in the Supplement). Similar percentages of students consumed snacks at school in states with laws (adjusted prevalence, 23.3% [95% CI, 18.3% to 28.4%]) as did in states without laws (adjusted prevalence, 26.9% [95% CI, 23.8% to 30.0%]; P = .25). Discussion This cross-sectional study examining differences in the dietary intake of students in states subject to laws regarding Smart Snacks vs students in states without such laws found that some dietary outcomes differed by state law among the overall sample and among students who consumed snacks at school on the measurement day. Among all students, those in states with laws requiring schools to implement Smart Snacks consumed an adjusted mean of 53.9 fewer total kcal per day from solid fats and added sugars. More of this difference came from solid fats (37.7 kcal) than added sugars (16.2 kcal); when examined as separate outcomes, solid fat consumption differed significantly by state law, but consumption of added sugars did not. When considering the subsample of students who consumed snacks at school, while the reduction limited statistical power, a similar pattern was seen, although the difference was not statistically significant. Students who consumed snacks at school in states with laws consumed 53.5 fewer kcal from solid fats compared with students in states without laws. Differences in daily consumption of solid fats and added sugars combined and sodium were not statistically significant. Policy approaches to improving the school food environment are hypothesized to work by improving the nutritional composition of foods and beverages sold at school, and thereby changing students’ dietary intake behaviors. However, another way they may affect behavior is by reducing students’ snacking behaviors—that is, decreasing the frequency of purchasing and consuming any snacks at school. We did not find that state laws were associated with differences in the percentages of students who consumed snacks at school; however, for students who consumed snacks at school, Table 2. Mean Daily Energy Intake and Sodium Intake Mean (95% CI), kcal Students Who Consumed Snacks Intake All Students (N = 1959) Obtained at School (n = 528) Total daily energy 1982.2 (1919.2 to 2045.2) 1997.1 (1894.0 to 2100.2) Solid fats and added sugars 550.4 (524.5 to 576.4) 568.3 (526.0 to 610.7) Solid fats only 286.9 (273.9 to 299.8) 289.1 (262.6 to 315.6) Added sugars only 263.6 (246.6 to 280.6) 279.2 (257.4 to 301.1) Sodium, mg 3165.8 (3057.7 to 3273.9) 3163.0 (2977.6 to 3348.4) JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 (Reprinted) January 15, 2020 6/12 JAMA Network Open | Nutrition, Obesity, and Exercise State Laws Regarding Snacks in US Schools Students' Consumption of Solid Fats and Added Sugars Table 3. Multivariable Linear Regression Model Examining the Association of State-Level Smart Snacks Policy With Students’ Total Daily Energy Intake All Students (N = 1959) Students Who Consumed Snacks Obtained at School (n = 528) Coefficient Adjusted Mean Coefficient Adjusted Mean Variable (95% CI), kcal P Value (95% CI), kcal (95% CI), kcal P Value (95% CI), kcal State law requires Smart Snacks standards to be followed in all school venues No 0 [Reference] NA 1983.5 (1919.1 to 0 [Reference] NA 2032.0 (1922.7 to 2047.9) 2141.4) Yes −5.77 (−145.65 to .94 1977.7 (1841.6 to −162.64 (−334.30 to .06 1869.4 (1705.2 to 134.10) 2113.9) 9.03) 2033.6) Student grade, 17.49 (1.22 to .04 NA 10.98 (−14.49 to .39 NA per increased grade level 33.75) 36.46) Sex Boys 0 [Reference] NA 2144.4 (2050.3 to 0 [Reference] NA 2292.2 (2132.4 to 2238.4) 2452.0) Girls −330.10 (−437.06 to <.001 1814.3 (1750.2 to −542.50 (−723.25 to <.001 1749.7 (1655.5 to −223.14) 1878.4) −361.74) 1843.9) Race/ethnicity Non-Hispanic white 0 [Reference] NA 1970.2 (1880.6 to 0 [Reference] NA 2018.6 (1876.2 to 2059.9) 2160.9) Non-Hispanic black 67.31 (−100.08 to .43 2037.5 (1884.9 to 15.22 (−183.67 to .88 2033.8 (1886.1 to 234.69) 2190.2) 214.10) 2181.5) Hispanic 19.08 (−111.42 to .77 1989.3 (1891.6 to −20.64 (−347.57 to .90 1997.9 (1722.2 to 149.57) 2087.0) 306.29) 2273.6) Other or multiracial −20.94 (−179.65 to .79 1949.3 (1797.6 to −230.07 (−518.12 to .12 1788.5 (1546.6 to 137.76) 2101.0) 57.97) 2030.4) School urbanicity Urban 0 [Reference] NA 2048.0 (1871.4 to 0 [Reference] NA 2042.4 (1908.8 to 2224.5) 2176.0) Suburban −92.09 (−295.95 to .37 1955.9 (1883.1 to −26.47 (−177.72 to .73 2015.9 (1872.9 to 111.78) 2028.6) 124.79) 2159.0) Rural −74.80 (−265.69 to .44 1973.2 (1866.2 to −119.08 (−281.63 to .15 1923.3 (1791.3 to 116.08) 2080.1) 43.47) 2055.3) School size, No. of students ≥1000 0 [Reference] NA 2067.1 (1957.9 to 0 [Reference] NA 2094.4 (1907.3 to 2176.2) 2281.4) 500-999 −140.48 (−277.71 to .045 1926.6 (1845.8 to −222.21 (−428.85 to .04 1872.2 (1756.7 to −3.26) 2007.3) −15.58) 1987.6) <500 −90.05 (−209.71 to .14 1977.0 (1883.7 to −20.14 (−279.84 to .88 2074.2 (1901.5 to 29.61) 2070.3) 239.56) 2246.9) District-level racial/ethnic composition ≥66% White 0 [Reference] NA 2049.4 (1957.8 to 0 [Reference] NA 2056.3 (1901.4 to 2141.1) 2211.2) ≥50% Black −83.63 (−261.43 to .35 1965.8 (1801.7 to 10.25 (−351.80 to .96 2066.5 (1701.8 to 94.17) 2130.0) 372.30) 2431.2) ≥50% Hispanic −65.73 (−255.08 to .49 1983.7 (1826.0 to −27.85 (−228.60 to .78 2028.4 (1890.8 to 123.61) 2141.5) 172.90) 2166.1) Other −127.80 (−255.69 to .05 1921.6 (1834.0 to −146.14 (−326.36 to .11 1910.1 (1792.9 to 0.09) 2009.3) 34.08) 2027.4) District-level child poverty rate <20% 0 [Reference] NA 1986.2 (1893.3 to 0 [Reference] NA 1985.5 (1863.0 to 2079.0) 2108.0) ≥20% −12.05 (−137.72 to .85 1974.1 (1903.2 to 35.29 (−113.93 to .64 2020.8 (1892.6 to 113.61) 2045.0) 184.51) 2149.1) Region West 0 [Reference] NA 2025.8 (1933.3 to 0 [Reference] NA 1997.5 (1863.7 to 2118.3) 2131.2) Midwest 52.90 (−83.87 to .45 2078.7 (1978.3 to 79.09 (−132.98 to .46 2076.5 (1912.3 to 189.68) 2179.1) 291.15) 2240.8) South −39.35 (−190.25 to .61 1986.5 (1894.5 to 51.14 (−130.12 to .58 2048.6 (1909.3 to 111.55) 2078.4) 232.40) 2187.9) Northeast −291.07 (−498.30 to .006 1734.7 (1531.8 to −379.80 (−685.14 to .02 1617.7 (1318.6 to −83.85) 1937.7) −74.45) 1916.7) Abbreviation: NA, not applicable. JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 (Reprinted) January 15, 2020 7/12 JAMA Network Open | Nutrition, Obesity, and Exercise State Laws Regarding Snacks in US Schools Students' Consumption of Solid Fats and Added Sugars Table 4. Multivariable Linear Regression Model Examining the Association of State-Level Smart Snacks Policy With Students’ Daily Energy Intake From Solid Fats and Added Sugars All Students (N = 1959) Students Who Consumed Snacks Obtained at School (n = 528) Coefficient Adjusted Mean Coefficient Adjusted Mean Variable (95% CI), kcal P Value (95% CI), kcal (95% CI), kcal P Value (95% CI), kcal State law requires Smart Snacks standards to be followed in all school venues No 0 [Reference] NA 562.5 (534.3 to 0 [Reference] NA 582.0 (539.0 to 590.8) 625.0) Yes −53.87 (−104.52 to .04 508.7 (463.0 to −63.53 (−137.93 to .09 518.5 (444.2 to −3.22) 554.4) 10.86) 592.8) Student grade, 4.76 (−3.00 to .23 NA 3.79 (−8.46 to .54 NA per increased grade level 12.51) 16.04) Sex Boys 0 [Reference] NA 593.6 (559.6 to 0 [Reference] NA 650.6 (589.1 to 627.7) 712.0) Girls −87.90 (−123.58 to <.001 505.7 (478.0 to −151.11 (−216.69 to <.001 499.4 (458.8 to −52.23) 533.4) −85.53) 540.1) Race/ethnicity Non-Hispanic white 0 [Reference] NA 553.0 (522.2 to 0 [Reference] NA 574.0 (514.5 to 583.8) 633.5) Non-Hispanic black 37.28 (−33.58 to .30 590.2 (519.5 to 50.21 (−69.01 to .41 624.2 (521.3 to 108.14) 661.0) 169.44) 727.1) Hispanic −20.99 (−73.86 to .43 532.0 (485.7 to −29.44 (−158.78 to .65 544.5 (438.9 to 31.89) 578.3) 99.91) 650.1) Other or multiracial −24.38 (−94.69 to .49 528.6 (459.1 to −86.31 (−232.78 to .25 487.7 (370.1 to 45.93) 598.1) 60.15) 605.3) School urbanicity Urban 0 [Reference] NA 600.3 (546.4 to 0 [Reference] NA 647.4 (589.1 to 654.1) 705.6) Suburban −77.88 (−138.23 to .01 522.4 (493.0 to −102.92 (−163.38 to .001 544.4 (497.5 to −17.53) 551.8) −42.47) 591.4) Rural −42.00 (−108.76 to .22 558.3 (510.8 to −106.06 (−184.09 to .008 541.3 (468.0 to 24.76) 605.7) −28.04) 614.6) School size, No. of students ≥1000 0 [Reference] NA 554.5 (513.5 to 0 [Reference] NA 603.5 (529.0 to 595.5) 678.0) 500-999 −13.60 (−67.84 to .62 540.9 (503.7 to −82.62 (−164.31 to .047 520.9 (469.6 to 40.64) 578.2) −0.94) 572.1) <500 8.56 (−45.83 to .76 563.1 (522.1 to −2.79 (−115.49 to .96 600.7 (528.4 to 62.96) 604.1) 109.91) 673.0) District-level racial/ethnic composition ≥66% White 0 [Reference] NA 602.4 (562.5 to 0 [Reference] NA 611.9 (546.5 to 642.3) 677.4) ≥50% Black −62.17 (−149.31 to .16 540.2 (461.6 to −95.35 (−231.44 to .17 516.6 (391.6 to 24.98) 618.8) 40.74) 641.6) ≥50% Hispanic −90.19 (−171.99 to .03 512.2 (445.8 to −69.94 (−168.48 to .16 542.0 (464.2 to −8.40) 578.5) 28.60) 619.8) Other −82.01 (−134.12 to .002 520.4 (486.0 to −67.88 (−147.48 to .09 544.0 (491.8 to −29.90) 554.7) 11.72) 596.3) District-level child poverty rate <20% 0 [Reference] NA 551.5 (514.4 to 0 [Reference] NA 563.0 (505.8 to 588.6) 620.1) ≥20% −3.22 (−55.59 to .90 548.3 (515.4 to 16.46 (−57.58 to .66 579.4 (530.1 to 49.15) 581.1) 90.50) 628.8) Region West 0 [Reference] NA 558.9 (516.7 to 0 [Reference] NA 580.1 (528.2 to 601.2) 632.1) Midwest 19.40 (−35.13 to .48 578.3 (547.0 to 46.42 (−30.98 to .24 626.6 (570.8 to 73.93) 609.7) 123.83) 682.4) South 5.68 (−62.88 to .87 564.6 (522.0 to −3.39 (−83.91 to .93 576.7 (516.1 to 74.24) 607.3) 77.13) 637.4) Northeast −117.42 (−198.95 to .005 441.5 (363.0 to −187.83 (−300.95 to .001 392.3 (287.0 to −35.90) 520.0) −74.70) 497.6) Abbreviation: NA, not applicable. JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 (Reprinted) January 15, 2020 8/12 JAMA Network Open | Nutrition, Obesity, and Exercise State Laws Regarding Snacks in US Schools Students' Consumption of Solid Fats and Added Sugars dietary outcomes were significantly different. This suggests that the association of policy with dietary outcomes works by changing the nutritional composition of items sold in school without an associated change in snacking behaviors—in other words, students may keep snacking, but when the snacks sold are more healthful, the dietary patterns of those students may improve. The differences observed in students’ total dietary energy intake may seem modest, but a 54 kcal reduction in solid fats and added sugars is significant practically as well as statistically. Consumption of solid fats and added sugars adds between 500 to 1050 kcal to total daily energy intake for people in the United States, far higher than optimal levels: the 2015 to 2020 Dietary Guidelines reiterated the importance of limiting solid fats and added sugars consumption, recommending no more than 10% of kcal daily from sugar and no more than 10% from saturated fat for all age groups. Our findings, which used data from a nationally-representative sample of children and adolescents, suggest that more than 25% of daily energy intake was derived from solid fats and added sugars, with solid fats and added sugars consumption each over 10%. While the subset of students who consumed snacks at school also exceeded those recommendations, students in states with laws that address the nutritional standard of school snacks had significantly better dietary patterns. Studies have shown that over time, small changes in daily dietary intake can substantially improve health outcomes, including weight status and cardiovascular outcomes 3,4 associated with consumption of solid fats and added sugars. Limitations This study has several limitations. National-level policy interventions, such as Smart Snacks, and state laws to support implementation are expected to improve the nutritional characteristics of foods and beverages sold to students on campus. Mediation analyses to examine the hypothesized associations could not be conducted because we did not have intermediate analytic weights for schools, which would be necessary for multilevel analyses. Furthermore, data were not available on the nutrient profiles of snacks sold at each school. As a result, these analyses do not explicitly demonstrate that policy changed the types of foods and beverages sold at the school that each student attended. Such analyses should be conducted in the future. Furthermore, the cross-sectional nature of these data does not allow for examination of changes in students’ dietary patterns. It is expected that policy changes would result in measurable changes in school-level food and beverage nutritional quality, as well as student-level dietary outcomes. Such changes cannot be examined with cross-sectional data; thus, these associations may not be causal or directional—third variables may explain the associations, such as other characteristics of the states with laws. However, we accounted for student demographic characteristics and other contextual characteristics. Furthermore, the states with laws are located in various regions of the United States and do not include states that have a history of state-level intervention to change school food environments. For example, California has a state nutrition policy, but we did not code it as having a Smart Snacks law because the state law was less stringent than Smart Snacks on some topics. The association of nutrition standards with dietary Table 5. Covariate-Adjusted Student Dietary Outcomes by State-Level Smart Snacks Policy All Students (N = 1959) Students Who Consumed Snacks Obtained at School (n = 528) Adjusted Mean (95% CI) Adjusted Mean (95% CI) Variable In States Without a Law In States With a Law Difference P Value In States Without a Law In States With a Law Difference P Value Total daily energy intake, 1983.5 (1919.1 to 1977.7 (1841.6 to −5.8 .94 2032.0 (1922.7 to 1869.4 (1705.2 to −162.6 .06 kcal 2047.9) 2113.9) 2141.4) 2033.6) Solid fats and added 562.5 (534.3 to 508.7 (463.0 to −53.9 .04 582.0 (539.0 to 518.5 (444.2 to −63.5 .09 sugars, kcal 590.8) 554.4) 625.0) 592.8) Solid fats, kcal 295.3 (280.5 to 257.6 (237.1 to −37.7 .004 300.6 (272.1 to 247.1 (208.2 to −53.5 .02 310.2) 278.1) 329.1) 286.0) Added sugars, kcal 267.2 (248.4 to 251.1 (219.4 to −16.2 .36 281.4 (259.8 to 271.4 (224.1 to −10.0 .67 286.0) 282.7) 303.0) 318.7) Sodium, mg 3162.7 (3056.0 to 3176.3 (2927.8 to 13.5 .92 3223.3 (3006.6 to 2942.7 (2648.4 to −280.6 .12 3269.5) 3424.8) 3440.1) 3237.0) JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 (Reprinted) January 15, 2020 9/12 JAMA Network Open | Nutrition, Obesity, and Exercise State Laws Regarding Snacks in US Schools Students' Consumption of Solid Fats and Added Sugars outcomes might be larger if a broader definition were used. The exposure variable was a state-level Smart Snacks law because our goal was to examine the potential for state-level policy actions to facilitate and support national-level policy intervention. Although all schools participating in USDA child nutrition programs must comply with Smart Snacks, some states reinforced this requirement and were quicker to implement those standards. As we note elsewhere, states incorporated Smart Snacks in various ways. For example, state laws in Arkansas, Arizona, Florida, Iowa, and Mississippi adopted the full text of Smart Snacks or linked to the USDA website or another state-adopted policy that listed the full text of the standards. The District of Columbia, Georgia, Illinois, and Utah law instead provided a general reference to the federal rule without listing details for compliance. Additionally, several points are noteworthy regarding the outcome. Only 1 day of dietary recall data were used, so although the sample of students was representative, their dietary intake on the measurement day represents only that day and may not be representative of each student’s overall dietary pattern owing to intraindividual variation in dietary intake. Response biases may have affected dietary self-report, and parental assistance for younger children may have biased responses. Regressions did not include a student-level measure of economic status, such as free or reduced- priced meal eligibility; students from wealthier families are more likely to be able to afford snacks, so the results for the sample of 528 students who consumed snacks at schools may include more students from wealthier families than the sample overall. Additionally, we recognize that there is not agreement among nutritional scientists that all solid fats and added sugars should be limited because some foods high in added sugars, such as some cereals and grain products, can contribute micronutrients to people’s diets. Nevertheless, the Dietary Guidelines focus on limiting energy intake from solid fats and added sugars and consuming nutrient-dense foods, such as lean meat, low-fat dairy, and fruits and vegetables; these are evidence-based strategies with scientific support. Conclusions In summary, our findings suggest that policy interventions, such as the Smart Snacks standards and state laws that support the implementation of these changes in schools, may be promising interventions for improving the dietary habits of children and adolescents. Owing to the significant negative health consequences associated with suboptimal diets, interventions to improve the dietary habits of people in the United States may be of significant value for the nation’s health. ARTICLE INFORMATION Accepted for Publication: November 6, 2019. Published: January 15, 2020. doi:10.1001/jamanetworkopen.2019.18436 Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Turner L et al. JAMA Network Open. Corresponding Author: Lindsey Turner, PhD, College of Education, Boise State University, 1910 University Dr, Boise, ID 83725-1740 (lindseyturner1@boisestate.edu). Author Affiliations: College of Education, Boise State University, Boise, Idaho (Turner); Institute for Health Research and Policy, University of Illinois at Chicago, Chicago (Leider, Piekarz-Porter, Chriqui); Division of Health Policy and Administration, School of Public Health, University of Illinois at Chicago, Chicago (Chriqui). Author Contributions: Mr Leider and Dr Chriqui had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Turner, Chriqui. Acquisition, analysis, or interpretation of data: Turner, Leider, Piekarz-Porter. Drafting of the manuscript: Turner, Piekarz-Porter. Critical revision of the manuscript for important intellectual content: All authors. JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 (Reprinted) January 15, 2020 10/12 JAMA Network Open | Nutrition, Obesity, and Exercise State Laws Regarding Snacks in US Schools Students' Consumption of Solid Fats and Added Sugars Statistical analysis: Turner, Leider. Obtained funding: Turner, Chriqui. Administrative, technical, or material support: Turner, Piekarz-Porter. Supervision: Turner, Chriqui. Conflict of Interest Disclosures: Dr Chriqui reported receiving grants from the Robert Wood Johnson Foundation and National Cancer Institute outside the submitted work and serving as an unpaid advisor for several nonprofit or academic institutions on specific projects, including Voices for Healthy Kids for American Heart Association, the Alliance for a Healthier Generation, Action for Healthy Kids, and the Consortium to Lower Obesity in Chicago’s Children. No other disclosures were reported. Funding/Support: This work was funded by a US Department of Agriculture (USDA) cooperative agreement (USDA-FNS-OPS-SWP-15-IL-01). Role of the Funder/Sponsor: The USDA was involved in the design and conduct of the study. The USDA was not involved in the collection, management, analysis, and interpretation of the data; The USDA reviewed the manuscript to provide feedback but was not involved in the preparation or approval of the manuscript or decision to submit the manuscript for publication. Disclaimer: The contents of this publication do not necessarily reflect the views or policies of the USDA, nor does mention of trade names, commercial products, or organizations imply endorsement by the US government. Additional Contributions: Wanting Lin, JD, PhD, and Rebecca Schermbeck, MPH, MS, RD (University of Illinois at Chicago), assisted with coding the policy data. They were not compensated for their contribution. REFERENCES 1. Murray CJ, Atkinson C, Bhalla K, et al; US Burden of Disease Collaborators. The state of US health, 1990-2010: burden of diseases, injuries, and risk factors. JAMA. 2013;310(6):591-608. doi:10.1001/jama.2013.13805 2. Mozaffarian D. Dietary and policy priorities for cardiovascular disease, diabetes, and obesity: a comprehensive review. Circulation. 2016;133(2):187-225. doi:10.1161/CIRCULATIONAHA.115.018585 3. Dietary Guidelines Advisory Committee. Report of the Dietary Guidelines Advisory Committee on the Dietary Guidelines for Americans, 2010, to the Secretary of Agriculture and the Secretary of Health and Human Services. Washington, DC: US Department of Agriculture; 2010. 4. Nicklas TA, O’Neil CE. Development of the SoFAS (solid fats and added sugars) concept: the 2010 Dietary Guidelines for Americans. Adv Nutr. 2015;6(3):368S-375S. doi:10.3945/an.114.007021 5. Reedy J, Krebs-Smith SM. Dietary sources of energy, solid fats, and added sugars among children and adolescents in the United States. J Am Diet Assoc. 2010;110(10):1477-1484. doi:10.1016/j.jada.2010.07.010 6. Templeton S. Sugar intake from combined school lunch and competitive food consumption. J Am Diet Assoc. 2005;105(7):1066-1067. doi:10.1016/j.jada.2005.05.259 7. Poti JM, Slining MM, Popkin BM. Where are kids getting their empty calories: stores, schools, and fast-food restaurants each played an important role in empty calorie intake among US children during 2009-2010. J Acad Nutr Diet. 2014;114(6):908-917. doi:10.1016/j.jand.2013.08.012 8. Poti JM, Slining MM, Popkin BM. Solid fat and added sugar intake among US children: the role of stores, schools, and fast food, 1994-2010. Am J Prev Med. 2013;45(5):551-559. doi:10.1016/j.amepre.2013.06.013 9. Fox MK, Gordon A, Nogales R, Wilson A. Availability and consumption of competitive foods in US public schools. J Am Diet Assoc. 2009;109(2)(suppl):S57-S66. doi:10.1016/j.jada.2008.10.063 10. Briefel RR, Crepinsek MK, Cabili C, Wilson A, Gleason PM. School food environments and practices affect dietary behaviors of US public school children. J Am Diet Assoc. 2009;109(2)(suppl):S91-S107. doi:10.1016/j.jada. 2008.10.059 11. Turner L, Chaloupka FJ. Wide availability of high-calorie beverages in US elementary schools. Arch Pediatr Adolesc Med. 2011;165(3):223-228. doi:10.1001/archpediatrics.2010.215 12. Healthy, Hunger-Free Kids Act of 2010, 42 USC §1751 (2010). 13. Food and Nutrition Service, USDA. National School Lunch Program and School Breakfast Program: nutrition standards for all foods sold in school as required by the Healthy, Hunger-Free Kids Act of 2010: interim final rule. Fed Regist. 2013;78(125):39067-39120. 14. Institute of Medicine. School Meals: Healthy Building Blocks for Children. Washington, DC: National Academies Press; 2009. JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 (Reprinted) January 15, 2020 11/12 JAMA Network Open | Nutrition, Obesity, and Exercise State Laws Regarding Snacks in US Schools Students' Consumption of Solid Fats and Added Sugars 15. Piekarz-Porter E, Chriqui JF, Schermbeck RM, Leider J, Lin W; Bridging the Gap Program, National Wellness Policy Study. The Active Role States Have Played in Helping to Transform the School Wellness Environment Through Policy. Chicago, IL: University of Illinois at Chicago; 2017. 16. Chriqui JF, Pickel M, Story M. Influence of school competitive food and beverage policies on obesity, consumption, and availability: a systematic review. JAMA Pediatr. 2014;168(3):279-286. doi:10.1001/ jamapediatrics.2013.4457 17. Asada Y, Chriqui J, Chavez N, Odoms-Young A, Handler A. USDA snack policy implementation: best practices from the front lines, United States, 2013-2014. Prev Chronic Dis. 2016;13:E79. doi:10.5888/pcd13.160023 18. US Department of Agriculture, Food and Nutrition Service. School nutrition and meal cost study. https://www. fns.usda.gov/school-nutrition-and-meal-cost-study. Accessed May 30, 2019. 19. US Department of Agriculture, Food and Nutrition Service. Nutrition and meal cost study: data collection instruments. https://fns-prod.azureedge.net/sites/default/files/resource-files/SNMCS-DataCollection-Instruments.pdf. Accessed August 5, 2019. 20. Raper N, Perloff B, Ingwersen L, Steinfeldt L, Anand J. An overview of USDA’s Dietary Intake Data System. J Food Compos Anal. 2004;17:545-555. doi:10.1016/j.jfca.2004.02.013 21. US Department of Education, National Center for Education Statistics. Documentation to the NCES Common Core of Data Public Elementary/Secondary School Universe Survey: school year 2011-12. https://nces.ed.gov/ccd/ pdf/SC2011_1a_doc.pdf. Accessed April 15, 2019. 22. US Department of Education, National Center for Education Statistics. Documentation to the NCES Common Core of Data Public Elementary/Secondary School Universe Survey: school year 2013-14. https://nces.ed.gov/ccd/ pdf/2015147_2013-14_LEA_documentation_v1a.pdf. Accessed April 1, 2017. 23. US Census Bureau. Small Area Income and Poverty Estimates: 2011 highlights. https://www.census.gov/library/ publications/2012/demo/SAIPE-highlights-2011.html. Accessed June 1, 2017. 24. Piekarz-Porter E, Schermbeck RM, Leider J, Young SK, Chriqui JF; National Wellness Policy Study, Institute for Health Research and Policy. Working on Wellness: How Aligned Are District Wellness Policies With the Soon-To-Be Implemented Federal Wellness Policy Requirements? Chicago, IL: University of Illinois at Chicago; 2017. 25. National Cancer Institute. Classification of Laws Associated with School Students. https://class.cancer.gov/. Accessed June 1, 2019. 26. National Association of School Boards of Education. State School Health Policy Database on School Health. https://statepolicies.nasbe.org/health. Accessed June 1, 2017. 27. Schwartz MB, Lund AE, Grow HM, et al. A comprehensive coding system to measure the quality of school wellness policies. J Am Diet Assoc. 2009;109(7):1256-1262. doi:10.1016/j.jada.2009.04.008 28. US Department of Agriculture. Nutrition standards in the National School Lunch and School Breakfast Programs. https://www.gpo.gov/fdsys/pkg/FR-2012-01-26/pdf/2012-1010.pdf. Accessed June 1, 2019. 29. US Department of Agriculture. National School Lunch Program and School Breakfast Program: nutrition standards for all foods sold in school as required by the Healthy, Hunger-Free Kids Act of 2010. https://www. federalregister.gov/documents/2013/06/28/2013-15249/national-school-lunch-program-and-school- breakfast-program-nutrition-standards-for-all-foods-sold-in. Accessed June 1, 2019. 30. US Department of Health and Human Services, US Department of Agriculture. Dietary guidelines for Americans 2015-2020: eighth edition. https://health.gov/dietaryguidelines/2015/guidelines/. Accessed June 1, 2019. SUPPLEMENT. eTable 1. Multivariable Linear Regression Model Examining the Association of State-Level Smart Snacks Policy With Students’ Daily Energy Intake From Solid Fats in Kilocalories eTable 2. Multivariable Linear Regression Model Examining the Association of State-Level Smart Snacks Policy With Students’ Daily Energy Intake From Added Sugars in Kilocalories eTable 3. Multivariable Linear Regression Model Examining the Association of State-Level Smart Snacks Policy With Students’ Daily Intake of Sodium in Milligrams eTable 4. Multivariable Logistic Regression Model Examining the Association of State-Level Smart Snacks Policy With Student Consumption of Snacks Obtained at School Among 1959 Students JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 (Reprinted) January 15, 2020 12/12 Supplementary Online Content Turner L, Leider J, Piekarz-Porter E, Chriqui JF. Association of state laws regarding snacks in US schools with students' consumption of solid fats and added sugars. JAMA Network Open. 2020;3(1):e1918436. doi:10.1001/jamanetworkopen.2019.18436 eTable 1. Multivariable Linear Regression Model Examining the Association of State-Level Smart Snacks Policy With From Solid Fats in Kilocalories eTable 2. Multivariable Linear Regression Model Examining the Association of State-Level Smart Snacks Policy With From Added Sugars in Kilocalories eTable 3. Multivariable Linear Regression Model Examining the Association of State-Level Smart Snacks Policy With in Milligrams eTable 4. Multivariable Logistic Regression Model Examining the Association of State-Level Smart Snacks Policy With Student Consumption of Snacks Obtained at School Among 1959 Students This supplementary material has been provided by the authors to give readers additional information about their work. © 2020 Turner L et al. JAMA Network Open. eTable 1. Multivariable Linear Regression Model Examining the Association of State-Level Smart Snacks Daily Energy Intake From Solid Fats in Kilocalories All Students (n = 1959) Students who Consumed Snacks Purchased at School (n = 528) Variable Adjusted 95% CI Adjusted Coefficient (95% CI) P Mean Coefficient (95% CI) P Mean Value Value State Law Requires Smart Snacks Standards to Be Followed in All School Venues No 0 [Reference] -- 295.3 280.5, 310.2 0 [Reference] -- 300.6 272.1, 329.1 Yes -37.70 (-62.78, -12.62) .004 257.6 237.1, 278.1 -53.50 (-97.03, -9.97) .02 247.1 208.2, 286.0 Student Grade 2.89 (-1.44, 7.21) .19 -- 2.53 (-4.77, 9.83) .49 -- (continuous) Student Gender Male 0 [Reference] -- 312.7 295.7, 329.8 0 [Reference] -- 346.3 306.1, 386.6 Female -52.69 (-71.87, -33.50) <.001 260.1 245.4, 274.7 -105.18 (-147.42, -62.95) <.001 241.1 217.5, 264.7 Student Race/Ethnicity White, non-Hispanic 0 [Reference] -- 290.5 274.2, 306.7 0 [Reference] -- 299.0 258.9, 339.0 Black, non-Hispanic -0.88 (-39.88, 38.13) .96 289.6 252.0, 327.1 1.20 (-79.18, 81.58) .98 300.2 236.7, 363.6 Hispanic -12.68 (-43.87, 18.51) .42 277.8 251.2, 304.4 -22.30 (-103.22, 58.61) .59 276.7 209.9, 343.4 Other -3.03 (-44.80, 38.74) .89 287.4 246.6, 328.3 -55.31 (-136.96, 26.33) .18 243.7 182.5, 304.8 School Urbanicity Urban 0 [Reference] -- 303.5 272.4, 334.6 0 [Reference] -- 315.1 275.5, 354.6 Suburban -27.30 (-65.23, 10.63) .16 276.2 258.6, 293.8 -25.75 (-66.30, 14.81) .21 289.3 258.4, 320.3 Rural -11.71 (-47.52, 24.10) .52 291.8 269.2, 314.4 -49.27 (-99.02, 0.47) .052 265.8 223.1, 308.4 School Size Large (>1000 students) 0 [Reference] -- 286.3 264.5, 308.1 0 [Reference] -- 303.3 252.4, 354.1 Medium (500 to 999 -3.21 (-34.03, 27.61) .84 283.1 263.8, 302.4 -36.51 (-95.59, 22.57) .22 266.8 233.0, 300.5 students) Small (< 500 students) 8.26 (-20.81, 37.33) .57 294.6 270.5, 318.7 5.07 (-55.97, 66.12) .87 308.3 271.3, 345.3 Race/Ethnicity (District) 0 [Reference] -- 291.8 272.4, 311.3 0 [Reference] -- 281.8 237.3, 326.4 4.12 (-46.44, 54.68) .87 296.0 247.7, 344.3 -3.94 (-93.84, 85.96) .93 277.9 198.5, 357.3 -5.37 (-55.79, 45.06) .83 286.5 243.6, 329.4 25.27 (-38.73, 89.27) .44 307.1 260.9, 353.4 Other -10.47 (-36.30, 15.35) .42 281.4 264.6, 298.1 7.38 (-46.65, 61.41) .79 289.2 257.3, 321.2 Child Poverty Rate (District) <20% 0 [Reference] -- 289.0 269.5, 308.6 0 [Reference] -- 292.0 256.6, 327.4 -6.63 (-36.77, 23.51) .66 282.4 264.1, 300.7 -8.99 (-58.32, 40.34) .72 283.0 248.2, 317.9 Region West 0 [Reference] -- 298.4 274.1, 322.7 0 [Reference] -- 288.4 248.0, 328.7 Midwest 10.20 (-21.83, 42.23) .53 308.6 291.8, 325.4 41.15 (-13.56, 95.86) .14 329.5 293.0, 366.0 © 2020 Turner L et al. JAMA Network Open. South -11.23 (-52.63, 30.16) .59 287.2 263.7, 310.6 -1.92 (-59.55, 55.72) .95 286.4 246.8, 326.1 Northeast -67.30 (-110.47, -24.13) .003 231.1 193.2, 269.0 -75.12 (-150.74, 0.50) .052 213.2 148.3, 278.2 © 2020 Turner L et al. JAMA Network Open. eTable 2. Multivariable Linear Regression Model Examining the Association of State- Daily Energy Intake From Added Sugars in Kilocalories All Students Students who Consumed Snacks Obtained at School (n = 1959) (n = 528) Variable Coefficient (95% CI) P Adjusted Coefficient (95% CI) P Adjusted Value Mean 95% CI Value Mean 95% CI State Law Requires Smart Snacks Standards to Be Followed in All School Venues No 0 [Reference] -- 267.2 248.4, 286.0 0 [Reference] -- 281.4 259.8, 303.0 Yes -16.17 (-51.28, 18.95) .36 251.1 219.4, 282.7 -10.04 (-56.92, 36.85) .67 271.4 224.1, 318.7 Student Grade (continuous) 1.87 (-2.63, 6.37) .41 -- 1.25 (-6.73, 9.24) .76 -- Student Gender Male 0 [Reference] -- 280.9 258.3, 303.5 0 [Reference] -- 304.2 273.6, 334.8 Female -35.22 (-56.92, -13.52) .002 245.7 228.7, 262.6 -45.93 (-80.19, -11.67) .009 258.3 234.3, 282.3 Student Race/Ethnicity White, non-Hispanic 0 [Reference] -- 262.5 242.7, 282.4 0 [Reference] -- 275.0 246.3, 303.7 Black, non-Hispanic 38.16 (-1.24, 77.55) .06 300.7 261.4, 339.9 49.02 (0.05, 97.99) .050 324.0 275.5, 372.5 Hispanic -8.31 (-38.56, 21.93) .59 254.2 227.2, 281.2 -7.13 (-67.43, 53.16) .82 267.9 220.7, 315.0 Other -21.35 (-60.73, 18.03) .29 241.2 201.4, 280.9 -31.00 (-106.20, 44.20) .42 244.0 177.2, 310.8 School Urbanicity Urban 0 [Reference] -- 296.8 267.3, 326.2 0 [Reference] -- 332.3 297.0, 367.6 Suburban -50.57 (-81.13, -20.02) .001 246.2 227.4, 264.9 -77.17 (-116.39, -37.96) <.001 255.1 230.6, 279.6 Rural -30.28 (-71.16, 10.59) .15 266.5 233.9, 299.1 -56.79 (-107.13, -6.45) .03 275.5 233.2, 317.8 School Size Large (>1000 students) 0 [Reference] -- 268.2 238.8, 297.6 0 [Reference] -- 300.2 259.7, 340.8 Medium (500 to 999 -10.39 (-42.32, 21.54) .52 257.8 235.0, 280.6 -46.11 (-90.86, -1.36) .04 254.1 227.0, 281.3 students) Small (< 500 students) 0.30 (-39.92, 40.53) .99 268.5 244.4, 292.6 -7.86 (-84.18, 68.45) .84 292.4 244.1, 340.6 Race/Ethnicity (District) White 0 [Reference] -- 310.5 280.8, 340.2 0 [Reference] -- 330.1 297.1, 363.0 -66.29 (-116.43, -16.15) .01 244.2 201.8, 286.6 -91.41 (-161.69, -21.12) .01 238.7 172.8, 304.6 -84.82 (-130.14, -39.51) <.001 225.7 195.6, 255.8 -95.20 (-156.03, -34.37) .002 234.9 185.6, 284.2 Other -71.54 (-109.09, -33.98) <.001 239.0 217.0, 261.0 -75.26 (-117.77, -32.76) .001 254.8 223.7, 285.9 Child Poverty Rate (District) <20% 0 [Reference] -- 262.5 240.3, 284.7 0 [Reference] -- 270.9 242.4, 299.4 3.40 (-23.97, 30.77) .81 265.9 246.5, 285.3 25.45 (-15.46, 66.36) .22 296.4 266.6, 326.2 Region West 0 [Reference] -- 260.6 235.4, 285.7 0 [Reference] -- 291.8 259.9, 323.6 Midwest 9.21 (-25.59, 44.00) .60 269.8 245.9, 293.6 5.28 (-40.81, 51.36) .82 297.1 262.2, 331.9 South 16.91 (-17.99, 51.82) .34 277.5 251.8, 303.2 -1.47 (-47.08, 44.15) .95 290.3 260.2, 320.4 Northeast -50.13 (-102.23, 1.98) .06 210.4 161.3, 259.6 -112.70 (-168.44, -56.96) <.001 179.1 128.7, 229.4 © 2020 Turner L et al. JAMA Network Open. eTable 3. Multivariable Linear Regression Model Examining the Association of State-Level Smart Snacks Policy With Daily Intake of Sodium in Milligrams All Students Students who Consumed Snacks Obtained at School (n = 1959) (n = 528) Variable P Adjusted P Adjusted Coefficient (95% CI) Value Mean 95% CI Coefficient (95% CI) Value Mean 95% CI State Law Requires Smart Snacks Standards to Be Followed in All School Venues No 0 [Reference] -- 3162.7 3056.0, 0 [Reference] -- 3223.3 3006.6, 3269.5 3440.1 Yes 13.55 (-238.71, 265.81) .92 3176.3 2927.8, -280.60 (-634.20, 72.99) .12 2942.7 2648.4, 3424.8 3237.0 Student Grade 45.36 (19.14, 71.58) .001 -- 46.24 (-10.86, 103.33) .11 -- (continuous) Student Gender Male 0 [Reference] -- 3453.5 3297.1, 0 [Reference] -- 3636.9 3337.3, 3609.9 3936.6 Female -585.56 (-764.04, -407.09) <.001 2867.9 2754.8, -871.15 (-1206.22, -536.08) <.001 2765.8 2591.9, 2981.0 2939.7 Student Race/Ethnicity White, non-Hispanic 0 [Reference] -- 3106.9 2948.1, 0 [Reference] -- 3154.7 2883.3, 3265.6 3426.0 Black, non-Hispanic 102.52 (-186.59, 391.63) .48 3209.4 2968.9, 3.83 (-435.40, 443.05) .99 3158.5 2843.4, 3449.9 3473.5 Hispanic 121.85 (-113.02, 356.71) .31 3228.7 3058.2, -87.23 (-575.70, 401.23) .72 3067.4 2684.9, 3399.3 3449.9 Other 164.08 (-181.47, 509.62) .35 3271.0 2938.6, 350.48 (-629.38, 1330.34) .48 3505.1 2543.5, 3603.3 4466.8 School Urbanicity Urban 0 [Reference] -- 3213.8 2914.2, 0 [Reference] -- 3105.1 2836.5, 3513.3 3373.6 Suburban -102.26 (-453.36, 248.84) .57 3111.5 2980.1, 79.71 (-330.56, 489.99) .70 3184.8 2890.1, 3242.9 3479.4 Rural 9.06 (-343.47, 361.59) .96 3222.8 3010.2, 70.14 (-396.44, 536.73) .77 3175.2 2705.3, 3435.5 3645.1 School Size Large (>1000 students) 0 [Reference] -- 3368.0 3168.6, 0 [Reference] -- 3393.9 2954.5, 3567.3 3833.2 Medium (500 to 999 -303.83 (-553.41, -54.26) .02 3064.2 2925.2, -425.93 (-911.23, 59.38) .09 2967.9 2748.2, students) 3203.2 3187.7 Small (< 500 students) -272.92 (-521.95, -23.90) .03 3095.1 2917.6, -242.22 (-864.42, 379.99) .44 3151.6 2835.6, 3272.5 3467.7 Race/Ethnicity (District) © 2020 Turner L et al. JAMA Network Open. 0 [Reference] -- 3232.4 3067.2, 0 [Reference] -- 3278.1 2968.3, 3397.6 3587.8 246.10 (-59.99, 552.19) .11 3478.5 3211.0, 292.15 (-653.02, 1237.33) .54 3570.2 2624.7, 3746.0 4515.7 -89.25 (-395.08, 216.58) .56 3143.2 2923.3, -249.37 (-775.86, 277.13) .35 3028.7 2701.1, 3363.0 3356.2 Other -153.82 (-393.28, 85.65) .21 3078.6 2908.0, -225.96 (-645.90, 193.99) .29 3052.1 2770.1, 3249.1 3334.1 Child Poverty Rate (District) <20% 0 [Reference] -- 3193.8 3051.6, 0 [Reference] -- 3103.2 2909.9, 3336.0 3296.6 -85.41 (-271.30, 100.47) .36 3108.4 2978.1, 182.96 (-89.88, 455.80) .19 3286.2 3010.1, 3238.7 3562.2 Region West 0 [Reference] -- 3273.5 3036.8, 0 [Reference] -- 3396.9 2820.7, 3510.3 3973.1 Midwest 46.15 (-269.95, 362.24) .77 3319.7 3103.9, -227.81 (-815.22, 359.60) .44 3169.1 2929.0, 3535.4 3409.1 South -127.52 (-408.46, 153.42) .37 3146.0 3027.1, -179.26 (-838.28, 479.75) .59 3217.6 2951.5, 3264.9 3483.7 Northeast -464.44 (-794.67, -134.22) .006 2809.1 2532.9, -787.78 (-1458.21, -117.35) .02 2609.1 2066.6, 3085.3 3151.6 © 2020 Turner L et al. JAMA Network Open. eTable 4. Multivariable Logistic Regression Model Examining the Association of State-Level Smart Snacks Policy With Student Consumption of Snacks Obtained at School Among 1959 Students Adjusted Variable Odds Ratio (95% CI) P Value Prevalence 95% CI State Law Requires Smart Snacks Standards to Be Followed in All School Venues No 1 [Reference] -- 26.9 23.8, 30.0 Yes 0.82 (0.59, 1.15) .25 23.3 18.3, 28.4 Student Grade (continuous) 1.06 (1.02, 1.11) .007 -- Student Gender Male 1 [Reference] -- 23.3 19.7, 27.0 Female 1.35 (1.03, 1.75) .03 28.9 25.3, 32.6 Student Race/Ethnicity White, non-Hispanic 1 [Reference] -- 23.8 19.9, 27.8 Black, non-Hispanic 1.82 (1.22, 2.71) .004 36.0 28.4, 43.7 Hispanic 1.13 (0.80, 1.61) .48 26.1 21.1, 31.2 Other 1.08 (0.65, 1.77) .77 25.2 17.7, 32.6 School Urbanicity Urban 1 [Reference] -- 27.0 18.9, 35.0 Suburban 0.93 (0.56, 1.53) .77 25.6 21.7, 29.4 Rural 0.97 (0.60, 1.55) .89 26.3 22.5, 30.1 School Size Large (>1000 students) 1 [Reference] -- 27.4 22.3, 32.5 Medium (500 to 999 students) 0.90 (0.64, 1.28) .57 25.5 21.4, 29.6 Small (< 500 students) 0.90 (0.57, 1.42) .64 25.4 18.8, 31.9 Race/Ethnicity (District) 1 [Reference] -- 28.4 23.6, 33.1 0.94 (0.57, 1.56) .82 27.3 19.1, 35.4 0.98 (0.51, 1.89) .95 27.9 17.2, 38.7 Other 0.76 (0.53, 1.08) .12 23.2 19.3, 27.0 Child Poverty Rate (District) <20% 1 [Reference] -- 26.6 22.3, 30.8 0.93 (0.63, 1.37) .70 25.2 20.5, 29.8 Region West 1 [Reference] -- 25.5 16.6, 34.4 Midwest 1.05 (0.60, 1.86) .86 26.4 21.5, 31.4 South 1.08 (0.61, 1.92) .79 27.0 22.8, 31.1 Northeast 0.89 (0.44, 1.80) .74 23.3 14.8, 31.8 © 2020 Turner L et al. JAMA Network Open.

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Published: Jan 15, 2020

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