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Methodological note on new estimates of the prevalence of undernourishment in China













Cafiero, C., Feng, J. & Ishaq, A. 2020. Methodological note on new estimates of the prevalence of undernourishment in China. FAO Statistics Working Paper 20-18. Rome, FAO. 




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    Refinements to the FAO methodology for estimating the prevalence of undernourishment indicator 2014
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    The FAO prevalence of undernourishment (PoU) indicator monitors progress towards Millennium Development Goal target 1C of halving, between 1990 and 2015, the proportion of people suffering from hunger. Estimates of the number of undernourished (NoU) - calculated by multiplying the PoU by the size of the reference population - are used to monitor progress towards the World Food Summit goal of reducing by half the number of people suffering from undernourishment. The PoU indicator is defined a s the probability that a randomly selected individual from the reference population is found to consume less than his/her calorie requirement for an active and healthy life. This paper reports on refinements to the methodology for estimating the Prevalence of Undernourishment that were adopted during the preparation of the State of Food Insecurity in the World Report 2014. The paper reviews the method adopted for selecting the functional form of the probability density function for the calculati on of the PoU, which uses a data-driven criterion. It proposes revised methods for estimating the variability (CV) and asymmetry (SK) parameters from available household survey, based on a leave-out-one cross validation approach. This approach is shown to be more conservative and stable across different country datasets than alternative methods. Following, the paper describes a regression approach for controlling for excess variability due to differences between food acquisition and consumptio n in surveys, which allows for a seasonality adjustment. Finally, the paper introduces an updated regression for computing variability measures in the absence of reliable household surveys, which incorporates the effect of food prices along with those of per capita income levels and inequality.
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    Methodological issues in the estimation of the prevalence of undernourishment based on dietary energy consumption data: A review and clarification 2014
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    Sukhatme had in the early 1960’s originally formulated the estimate of the proportion of undernourished in a population (PU) within a bivariate distribution framework where dietary energy consumption (DEC) and dietary energy requirement (DER) are considered as random variables. However, in the absence of data on DEC and DER of individuals expressed in the form of bivariate distribution, Sukhatme had suggested a formula that considers the part of the distribution of DEC below a cut-off point repr esenting the lower limit of the distribution of DER as an estimate of PU. However, this univariate approach has been criticised as yielding an underestimate of the magnitude of the prevalence undernourishment in a population. In response to this critic, Sukhatme has attempted to justify the approach by invoking the theory of intra-individual changes in DER. As this theory has led to a controversy rather than a clarification of the univariate approach, doubts regarding its validity still prevail. Following a review of these developments including the concept of DER, this article shows that the formulation of PU within the bivariate distribution framework is inappropriate. Subsequently, the relevance of the univariate approach is clarified. Finally, the article addresses certain issues relating to practical estimation of the prevalence measures based on household rather than individual data pertaining to DEC.
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    Estimating the prevalence of nutrient inadequacy from household consumption and expenditure surveys 2022
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    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.

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