Food Waste in the National School Lunch Program 1978-2015: A Systematic Review RESEARCH 1792 JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS ª 2 artic licen Review Food Waste in the National School Lunch Program 1978-2015: A Systematic Review Carmen Byker Shanks, PhD, RDN; Jinan Banna, PhD, RD; Elena L. Serrano, PhD ARTICLE INFORMATION Article history: Submitted 19 April 2016 Accepted 6 June 2017 Available online 11 August 2017 Keywords: Food waste Plate waste School lunch Consumption Diet 2212-2672/Copyright ª 2017 by the Academy of Nutrition and Dietetics. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/). http://dx.doi.org/10.1016/j.jand.2017.06.008 ABSTRACT Background Food waste studies have been used for more than 40 years to assess nutrient intake, dietary quality, menu performance, food acceptability, cost, and effec- tiveness of nutrition education in the National School Lunch Program (NSLP). Objective Describe methods used to measure food waste and respective results in the NSLP across time. Methods A systematic review using PubMed, Science Direct, Informaworld, and Insti- tute of Scientific Information Web of Knowledge was conducted using the following search terms: waste, school lunch, plate waste, food waste, kitchen, half method, quarter method, weight, and photography. Studies published through June 2015 were included. The systematic review followed preferred reporting items for systematic reviews and meta-analyses recommendations. Results The final review included 53 articles. Food waste methodologies included in- person visual estimation (n¼11), digital photography (n¼11), direct weighing (n¼23), and a combination of in-person visual estimation, digital photography, and/or direct weighing (n¼8). A majority of studies used a preepost intervention or cross-sectional design. Fruits and vegetables were the most researched dietary component on the lunch tray and yielded the greatest amount of waste across studies. Conclusions Food waste is commonly assessed in the NSLP, but the methods are diverse and reporting metrics are variable. Future research should focus on establishing more uniform metrics to measure and report on food waste in the NSLP. Consistent food waste measurement methods will allow for better comparisons between studies. Such measures may facilitate better decision making about NSLP practices, programs, and policies that influence student consumption patterns across settings and interventions. J Acad Nutr Diet. 2017;117:1792-1807. HE NATIONAL SCHOOL LUNCH PROGRAM (NSLP) national nutrition standards, food portions are standardized, Tserves more than 31 million children in more than100,000 schools each school day.1,2 The NSLP aims tooffer balanced meals to schoolchildren, provided at free or reduced costs for low-income populations and sub- sidized by the federal government.2 The Healthy Hunger Free Kids Act of 2010 required updated nutrition standards for schools based on the most recent Dietary Guidelines for Americans and Institute of Medicine recommendations.3 The requirements consist of five meal components: fruits, vege- tables, whole grains, low-fat dairy, protein, and sodium content in a specified range. The serving size and caloric limits for each meal for children enrolled in grades kinder- garten through 12 are based on age group. A lunch provided to a student must consist of three out of the five components offered to be considered a reimbursable meal, with one of the components being a fruit or vegetable.3 The NSLP setting provides an important opportunity for researchers and practitioners to study how much and what types of nutrients children consume and waste. The lunch- room is experimental in nature because menus are designed (and can be changed) by local school food authorities per and many students dine in the cafeteria every school day. Study results with high external validity have far reaching implications for the NSLP nationwide. Since the 1970s,4 researchers have used plate and food waste studies to observe nutrient intake, dietary quality, menu performance, food acceptability, cost, and effectiveness of nutrition education in the NSLP. Plate and food waste are used synonymously throughout most of the school foods research literature and will herein be referred to as food waste. Food waste studies measure the uneaten edible portion of food served to an individual.5 Food waste meth- odology can measure several important food and nutrition outcomes,6 including the amount of a specific nutrient available, consumed, and wasted, the types of food groups most likely being eaten or thrown away, compliance with nutrition practices and policies, the effect of nutrition edu- cation on food choice and consumption, acceptability of menu items, and the influence of waste on an institution’s budget and on natural resources. The resulting data can be used to drive important changes in practices, programs, and policies in a school lunch program. In addition, in recent 017 by the Academy of Nutrition and Dietetics. This is an open access le under the CC BY-NC-ND license (http://creativecommons.org/ ses/by-nc-nd/4.0/). http://creativecommons.org/licenses/by-nc-nd/4.0/ http://creativecommons.org/licenses/by-nc-nd/4.0/ http://dx.doi.org/10.1016/j.jand.2017.06.008 http://crossmark.crossref.org/dialog/?doi=10.1016/j.jand.2017.06.008&domain=pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ http://creativecommons.org/licenses/by-nc-nd/4.0/ Records identified through database search (PubMed, Science Direct, Informaworld, ISI Web of Knowledge) (n=10,892) Remaining records after 1,562 duplicates removed across and within databases (n=9,330) Remaining records after screening by title (n=1,496) Studies included in synthesis (n=53) Remaining records after screening by abstract (n=66) Excluded based upon irrelevanta title (n=7,834) Excluded based upon irrelevanta abstract (n=1,430) Excluded based upon irrelevanta full- text (n=13) Remaining records after screening full-text (n=53) None excluded based upon quality Id en tif ic at io n Sc re en in g E lig ib ili ty In cl ud ed Figure. Preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2009 flow diagram for selecting studies to include in the systematic review of food waste in the National School Lunch Program across time. Terms used in this search included a combination of the following: waste, school lunch, plate waste, food waste, kitchen waste, half method, quarter method, weight, and photography. aRelevance determined by inclusion and exclusion criteria. Inclusion criteria for articles were peer reviewed, English language, and conducted in US National School Lunch Program (NSLP). Exclusion criteria for articles were no focus on the US NSLP, food waste not used as a measurement tool, review of literature, or a conference meeting abstract. ISI¼Institute for Scientific Information. RESEARCH years, global and national food waste campaigns have further amplified the importance of reducing food waste.7,8 The purpose of this systematic review was to provide a summary of the literature describing the measurement and results of food waste studies in the NSLP across time. METHODS Search Strategy Articles included in this systematic literature review were extracted from PubMed, Science Direct, Informaworld, and ISI Web of Knowledge using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) format pub- lished through June 2015.9 When testing key words, these databases yielded relevant articles. The authors tested poten- tial key words related to NSLP and food waste through mock searches to ensure that the final list of terms captured relevant articles that met inclusion and exclusion criteria. Keywords November 2017 Volume 117 Number 11 JO entered with Boolean operators included waste, school lunch, plate waste, food waste, kitchen, half method, quarter method, weight, and photography. The following are two search strate- gies used in Science Direct: waste OR “food waste” OR “plate waste” OR “kitchen waste” AND school AND lunch; waste OR “food waste” OR “plate waste” AND school AND lunch AND “quarter method” OR “half method” OR weight OR photog- raphy. No limits or filters were used in the search. The search strategy was modified for individual databases. Study Selection The main criterion for inclusion was the explicit use and description of a method to measure food waste in the NSLP. Articles included were peer-reviewed, written in the English language, and based on studies conducted in the United States covering the NSLP. Journal articles that collected pri- mary data were considered. Articles were excluded in cases URNAL OF THE ACADEMY OF NUTRITION AND DIETETICS 1793 Table 1. In-person visual estimation through observation for food waste studies conducted in the National School Lunch Program Reference Green and colleagues, 198711 Reger and colleagues, 199612 Auld and colleagues, 199913 Blom-Hoffman and colleagues, 200414 Just and colleagues, 201315 Wansink and colleagues, 201316 Just and colleagues, 201417a Cullen and colleagues, 201518 Cullen and colleagues, 201519a Price and colleagues, 201520 Wansink and colleagues, 201521 Study design Ibc CSd Ie RCTf Ie RCT Ie RCT Ie Ie Ie Specific data collection method 1/2 gh 6i Ej 6k 1/2 l 1/4 mn 1/4 n 1/4 n 1/4 n 1/2 l 1/4 n Type and no. of schools Elementary 1 1 4 1 18 8 8 7 Middle 6 4 High 1 1 Grade level 3 3-6 2-4 Kindergarten-1 NRo NR NR Kindergarten-8 NR 1-6 NR Average percent wasted for dietary components measuredp Grains/bread 37 27 34 Vegetables 12 58 >q > 19 48 32 > 19 Fruits/fruit juice 31 39 > 41r 15 27 23 > Meat/meat alternate 1 18 Milk 50 17 18 27 Other 33r 62s 11s 95t 64t Days of food waste data collectionu 70 20 NR 3 NR 6 3 NR NR 14 3 No. of waste observationsv 123 240 502 NR 47,414 640 3,330 1,576 1,045 22,939 554 (continued on next page) R E S E A R C H 1 7 9 4 JO U R N A L O F T H E A C A D E M Y O F N U T R IT IO N A N D D IE T E T IC S N o vem b er 2017 Vo lu m e 117 N u m b er 11 Table 1. In-person visual estimation through observation for food waste studies conducted in the National School Lunch Program (continued) Reference Green and colleagues, 198711 Reger and colleagues, 199612 Auld and colleagues, 199913 Blom-Hoffman and colleagues, 200414 Just and colleagues, 201315 Wansink and colleagues, 201316 Just and colleagues, 201417a Cullen and colleagues, 201518 Cullen and colleagues, 201519a Price and colleagues, 201520 Wansink and colleagues, 201521 Effective public health practice project quality rating10 Strong Weak Strong Strong Moderate Strong Moderate Strong Strong Strong Strong aData were collected to assess food waste after new school lunch meal patterns were implemented beginning 2012. bI¼intervention. cPre-post-follow-up intervention. dCS¼cross-sectional. ePre-post intervention. fRCT¼randomized controlled trial. g1/2¼half waste method. hA þ sign was recorded for more than half of food wasted and e sign was recorded for less than half of food wasted. i6¼six-point scale scored as 1¼ate all of food to 6¼ate none of food. jE¼estimation. kMeasured with 6-point scale: 5¼91% to 100%; 4¼76% to 90%; 3¼51% to 75%; 2¼26% to 50%; 1¼11% to 25%; 0¼0% to 10%. lMeasured in increments of 1/2 a serving. m1/4¼quarter waste method. nMeasured in increments of none, 1/4 , 1/2, 3/4 , or all wasted. oNR¼not reported with specificity. pIn some cases, the average percent waste within a dietary component was reported within the cited article. In other cases, this study’s authors calculated average percent wasted within a dietary component when research design collected waste across multiple intervention periods. When percent consumed was reported (instead of percent waste), this study’s authors calculated average percent waste by subtracting the percent consumed from 100% and, if necessary, averaged across multiple intervention periods or groups. q>¼study indicated dietary component measured but not average percent wasted within dietary component. rSpecific macro- and/or micronutrients measured in whole meal. sMeasured waste of a mixed entrée. tMeasured waste of legumes. uData calculated as number of days reported for study multiplied by number of schools involved in food waste collections. vData reported according to study as individual food items or entire student tray. R E S E A R C H N o vem b er 2017 Vo lu m e 117 N u m b er 11 JO U R N A L O F T H E A C A D E M Y O F N U T R IT IO N A N D D IE T E T IC S 1 7 9 5 Table 2. Visual estimation through digital photography for food waste studies conducted in the National School Lunch Program Reference Marlette and colleagues, 200522 Martin and colleagues, 200623 Martin and colleagues, 201024 Smith and colleagues, 201325 Williamson and colleagues, 201326 Bontrager and colleagues, 201427 Bontrager and colleagues, 201428 Hubbard and colleagues, 201429a Alaimo and colleagues, 201530 Bontrager and colleagues, 201531a Monlezun and colleagues, 201532a Study design CSb CSc CS CS RCTd Ief CS If If If CS Specific data collection methode RPg RP RP PIh PI PI PI PI PI PI PI Type and no. of schools Elementary 33 3 21 8 9 6 11 1 Middle 3 1 2 1 Other 1 Grade level 6 6 4-6 1-8 4-6 3-5 3-5 NRi 3-5 3-5 Kindergarten-8 Average percent wasted for dietary components measuredj Grains/bread 16 >k 27 32 > > Vegetables 32 > 37l 32 > > > > > > Fruits/fruit juice 38 > 40 > > > > > > Meat/meat alternate 21 > > Milk 15 > 30 27 > > > Other 32m >mn >n 22m >n > >o >m >mn Days of food waste data collectionp 24 5 3 23 3 64 32 10 12 NR 5 No. of waste observationsq 743 215 2,049 899 NRf 4,451 2,292 644 1,192 7,117 1,750 (continued on next page) R E S E A R C H 1 7 9 6 JO U R N A L O F T H E A C A D E M Y O F N U T R IT IO N A N D D IE T E T IC S N o vem b er 2017 Vo lu m e 117 N u m b er 11 Table 2. Visual estimation through digital photography for food waste studies conducted in the National School Lunch Program (continued) Reference Marlette and colleagues, 200522 Martin and colleagues, 200623 Martin and colleagues, 201024 Smith and colleagues, 201325 Williamson and colleagues, 201326 Bontrager and colleagues, 201427 Bontrager and colleagues, 201428 Hubbard and colleagues, 201429a Alaimo and colleagues, 201530 Bontrager and colleagues, 201531a Monlezun and colleagues, 201532a Effective public health project practice quality rating10 Weak Weak Weak Weak Strong Weak Weak Moderate Moderate Moderate Weak aData were collected to assess food waste after new school lunch meal patterns were implemented, beginning 2012. bCS¼cross-sectional. cCross-sectional study used for validation purposes. dRCT¼randomized controlled trial. eI¼intervention. fPre-post intervention. gRP¼raw percent, meaning percent of food selection and plate waste in photograph compared with reference photographed and weighed portion. hPI¼percent increments, meaning percent increments (eg, in 10% or 25% increments) of food selection and plate waste in photograph compared with reference photographed and weighed portion. iNR¼not reported with specificity. jData calculated as number of days reported for study multiplied by number of schools involved in food waste collections. k>¼study indicated dietary component measured but not average percent wasted within dietary component. lFruits and vegetables combined. mMeasured waste of a mixed entrée. nSpecific macro- and/or micronutrients measured in whole meal. oMeasured waste of legumes. pIn some cases, the average percent waste within a dietary component was reported within the cited article. In other cases, this study’s authors calculated average percent wasted within a dietary component when research design collected waste across multiple intervention periods. When percent consumed was reported (instead of percentage waste), this study’s authors calculated average percent waste by subtracting the percent consumed from 100% and, if necessary, averaged across multiple intervention periods or groups. qData reported according to study as individual food items or entire student tray. R E S E A R C H N o vem b er 2017 Vo lu m e 117 N u m b er 11 JO U R N A L O F T H E A C A D E M Y O F N U T R IT IO N A N D D IE T E T IC S 1 7 9 7 Table 3. Direct weighing for food waste studies in the National School Lunch Programa Reference Jansen and colleagues, 197833 Davidson and colleagues, 197934 Comstock and colleagues, 198235 Getlinger and colleagues, 199636 Whatley and colleagues, 199637 Adams and colleagues, 200538 Toma and colleagues, 200939 Hoffman and colleagues, 201040 Lazor and colleagues, 201041 Chu and colleagues, 201142 Hoffman and colleagues, 201143 Study design Qb CSc CS Ide If CS Ie If CS CS Lg Specific data collection method DWjk DWl DWk DWk DWl DWm DWk DWk DWk DWk DWk Type and no. of schools Elementary 29 23 11 1 2 4 1 4 12 4 Middle 5 3 High 29 2 Grade level 5 and 10 1-3 1-5 or 6 1-3 3-5 1-5 Kindergarten-6 Kindergarten-1 NRr NR Kindergarten-1 Average percent wasted for dietary components measureds Grains/bread 21 >t 18 > 35 Vegetables 51 > 16 > > > Fruits/fruit juice 30 > 12 > > > Meat/meat alternate 18 > 18 > Milk 9 > 82 Other 32u >uv > 2 >v >u >v Days of food waste data collectionw 10 NR 33 8 76 4 7 36 NR NR 60 No. of waste observationsx 130,000 230 13,749 NR 560 294 NR 1,414 1,933 NR 1,060 Effective public health practice project quality rating10 Weak Weak Weak Moderate Moderate Weak Moderate Moderate Weak Weak Strong aData were collected to assess food waste after new school lunch meal patterns implemented beginning 2012. bQ¼quasiexperimental. cCS¼cross-sectional. dI¼intervention. ePre-post intervention. fPre-post-follow-up intervention. gL¼longitudnal. hMM¼mixed methods. iRCT¼randomized controlled trial. jDW¼direct weighing. kDifference weight of plate waste for each food minus weight of average selected serving. lPercent plate waste calculated by dividing the weight of edible food waste by the mean serving weight. mDifference weight of plate waste for each food minus pre consumption selections for all students’ plates. nWeight of fluid milk remaining was determined using the full weight and empty container weight of the carton. oFruit and vegetable consumption was calculated by weighing all produce prepared and subtracting unserved and waste weights, divided by number of students. pWaste was sorted by hand and weighed on a digital scale. qAt least one study school was not identified as elementary or middle, but identified kindergarten through eighth grade or was not identified as middle or high, but identified as grades six through 12. rNR¼not reported with specificity. sIn some cases, the average percent waste within a dietary component was reported within the cited article. In other cases, this study’s authors calculated average percent wasted within a dietary component when research design collected waste across multiple intervention periods. When percent consumed was reported (instead of percentage waste), this study’s authors calculated average percent waste by subtracting the percent consumed from 100% and, if necessary, averaged across multiple intervention periods or groups. t>¼study indicated dietary component measured but not average percent wasted within dietary component. uMeasured waste of a mixed entrée. vSpecific macro- and/or micronutrients measured in whole meal. wData calculated as number of days reported for study multiplied by number of schools involved in food waste collections. xData reported according to study as individual food items or entire student tray. RESEARCH 1798 JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS November 2017 Volume 117 Number 11 Table 3. Direct weighing for food waste studies in the National School Lunch Programa (continued) Reference Cohen and colleagues, 201244 Yon and colleagues, 201245 Cohen and colleagues, 201346 Ramsay and colleagues, 201347 Byker and colleague, 20145 Cohen and colleaugues, 201448 Hunsberger and colleagues, 201449 Jones and colleagues, 201450 Jones and colleagues, 201451 Cohen and colleagues, 201552 Miller and colleagues, 201553 Wilkie and colleagues, 201554 CS MMh CS Q CS Ie MM Ie Ie RCTi Ie CS DWk DWn DWk DWk DWk DWk DWk DWo DWo DWk DWk DWp 9 1 1 4q 1 1q 1 7 1 1 4 4 7 2q NR 3-5 6-8 K Prekindergarten- Kindergarten 1-8 Kindergarten-2 Kindergarten-8 1-5 3-8 Kindergarten-5 Kindergarten-12 > > > 73 > 51 67 > > > 73 > > 47 > 33 43 > > > 36 > > 75 > 25 > 46 41 > 18u 19u 51u 20u >u 27u > 8 9 8 4 5 16 5 23 64 84 3 20 3,049 793 3,049 473 304 1,030 261 180 251 2,638 2,027 NR Moderate Weak Moderate Moderate Weak Strong Strong Moderate Moderate Strong Strong Weak RESEARCH November 2017 Volume 117 Number 11 JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS 1799 RESEARCH where they did not focus on the NSLP, were conducted outside of the United States, did not measure food waste, or presented a review of literature. Meeting abstracts were excluded due to limited information about methodology conducted. Cross-sectional, intervention, quasiexperimental, randomized controlled trial, and mixed-methods study de- signs and methods were considered. Data Extraction Two reviewers first evaluated articles by titles, abstracts, and key words. In cases where food waste and kindergarten through 12th-grade schools were discussed in the title of an article, abstract, or key words, the full article was reviewed to determine relevance. Titles and abstracts that met the in- clusion criteria were recorded for full text review. The refer- ences in each article included were reviewed to determine whether any other additional studies were relevant, although no additional articles were found that were not already captured in the search. The authors reviewed each article independently and met to determine inclusion or exclusion; disagreements were resolved via discussion. For each article included in the review, one coder collected and entered data into an extraction template. Information recorded included: first author and year published, purpose, study design and specific data collection method, school type, number of schools involved, location of school, number of students, free and reduced NSLP eligibility, race/ethnicity, grade level or age, dietary component measures, duration and frequency of the data collected, food waste results, other relevant findings to food waste, and whether conducted before or after implementation of the NSLP standards updated by the Healthy Hunger Free Kids Act of 2010. The categories for data extraction were determined based on factors that may inform a researcher’s decision to select a particular food waste measurement method. For example, it may be useful for re- searchers to understand the various ways results are reported when using a particular method (ie, waste of nutrients, spe- cific foods, or food groups). The data collected, along with the publication, were reviewed by at least two additional coders to ensure accuracy; all disagreements were resolved by dis- cussing inclusion and exclusion criteria to reach consensus. Quality Appraisal of Individual Studies Study quality was assessed using the Effective Public Health Policy Project (EPHPP) Quality Assessment Tool.10 The EPHPP Quality Assessment Tool provides researchers with criteria to evaluate studies on the basis of selection bias, study design, confounders, blinding, data collection methods, withdraws and dropouts, intervention integrity, and analysis. Each criteria is scored numerically according to provided guidelines by the EPHPP Quality Assessment Tool as strong (score¼1), moderate (score¼2), or weak (score¼3). Subsequently, the entire article is rated as strong (no weak ratings), moderate (one weak rating), or weak (two or more weak ratings). This study was exempt from institutional review board re- view because there was no interaction with human subjects. RESULTS A total of 10,892 articles were retrieved using the database search. After eliminating duplicates and articles that did not meet inclusion criteria based on title and abstract screening, 1800 JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS 66 articles remained for content review. After reviewing the full articles, 13 studies were excluded due to the following reasons: four were conducted outside of the United States; four did not involve the NSLP; three were in preschools; and two were conference abstracts, not full articles (see the Figure). The 53 studies included in this review used four major types of food waste measurement methodologies: in-person visual estimation (n¼11) (Table 1), digital photography (n¼11) (Table 2), direct weighing (n¼23) (Table 3), and a combination of in-person visual estimation, digital photog- raphy, and/or direct weighing (n¼8) (Table 4). With regard to study design and methods, most studies identified in- terventions with a preepost or preepost-follow-up design (n¼20) or cross-sectional (n¼23), two were quasiexper- imental, two were mixed methods, one study was longitu- dinal, and five were randomized controlled trials. Fourteen studies were rated as strong, 20 studies were rated as mod- erate, and 19 studies were rated as weak according to the EPHPP Quality Assessment Tool. Studies labeled as moderate were likely to have a weak rating for study design, whereas studies labeled as weak were likely to have weak ratings for selection bias or confounders and study design. See Tables 1 through 4 for quality assessment ratings. In-Person Visual Estimation of Food Waste through Observation In-person visual estimation through observation of food waste occurred in 11 studies (Table 1).11-21 Researchers con- ducted in-person visual estimation through observation by first viewing several serving sizes of school lunch foods of interest to understand the appearance of the average plated food component. Researchers then weighed several samples of the plated food item of interest to find the average serving weight in grams or ounces. Finally, student trays were collected and assessed for the amount of food wasted in validated increments. Increments included less or more than half wasted,11,15,20 quarters (eg, none, half, three-quarters, or all),16-19,21 or a 6-point scale (eg, 0¼0% to 10% and 5¼91% to 100%),12,14 or a percent estimation (eg, on a scale of 0% to 100%).13 In some studies, a computer program was used to estimate the grams or ounces and energy of food consumed from the in-person visual estimation through observation. One study focused on the total amount of food wasted.12 Other studies used food waste measurement as a proxy for the amount of foods students consumed. The research had a variety of aims, including to understand the influence of nutrition education11,13,14,21 or changes in nutrition re- quirements.18,19 In addition, studies examined the effects of lunchtime procedures or the food environment or infra- structure15,16,20 and food acceptability on consumption levels.17 Studies were concentrated in the West,15,20 North- east,14,16,17,21 and South,12,18,19 with two studies not reporting geographic location.11,13 Three studies examined schools with free and reduced lunch eligibility rates of more than 80%.12-14 By far, fruits and vegetables were the most frequently studied food groups.12-21 Nutrition education was minimally effective in decreasing the amount of food waste.11,13,14,21 Modifying lunchtime procedures or the food itself increased consumption of foods and decreased waste.15-17,20 New nutrition standards resulted in no significant differences in the percentage of fruits, vegetables, or whole grains November 2017 Volume 117 Number 11 RESEARCH consumed or wasted.19 Sex and age significantly influenced waste in Reger’s study.12 Visual Estimation of Food Waste through Digital Photography Visual estimation through digital photography was used in 11 studies (Table 2).22-32 Researchers conducted visual estima- tion of food waste through digital photography by photo- graphing either or both reference serving sizes of the food component of interest, or the student’s selected food pre- consumption. When taking photographs of the reference serving sizes, researchers generally calculated an average weight for the food component as well. Each student’s tray was then photographed at the tray return area (post- consumption). In reviewing the photographs, food con- sumption was estimated as a percentage of the reference serving size or student’s preconsumption selection. Food waste estimates were made as a raw percent22-24 or in in- crements of 10%,25,26,32 25%,27-31 or 0% to 10% to 25% to 50% to 100%.26,27 Computer applications were used to estimate the weight and energy of food consumed from the visual esti- mation through digital photography in studies using this method. The purposes of each study varied, with food waste mea- sures aimed at primarily understanding the amount of food waste22,31 and food consumption,25,27,28 modification of food environment or lunchtime procedures,26,29 instrument val- idity,23 compliance with nutrition recommendations,24 and nutrition education.30,32 Studies were conducted in the West,25,26 Midwest,27,28,30,31 Northeast,29 and South,22,24,32 although one did not report geographic location.23 Alaimo and colleagues30 and Monlezun and colleagues32 reported free and reduced rates near 100%, whereas several other studies did not report free and reduced rates. As in the studies using visual estimation techniques to measure waste, studies using digital photography also focused predominantly on fruits and vegetables. Several distinguished between forms of fruits and vegetables, such as cooked, raw, canned, and fresh.25,29,31 Two studies reported that waste of fruit and vegetables was the highest when compared with other dietary components.22,24 Three studies reported a decrease in waste of fruit and vegetables and other dietary components as a result of an intervention.25,27,29 Several studies expressed food waste in terms of calories rather than as a percentage of food wasted.26,28,32 Direct Weighing of Food Waste Direct weighing of food waste was used as the main research method in 23 studies (Table 3).5,33-54 The process for direct weighing of food waste generally includes to determine what is being served in the cafeteria on the day of the study, to determine which food(s) will be included in the study, to weigh random samples of the food(s) and calculate an average weight, to collect and weigh food waste from student trays, to calculate percent or grams or ounces consumed by subtracting the food waste collected in Step 4 from the average weight determined in Step 3 and multiplying by 100. Some research measured waste for all foods on the tray,5,33-36,44,46-49,53,54 whereas others focused on collecting waste data about specific foods or food compo- nents.37,39-43,45,50-52 Three additional studies measured the November 2017 Volume 117 Number 11 JO weight of all food before it was served, collected all food waste from student trays, and subtracted the total amount leftover.38,50,51 About three-fourths of studies used food waste as a proxy for understanding the amount of food stu- dents consumed. Research aimed to understand the impacts of nutrition education,40,43,50,51 changes in nutrition requirements,47 lunchtime procedures or the food environment,36,38,49,52 or food acceptability on consumption levels.37,39,41,42,44,45,52,53 Six studies specifically aimed to directly measure the amount of waste produced in the NSLP.5,33-35,46,48,54 Studies were concentrated in the West,38,39,49-51 Midwest,36,42,53 Northeast,40,43,44,46,48,52 South,5,34,32,54 and mixed loca- tions,33,35,41,45 with two studies not reporting geographic location.37,47 Seven studies reported free and reduced lunch eligibility rates above 80%.42-44,46,48,49,52 The most common food components examined in studies involving direct weighing were fruits and vegetables. Sixteen studies reported the quantity of waste from fruits and vege- tables. Other dietary components examined included milk, grains, and high-protein items such as soy-based products. Studies examined acceptance of specific foods in the cafeteria, such as whole grains.38,39,41,42,45 Two studies found a reduc- tion in food waste from changing recess to before lunch.36,49 Many interventions (eg, nutrition education, changes in nutrition requirements, lunchtime procedures or the food environment, or food acceptability on consumption levels) led to a decrease in waste for some foods. Combination of Methods Eight studies used a combination of in-person visual esti- mation through observation, visual estimation through digital photography, and/or direct weighing methods (Table 4).55-62 One study used direct weighing, visual obser- vation, and children’s ratings.55 Three studies used direct weighing and visual observation.56,58,59 Three studies used direct weighing and digital photography.57,61,62 One study used direct weighing, two types of visual observation, and visual photography.60 Four studies were designed to validate or compare food waste measures,55,56,60,61 one study validated a questionnaire against a food waste methodology,58 and three used food waste as a proxy for measuring the amount of food students consumed.57,59,62 Research aiming to understand food waste and consumption examined responses to changes in food requirements.57,59,62 Studies were concentrated in the West,58,59 Northeast,62 South,57 with four studies not reporting the geographic location in the United States.55,56,60,62 Rates for free or reduced school lunch eligibility ranged from 35.0%61 to 93.6%58; however, more than half did not report this information. Fruit and vegetables or components were consistently assessed across all studies except one, which was focused on competitive (snack) foods.57 Researchers used a combination of measures to validate a food waste measurement tool through comparison with a gold standard of direct weighing of waste. For the validity studies, the digital imaging and obser- vation technique was found to be comparable to weighed plate waste with 96%61 agreement and the quarter-waste method had a reliability measure of 0.9,60 both showing promise as URNAL OF THE ACADEMY OF NUTRITION AND DIETETICS 1801 Table 4. Combination of methodologies for food waste studies conducted in the National School Lunch Program (visual estimation, digital photography, direct weighing)a Reference Comstock and colleagues, 198155 Graves and colleagues, 198356 Templeton and colleagues, 200557 Wallen and colleagues, 201158 Gase and colleagues, 201459 Hanks and colleagues, 201460 Taylor and colleagues, 201461 Schwartz and colleagues, 201562 Study design CSbc CSc CS CSc CS CSc CSc Ide Specific data collection method Wf VOg Wh VOi Wh DPjk Wh VOi W VOi W VOi DPk Wh DPk Wh DP Type and no. of schools Elementary 5 1 2 1 2 Middle 3 4 12 Grade level Kindergarten-6 1-6 6 4 NRl Kindergarten-5 3-5 5-7 Average percent wasted for dietary components measuredm Grains/bread >n > > > Vegetables > > > > > > 51 Fruits/fruit juice > > > > > > 31 Meat/meat alternate > > Milk > > > 45 Other >o >o >p >o >o >o 26o Days of food waste data collectionq 4 8 24 1 20 1 8 36 No. of waste observationsr 2,000 450 743 125 2,228 197 276 1,340 (continued on next page) R E S E A R C H 1 8 0 2 JO U R N A L O F T H E A C A D E M Y O F N U T R IT IO N A N D D IE T E T IC S N o vem b er 2017 Vo lu m e 117 N u m b er 11 Table 4. Combination of methodologies for food waste studies conducted in the National School Lunch Program (visual estimation, digital photography, direct weighing)a (continued) Reference Comstock and colleagues, 198155 Graves and colleagues, 198356 Templeton and colleagues, 200557 Wallen and colleagues, 201158 Gase and colleagues, 201459 Hanks and colleagues, 201460 Taylor and colleagues, 201461 Schwartz and colleagues, 201562 Effective public health practice project quality rating10 Weak Weak Moderate Moderate Moderate Moderate Moderate Moderate aData were collected to assess food waste after new school lunch meal patterns were implemented beginning 2012. bCS¼cross-sectional. cCross-sectional study used for validation purposes. dI¼intervention. ePre-post intervention. fW¼direct weighing. gVO¼visual observation. hDifference weight of plate waste for each food minus weight of average selected serving. iQuarter waste method (none, half, three-quarters, or all). jDP¼digital photography. kEstimate percent of food selected and plate waste in photograph compared with reference photograph or a sample tray. lNR¼not reported with specificity. mIn some cases, the average percent waste within a dietary component was reported within the cited article. In other cases, this study’s authors calculated average percentage wasted within a dietary component when research design collected waste across multiple intervention periods. When percent consumed was reported (instead of percentage waste), this study’s authors calculated average percetage waste by subtracting the percentage consumed from 100% and, when necessary, averaged across multiple intervention periods or groups. n>¼Study indicated dietary component measured but not average percentage wasted within dietary component. oMeasured waste of a mixed entrée. pSpecific macro- and/or micronutrients measured in whole meal. qData calculated as number of days reported for study multiplied by number of schools involved in food waste collections. rData reported according to study as individual food items or entire student tray. R E S E A R C H N o vem b er 2017 Vo lu m e 117 N u m b er 11 JO U R N A L O F T H E A C A D E M Y O F N U T R IT IO N A N D D IE T E T IC S 1 8 0 3 RESEARCH alternatives to direct weighing. One other study found that the Day in the Life Questionnaire-Colorado dietary assessment had a high level of validity compared with plate waste.58 DISCUSSION This literature review highlights methods and results from four main research methodologies found across 53 food waste studies in the NSLP across time. Studies using in- person visual estimation, digital photography, direct weigh- ing, and a combination of in-person visual estimation, digital photography, and/or direct weighing varied greatly in research goals, protocol, and reporting. The results of this review may be useful for researchers seeking to measure food waste in school meals, influence what is consumed and wasted at schools, implement effective interventions, and develop new methods for measurement of food waste. Study aims ranged from evaluating the effects of programs on food consumption and/or waste to generally assessing food waste. No discernible trends in food consumption or food waste outcomes were observed based on study design (cross-sectional, intervention, quasiexperimental, mixed methods, or randomized controlled trial), the percentage of students who were eligible for free or reduced school lunch, geographic location of the school, and/or race or ethnicity. Most studies covered elementary schools, followed by middle schools; only five studies were conducted in high schools. Inconsistencies were noted in reporting key study design features (eg, number of schools, location of school, and di- etary component measured), and participant characteristics (eg, eligibility for free or reduced school lunch eligibility, race/ethnicity, and specific grade of students). There was a large degree of variability regarding how food waste was characterized in results. For example, units of measurement were reported in grams, ounces, percentages, or kilocalories. More uniform reporting metrics would lead to pooling food waste data across studies with potential to un- derstand consumption patterns and influence the school lunch field. Across methodologies, most studies reported the percentage of food groups or specific foods wasted. Some studies using in-person visual estimation through observa- tion or digital photography reported food waste in terms of calories or number of servings wasted.15,20,27,29,32 In one study using direct weighing, findings were presented by cost and the percentage of the total food budget wasted.46 Re- searchers also reported findings in terms of nutrients wasted and weight of food wasted. This variability contributes to the difficulty in understanding changes in food waste over time and difference across settings and populations by methodology. Many studies used observation, photography, and/or weighing of food waste as a proxy for measuring food con- sumption. Perhaps using “plate consumption” rather than “food waste” or “plate waste,” as Alaimo suggests,30 would increase the relevance of the measurement method to a study’s purpose. The language around plate waste and food waste should be selected carefully, especially in light of the attention that the NSLP receives from the public, media, and policymakers.63 In addition, plate waste and food waste are used interchangeably in the school lunch literature and re- searchers should choose one term to reduce confusion. 1804 JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS Several trends were noted across the methodologies. Nearly all studies were cross-sectional or interventions; only two studies were quasiexperimental, two studies used mixed methods, one study was longitudinal, and five were ran- domized controlled trials. Few longitudinal food waste studies existed; thus, there is no clear understanding of how much food is wasted or not wasted as a result of an inter- vention in the long term. For example, studying the long- term influences on waste of Smarter Lunchrooms design64 is important for knowing how changing the cafeteria food environment changes student consumption and waste throughout kindergarten through grade 12. Some studies aimed to validate a method, compare methods, or to assess intake or another method to assess waste. The five studies that validated or compared measures found acceptable correlation values or similar results be- tween measures.55,56,58,60,61 More studies should incorporate qualitative data in a mixed-methods design. Pairing qualitative with quantitative data allows for study designs that address research questions that are complex and multifaceted.65 Food waste researchers could address several qualitative questions along with quantitative food waste research, such as: How does student perception of the quality of the particular school’s food in- fluence the amount of waste? And, why do students waste food in general, from their own perspective? Overall, researchers using the in-person visual estimation through observation methodology collected food waste data for a greater period of time and at a higher frequency than those who used visual estimation through digital photography and direct weighing, likely given the lower burden on the researchers for data collection and analysis. Direct weighing has been used for a longer period of time when compared with visual estimation through both in- person observation and digital photography. Eighteen arti- cles published before 2014 used weighing compared with eight in-person observation and four digital photography studies. In 2014-2015, 10 studies used direct weighing, seven used in-person observation, and six used digital photography—evidence of the increasing popularity of visual methodologies. Fruits and vegetables were the most consistent dietary components measured, except for 12 studies. Fruits and vegetables were often reported to be the foods wasted in the largest quantities across the methodologies to assess waste. Adequate and balanced nutrition is of vital importance in assisting children to grow and learn. It is important to un- derstand fruit and vegetable consumption within the context of the entire tray (meal). Examining only a segment of the diet does not account for understanding the other foods that compete with a student’s food consumption patterns. Ana- lyses of food preferences toward studied food components, as well as food exposures, would also provide insight into food waste and consumption, especially when research has demonstrated that several exposures may be needed to in- fluence food acceptance.66,67 In addition, a few studies noted that older students wasted more than younger students and girls wasted more than boys; therefore, when addressing food waste, it may be important to consider consumption differences between boys and girls as well as in different age groups. November 2017 Volume 117 Number 11 RESEARCH Of note, no studies reported zero food waste. Since the 1970s, most studies reported more than 30% food waste and, furthermore, no studies have reported <5%. With an increasing focus on supporting self-regulation (eg, internal cues for satiety and hunger) instead of a clean plate or responding to visual cues to consume more,66 some level of waste should be expected. A multitude of other factors also influence food waste, including balancing caloric re- quirements with energy expenditure, metabolic and physical factors, food preferences, serving sizes, the school environ- ment, and what and how much children eat before the meal and in the home environment. However, how can food waste be minimized? This is long-standing question and a complex issue that should be addressed by the NSLP and food waste researchers strategically.68,69 Summarizing and aggregating data will become easier when researchers establish standardized food waste data collection measures and reporting techniques. Selection of a uniform metric to report results is an important consider- ation for researchers because consistent reporting may allow for comparison of findings. Further, the EPHPP Quality Assessment Tool10 ratings were fairly mixed between strong, moderate, and weak. Weaker ratings raise questions about the validity of the findings, potentially due to bias in the selection of subjects, lack of description in the measurement of outcomes, or bias in methods or reporting. Therefore, a standardized food waste data collection measure and reporting technique has the potential to simultaneously increase quality assessment ratings. Limitations exist in this systematic review. The search terms used may not have retrieved all articles relevant to food waste in the NSLP. Therefore, conclusions made in this research are limited to the publications retrieved during the search process. Excluding nonepeer-reviewed research may have overlooked important work conducted addressing food waste in schools. For example, Buzby and colleagues6 pub- lished a Report to Congress about plate waste amounts and measures in the NSLP before 2002. In addition, food waste connected to other food programs for children have been studied, including the School Breakfast Program and the Summer Food Service Program. CONCLUSIONS Generally, studies of food waste and consumption in the NSLP through the use of in-person visual estimation, digital photography, and/or weighing over the past 40 years has yielded mixed results about the amounts of food waste yielded within differing dietary components. The NSLP has the important purpose of feeding a large majority of our nation’s children with balanced and nutritious meals. As such, improving measurement methods to understand the amount of foods consumed and wasted in the lunchroom is an important charge for the public health and dietetics fields. There is a need for development of methods using technology that are low cost, have a low subject burden, and allow for measurement of food waste with limited involvement of re- searchers. Researchers need to better understand the causes and consequences of food waste on the school lunch tray by designing studies with consistent research protocols that examine dietary quality and food preferences of students. The November 2017 Volume 117 Number 11 JO ultimate goal should be to produce food waste data and implementable strategies that promote continuous improvement in the cafeteria food environment and health- ful eating habits among students, especially since wasted food is wasted nutrients.70 References 1. US Department of Agriculture, Food and Nutrition Service. Child nutrition programs. http://www.fns.usda.gov/school-meals/child- nutrition-programs. Published February 2016. Accessed March 2016. 2. US Department of Agriculture, Food and Nutrition Service. National School Lunch Program fact sheet. http://www.fns.usda.gov/cnd/ lunch/AboutLunch/NSLPFactSheet.pdf. Published September 2013. Accessed March 2016. 3. US Government Publishing Office. 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http://refhub.elsevier.com/S2212-2672(17)30598-1/sref57 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref58 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref58 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref58 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref59 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref59 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref59 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref59 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref60 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref60 RESEARCH Validating the quarter-waste method. J Acad Nutr Diet. 2014;114(3): 470-474. 61. Taylor JC, Yon BA, Johnson RK. Reliability and validity of digital im- aging as a measure of school children’s fruit and vegetable con- sumption. J Acad Nutr Diet. 2014;114(9):1359-1366. 62. Schwartz MB, Henderson KE, Read M, Danna N, Ickovics JR. New school regulations increase fruit consumption and do not in- crease total plate waste. Child Obes. 2015;11(3):242-247. 63. Byker C, Pinard C, Yaroch A, Serrano E. New NSLP guidelines: Chal- lenges and opportunities for nutrition education practitioners and researchers. J Nutr Educ Behav. 2013;45(6):683-968. 64. Hanks AR, DR Just, Wansink B. Smarter lunchrooms can address new school lunchroom guidelines and childhood obesity. Pediatrics. 2013;162(4):867-869. November 2017 Volume 117 Number 11 JO 65. Creswell JW, Plano Clark VL. Designing and Conducting Mixed Methods Research. London, UK: Sage Publications; 2007. 66. Birch LL, Fisher JO. Development of eating behaviors among children and adolescents. Pediatrics. 1998;101(2):539-549. 67. Birch L. Development of food preferences. Annu Rev Nutr. 1999;19(1):41-62. 68. Waste from School Lunches. Washington, DC: US General Accounting Office; 1996. Publication no. GAO/RCED-96-128R. 69. US Department of Agriculture. Let’s talk trash. http://www. choosemyplate.gov/lets-talk-trash. Published September 2015. Accessed March 2016. 70. Spiker ML, Hiza HA, Siddiqi SM, Neff RA. Wasted food, wasted nutrients: Nutrient loss from wasted food in the United States and comparison to gaps in dietary intake. J Acad Nutr Diet. 2017;117(7):1031-1040. AUTHOR INFORMATION C. Byker Shanks is an associate professor, Department of Health and Human Development, Montana State University Food and Health Lab, Bozeman. J. Banna is an assistant professor, Department of Human Nutrition, Food, and Animal Sciences, University of Hawaii-Manoa, Manoa. E. L. Serrano is director, Department of Human Nutrition, Foods and Exercise, Virginia Family Nutrition Program, Virginia Polytechnic Institute and State University, Blacksburg. Address correspondence to: Carmen Byker Shanks, PhD, RDN, Department of Health and Human Development, Montana State University Food and Health Lab, 960 Technology Blvd, Room 215, Bozeman, MT 59718. E-mail: cbykershanks@montana.edu STATEMENT OF POTENTIAL CONFLICT OF INTEREST No potential conflict of interest was reported by the authors. FUNDING/SUPPORT Research reported in this publication was partially supported by the National Institute of General Medical Sciences of the National Institutes of Health under award nos. P20GM103474 and 5P20GM104417. Research reported in this publication was partially supported by Cornell Behavioral Economics in Child Nutrition Programs (BEN) Center Grants Program under award no. 77867-10660. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or Cornell BEN Center. ACKNOWLEDGEMENTS The authors thank Lindsay Kummer, RDN; Susan Chen; Chloe Panizza, MS; Rise Morisato; Alicia Leitch, MS; Bonnie Billingsley, RDN; Allison Milodragovich, MS; and Erin Smith for their contributions to the article. URNAL OF THE ACADEMY OF NUTRITION AND DIETETICS 1807 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref60 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref60 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref61 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref61 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref61 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref62 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref62 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref62 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref63 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref63 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref63 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref64 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref64 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref64 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref65 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref65 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref66 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref66 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref67 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref67 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref68 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref68 http://www.choosemyplate.gov/lets-talk-trash http://www.choosemyplate.gov/lets-talk-trash http://refhub.elsevier.com/S2212-2672(17)30598-1/sref70 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref70 http://refhub.elsevier.com/S2212-2672(17)30598-1/sref70 mailto:cbykershanks@montana.edu Food Waste in the National School Lunch Program 1978-2015: A Systematic Review Methods Search Strategy Study Selection Data Extraction Quality Appraisal of Individual Studies Results In-Person Visual Estimation of Food Waste through Observation Visual Estimation of Food Waste through Digital Photography Direct Weighing of Food Waste Combination of Methods Discussion Conclusions References