Differences in Diet Quality between School Lunch Participants and Nonparticipants in the United States by Income and Race
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design and Data Sources
2.2. Measuring the Nutritional Quality of Students’ Diets
2.3. Defining Income and Race/Ethnicity Subgroups
2.4. Statistical Methods
3. Results
3.1. Healthy Eating Index-2010 Scores by Household Income
3.1.1. Lunch Intakes
3.1.2. Intakes over 24 H
3.2. Healthy Eating Index-2010 Scores by Student Race and Ethnicity
3.2.1. Lunch Intakes
3.2.2. Intakes over 24 H
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- U.S. Department of Agriculture, Food and Nutrition Service. National Level Annual Summary Tables: FY 1969–2019, National School Lunch: Participation and Lunches Served. Available online: https://fns-prod.azureedge.net/pd/child-nutrition-tables (accessed on 27 August 2020).
- National School Lunch Program and School Breakfast Program: Eliminating Applications through community eligibility as required by the Healthy, Hunger-Free Kids Act of 2010. Federal Register. 29 July 2016. 81(46). U.S. Department of Agriculture, Food and Nutrition Service. Available online: https://www.govinfo.gov/content/pkg/FR-2016-07-29/pdf/2016-17232.pdf (accessed on 27 August 2020).
- Nutrition Standards in the National School Lunch and School Breakfast Programs: Final Rule. Federal Register. 26 January 2012. 77(17). U.S. Department of Agriculture, Food and Nutrition Service. Available online: https://www.federalregister.gov/articles/2012/01/26/2012-1010/nutrition-standards-in-the-national-school-lunch-and-school-breakfast-programs (accessed on 27 August 2020).
- U.S. Department of Agriculture and U.S. Department of Health and Human Services. Dietary Guidelines for Americans 2010, 7th ed.; U.S. Government Printing Office: Washington, DC, USA, 2010.
- Fox, M.K.; Gearan, E. School Nutrition and Meal Cost Study: Summary of Findings; Mathematica: Princeton, NJ, USA, 2019. [Google Scholar]
- Kinderknecht, K.; Harris, C.; Jones-Smith, J. Association of the Healthy, Hunger-Free Kids Act with dietary quality among children in the US National School Lunch Program. JAMA 2020, 324, 359–368. [Google Scholar] [CrossRef] [PubMed]
- Au, L.E.; Rosen, N.J.; Fenton, K.; Hecht, K.; Ritchie, L.D. Eating school lunch is associated with higher diet quality among elementary school students. J. Acad. Nutr. Diet. 2016, 116, 1817–1824. [Google Scholar] [CrossRef]
- Bergman, E.A.; Saade, C.; Shaw, E.; Englund, T.; Cashman, L.; Taylor, K.W.; Watkins, T.; Rushing, K. Lunches selected and consumed from the National School Lunch Program in schools designated as Healthier US School Challenge schools are more nutritious than lunches brought from home. J. Child Nutr. Manag. 2014, 38, 1–17. [Google Scholar]
- Johnston, C.A.; Moreno, J.P.; El-Mubasher, A.; Woehler, D. School lunches and lunches brought from home: A Comparative analysis. Child Obes. 2020, 8, 364–368. [Google Scholar] [CrossRef] [PubMed]
- Krebs-Smith, S.M.; Guenther, P.M.; Subar, A.F.; Kirkpatrick, S.I.; Dodd, K.W. Americans do not meet federal dietary recommendations. J. Nutr. 2010, 140, 1832–1838. [Google Scholar] [CrossRef] [PubMed]
- Jackson, S.L.; King, S.M.C.; Zhao, L.; Cogswell, M.E. Prevalence of excess sodium intake in the United States—NHANES, 2009–2012. MMWR Morb. Mortal. Wkly. Rep. 2016, 64, 1393–1397. [Google Scholar] [CrossRef]
- Banfield, E.C.; Liu, Y.; Davis, J.S.; Chang, S.; Frazier-Wood, A.C. Poor adherence to US dietary guidelines for children and adolescents in the National Health and Nutrition Examination Survey population. J. Acad. Nutr. Diet. 2016, 116, 21–27. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Misra, M.; Pacaud, D.; Petryk, A.; Collett-Solberg, P.F.; Kappy, M. Vitamin D deficiency in children and its management: Review of current knowledge and recommendations. Pediatrics 2008, 122, 398–417. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Deegan, H.; Bates, H.M.; McCarger, L.J. Assessment of iron status in adolescents: Dietary, biochemical and lifestyle determinants. J. Adolesc. Health 2005, 37, 75.e15–75.e21. [Google Scholar] [CrossRef]
- Hess, J.; Slavin, J. Snacking for a cause: Nutritional insufficiencies and excesses of U.S. children, a critical review of food consumption patterns and macronutrient and micronutrient intake of U.S. children. Nutrients 2014, 6, 4750–4759. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kaganov, B.; Caroli, M.; Mazur, A.; Singhal, A.; Vania, A. Suboptimal micronutrient intake among children in Europe. Nutrients 2015, 7, 3524–3535. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sahoo, K.; Sahoo, B.; Choudhury, A.K.; Sofi, N.Y.; Kumar, R.; Bhadoria, A.S. Childhood obesity: Causes and consequences. J. Fam. Med. Prim. Care 2015, 4, 187–192. [Google Scholar] [CrossRef]
- Andes, L.J.; Cheng, Y.J.; Rolka, D.B.; Gregg, E.W.; Imperatore, G. Prevalence of prediabetes among adolescents and young adults in the United States, 2005–2016. JAMA Pediatr. 2020, 174, e194498. [Google Scholar] [CrossRef] [PubMed]
- O’Neil, A.; Quirk, S.E.; Housden, S.; Brennan, S.L.; Williams, L.J.; Pasco, J.A.; Berk, M.; Jacka, F.N. Relationship between diet and mental health in children and adolescents: A systematic review. Am. J. Public Health 2014, 104, e31–e42. [Google Scholar] [CrossRef] [PubMed]
- Hiza, H.A.B.; Casavale, K.O.; Guenther, P.M.; Davis, C.A. Diet quality of Americans differs by age, sex, race/ethnicity, income, and education level. J. Acad. Nutr. Diet. 2013, 113, 297–306. [Google Scholar] [CrossRef] [PubMed]
- Thomson, J.L.; Tussing-Humphreys, L.M.; Goodman, M.H.; Landry, A.S. Diet quality in a nationally representative sample of American children by sociodemographic characteristics. Am. J. Clin. Nutr. 2019, 109, 127–138. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gu, X.; Tucker, K.L. Dietary quality of the US child and adolescent population: Trends from 1999 to 2012 and associations with the use of federal nutrition assistance programs. Am. J. Clin. Nutr. 2017, 105, 194–202. [Google Scholar] [CrossRef] [Green Version]
- Kirkpatrick, S.I.; Dodd, K.W.; Reedy, J.; Krebs-Smith, S.M. Income and race/ethnicity are associated with adherence to food-based dietary guidance among US adults and children. J. Acad. Nutr. Diet. 2012, 112, 624–635.e6. [Google Scholar] [CrossRef] [Green Version]
- Liu, J.; Rehm, C.D.; Onopa, J. Trends in diet quality among youth in the United States, 1999–2016. JAMA 2020, 23, 1161–1174. [Google Scholar] [CrossRef]
- Fox, M.K.; Gearan, E.; Cabili, C.; Dotter, D.; Niland, K.; Washburn, L.; Paxton, N.; Olsho, L.; LeClair, L.; Tran, V. School Nutrition and Meal Cost Study, Final Report Volume 4: Student Participation, Satisfaction, Plate Waste, and Dietary Intakes; U.S. Department of Agriculture, Food and Nutrition Service, Office of Policy Support: Alexandria, VA, USA, 2019; Available online: https://fns-prod.azureedge.net/sites/default/files/resource-files/SNMCS-Volume4.pdf (accessed on 17 September 2020).
- 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. [Google Scholar] [CrossRef]
- U.S. Department of Agriculture, Agricultural Research Service. USDA Food and Nutrient Database for Dietary Studies 2011-2012. 2014. Available online: https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/fndds/ (accessed on 8 June 2020).
- U.S. Department of Agriculture, Agricultural Research Service. Food Patterns Equivalents Database 2011–2012. 2014. Available online: https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/fped-overview/ (accessed on 8 June 2020).
- Gordon, A.; Fox, M.K.; Clark, M.; Nogales, R.; Condon, E.; Gleason, P.; Sarin, A. School Nutrition Dietary Assessment Study-III, final report volume II: Student participation and dietary intakes; U.S. Department of Agriculture, Food and Nutrition Service: Alexandria, VA, USA, November 2007; Available online: https://fns-prod.azureedge.net/sites/default/files/SNDAIII-Vol2.pdf (accessed on 4 October 2020).
- Zeidman, E.; Beyler, N.; Gearan, E.; Morrison, N.; Niland, K.; Washburn, L.; Carlson, B. School Nutrition and Meal Cost Study: Study Design, Sampling, and Data Collection; U.S. Department of Agriculture, Food and Nutrition Service, Office of Policy Support: Alexandria, VA, USA, 2019; Available online: https://fns-prod.azureedge.net/sites/default/files/resource-files/SNMCS-Methods-Report.pdf (accessed on 17 September 2020).
- Guenther, P.M.; Casavale, K.O.; Reedy, J.; Kirkpatrick, S.I.; Hiza, H.A.B.; Kuczynski, K.J.; Kahle, L.L.; Krebs-Smith, S.M. Update of the Healthy Eating Index: HEI-2010. J. Acad. Nutr. Diet. 2013, 113, 569–580. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- U.S. Department of Agriculture, Center for Nutrition Policy and Promotion. Healthy Eating Index-2010. CNPP Fact Sheet No. 2; USDA: Washington, DC, USA, 2013. Available online: https://www.fns.usda.gov/cnpp/healthy-eating-index-hei-reports (accessed on 17 September 2020).
- SAS Code, Population Ratio Method. Epidemiology and Genomics Research Program, National Cancer Institute. Available online: epi.grants.cancer.gov/hei/sas-code.html (accessed on 8 June 2020).
- Freedman, L.S.; Guenther, P.M.; Krebs-Smith, S.M.; Kott, P.S. A population’s mean Healthy Eating Index-2005 scores are best estimated by the score of the population ratio when one 24-hour recall is available. J. Nutr. 2008, 138, 1725–1729. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Imai, K.; Ratkovic, M. Covariate balancing propensity score. J. R. Stat. Soc. B 2014, 76, 243–263. [Google Scholar] [CrossRef]
- DuGoff, E.H.; Schuler, M.; Stuart, E.A. Generalizing observational study results: Applying propensity score methods to complex surveys. Health Serv. Res. 2014, 49, 284–303. [Google Scholar] [CrossRef]
- Forrestal, S.; Potamites, E.; Guthrie, J.; Paxton, N. Associations among food security, school meal participation, and students’ diet quality in the first School Nutrition and Meal Cost Study. Nutrients. (under review).
- Cullen, K.W.; Chen, T.A. The contribution of the USDA school breakfast and lunch program meals to student daily dietary intake. Prev. Med. Rep. 2016, 28, 82–85. [Google Scholar] [CrossRef]
- Aune, D.; Norat, T.; Romundstad, P.; Vatten, L.J. Whole grain and refined grain consumption and the risk of type 2 diabetes: A systematic review and dose-response meta-analysis of cohort studies. Eur. J. Epidemiol. 2013, 28, 845–858. [Google Scholar] [CrossRef]
- Aune, D.; Keum, N.; Giovannucci, E.; Fadnes, L.T.; Boffetta, P.; Greenwood, D.C.; Tonstad, S.; Vatten, L.J.; Riboli, E.; Norat, T. Whole grain consumption and risk of cardiovascular disease, cancer, and all cause and cause specific mortality: Systematic review and dose-response meta-analysis of prospective studies. Br. Med. J. 2016, 353, i2716. [Google Scholar] [CrossRef] [Green Version]
- Ye, E.Q.; Chacko, S.A.; Chou, E.L.; Kugizaki, M.; Liu, S. Greater whole-grain intake is associated with lower risk of type 2 diabetes, cardiovascular disease, and weight gain. J. Nutr. 2012, 142, 1304–1313. [Google Scholar] [CrossRef]
- Kouvelioti, R.; Josse, A.R.; Klentrou, P. Effects of dairy consumption on body composition and bone properties in youth: A systematic review. Curr. Dev. Nutr. 2017, 1, e001214. [Google Scholar] [CrossRef] [Green Version]
- Kant, A.K. Reported consumption of low-nutrient-density foods by American children and adolescents: Nutritional and health correlates, NHANES III, 1988 to 1994. Arch. Pediatr. Adolesc. Med. 2003, 157, 789–796. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Coleman-Jensen, A.; Rabbitt, M.P.; Gregory, C.A.; Singh, A. Household Food Security in the United States in 2017; U.S. Department of Agriculture, Economic Research Service: Washington, DC, USA, 2018. [Google Scholar]
- Ralston, K.; Treen, K.; Coleman-Jensen, A.; Guthrie, J. Children’s Food Security and USDA Child Nutrition Programs; U.S. Department of Agriculture, Economic Research Service: Washington, DC, USA, 2017. [Google Scholar]
- Boeing, H.; Bechthold, A.; Bub, A.; Ellinger, S.; Haller, D.; Kroke, A.; Leschik-Bonnet, E.; Mueller, M.J.; Oberritter, H.; Schulze, M.; et al. Critical review: Vegetables and fruit in the prevention of chronic diseases. Eur. J. Nutr. 2012, 51, 637–663. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- He, F.J.; MacGregor, G.A. Importance of Salt in Determining Blood Pressure in Children. Hypertension 2006, 48, 861–869. [Google Scholar] [CrossRef] [PubMed]
- Rosner, B.; Cook, N.R.; Daniels, S.; Falkner, B. Childhood blood pressure trends and risk factors for high blood pressure: The NHANES experience 1988–2008. Hypertension 2013, 62, 247–254. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Maximum Score | Standard for Maximum Score a | Standard for Minimum Score of Zero a | |
---|---|---|---|
Adequacy components: Higher scores reflect higher concentrations in students’ diets | |||
Total fruit b | 5 | ≥0.8 c equivalent per 1000 kcal | No fruit |
Whole fruit c | 5 | ≥0.4 c equivalent per 1000 kcal | No whole fruit |
Total vegetables d | 5 | ≥1.1 c equivalent per 1000 kcal | No vegetables |
Greens and beans d | 5 | ≥0.2 c equivalent per 1000 kcal | No dark green vegetables, beans, or peas |
Whole grains | 10 | ≥1.5 oz equivalent per 1000 kcal | No whole grains |
Dairy e | 10 | ≥1.3 c equivalent per 1000 kcal | No dairy |
Total protein foods f | 5 | ≥2.5 oz equivalent per 1000 kcal | No protein foods |
Seafood and plant proteins f,g | 5 | ≥0.8 oz equivalent per 1000 kcal | No seafood or plant proteins |
Fatty acids h | 10 | (PUFAs i + MUFAs j)/saturated fatty acids ≥ 2.5 | (PUFAs + MUFAs)/saturated fatty acids ≤ 1.2 |
Moderation components: Higher scores reflect lower concentrations in students’ diets | |||
Refined grains | 10 | ≤1.8 oz equivalent per 1000 kcal | ≥4.3 oz equivalent per 1000 kcal |
Sodium | 10 | ≤1.1 g per 1000 kcal | ≥2.0 g per 1000 kcal |
Empty calories k | 20 | ≤19% of energy | ≥50% of energy |
Total Score | 100 |
Characteristics | All Students (n = 2097) | NSLP Participants (n = 1254) | Nonparticipants (n = 843) |
---|---|---|---|
Gender (%) | |||
Male | 50.4 | 53.2 | 46.7 * |
Age (mean years) | 12.1 | 11.2 | 13.1 * |
School level (%) | |||
Elementary | 45.4 | 56.8 | 30.0 * |
Middle | 20.7 | 19.3 | 22.6 |
High | 33.9 | 23.9 | 47.3 * |
Race/ethnicity (%) | |||
White, non-Hispanic | 48.9 | 42 | 58.1 * |
Hispanic | 24.4 | 30.3 | 16.4 * |
Black, non-Hispanic | 12.4 | 15.7 | 8.0 * |
Multiracial/other | 8 | 7.1 | 9.2 |
Missing | 6.3 | 4.8 | 8.2 * |
Household income as percentage of federal poverty level (%) | |||
Eligible for free or reduced-price meals (≤185%) | 42.5 | 59.7 | 19.5 * |
Not eligible for free or reduced-price meals (>185%) | 55.8 | 38.9 | 78.3 * |
Missing | 1.8 | 1.6 | 2.1 |
Lower-Income Students | Higher-Income Students | |||||
---|---|---|---|---|---|---|
NSLP Participants (n = 757) | Nonparticipants (n = 210) | Difference (NSLP Participants − Nonparticipants) | NSLP Participants (n = 475) | Nonparticipants (n = 614) | Difference (NSLP Participants − Nonparticipants) | |
Adequacy components: Higher scores reflect higher concentrations in students’ lunch intakes | ||||||
Total fruit | 100.0 | 82.6 | 17.4 | 98.5 | 99.6 | −1.1 |
Whole fruit | 100.0 | 98.4 | 1.6 | 100.0 | 100.0 | 0.0 |
Total vegetables | 55.1 | 33.5 | 21.6 * | 48.9 | 35.8 | 13.1 * |
Greens and beans | 35.3 | 26.9 | 8.5 | 12.1 | 26.5 | −14.4 |
Whole grains | 100.0 | 61.6 | 38.4 * | 100.0 | 61.0 | 39.0 * |
Dairy | 100.0 | 69.5 | 30.5 * | 100.0 | 64.5 | 35.5 * |
Total protein foods | 99.0 | 99.5 | −0.5 | 98.1 | 99.9 | −1.9 |
Seafood and plant proteins | 39.6 | 94.9 | −55.3 * | 77.4 | 86.7 | −9.3 |
Fatty acids | 60.3 | 72.0 | −11.7 | 69.2 | 58.4 | 10.8 |
Moderation components: Higher scores reflect lower concentrations in students’ lunch intakes | ||||||
Refined grains | 91.3 | 48.8 | 42.5 * | 82.7 | 26.9 | 55.7 * |
Sodium | 40.2 | 54.5 | −14.3 | 45.7 | 27.7 | 17.9 * |
Empty calories | 95.7 | 77.9 | 17.8 * | 94.9 | 79.2 | 15.7 * |
Total HEI score | 79.8 | 68.0 | 11.8 * | 80.5 | 62.1 | 18.4 * |
Lower-Income Students | Higher-Income Students | |||||
---|---|---|---|---|---|---|
NSLP Participants (n = 757) | Nonparticipants (n = 210) | Difference (NSLP Participants − Nonparticipants) | NSLP Participants (n = 475) | Nonparticipants (n = 614) | Difference (NSLP Participants − Nonparticipants) | |
Adequacy components: Higher scores reflect higher concentrations in students’ 24 h intakes | ||||||
Total fruit | 95.9 | 77.2 | 18.7 | 80.4 | 93.8 | −13.4 |
Whole fruit | 100 | 93.7 | 6.3 | 99.5 | 100 | −0.5 |
Total vegetables | 46.2 | 46.2 | 0 | 42.2 | 41.4 | 0.8 |
Greens and beans | 17.1 | 30.7 | −13.6 | 13.1 | 33.1 | −20.0 * |
Whole grains | 57.4 | 41.4 | 16.0 * | 58 | 43.4 | 14.5 * |
Dairy | 98.5 | 79.6 | 18.9 * | 97.7 | 82 | 15.7 * |
Total protein foods | 96.1 | 99.1 | −3.0 | 97.7 | 98.7 | −1.1 |
Seafood and plant proteins | 59.6 | 81.5 | −21.9 | 71 | 73.1 | −2.1 |
Fatty acids | 41.2 | 43.3 | −2.0 | 37.3 | 38.6 | −1.2 |
Moderation components: Higher scores reflect lower concentrations in students’ 24 h intakes | ||||||
Refined grains | 57.9 | 53.1 | 4.7 | 62.1 | 41.9 | 20.2 * |
Sodium | 45.4 | 50.7 | −5.3 | 48.8 | 38.8 | 10.0 * |
Empty calories | 72.5 | 65.9 | 6.6 | 72 | 72.3 | −0.3 |
Total HEI score | 65.3 | 61.4 | 3.9 | 65 | 60.9 | 4.0 * |
Non-Hispanic White Students | Non-Hispanic Black Students | Hispanic Students | |||||||
---|---|---|---|---|---|---|---|---|---|
NSLP Participants (n = 485) | Nonparticipants (n = 442) | Difference (NSLP Participants − Nonparticipants) | NSLP Participants (n = 183) | Nonparticipants (n = 74) | Difference (NSLP Participants − Nonparticipants) | NSLP Participants (n = 376) | Nonparticipants (n = 161) | Difference (NSLP Participants −Nonparticipants) | |
Adequacy components: Higher scores reflect higher concentrations in students’ lunch intakes | |||||||||
Total fruit | 99.5 | 92.2 | 7.4 | 99.7 | 92.6 | 7.1 | 100.0 | 96.3 | 3.7 |
Whole fruit | 100.0 | 100.0 | 0.0 | 100.0 | 99.9 | 0.1 | 100.0 | 99.9 | 0.1 |
Total vegetables | 56.3 | 30.1 | 26.3 * | 34.9 | 27.2 | 7.7 | 51.4 | 50.8 | 0.6 |
Greens and beans | 29.0 | 16.3 | 12.7 | 19.1 | 19.5 | −0.4 | 12.9 | 37.3 | −24.4 |
Whole grains | 99.9 | 53.5 | 46.4 * | 99.2 | 68.2 | 31.0 * | 100.0 | 74.4 | 25.6 |
Dairy | 100.0 | 65.0 | 35.0 * | 99.9 | 60.6 | 39.2 * | 100.0 | 75.1 | 24.9 * |
Total protein foods | 97.2 | 99.3 | −2.1 | 99.2 | 99.7 | −0.4 | 91.2 | 96.9 | −5.7 |
Seafood and plant proteins | 67.3 | 99.6 | −32.3 * | 50.6 | 34.7 | 15.8 | 52.2 | 63.7 | −11.4 |
Fatty acids | 62.1 | 67.1 | −5.0 | 68.2 | 46.6 | 21.6 | 67.7 | 55.0 | 12.7 |
Moderation components: Higher scores reflect lower concentrations in students’ lunch intakes | |||||||||
Refined grains | 86.8 | 22.1 | 64.7 * | 94.8 | 37.8 | 57.0 * | 88.0 | 60.4 | 27.5 * |
Sodium | 45.4 | 38.2 | 7.1 | 35.3 | 14.5 | 20.8 | 42.7 | 43.5 | −0.8 |
Empty calories | 93.2 | 76.5 | 16.7 * | 95.0 | 72.1 | 22.8 * | 98.5 | 76.5 | 22.0 * |
Total HEI score | 80.5 | 61.8 | 18.8 * | 78.9 | 55.9 | 23.0 * | 79.9 | 68.4 | 11.5 |
Non-Hispanic White Students | Non-Hispanic Black Students | Hispanic Students | |||||||
---|---|---|---|---|---|---|---|---|---|
NSLP Participants (n = 485) | Nonparticipants (n = 442) | Difference (NSLP Participants − Nonparticipants) | NSLP Participants (n = 183) | Nonparticipants (n = 74) | Difference (NSLP Participants − Nonparticipants) | NSLP Participants (n = 376) | Nonparticipants (n = 161) | Difference (NSLP Participants − Nonparticipants) | |
Adequacy components: Higher scores reflect higher concentrations in students’ 24 h intakes | |||||||||
Total fruit | 78.9 | 76.8 | 2.1 | 97.1 | 81.0 | 16.1 | 98.7 | 92.3 | 6.4 |
Whole fruit | 99.3 | 99.2 | 0.1 | 95.5 | 86.6 | 8.9 | 100.0 | 99.7 | 0.3 |
Total vegetables | 45.9 | 36.1 | 9.7 * | 40.7 | 47.7 | −7.0 | 44.9 | 55.7 | −10.8 |
Greens and beans | 13.9 | 22.8 | −8.9 | 18.3 | 73.6 | −55.2 * | 11.5 | 23.2 | −11.7 |
Whole grains | 54.7 | 38.5 | 16.2 * | 49.1 | 51.6 | −2.5 | 68.5 | 39.1 | 29.4 * |
Dairy | 99.3 | 84.7 | 14.5 * | 79.5 | 58.0 | 21.6 * | 100.0 | 84.2 | 15.7 * |
Total protein foods | 94.7 | 97.9 | −3.2 | 99.8 | 93.5 | 6.2 | 93.3 | 94.5 | −1.3 |
Seafood and plant proteins | 67.2 | 82.8 | −15.6 | 49.5 | 27.6 | 21.9 | 69.3 | 84.9 | −15.6 |
Fatty acids | 35.9 | 40.6 | −4.7 | 51.6 | 44.8 | 6.8 | 40.3 | 35.4 | 4.9 |
Moderation components: Higher scores reflect lower concentrations in students’ 24 h intakes | |||||||||
Refined grains | 63.0 | 42.4 | 20.6 * | 65.9 | 47.3 | 18.7 | 53.7 | 46.9 | 6.9 |
Sodium | 50.8 | 48.0 | 2.8 | 46.0 | 38.7 | 7.3 | 42.7 | 44.9 | −2.2 |
Empty calories | 68.2 | 66.8 | 1.3 | 70.4 | 62.8 | 7.6 | 79.9 | 72.9 | 7.0 |
Total HEI score | 64.0 | 59.6 | 4.4 * | 63.3 | 57.1 | 6.3 | 67.4 | 62.1 | 5.2 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Gearan, E.C.; Monzella, K.; Jennings, L.; Fox, M.K. Differences in Diet Quality between School Lunch Participants and Nonparticipants in the United States by Income and Race. Nutrients 2020, 12, 3891. https://doi.org/10.3390/nu12123891
Gearan EC, Monzella K, Jennings L, Fox MK. Differences in Diet Quality between School Lunch Participants and Nonparticipants in the United States by Income and Race. Nutrients. 2020; 12(12):3891. https://doi.org/10.3390/nu12123891
Chicago/Turabian StyleGearan, Elizabeth C., Kelley Monzella, Leah Jennings, and Mary Kay Fox. 2020. "Differences in Diet Quality between School Lunch Participants and Nonparticipants in the United States by Income and Race" Nutrients 12, no. 12: 3891. https://doi.org/10.3390/nu12123891