Table 4 - uploaded by Cristina Álvarez Sánchez
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16-food group classification from the dietary diversity questionnaire used as a base to create the Household Dietary Diversity Score (HDDS).
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This document is available at: http://www.fao.org/fileadmin/templates/ess/foodsecurity/Optimizing_the_use_of_ADePT_FSM_for_nutrient_analysis.pdf
This document describes the updates introduced in ADePT-FSM version 3 to improve its capacity for nutritional analysis by adding new indicators and refining methodologies. It also describes how to optimiz...
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... After careful consideration and analysis, folate was excluded because of the lack of folate data in FCTs that is needed to perform analyses of the prevalence of folate inadequacy. Recommended folate intakes are expressed in dietary folate equivalents (DFEs) [18,19], and, based on our own research of nutrient availability in FCTs [20], out of 66 FCTs assessed, only 10 of them published DFEs and only one published all the needed components to compute DFEs. In 2023, Bouckaert and colleagues [21] further performed a critical evaluation of folate data in European and international databases and suggested the adoption of DFEs in the future provided that both folic acid and total folate are available. ...
This paper presents an approach to estimate the between-subject variability in nutrient intake (through the coefficient of variation [CV]) and a method to estimate the prevalence of nutrient inadequacy (PoNI) (for eight micronutrients) using household consumption and expenditure survey (HCES) data. Prevalence values are compared to individual-level estimates derived using the National-Cancer-Institute method. Data come from the 2015 Bangladesh Integrated-Household-Survey, which conducted a household-level 7-day recall (7DR) and two rounds of individual-level 24-hour recall (24HR), filled by one respondent on behalf of all members, for the same rural households. The PoNI values based on 7DR are lower than those calculated from 24HR data, due to the larger average intake estimates from 7DR data. After controlling for differences in average intake estimates and adjusting household-level data for random measurement errors, the PoNI values from 7DR and 24HR data are remarkably close. This highlights the potential use of HCES data (conducted according to international agreed standards) for estimating the level of between-subject variability in usual nutrient intake in a population. The CVs from HCES could be used to compute the PoNI using average intake estimates from individual-level data; and the inadequacy of global nutrient supply using Supply and Utilization Accounts data.
... In the case of multi-ingredient dishes prepared and consumed away from home, the 7DR module did not collect information on quantities consumed, but only on the associated expenditures. Therefore, the contribution of each nutrient from these items was estimated from the corresponding expenditures, based on the median (at the region-income quintile level) of the ratio between the expenditure and quantity of each nutrient from the item's at-home consumption (Moltedo et al., 2018). Outliers and seasonal effects were treated in the same way as described for individual-level data. ...
... Possible reasons why 7DR modules may overestimate consumption includes: (a) the methodology used to estimate the nutrient content in food prepared and consumed away from home based on monetary values may results in too high energy values, as part of the expenditure on FAFH covers services provided the caterers (Moltedo et al., 2018); (b) the fact that households may be misreporting the total quantities of food obtained from own production rather than the share that is actually consumed (based on the authors' experience in processing data from many HCES, this is a problem commonly encountered in surveys conducted in rural areas); and (c) the fact that quantities for foods that are actually consumed as cooked but reported in quantities acquired in their raw form (e.g. chicken), may be incorrectly matched with the nutrient contained in the raw form of the food, due to lack of information on the preparation method (e.g. ...
Malnutrition is pervasive in both low- and middle-income countries. Yet, there is a scarcity of food intake data collected at the individual level to describe diets, determine the prevalence of inadequate nutrient consumption in populations, and shed light on how diets contribute to the malnutrition burden. In the absence of nationally representative individual-level food intake surveys, particularly in low- and middle-income countries, dietary data collected in household consumption and expenditure surveys (HCES) are being used as a second-best option to make inferences on the food and nutrient consumption of populations. This paper proposes an innovative approach to estimate variability in nutrient intake that uses food data collected in HCES to estimate the prevalence of nutrient inadequacy in a country. This method builds on the approach developed by FAO to estimate the indicator of inequality used in the Prevalence of Undernourishment used in the global monitoring of food insecurity.
... To assess the diet in terms of macronutrients, we calculate the calories obtained from each of the energy-yielding macronutrients (fat, proteins and carbohydrates) per day and per capita following the methodology described by the Food and Agriculture Organization of the United Nations (FAO) to be used with the ADePT-FSM software (Moltedo et al., 2014(Moltedo et al., , 2018. ...
... Beyond the sole calories consumed, literature highlights the importance of analysing the quality of consumption in developing countries (Donini et al., 2016;Ruel et al., 2013;Kennedy et al., 2007Kennedy et al., , 2010Hoddinott & Yohannes, 2002). As the 2015/16 Kenya Integrated Household Budget survey (KIHBS) (Kenya National Bureau of Statistics, 2018) collects food consumption data for a 7-days period, we use the household consumption and expenditure surveys dietary diversity score (HCES-DDS), an indicator proposed by the FAO, which can be used for longer reference periods (Moltedo et al., 2018). ...
... The following household survey and the food composition information have been treated to become compatible in terms of food items. Table (KNCT) following Moltedo et al. (2014Moltedo et al. ( , 2018. ...
The sustainable development goal #2 aims at ending hunger and malnutrition by 2030. Given the numbers of food insecure and malnourished people on the rise, the heterogeneity of nutritional statuses and needs, and the even worse context of COVID-19 pandemic, this has become an urgent challenge for food-related policies. This paper provides a comprehensive microsimulation approach to evaluate economic policies on food access, sufficiency (energy) and adequacy (protein, fat, carbohydrate) at household level. The improvement in market access conditions in Kenya is simulated as an application case of this method, using original insights from households' surveys and biochemical and nutritional information by food item. Simulation's results suggest that improving market access increases food purchasing power overall the country, with a pro-poor impact in rural areas. The daily energy consumption per capita and macronutrients intakes per capita increase at the national level, being the households with at least one stunted child under 5 years old, and poor households living areas outside Mombasa and Nairobi, those which benefit the most. The developed method and its Kenya's application contribute to the discussion on how to evaluate nutrition-sensitive policies, and how to cover most households suffering food insecurity and nutrition deficiencies in any given country.
Supplementary information:
The online version contains supplementary material available at 10.1007/s12571-021-01215-2.
... Some foods consumed away from home (FCAH) only had a monetary value attached (i.e. no quantity), for those particular food items (2 percent in Kenya, 0.8 percent in Samoa, and 3 percent in the Sudan), the calculation of amounts of dietary energy and nutrients provided by these foods was performed using median at-home calorie and nutrient unit values, respectively (Moltedo, Álvarez-Sánchez, Troubat, Cafiero, et al., 2018). The at-home median values were obtained at the regional income quintile urban/rural level. ...
... The statistics on usual food quantities consumed by food group, as well as those for usual dietary energy and nutrient consumption -by food insecurity level -were obtained using the ADePT-FSM software (Moltedo, Álvarez-Sánchez, Troubat and Cafiero, 2018;Moltedo, Troubat, Lokshin and Sajaia, 2014). Regression analyses, followed by Tukey's pairwise post-hoc tests (family-wise error rate of 5 percent) in the case of Kenya and the Sudan, were used to determine whether there was a difference between the mean of all possible pairs. ...
Households and individuals that experience moderate or severe food insecurity may have poorer diets, in quantity or in quality, than their food secure counterparts. Much of the evidence highlighting the associations between food insecurity and diet comes from North America. However, far less research has been conducted on the association of food insecurity, particularly at the moderate level, and dietary consumption in low- and middle-income countries. This study expands on previous work by considering cross-country comparable measures of food insecurity that are calibrated against the global Food
Insecurity Experience Scale (FIES). We use three household consumption and expenditure surveys, two from lower-middle-income countries (Kenya and the Sudan), and one from an upper-middle-income country (Samoa); as well as an individual dietary intake survey from Mexico (upper-middle-income country). The four surveys are nationally representative and include the FIES (Kenya, Samoa and the Sudan) or a similar experience-based food insecurity measure (Mexico). Estimates of the average usual consumption of foods from ten food groups, dietary energy, and macronutrients are computed by food
insecurity class. Overall, the analysis reveals that people who experience moderate or severe food insecurity consume less meat and dairy products (in all four countries) and less fruits and vegetables (Kenya and the Sudan) than those who are food secure or mildly food insecure. Consumption of cereals, roots, tubers and plantains, and pulses, seeds and nuts either decreases slightly, remains similar, or increases, resulting in a higher proportional contribution of these food groups to the total diet.
Consequently, the more food insecure people are, the larger the share of staples in their diet. This holds true even if food insecure people in Kenya and the Sudan reduce their consumption of staples, because they reduce consumption of other food groups even more.
It could be downloaded from http://www.fao.org/3/cb6217en/cb6217en.pdf
... All five HCES datasets contained poorly defined foods (such as "dinner in restaurant", "lunch at work" and "meal at a street vendor") in their food module list without reporting food quantities for them. Therefore, the amount of vitamin A contributed by these foods was not estimated using a food matching approach but through the ADePT-FSM tool, using the respective food monetary values combined with median at-home vitamin A unit monetary values (Moltedo et al., 2018). Then, for each household, we summed the vitamin A content in foods obtained from the food matching approach and from the monetary value approach. ...
Producing, reporting, and interpreting vitamin A statistics present multiple challenges largely attributable to the systems of equivalence used to convert pro-vitamin A carotenoids into retinol equivalents, and to the criteria used by institutions to set recommendations. This study describes the information on total vitamin A, retinol and pro-vitamin A carotenoids available in 89 food composition tables/databases (FCTs/FCDBs). It also evaluates the effect of the unit of vitamin A intake (Retinol Equivalents [RE] or Retinol Activity Equivalents [RAE]) and the source of requirements on the potential contribution of dietary intake to the population’s requirements. We found that 44 percent of the FCTs/FCDBs reviewed, many of them from high-income countries, do not provide total vitamin A or sufficient information for computing it, or present inconsistencies between the metadata and the published values; 9 percent publish total vitamin A in RE and RAE; and 26 percent provide information on retinol and pro-vitamin A carotenoids that enables calculating total vitamin A in both units.
Vitamin A adequacy ratios are lowest when the consumption unit is RAE and the source of requirements is the US Health and Medicine Division. When the consumption unit is RE, adequacy ratios are higher using FAO/WHO than EFSA requirements. It is imperative to reach consensus on the system of conversion of pro-vitamin A carotenoids into retinol equivalents.
... HCES collect information on households' food acquisition and/or consumption; however, their use for dietary assessment is limited to complying with minimum reliability standards, such as considering all possible food sources, capturing the most relevant foods consumed by households and using a food recall period of two weeks or less (Smith et al., 2014). Furthermore, these surveys suffer from limitations such as the impossibility to assess individual intakes (Bromage et al., 2018), challenges in computing the nutrient content from foods consumed away from home (Moltedo et al., 2018), lack of specificity with regard to food use (consumption, waste, food given to pets), and lack of information on food preparation and cooking phases (Fabbri and Crosby, 2016). ...
In many countries, statistics from household consumption and expenditure surveys are increasingly being used to inform policies and programs. In household surveys, foods are typically reported as they are acquired (the majority are raw). However, the micronutrient content of some foods diminishes during processing and cooking. Using food consumption data from the 2015/16 Kenya Integrated Household Budget Survey, this study analyzes whether mean consumption estimates of dietary energy, macronutrients, and eight micronutrients are equivalent (applying a two-side paired equivalence test) when matching foods: (1) considering the nutrient content in raw foods (as reported in the survey), and (2) considering the nutrient content in foods as typically consumed, thus applying yield and retention factors as needed. Both food matching approaches rendered statistically equivalent mean consumption estimates, at national and county levels, for dietary energy, protein, fats, available carbohydrates, total fiber, calcium and zinc. Non-equivalent means were found for iron, vitamins A, B1, B2, B12, and C. The higher differences between the means were, in percentage change, for vitamin C (47%), B1 (34%) and B12 (26%).
... HCES are conducted in a large number of countries every two to ten years, enabling the analysis of trends over time. In the past two decades, data from HCES have increasingly been used in an effort to derive food and nutrition information for policy decisions (Fiedler, 2013;Coates et al., 2017;Moltedo et al., 2018). ...
... Problems with non-comparability of food lists and recall periods of food consumption modules across HCES have been documented by the International Household Survey Network in a large review ( Smith et al., 2014). There is ongoing work to standardize food consumption modules in HCES, and a dietary diversity indicator for household data has been proposed by FAO ( Moltedo et al., 2018), which should improve comparability across HCES in the future. ...
In the past decade, food group dietary diversity indicators (FGIs) have increasingly been used to assess the impact of agriculture on food security or nutrition. Following a structured search strategy and a two-phase screening process, 46 studies investigating associations between agriculture and food security or nutrition through the use of simple FGIs were assessed for how the indicators were constructed and interpreted. Most studies based on individual level FGIs were consistent with published guidance, while many of the studies measuring households' dietary diversity were not, particularly in terms of interpretation of the indicators or of food group classification. Efforts are needed to harmonize the way FGIs are used and interpreted in order to enhance comparability across studies and allow meta-analyses of the association between agriculture and food security or nutrition.
