Malnutrition Calculation 1 Last Updated: December 17, 2019; First Released: June 10, 2014 Author: Kevin Boyle, President, DevTreks (1*) Version: DevTreks 2.2.0 A. Introduction This reference explains how to start to calculate food nutrition input and output data (2*). Appendix A. Introduction to Malnutrition provides context for this topic. Section Page Data URLs 1 Work Breakdown Structure and Rules 2 Malnutrition and Sustainable Food Stock Balanances 3 ARS Standard Reference (SR) Food Nutrition Input Calculator 6 ARS Standard Reference (SR) Food Nutrition Output Calculator 12 Input and Output Nutrient Calculations in Analyzers 15 Food Nutrition and Food Systems Calculations and Analyses 24 Digital Support for Malnutrition Improvement 27 Summary and Conclusion 29 Appendix A. Introduction to Malnutrition Analysis 33 B. Data URLs (3*) The calculators explained in this reference can be found at: https://www.devtreks.org/hometreks/preview/smallholders/linkedviewgroup/Food Nutrition Calculators/6/none/ Examples of input and output calculations can be found at the following URLs. All of the food nutrition datasets are owned by the Family Budgeting and Food Nutrition club in the HomeTreks network group (if needed, switch default clubs). [The pdf version of this reference may add spaces to URLs with linebreaks. Remove the space before going to the URL.] https://www.devtreks.org/hometreks/preview/farmworkers/input/BARLEY,PEARLED,RAW/2147395842/none/ https://localhost:5001/hometreks/preview/farmworkers/input/BARLEY,PEARLED,RAW/2147408123/none https://www.devtreks.org/hometreks/preview/farmworkers/input/TURKEY BREAST,SLICED,PREPACKAGED/2147391222/none/ https://www.devtreks.org/hometreks/preview/smallholders/output/BARLEY, PEARLED, RAW/2141211289/none/ https://localhost:5001/hometreks/preview/smallholders/output/2011 BARLEY, PEARLED, RAW/2141211289/none This reference used the Azure deployment (Version 2.1.0) and localhost, or blue motif, deployments (Version 1.6.3 and 2.2.0) to document calculations. The source calculations were not changed from Version 1.6.3 to Version 2.1.0, but the latter Version refactored the underlying calculator and analyzer patterns. The video tutorial also uses Version 2.2.0 because of further advancements in code and tutorials. C. Work Breakdown Structure (WBS) and Rules The food input and output data derive from the USDA, Agricultural Research Service (ARS) datasets, including Standard Reference (SR) and Food And Nutrient Database For Dietary Studies, 5.0 (FNDDS5). Only the input data contains a complete list of the 7,000+ SR food items from that reference. Although all of the SR food items had their nutrient characteristics bulk uploaded into them, only a sample number of calculations have actually been run. The localhost database includes a 2nd ARS dataset, FNDDS5 (i.e. from 2010), but does not include the bulk uploaded calculators. The SR dataset has capitalized names, while FNDDS5 does not. D. Malnutrition and Sustainable Food Stock Balances The Resource Stock tutorial explained the importance of maintaining the balances of community capital stocks, including food system stocks, to ensure the well-being of households and their only planet. The tutorial introduced a basic resource stock accounting framework consisting of: Time Period 1: Stock Ending Balance = Stock Starting Balance + Stock Flows (i.e. Stock Credits – Stock Debits). The Resource Stock calculators and analyzers are used to conduct the following types of assessments. Conservation Technology Assessment (CTA) is the analysis of resource stock flows and balances, and conservation technologies that are designed to prevent or correct imbalances in the stocks. This reference introduces calculators that support the following examples of this stock accounting, as applied in CTAs, which are relevant to malnutrition analysis: 1. Food Nutrient Input and Output Analysis: Buyers of agricultural products are paying greater attention to the qualitative and quantitative properties of agricultural outputs. They want organic tomatoes that have no pesticide residues, coffee beans that taste better, and subsistence crops that supply more nutrients. Farmers and agricultural advisors are actively seeking better inputs and management practices that lessen external environmental impacts including eutrophication, GHG, air quality, sediment runoff, acidification, water withdrawals, and biodiversity loss. 2. Nutrient Budget Balances: Nutrient budgets examine the flow of nutrients entering a system (i.e. an orchard) and the flow of nutrients leaving the system (i.e. fruits). The “system” can be humans. For example, USDA 2010) defines human calorie balances as “The balance between the calories consumed in food and the calories expended through physical activity and metabolic processes”. These studies help producers manage achieve better balance between the inputs expended in production and the left over residues. 3. Carbon and Ecological Footprint Balances: The following image (TEEB, 2018) shows the relation between diets and climate change (9*). The accompanying Malnutrition Analysis reference demonstrates how to conduct simple natural capital stock balances (i.e. GHG emissions and Ecological footprints). Version 2.2.0 added new References (Poore et al, 2019; Rose et al., 2018) that include databases containing actual emission amounts that can be used with these properties. 4. Agriculture and Food System Analysis: The following image (TEEB, 2018) provides context for helping consumers and producers make sustainable food system choices. The RCA Value Framework, which is explained in the Social Performance Analysis tutorial, incorporates this framework (as of Version 2.2.0). In summary, the overall goal of any production or consumption system is to achieve a higher quality of life for households by using impact pathways to better understand resource stock flows and balances so that mitigation and adaptation actions can be taken that prevent or correct imbalances in the stocks (i.e. preventing GHG from wrecking our planet). Example 12 in the Social Performance Analysis tutorial demonstrates how to conduct these analyses. 5. Household Quality of Life Assessments. The USDA (2015) uses the following Healthy Eating Index, HEI-2015, to help consumers assess the healthiness of their diets. Example 12 in the Social Performance Analysis tutorial demonstrates using general sustainability assessments of household quality of life that include these types of indexes. E. ARS Standard Reference (SR) Food Nutrition Input Calculator These calculators use the USDA, Agricultural Research Service (ARS) Standard Reference (SR) database to calculate the food nutritional composition of food Inputs. They compute “unit nutrient values per actual common household measure units”. In effect, these calculators compute a type of “unit nutrients” that enable them to be reused in any Operation (or meal) or Component. Example Input 1. Barley, Pearled, Raw The following image displays the properties entered for this food item. These properties are defined as follows: * Typical USDA Serving Size and Unit: The USDA SR food nutrient database contains two typical household portions, or serving, sizes. Either size can be chosen using metric or standard USA imperial units of measurement. Weight 1 is larger, and contains more measurements, than weight 2. Use weight 1 as the default. * Actual Serving Size: Adjust the Typical USDA Serving Size to the actual serving size consumed in a typical meal. In this example, the typical serving size is actually 0.25 cups, rather than the 1 cup Typical Serving Size. In order to keep this a unit cost, the typical meal should not be tied to one particular individual or age group. * Container Size in USDA Serving Unit: The size the container holding this food item. If necessary, convert the container units of measurement to the same units found in the typical serving size units. In this example, the container holds 2 cups of the 1 cup Typical USDA serving size. * Container Price and Unit: Grocery store price of the container. The unit is entered in terms of the USDA serving size, not the actual container unit (i.e. package). For example, if the common household measure is ounce, a one pound container should specify the unit as “16 ounce package”. Operating budgets usually use the Actual Serving Size to figure food nutrients consumed and serving cost. Capital budgets can use the container size to figure the “bulk” nutrient composition of foods, but care is needed to use the correct container size (16 ounces). * Extra 1 and Extra 2: Extra parameters to include in the calculations. Version 2.2.0 upgraded these properties to use the same adjustment for actual serving size as all other nutrients. The accompanying Malnutrition Analysis tutorial demonstrates how to use these properties to conduct simple carbon footprint balances (i.e. GHG emissions and energy use). Use an accompanying story to explain these extra properties. The following image displays the calculated properties for the actual serving size portion: These numbers are calculated as follows: Actual Water g: 20.18 g Typical Nutrient Value of Common Measure = 10.09 Typical Water (per 100 grams) * 200 (grams of common measure) / 100 5.045 (Actual nutrients per actual amount of common household measure) = (20.18 g Typical Nutrient Value of Common Measure * 1 cup (common household measure)) * (0.25 cup (actual household serving size) * 1 Input.OCAmount)) 5.045 (Actual nutrients per actual amount of common household measure adjusted for waste) = 5.045 * ((100 – 0 (refuse percent) / 100) 8 Actual Servings per Container = 2 cups of USDA Typical Serving (1 cup) per container / 0.25 cups consumed per actual serving 0.19 Total Cost per Actual Serving or Serving Cost = 0.19 Input.OCPrice * 1 Input.OCAmount The next image shows that the following Input and Series properties are updated: These properties are automatically updated in base Inputs as follows: * Input.OCAmount: 1 (unit nutrients and prices) * Input.OCUnit: 0.25 cup (Actual Serving Size in Typical Serving Size Units (cups)) * Input.OCPrice: 0.19 = 1.50 (2 cup container) / 8 (Actual Servings per Container) * Input.CAPPrice = 1.50 (Container Price) * Input.CAPUnit = 2 cup (Container Size in USDA Serving Unit) Pay close attention to the Input.OCUnit and Input.CAPUnit. When this Input is added to an Operation or Component, the Input.OCAmount or Input.CAPAmount can be changed to an “actual” amount. If the Input.CAPAmount is set to a value greater than zero, the container size will be used to calculate food nutrient composition amounts and serving cost. Otherwise the Input.OCAmount and Input.OCUnits will be used to calculate food nutrient composition amounts and serving cost. The Input.CAPAmount change is appropriate for Capital Budgets that need “bulk food nutrient” data. Operating Budgets should use the Input.OCAmount. Numeric examples can be found in the Malnutrition Analysis 1 reference. Make sure the amounts reflect the units being used. F. ARS Standard Reference (SR) Food Nutrition Output Calculator Unlike Inputs, Outputs do not include a list of ARS SR food items (and the cloud’s Output nutrient calculations use sample, rather than actual, data). As explained in Footnote 3 and 7, those data sets can be bulk uploaded into the database when the need arises. Example Output 1. Barley, Pearled, Raw The following images display the same food item, but with different properties than Example Input 1. The only difference that Outputs have from Inputs are that instead of using Input.OCAmount and Input.OCPrice in the calculations, Output.Amount and Output.Price are used: Output.Amount: 1 (unit nutrients and prices) Output.Unit: 0.25 cup (Actual Serving Size in Typical Serving Size Units (cups)) Output.Price: 0.19 = 1.50 (2 cup container) / 8 (Actual Servings per Container) Output.CompositionAmount: 1 = default value Output.CompositionUnit: each = default value Pay close attention to the Output.Unit. When this Output is added to an Outcome, the Output.Amount can be changed to an “actual” amount. Make sure the amount reflects the Output.Unit being used. G. Input and Output SR Nutrient Calculations in Analyzers The ARS SR food nutrient Input and Output calculations are rerun in the Malnutrition Analyzers explained in the Malnutrition Analysis 1 reference. The main difference in the calculated amounts involves the Input and Output Amounts used in the food nutrient, serving size, and serving cost calculations. Base Input and Output Analysis always will use an Input.OCAmount = 1 and an Output.Amount = 1. These calculations result in “unit nutrient” calculations that can be added to any Operation, Component, or Outcome and then changed. Operations and Components can change either the Input.OCAmount for “consumed food” analysis or Input.CAPAmount for “bulk food container” analysis, such as a Capital Budget Analysis. Outcomes can change the Output.Amount property for “produced or expended food” analysis or the Output.CompositionAmount for “distributed food” analyses. The CompositionAmount property is often used in livestock budgets to set the number of head of livestock. Benchmark comparators used to analyze this data, such as the nutritional goals listed the following chart (USDA, 2015), can be set, at least to some degree, through careful setting of all Inputs and Outputs and their amounts. Examples of the four calculations are as follows (6*): Example 1. Food Consumed Nutrient Content. This is the standard calculation used by Operations and Operating Budgets to calculate food nutrient consumed in meals. The following 2014 image displays the food nutrient calculations for a Turkey Input in an Operation Analysis of a Turkey sandwich. The initial Input.OCAmount for the Turkey is 1 (10 slice unit). 2014 Image with OCAmount = 1 The following 2016 image shows that when the Turkey Input.OCAmount is changed from 1 (10 slice unit) to 10 (10 slice unit), the food nutrient composition amounts, serving size, and the serving costs are multiplied by 10. These calculations are as follows: * Input.Times: 1 (general multiplier) * Input.OCAmount: 10 (unit nutrients and prices) * Input.OCUnit: 10 slice (Actual Serving Size in Typical Serving Size Units (slice)) * Input.OCPrice: 1.20 = 2.40 (20 slice container) / 2 (Actual Servings per Container) * Input.CAPAmount = 0 * Input.CAPPrice = 2.40 (20 slice container price) * Input.CAPUnit = 20 slice (Container Size in USDA Serving Unit) Serving Size: 100 slice = 10 slice (actual serving size) * 10 (Input.OCAmount) 12.50 Serving Cost = 1.20 Input.OCPrice * 10 Input.OCAmount Actual Water g: 11.36 g Typical Nutrient Value of Common Measure = 75.72 Typical Water (per 100 grams) * 15 (grams of common measure) / 100 1,135 (Actual nutrients per actual amount of common household measure) = (11.36 g Typical Nutrient Value of Common Measure * 10 slice (common household measure)) * 20 slice (serving size) 1,135 (Actual nutrients per actual amount of common household measure adjusted for waste) = 1,135 * ((100 – 0 (refuse percent) / 100) 2 Servings per Container = 20 slices of USDA Typical Serving (1 slice) per container / 10 slices (original actual serving size) Example 2. Food Supplied Container Nutrient Content. This is the standard calculation used by Components and Capital Budgets to calculate the food supplied using containers for measurement. It uses the same Input as the previous example, but the Input.OCAmount has been changed to 0, the Input.Times has been changed to 1, and the Input.CAPAmount has been changed to 2. The 2014 calculations are as follows: * Input.Times: 1 (general multiplier) * Input.OCAmount: 0 (unit nutrients and prices) * Input.OCUnit: 10 slice (Actual Serving Size in Typical Serving Size Units (slice)) * Input.OCPrice: 1.20 = 1.20 (20 slice container) / 2 (Actual Servings per Container) * Input.CAPAmount = 2 * Input.CAPPrice = 2.20 (20 slice container price) * Input.CAPUnit = 20 slice (Container Size in USDA Serving Unit) Serving Size: 40 slice = 20 slice (Container Size) * 2 (Input.CAPAmount) 4.80 Serving Cost = 2.40 Input.CAPPrice * 2 Input.CAPAmount Actual Water g: 11.36 g Typical Nutrient Value of Common Measure = 75.72 Typical Water (per 100 grams) * 15 (grams of common measure) / 100 454.32 (Actual nutrients per actual amount of common household measure) = (11.36 g Typical Nutrient Value of Common Measure * 1 slice (common household measure)) * 40 slice (serving size) 454.32 (Actual nutrients per actual amount of common household measure adjusted for waste) = 454.32 * ((100 – 0 (refuse percent) / 100) 2 Servings per Container = 20 slices of USDA Typical Serving (1 slice) per container / 10 slices (original actual serving size) Example 3. Food Produced or Expended Nutrient Content. This is the standard calculation used by Outcomes and Operating Budgets to calculate the nutrient content of agricultural outputs. They can also measure the food nutrients expended in Outcomes, such as physical activities and metabolic processes. The following 2014 image uses the Barley Output example from above. It shows that when the Output.Amount property is changed from 1 to 2, the food nutrition, serving size, and serving cost properties double (the calculations are similar to Example 1. Food Consumed Nutrient Content). Example 4. Food Distributed Container Nutrient Content. This is the standard calculation used by Components and Capital Budgets to calculate the bulk nutrient content of the food distributed, or wasted, using containers for measurement. It uses the same Output as the original example, with an Output.Amount equal to 1, an Output.Times equal to 1, and an Output.CompositionAmount changed from 1 to 2 (i.e.. to an amount greater than 1). Unlike Input.CapAmounts that default to 0, CompositionAmounts default to 1. Setting the Output.CompositionAmount property to an amount greater than 1 tells the calculator to use container size and container cost in the calculations (rather than Output.Amount and Output.Price). These 2014 calculations are as follows: * Output.Times: 1 (general multiplier) * Output.Amount: 1 (unit nutrients and prices) * Output.Unit: 0.25 cup (Actual Serving Size in Typical Serving Size Units (cups)) * Output.Price: 1.50 (2 cup container price) * Output.CompositionAmount: 2 (2 cups per container) * Output.CompositionUnit: 2 cups (Container Size in USDA Serving Unit) Serving Size: 4 cup = 2 cups (Container Size) * 2 (Output.CompositionAmount) 3.00 Serving Cost = 1.50 Output.Price * 2 Output.CompositionAmount Actual Water g: 20.18 g Typical Nutrient Value of Common Measure = 10.09 Typical Water (per 100 grams) * 200 (grams of common measure) / 100 80.7 (Actual nutrients per actual amount of common household measure) = (20.18 g Typical Nutrient Value of Common Measure * 1 cup (common household measure)) * 4 cups (serving size) 80.7 (Actual nutrients per actual amount of common household measure adjusted for waste) = 80.7 * ((100 – 0 (refuse percent) / 100) 8 Servings per Container = 2 cups of USDA Typical Serving per container / 0.25 (original actual serving size) H. Additional Food Nutrition and Food Systems Calculations and Analyses (6*) The Technology Assessment, Performance Analysis, and Social Performance Analysis tutorials demonstrate additional ways to calculate and analyze scientific data related to food systems. Specifically, many of the examples in those tutorials use a software pattern consisting of a metadata Indicator user interface, TEXT datasets that store both the data to calculate and the calculated results, custom algorithms for running the calculations and analyses, and backend statistical libraries for assisting with the analyses. Although the domain-specific calculation pattern demonstrated in this reference retains its utility, in the long run the alternative pattern will probably support more flexible and advanced food system analysis, including machine learning techniques (8*). The following image shows that, although not documented, another food calculator, along with related analyzers, is available in DevTreks (i.e. the FoodFacts calculator in the FoodNutrition extension). This calculator is used with the information available on the back of food containers in the USA. It is not currently supported (because of labor constraints). Refer to Footnotes 7 and 8 about additional sources of food data and potential future calculator development (i.e. uploading the USDA branded food database for use with this calculator). I. Support for Digital Malnutrition Improvement The Version 2.1.0 video tutorial attempted to “make purposeful mistakes” so that customers understood the need to directly work with software developers to “fix the bugs”. Version 2.2.0 fixed the following bugs. The following bug arose from setting the Typical Serving Size property incorrectly. This boolean had been set to false rather than true, causing the calculated results to be saved incorrectly at times. The video recommends that source code adopters work in properly funded teams to deal with common IT issues, including these types of bugs. Although DevTreks itself does not accept donations and prefers nonconventional approaches to software development (i.e. to freely criticize the culprits, 8*), we recommend that teams employ better practices for nonprofit businesses, software development, and tutorial support. Summary and Conclusions Food nutrients are a critical resource needed by everyone. When they get out of balance and malnutrition ensues, children go hungry, adolescents become obese, adults develop diabetes, and workers work less hard. This reference demonstrates how to calculate the basic nutritional value of Inputs and Outputs. These numbers may help people to manage malnutrition in ways that help them to improve the sustainability of their lives and livelihoods. Footnotes 1. The author has studied malnutrition as an important, but ancillary topic, in his agricultural science education at Cornell University, USA (B.S.) and U.C. Davis, USA (M.S.). He is not an expert in the field. The tools introduced in this reference were kept basic for that reason. 2. Analysts have developed a large number of techniques for calculating malnutrition. This reference introduces basic malnutrition calculation. Some of the more advanced techniques will be included in future releases (see footnote 7). For example, Example 5 in the Social Performance Analysis 3 reference demonstrates how to tie domain-specific calculations to complementary population algorithms to account for socioeconomic factors influencing food production and consumption. 3. All of the 7200+- SR24 food nutrient Inputs were uploaded at once to the database. Under most circumstances, this type of data should not be entered by manually adding nutrients for each Input or Output. Standard database techniques can be used to bulk upload the data. Retain the uploaded data files in case another club wants to work with their own data set. The logistics of uploading cloud computing data by non-database administrators is a technical matter. 4. Alternative tools were designed (and almost built) that could measure actual household nutrient consumption and production, but their result could not easily be reused as “unit nutrients”. DevTreks decided that these types of “advanced” tools need to be vetted by experts in this field. DevTreks encourages source code users to build their own calculators, algorithms, and tutorials that meet the needs of their own stakeholders. 5. The second ARS data set, FNDDS5, consists of 7200 food items that include combinations of food, such as mashed potatoes and gravy. Ditto –source code users need to be experimenting and increasing their experience in this field if malnutrition and food sustainability are to be tackled at global scale in affordable and fair ways. 6. Version 2.1.6 investigated the development of a lighter weight version of DevTreks. We concluded that viable alternatives might employ a metadata user interface, a document storage database that holds TEXT input and output files, custom algorithms, and an advanced search engine, with specific focus on sustainable supply chain analysis. We also concluded that this type of consumer software for conducting consequential digital activism was more appropriate for consumer oriented software development companies (i.e. but then again). 7. Version 2.2.0 began using the calculators’ Extra properties to conduct carbon and ecological footprint balances. The references used for those balances (Poore et al, 2019; Rose et al., 2018) show that new food databases are becoming available that include nutritional, environmental, and socioeconomic factors that can be used in sustainability studies. Additional food databases are documented in the following source: https://www.ers.usda.gov/about-ers/partnerships/strengthening-statistics-through-the-icars/food-related-data-sources/ DevTreks will consider bulk uploading datasets that focus on food supply chain sustainability in future releases (i.e. Footnote 13 in the SDG Plan reference in the Social Performance Analysis tutorial provides context and motivation). 8. The Poore and Nemecek (2019) supply chain database highlights the heroic efforts currently needed to build international supply chain databases that can be used to make sustainable production and consumption choices (i.e. see their supplementary authors’ reference). The inaccessibility of the Rose et al (2019) database (i.e. as of November, 2019) demonstrates the added difficulty of using socioeconomic data in support of sustainability decision making. The 2 efforts explain DevTreks continual criticism of conventional institutions misuse of modern IT. Global problems, particularly climate change and biodiversity loss, won’t be solved by thousands of scientists, researchers, government agencies, private businesses, non-profits, and individual volunteers, collecting scientific evidence consisting of thousands of incompatible datasets published in peer-siloed journals or added to proprietary data “places”. Although several scientific organizations have taken efforts to address specific parts of the problem (i.e. IPCC’s GHG inventories, IPBES’s proposed data platform, EU’s EEA approach, USDA’s central food platform at https://fdc.nal.usda.gov/), the underlying institutional IT failure suggests the need for a “next generation” of digitally savvy institutions staffed by IT professional who know how to use standard, open source, digital, algorithm platforms. 9. Within the past year the author switched from a standard Irish meat and potato diet to a 90% vegetarian diet for this reason. Climate change specialists who don’t believe consumers need to be taking similar actions don’t understand the importance of social norms (i.e. refer to the Social Performance Analysis tutorial) or recent science (i.e. refer to the TEEB and WRI references to those tutorials). References Food and Agriculture Organization of the United Nations. The State of Food Insecurity in the World. Strengthening the enabling environment for food security and nutrition. 2014 IPPC, 2013. Climate Change 2012, The Physical Science Evidence. Working Group 1 Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. [Stocker, Qin, Plattner, Tignor, Allen, Boschung, Nauels, Xia, Bex, and Midgely (EDS)]. Cambridge University Press, Cambridge, UK and USA IPPC, 2014. Climate Change 2014, Impacts, Adaptation, and Vulnerability, Part A Global and Sectoral Aspects. Working Group 2 Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. [Field, Barnes, Barros, Dockken, Mach, Mastrandrea, Bilir, Chatterjee, Ebi, Estrada, Genova, Girma, Kissel, Levy, MacCracken, Mastrandea, and White (EDS)]. J. Poore and T. Nemecek, Reducing food’s environmental impacts through producers and consumers. Science 360, 987–992 (2018) 1 June 2018 and Erratum published February, 2019 Donald Rose, Martin C Heller, Amelia M Willits-Smith, and Robert J Meyer. Carbon footprint of self-selected US diets: nutritional, demographic, and behavioral correlates. Am J Clin Nutr 2019;109:526–534. The Economics of Ecosystems and Biodiversity (TEEB) (2018). Measuring what matters in agriculture and food systems: a synthesis of the results and recommendations of TEEB for Agriculture and Food’s Scientific and Economic Foundations report. Geneva: UN Environment. 2018 USDA, ARS Composition of Foods Raw, Processed, Prepared USDA National Nutrient Database for Standard Reference, Release 24. September, 2011. (http://www.ars.usda.gov/nutrientdata) USDA and United States Department of Health and Human Services. Dietary Guidelines for Americans, 2015-2020. 8th Edition. Washington DC. US GPO, December, 2015 USDA. The Healthy Eating Index 2015 (HEI-2015) Fact Sheet. 2015 US Agency for International Development. Nutrition by Design, Design and Measuring for Nutrition Impacts. Presentation made December, 2012 for Agriculture and Nutrition Global Learning and Evidence Exchange (NGLEE). US CDC. last accessed Nov. 14 2019: https://www.cdc.gov/nchs/healthy_people/hp2020/hp2020_indicators.htm Related site: US Deparment of Health and Human Services. Office of Disease Prevention and Health Promotion. last accessed Nov. 14 2019: https://www.healthypeople.gov/ World Health Organization. Interventions on Diet and Physical Activity: What Works. Summary Report. 2009 World Bank. Improving Nutrition through Multisectoral Approaches. 2013 References Note We try to use references that are open access or that do not charge fees. Improvements, Errors, and New Features Please notify DevTreks (devtrekkers@gmail.com) if you find errors in these references. Also please let us know about suggested improvements or recommended new features. A video tutorial explaining this reference can be found at: https://www.devtreks.org/commontreks/preview/commons/resourcepack/Malnutrition Analysis 1/450/none/ Appendix A. Introduction to Malnutrition Substantial progress has been achieved in recent years in reducing malnutrition throughout the world. The following image (FAO 2014) demonstrates that, even with that progress, substantially more work needs to be done to alleviate malnutrition. The following image comes from the Social Performance references and introduce sustainable accounting system goals, targets, and indicators that the international community wants tackled by 2030. Many of these goals relate directly to the improved delivery of malnutrition-related and food system services, including No Poverty and Zero Hunger. The objective of malnutrition improvement interventions is to support decisions that involve scenarios similar to the following agricultural development and malnutrition project (USAID, 2012). The image shows that malnutrition projects may need to be integrated into other development purposes as well, such as agricultural development, or health care, programs. : The following graphic (USAID, 2012) shows a malnutrition intervention with a tightly focused goal –decrease Vitamin A deficiency in targeted children. The following image (WHO 2009) demonstrates the health care sector’s Health Technology Assessment approach to studying health care, which emphasize the metadata analysis of randomized control trial data, can also be applied to understanding effective malnutrition interventions (i.e. technologies). The following image (World Bank 2013) demonstrates that many malnutrition interventions must take place across the entire health sector of targeted regions. The following image (IPCC WG2 2014) demonstrates that many regions will need to adapt new food security strategies for dealing with the planet’s climate change crisis. The following image illustrates DevTreks Knowledge Bank approach to malnutrition. TEEB (2018) use the following image to show the current state of scientific evidence that supports understanding the source of the risks associated with health and food systems. These graphics demonstrate that professional malnutrition analysis requires a comprehensive approach with a comprehensive set of cloud computing tools. This reference only addresses one of the tools in any malnutrition toolkit –food nutritional analyses. These analyses will often be used in conjunction with additional nutrition decision support information, such as the socioeconomic factors documented in the following nutritional goal chart from the USDA (2015) (7*). Several tutorials demonstrate additional tools available to tackle more comprehensive malnutrition interventions (3*). For example, the Ag Production Analysis 1 tutorial demonstrates how to collect and analyze full agricultural production data sets. The nutrient stock budgeting techniques demonstrated by this reference can also be applied to other nutrient budgets, such as soil and plant nutrient budgeting (4*). The Construction Analysis 1 tutorial demonstrates how to track cost and benefit data for sanitation improvement investment projects. The Health Care Analysis 1 tutorial demonstrates how to collect and analyze health care data sets. The Monitoring and Evaluation tutorials demonstrate how to collect and analyze full data for malnutrition food distribution and delivery programs. The Resource Stock Analysis and Technology Assessment tutorials demonstrate how to collect and analyze full data for climate change mitigation and adaptation technologies. Version 2.1.0 introduced a new Social Performance Analysis tutorial demonstrating how to assess the sustainability of agricultural production and food system supply chains. The tutorial assists producers to take sustainable production actions and consumers to make sustainable consumption choices. The tutorial also explains how to uses Indicators similar to those shown in the following image (FAO, 2014?), to help with the safety of our neighbors and friends. DevTreks –social budgeting that improves lives and livelihoods 1