1. Can Household Dietary Data and Adult Male Equivalent Distribution Assumptions Accurately Predict Individual Level Food Consumption in Ethiopia? Lauer, Jacqueline MPH, MS; Coates, Jennie PhD; Rogers, Bea PhD; Blau, Alex MS; Roba, Alemzewed MS; Tesema, Yohannes MS Tufts’ Friedman School of Nutrition Science and Policy Background and Significance Methods Research Aims and Hypotheses Discussion and Conclusions Food and nutrition polices and programs require information about which foods and nutrients are consumed by which groups and in what quantity. Due to their low-cost, routine collection, and general availability, Household Consumption and Expenditure Surveys (HCES) are routinely used as a source of dietary data. However, because information is collected at the household level, assumptions must be made in order to derive individual level estimates of food consumption from these surveys. In general, it is assumed that macro- and micro-nutrients are distributed within a household according to an individual’s energy requirements. Adult Male Equivalents (AMEs) (also called Adult Consumption Equivalents), developed by FAO/WHO Joint Expert Consultations, express energy requirements on the basis of gender, age, and physiological status as a proportion of the requirements of an average adult male. 1 These AME factors are then applied to information about the household’s total food consumption and its demographic composition in order to estimate the proportion of total food and nutrient availability allocated to individuals in the household. Despite the fact that the AME approach to estimating individual level consumption is often utilized, the assumption of ‘equitable’ intra-household distribution based on energy requirements has only rarely been tested. Previous research by Rogers, Coates, and Blau (2012) analyzed household and individual level food consumption data from 600 households in Bangladesh. 2 Results from this prior study are presented below. Based on these findings from Bangladesh, AME distribution assumptions may not accurately predict individual-level dietary consumption from household level data. However, additional studies across different geographical areas are needed in order to further test the validity of this approach. In addition, it would be useful to have a better understanding of which household characteristics, if any, are most associated with deviations from AME distribution assumptions. This research study, which is modeled after the aforementioned Bangladesh study by Rogers, Coates, and Blau, has two primary aims: Aim #1: The first aim of this study is to determine if individual-level dietary information derived from household data using AME distribution rules differs from dietary information obtained directly from individual intake data for various age, sex, and physiological groups in Ethiopia. Hypothesis: Based on previous research, it is hypothesized that certain vulnerable groups in Ethiopia, like children under the age of five and pregnant/lactating women will receive less than what would be predicted by the AME distribution assumptions. Aim#2: The second aim of this study is to determine what household characteristics, if any, are most associated with deviations from AME distribution assumptions in Ethiopia. This study will look at factors such as household food security status, women’s empowerment, and the household’s dependency ratio. Hypothesis #2: It is hypothesized that households that have a lower food security status, lower women’s empowerment, and a higher dependency ratio will deviate more from AME distribution assumptions. Overall, research from Bangladesh by Rogers, Coates, and Blau shows that AME distribution assumptions may not always be accurate when compared to individual 24- hour consumption data. This is especially true in the case of the children under the age of five, which is often considered a nutritionally vulnerable demographic. This calls into question the practice of deriving individual level estimates using HCES surveys and AME distribution assumptions, but further studies are needed. Moving forward, it is important that we do similar analysis in varying geographic locations, such as Ethiopia, as intra-household food allocation practices likely vary across geographical region. Regression analysis will also be important in determining what household factors are associated with deviations from these assumptions. Results from these studies will have numerous policy implications, especially related to food distribution and food fortification programs. Finally, there are several methodological limitations worth mentioning. For one, it is challenging to obtain good quality dietary data due to factors such as difficulties with recall, coding errors, crude estimates of portion size, and limitations of FCTs. Furthermore, AME calculations are based on estimated caloric needs, which assume average heights, weights, and physical activity levels.  This research study makes use of data from round two of USAID’s ENGINE (Empowering New Generations to Improve Nutrition and Economic Development) Project’s Agriculture-Nutrition Survey in Ethiopia. The sample size for this dataset is 1,200 households and about 6,600 individuals.  This survey is unique in that it asked the household food preparer to report on all foods consumed by the household and what proportion was consumed by each member of the household in the previous 24 hours Therefore, there is dietary information at the household as well as the individual level.  STATA software is being used for all data analysis. Dietary data were first cleaned and coded to match the Ethiopian food composition table (FCT). 3 At the individual level, portion sizes for the various food items will be converted into grams, which will then converted into quantities of calories (kcals), protein (grams), animal source protein (grams), and Vitamin A (REs).  Adult Male Equivalents will be calculated by comparing individual caloric needs to the caloric needs of an adult male. Individual dietary intake will then be compared to intake calculated by applying AME distribution assumptions to total household consumption.  Regression analysis will be used to determine which, if any, household factors were significantly associated with deviations from AME distribution assumptions. Variables for this analysis will include the household’s food security status (using the Household Food Insecurity Access Scale), women’s empowerment (degree of control over household expenditures), and the household’s dependency ratio.  Results from this study will be formally compared to the results from the Bangladesh study. Results for protein, presented in Table 2, are similar to those for calorie consumption. Children under the age of five in Bangladesh receive only about 77% of the amount of protein they are expected to receive using AME distribution assumptions. Once again, the elderly are consuming more than their “fair share.” Throughout the analysis, there is no evidence that females consume less of their “fair share” compared to males in similar age categories. References 1. Weisell, Robert and Dop, Marie Claude (2012). The Adult Male Equivalent concept and its application to Household Consumption and Expenditures Surveys (HCES). Food and Nutrition Bulletin, 33(3S): S157-S162 2. Rogers B, Coates J, and Blau A. (2012). Estimating Individual Consumption from National Household Consumption and Expenditure Survey Data for Nutrition Programming Decisions. Presented at: UN FAO International Conference on Diet and Activity Methods, Rome, May 2012. 3. EHNRI/FAO. (1998) Food Composition Table for Use in Ethiopia IV (English) Bangladesh Table 1: Total Calories (Kcals) Age GroupSexAMEIndv. 24hr Recall Indv. 24hr Proportion of “Fair Share” N=13ExBFed902.5600 N=135PregLact2366.872124.940.983 N=204<5 Years1335.81871.70.681 N=259>5 & <18M2233.901942.600.926 N=363F1900.631798.820.981 N=565>18 & < 65M2607.662662.271.057 N=424F1935.702110.251.093 N=83>65 Years1700.352060.991.186 Bangladesh Table 2: Total Protein (grams) Age GroupSexAMEIndv. 24hr Recall Indv. 24hr Proportion of “Fair Share” N=13ExBFed15.2200 N=135PregLact35.4634.010.962 N=204<5 Years19.1014.940.767 N=259>5 & <18M32.8330.380.927 N=363F29.2629.390.986 N=565>18 & < 65M41.5543.341.059 N=424F31.3532.801.068 N=83>65 Years27.3432.791.199 As shown in Table 1, AME distribution assumptions applied to a household food consumption survey are shown to be fairly accurate compared to individual intake, measured using 24-hour recall for certain groups, mainly for adults and adolescents. However, there is a big departure when it comes to children under the age of five. According to these results, children under the age of five in Bangladesh receive only about 68% of the calories that they are expected to receive using AME distribution assumptions. The elderly in Bangladesh, on the other hand, receive more than their “fair share,” likely reflecting their preferred and respected position in the household.