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Objective: The present paper aimed to demonstrate how 24 h dietary recall data can be used to generate a nutrition-relevant food list for household consumption and expenditure surveys (HCES) using contribution analysis and stepwise regression.Design: The analysis used data from the 2011/12 Bangladesh Integrated Household Survey (BIHS), which is nationally representative of rural Bangladesh. A total of 325 primary sampling units (PSU=village) were surveyed through a two-stage stratified sampling approach. The household food consumption module used for the analysis consisted of a 24 h open dietary recall in which the female member in charge of preparing and serving food was asked about foods and quantities consumed by the whole household.Setting: Rural Bangladesh.ParticipantsA total of 6500 households.Results: The original 24 h open dietary recall data in the BIHS were comprised of 288 individual foods that were grouped into ninety-four similar food groups. Contribution analysis and stepwise regression were based on nutrients of public health interest in Bangladesh (energy, protein, fat, Fe, Zn, vitamin A). These steps revealed that a list of fifty-nine food items captures approximately 90 % of the total intake and up to 90 % of the between-person variation for the key nutrients based on the diets of the population.Conclusions: The study illustrates how 24 h open dietary recall data can be used to generate a country-specific nutrition-relevant food list that could be integrated into an HCES consumption module to enable more accurate and comprehensive household-level food and nutrient analyses. [ABSTRACT FROM AUTHOR]

Many researchers use data from Household Consumption and Expenditure Surveys (HCES) to estimate individual food and nutrient intake when individual dietary data are not available. They assume that food is allocated within households according to members’ proportional energy requirements relative to an adult male (called an adult male equivalent, or AME). This study sought to validate AME-based estimates of individual consumption of calories, protein, iron, and animal source protein (ASP) across 10 age-sex categories, using data from Bangladesh and Ethiopia containing both household and individual-level consumption data. The study also assessed the accuracy of adjusting for meal partakers and physical activity levels (PAL), and compared energy-weighted AMEs to nutrient-specific AME predictions. Energy AME-based predictions of nutrient intake were generally accurate within ten percentage points of individually reported intakes, but were less accurate for infants 6–23 months and children in Bangladesh than for other demographic groups. AME predictions were more accurate: (1) in Ethiopia than in Bangladesh, (2) for predicting intake of the three nutrients rather than ASP, (3) for estimating nutrient intake rather than adequacy, (4) using energy-weighted AMEs rather than nutrient specific weights, and (5) using moderate PAL for youth and adults rather than high PAL. Adjusting for meal partakers did not consistently improve the AME-based predictions. Energy based AME estimates from household data can produce a useful proxy of average intake for certain population subgroups, however individually measured dietary assessment remains the best approach to identify groups at risk of nutrient inadequacy. [ABSTRACT FROM AUTHOR]

Changing precipitation patterns caused by climate change are expected to have major impacts on food security and nutrition in agrarian areas in developing countries. However, the linkages between the duration and severity of precipitation shocks and their effects on child nutrition and household food security metrics remain underexplored. In this study, we used Feed the Future datasets from Ghana and Bangladesh to examine the impact of precipitation extremes on nutrition, measured by children's height-for-age and weight-for-height Z-scores, and food security, measured by the Household Hunger Scale. We used a spatial error regression to control for the effects of spatial autocorrelation, and we found an association between precipitation shocks and household hunger in both Ghana and Bangladesh, as well as an association between higher rainfall and worse child nutrition in Ghana. [ABSTRACT FROM AUTHOR]