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            "target": "<p><strong>Data availability:</strong></p>\n<p>Since 2017 FAO has reported separate estimates of PoU for 160 countries. </p>\n<p>While country-level estimates are presented as three-year averages, regional and global estimates are yearly estimates. </p>\n<p><strong>Time series: </strong></p>\n<p>2000 - current </p>\n<p><strong>Disaggregation: </strong></p>\n<p>Due to reliance on national Food Balance Sheets data to estimate mean caloric consumption levels in the population, the global monitoring of MDG Target 1C and of the WFS target has been based on estimates of the PoU at national level only. </p>\n<p>In principle, the indicator can be computed for any specific population group, provided sufficient accurate information exists to characterize the model&#x2019;s parameters for that specific group, that is, if data on the group&#x2019;s food consumption levels, age/gender structure and &#x2013; possibly &#x2013; physical activity levels, exist. </p>\n<p>The scope for disaggregation thus crucially depends on the availability of surveys designed to be representative at the level of sub national population groups. Given prevailing practice in the design of national household surveys, sufficient reliable information is seldom available for disaggregation beyond the level of macro area of residence (urban-rural) and of the main Provinces/Divisions in a country. To the extent that most of the used surveys are designed to accurately capture the distribution of income, inference can be drawn on the PoU in different income classes of the population. Gender disaggregation is limited by the possibility to identify and group households by gender-related information (such as sex of the head of the household, or male/female ratio).</p>",
            "old": "<p><strong>Data availability:</strong></p>\n<p>Since 2017 FAO has reported separate estimates of PoU for 170 countries, distributed as follows: </p>\n<p>World 170 </p>\n<p> Africa 37 </p>\n<p> Northern Africa 5 </p>\n<p> Sub-Saharan Africa 32 </p>\n<p> Eastern Africa 8 </p>\n<p> Middle Africa 6 </p>\n<p> Southern Africa 5 </p>\n<p> Western Africa 13 </p>\n<p> Asia 38 </p>\n<p> Central Asia 4 </p>\n<p> Eastern Asia 5 </p>\n<p> Southern Asia 7 </p>\n<p> South-Eastern Asia 9 </p>\n<p> Western Asia 13 </p>\n<p> Latin America and the Caribbean 28 </p>\n<p> Caribbean 8 </p>\n<p> Latin America 20 </p>\n<p> Central America 8 </p>\n<p> South America 12 </p>\n<p> Oceania 9</p>\n<p>Australia and New Zealand 2</p>\n<p>Oceania excluding Australia and New Zealand 7</p>\n<p> Northern America and Europe 42</p>\n<p>Northern America 2</p>\n<p>Europe 38</p>\n<p> Eastern Europe 9</p>\n<p> Northern Europe 10</p>\n<p> Southern Europe 12</p>\n<p> Western Europe 7</p>\n<p>While country-level estimates are presented as three-year averages, regional and global estimates are yearly estimates. </p>\n<p><strong>Time series: </strong></p>\n<p>2000 - current </p>\n<p><strong>Disaggregation: </strong></p>\n<p>Due to reliance on national Food Balance Sheets data to estimate mean caloric consumption levels in the population, the global monitoring of MDG Target 1C and of the WFS target has been based on estimates of the PoU at national level only. </p>\n<p>In principle, the indicator can be computed for any specific population group, provided sufficient accurate information exists to characterize the model&#x2019;s parameters for that specific group, that is, if data on the group&#x2019;s food consumption levels, age/gender structure and &#x2013; possibly &#x2013; physical activity levels, exist. </p>\n<p>The scope for disaggregation thus crucially depends on the availability of surveys designed to be representative at the level of sub national population groups. Given prevailing practice in the design of national household surveys, sufficient reliable information is seldom available for disaggregation beyond the level of macro area of residence (urban-rural) and of the main Provinces/Divisions in a country. To the extent that most of the used surveys are designed to accurately capture the distribution of income, inference can be drawn on the PoU in different income classes of the population. Gender disaggregation is limited by the possibility to identify and group households by gender-related information (such as sex of the head of the household, or male/female ratio).</p>",
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            "target": "<h2>Métodos y directrices a disposición de los países para la recopilación de los datos a nivel nacional: </h2>\n<p>Las tres principales fuentes de datos a nivel nacional son: </p>\n<ol>\n  <li>Informes oficiales sobre la producción, el comercio y la utilización de las principales producciones alimentarias y ganaderas. </li>\n  <li>Datos de las encuestas de hogares sobre el consumo de alimentos </li>\n  <li>Características demográficas de la población nacional </li>\n</ol>\n<p> Las fuentes de datos sobre la producción agrícola suelen ser encuestas nacionales realizadas por el Ministerio de Agricultura y Ganadería y/o la Oficina Nacional de Estadística. Las encuestas suelen ser anuales y, a falta de mediciones directas, utilizan información sobre superficies/número de animales y rendimiento de los cultivos/peso de las canales para calcular las cantidades de productos agrícolas o ganaderos. Los censos agrícolas, que la FAO recomienda realizar cada diez años, pueden complementar estas encuestas al proporcionar datos medidos más actualizados sobre los cultivos y el ganado, y permitir así proyecciones/revisiones más precisas. </p>\n<p> La fuente de datos para el comercio agrícola y alimentario es casi exclusivamente la oficina nacional de aduanas (con pocas excepciones en las que los datos pueden obtenerse del Banco Central). Los países suelen preparar estos informes comerciales siguiendo formatos internacionales estándar (clasificaciones de productos/países, unidades de medida, detalles de los socios comerciales). Aunque estos datos comerciales pueden considerarse bastante fiables, ya que son el resultado de la medición/notificación directa por parte de la oficina de aduanas, los problemas del comercio fronterizo no declarado (y el movimiento de animales), la clasificación errónea de las mercancías, la confidencialidad, el desfase temporal, por nombrar algunos, pueden requerir cierto análisis y validación de los datos (a menudo haciendo referencia a las estadísticas comerciales &#x2018; espejo&#x2019; para cotejar las cantidades y los valores). </p>\n<p> Los datos sobre la utilización de los cultivos primarios y procesados y del ganado pueden obtenerse mediante encuestas especializadas (complementadas por la investigación) a través del sistema nacional de la industria agroalimentaria. Las utilizaciones que interesan aquí son las cantidades destinadas, entre otras cosas, a la alimentación animal, a los usos industriales (por ejemplo, la producción de biocombustibles), a las existencias nacionales/empresariales/agrícolas, a las semillas (siembra para el ciclo agrícola sucesivo) &#x2013; para permitir una evaluación lo más precisa posible de las cantidades destinadas/disponibles para el consumo humano potencial. </p>\n<p> Estos conjuntos de datos (producción, comercio y utilización), una vez cotejados y validados, constituyen la base para la elaboración de los Balances Alimentarios (FBS, por sus siglas en inglés). Los FBS son un marco contable en el que la oferta (producción + importaciones + retiradas de existencias) debe ser igual a la utilización (exportación + transformación de alimentos + piensos + semillas + uso industrial, etc.). Cabe señalar que, en el marco de los FBS, las pérdidas posteriores a la cosecha o al sacrificio (hasta el nivel de la venta al por menor) se consideran una utilización y, por tanto, un componente del equilibrio de los FBS. El marco de los FBS ofrece una imagen de la situación de la oferta agrícola a nivel nacional y permite una estructura cruzada en la que los datos, oficiales o estimados/imputados, pueden analizarse y validarse más a fondo (por ejemplo, el número de animales puede resultar subestimado/subreportado). El principal resultado de la compilación de las FBS es el cálculo del Suministro de Energía Dietética (SED) en kilocalorías por persona (basado en las cifras de población) en un año determinado (las cantidades resultantes como disponibles para el consumo humano se convierten en sus equivalentes calóricos utilizando factores de conversión nutritiva adecuados por producto). El SED, a falta de datos de consumo directo procedentes de las encuestas de hogares, es uno de los componentes clave en el cálculo de la Prevalencia de la Subalimentación (PoU). La FAO está emprendiendo actualmente un programa más centrado en proporcionar capacidad de FBS a los países, incluida una herramienta de compilación actualizada. </p>\n<p> La FAO obtiene datos sobre la producción primaria/procesada de cultivos/ganado, y su utilización principal, a través de cuestionarios adaptados a cada país que se envían anualmente a todos ellos. Las estadísticas comerciales oficiales de los países se obtienen anualmente a través de descargas masivas de la base de datos de comercio de las Naciones Unidas (se espera que los países informen anualmente a la UNSD). En algunos casos, cuando están disponibles, también se utilizan los datos nacionales de las FBS. Estos conjuntos de datos se validan y forman parte de las FBS de los países que recopila la FAO. Cabe señalar que, cuando los datos no se comunican oficialmente o no están disponibles (como ocurre con frecuencia con los datos sobre la utilización de productos básicos), es necesario recurrir a imputaciones para cerrar las brechas de datos. </p>\n<p>Las nuevas directrices de las FBS para la compilación nacional (completadas recientemente en colaboración con la Estrategia Mundial) y la nueva herramienta de compilación (aplicación basada en R &#x2018;shiny&#x2019;). </p>\n<p><strong><em>Detalles sobre la metodología de las FBS: </em></strong><a href=\"http://www.fao.org/economic/ess/fbs/ess-fbs02/en/\">http://www.fao.org/economic/ess/fbs/ess-fbs02/en/</a>. </p>\n<p>El Manual de FBS que se muestra aquí no debe confundirse con las Directrices de FBS recientemente finalizadas. El Manual es de carácter más técnico y explica la metodología seguida por la FAO para la elaboración de las FBS de los países. Las Directrices, en cambio, aunque se basan en el Manual, proporcionan a los países una orientación más revisada y práctica y recomendaciones para la compilación a nivel nacional. </p>\n<p><strong><em>Algunos textos de referencia de las FBS también están disponibles en FAOSTAT:</em></strong> <a href=\"http://www.fao.org/faostat/en/#data/FBS\">http://www.fao.org/faostat/en/#data/FBS</a>. </p>",
            "old": "<h2>Métodos y directrices a disposición de los países para la recopilación de los datos a nivel nacional: </h2>\n<p>Las tres principales fuentes de datos a nivel nacional son: </p>\n<ol>\n  <li>Informes oficiales sobre la producción, el comercio y la utilización de las principales producciones alimentarias y ganaderas. </li>\n  <li>Datos de las encuestas de hogares sobre el consumo de alimentos </li>\n  <li>Características demográficas de la población nacional </li>\n</ol>\n<p> Las fuentes de datos sobre la producción agrícola suelen ser encuestas nacionales realizadas por el Ministerio de Agricultura y Ganadería y/o la Oficina Nacional de Estadística. Las encuestas suelen ser anuales y, a falta de mediciones directas, utilizan información sobre superficies/número de animales y rendimiento de los cultivos/peso de las canales para calcular las cantidades de productos agrícolas o ganaderos. Los censos agrícolas, que la FAO recomienda realizar cada diez años, pueden complementar estas encuestas al proporcionar datos medidos más actualizados sobre los cultivos y el ganado, y permitir así proyecciones/revisiones más precisas. </p>\n<p> La fuente de datos para el comercio agrícola y alimentario es casi exclusivamente la oficina nacional de aduanas (con pocas excepciones en las que los datos pueden obtenerse del Banco Central). Los países suelen preparar estos informes comerciales siguiendo formatos internacionales estándar (clasificaciones de productos/países, unidades de medida, detalles de los socios comerciales). Aunque estos datos comerciales pueden considerarse bastante fiables, ya que son el resultado de la medición/notificación directa por parte de la oficina de aduanas, los problemas del comercio fronterizo no declarado (y el movimiento de animales), la clasificación errónea de las mercancías, la confidencialidad, el desfase temporal, por nombrar algunos, pueden requerir cierto análisis y validación de los datos (a menudo haciendo referencia a las estadísticas comerciales &#x2018; espejo&#x2019; para cotejar las cantidades y los valores). </p>\n<p> Los datos sobre la utilización de los cultivos primarios y procesados y del ganado pueden obtenerse mediante encuestas especializadas (complementadas por la investigación) a través del sistema nacional de la industria agroalimentaria. Las utilizaciones que interesan aquí son las cantidades destinadas, entre otras cosas, a la alimentación animal, a los usos industriales (por ejemplo, la producción de biocombustibles), a las existencias nacionales/empresariales/agrícolas, a las semillas (siembra para el ciclo agrícola sucesivo) &#x2013; para permitir una evaluación lo más precisa posible de las cantidades destinadas/disponibles para el consumo humano potencial. </p>\n<p> Estos conjuntos de datos (producción, comercio y utilización), una vez cotejados y validados, constituyen la base para la elaboración de los Balances Alimentarios (FBS, por sus siglas en inglés). Los FBS son un marco contable en el que la oferta (producción + importaciones + retiradas de existencias) debe ser igual a la utilización (exportación + transformación de alimentos + piensos + semillas + uso industrial, etc.). Cabe señalar que, en el marco de los FBS, las pérdidas posteriores a la cosecha o al sacrificio (hasta el nivel de la venta al por menor) se consideran una utilización y, por tanto, un componente del equilibrio de los FBS. El marco de los FBS ofrece una imagen de la situación de la oferta agrícola a nivel nacional y permite una estructura cruzada en la que los datos, oficiales o estimados/imputados, pueden analizarse y validarse más a fondo (por ejemplo, el número de animales puede resultar subestimado/subreportado). El principal resultado de la compilación de las FBS es el cálculo del Suministro de Energía Dietética (SED) en kilocalorías por persona (basado en las cifras de población) en un año determinado (las cantidades resultantes como disponibles para el consumo humano se convierten en sus equivalentes calóricos utilizando factores de conversión nutritiva adecuados por producto). El SED, a falta de datos de consumo directo procedentes de las encuestas de hogares, es uno de los componentes clave en el cálculo de la Prevalencia de la Subalimentación (PoU). La FAO está emprendiendo actualmente un programa más centrado en proporcionar capacidad de FBS a los países, incluida una herramienta de compilación actualizada. </p>\n<p> La FAO obtiene datos sobre la producción primaria/procesada de cultivos/ganado, y su utilización principal, a través de cuestionarios adaptados a cada país que se envían anualmente a todos ellos. Las estadísticas comerciales oficiales de los países se obtienen anualmente a través de descargas masivas de la base de datos de comercio de las Naciones Unidas (se espera que los países informen anualmente a la UNSD). En algunos casos, cuando están disponibles, también se utilizan los datos nacionales de las FBS. Estos conjuntos de datos se validan y forman parte de las FBS de los países que recopila la FAO. Cabe señalar que, cuando los datos no se comunican oficialmente o no están disponibles (como ocurre con frecuencia con los datos sobre la utilización de productos básicos), es necesario recurrir a imputaciones para cerrar las brechas de datos. </p>\n<p>Las nuevas directrices de las FBS para la compilación nacional (completadas recientemente en colaboración con la Estrategia Mundial) y la nueva herramienta de compilación (aplicación basada en R &#x2018;shiny&#x2019;). </p>\n<p><strong><em>Detalles sobre la metodología de las FBS: </em></strong><a href=\"http://www.fao.org/economic/ess/fbs/ess-fbs02/en/\">http://www.fao.org/economic/ess/fbs/ess-fbs02/en/</a>. </p>\n<p>El Manual de FBS que se muestra aquí no debe confundirse con las Directrices de FBS recientemente finalizadas. El Manual es de carácter más técnico y explica la metodología seguida por la FAO para la elaboración de las FBS de los países. Las Directrices, en cambio, aunque se basan en el Manual, proporcionan a los países una orientación más revisada y práctica y recomendaciones para la compilación a nivel nacional. </p>\n<p><strong><em>Algunos textos de referencia de las FBS también están disponibles en FAOSTAT:</em></strong> <a href=\"http://www.fao.org/faostat/en/#data/FBS\">http://www.fao.org/faostat/en/#data/FBS</a>. </p>",
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                "source": "<p>The main three sources of data at national level are: </p>\n<p>a) Official reports on the production, trade and utilization of the major food crop and livestock productions. </p>\n<p>b) Household survey data on food consumption </p>\n<p>c) Demographic characteristics of the national population </p>\n<p>Data sources for agricultural production are usually national surveys that are conducted by the Ministry of Agricultural/Livestock and/or the National Statistical Office. The surveys are usually annual, and in the absence of direct measurements, use information on areas/animal numbers and crop yields/carcass weights to calculate crop or livestock product quantities. Agricultural censuses, which FAO recommends conducting every ten years, may complement these surveys by providing more updated measured data on crops and livestock, and thus enable more precise projections/revisions. </p>\n<p>The data source for agricultural and food trade is almost exclusively the national customs office (with few exceptions where data may be obtained from the Central Bank). Countries often prepare these trade reports following international standard formats (commodity/country classifications, units of measurement, trading partner detail). While such trade data may be considered quite reliable, being the result of direct measurement/reporting by/to the customs office, issues of unreported border trade (and animal movement), misclassification of commodities, confidentiality, time-lag, to name a few, may necessitate some data analysis and validation (often by referring to &#x2018;mirror&#x2019; trade statistics to cross-check quantities and values). </p>\n<p>Data on the utilization of primary and processed crops and livestock may be obtained through specialized surveys (supplemented by research) through the national agri-food industry system. Utilizations of interest here are those quantities destined for, among others, animal feed, for industrial uses (e.g. biofuel production), for national/enterprise/farm stocks, for seed (sowing for the successive agricultural cycle) &#x2013; to enable as accurate an assessment as possible of the quantities destined/available for potential human consumption. </p>\n<p>These datasets (production, trade and utilizations), once cross-checked and validated, form the basis for the compilation of the Food Balance Sheets (FBS). The FBS are an accounting framework whereby supply (production + imports + stock withdrawals) should equal utilization (export + food processing + feed + seed + industrial use, etc.). It should be noted that, within the FBS framework, post-harvest/slaughter losses (up to the retail level) are considered as utilization, and thus a component in the balancing of the FBS. The FBS framework provides a snapshot of the agricultural supply situation at the national level, and allows for a cross-referenced structure whereby data, official or estimated/imputed, may be further analyzed and validated (e.g. animal numbers may result as being under-reported/estimated). </p>\n<p>The main result of the compilation of the FBS is the calculation of the Dietary Energy Supply (DES) in kilocalories per person (based on population figures) in a given year (quantities resulting as available for human consumption are converted into their caloric equivalents by using appropriate nutritive conversion factors by commodity). The DES, in the absence of direct consumption data from household surveys, is one of the key components in the calculation of the Prevalence of Undernourishment (PoU). FAO is presently embarking on a more focused program of providing FBS capacity to countries, including an updated compilation tool. </p>\n<p>FAO obtains crop/livestock primary/processed production data, and principal utilization thereof, through country-tailored questionnaires that are dispatched to all countries annually. Official country trade statistics are obtained annually through bulk downloads of the United Nations trade database (countries are expected to report to UNSD annually). In some cases, when available, national FBS data are also used. These datasets are then validated and form inputs in the country FBS which FAO compiles. It should be noted that when data are not officially reported/available (as is frequently the case with commodity utilization data), and hence it is necessary to resort to imputations to fill the data gaps. </p>\n<p>The new FBS Guidelines for national compilation (completed recently in collaboration with the Global Strategy) and new compilation tool (R-based &#x2018;shiny&#x2019; application). </p>\n<p>Detail on FBS methodology: <a href=\"http://www.fao.org/economic/ess/fbs/ess-fbs02/en/\">http://www.fao.org/economic/ess/fbs/ess-fbs02/en/</a>. </p>\n<p>The FBS Handbook shown here should not be confused with the recently completed FBS Guidelines. The Handbook is of a more technical nature and explains the methodology followed by FAO in compiling country FBS. The Guidelines on the other hand, while based on the Handbook, provide countries with a more revised and practical guidance and recommendations for compilation at the national level. </p>\n<p>Some FBS background text also available on FAOSTAT: <a href=\"http://www.fao.org/faostat/en/#data/FBS\">http://www.fao.org/faostat/en/#data/FBS</a>. </p>",
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            "target": "<p>The main three sources of data at national level are: </p>\n<p>a) Official reports on the production, trade and utilization of the major food crop and livestock productions. </p>\n<p>b) Household survey data on food consumption </p>\n<p>c) Demographic characteristics of the national population </p>\n<p>Data sources for agricultural production are usually national surveys that are conducted by the Ministry of Agricultural/Livestock and/or the National Statistical Office. The surveys are usually annual, and in the absence of direct measurements, use information on areas/animal numbers and crop yields/carcass weights to calculate crop or livestock product quantities. Agricultural censuses, which FAO recommends conducting every ten years, may complement these surveys by providing more updated measured data on crops and livestock, and thus enable more precise projections/revisions. </p>\n<p>The data source for agricultural and food trade is almost exclusively the national customs office (with few exceptions where data may be obtained from the Central Bank). Countries often prepare these trade reports following international standard formats (commodity/country classifications, units of measurement, trading partner detail). While such trade data may be considered quite reliable, being the result of direct measurement/reporting by/to the customs office, issues of unreported border trade (and animal movement), misclassification of commodities, confidentiality, time-lag, to name a few, may necessitate some data analysis and validation (often by referring to &#x2018;mirror&#x2019; trade statistics to cross-check quantities and values). </p>\n<p>Data on the utilization of primary and processed crops and livestock may be obtained through specialized surveys (supplemented by research) through the national agri-food industry system. Utilizations of interest here are those quantities destined for, among others, animal feed, for industrial uses (e.g. biofuel production), for national/enterprise/farm stocks, for seed (sowing for the successive agricultural cycle) &#x2013; to enable as accurate an assessment as possible of the quantities destined/available for potential human consumption. </p>\n<p>These datasets (production, trade and utilizations), once cross-checked and validated, form the basis for the compilation of the Food Balance Sheets (FBS). The FBS are an accounting framework whereby supply (production + imports + stock withdrawals) should equal utilization (export + food processing + feed + seed + industrial use, etc.). It should be noted that, within the FBS framework, post-harvest/slaughter losses (up to the retail level) are considered as utilization, and thus a component in the balancing of the FBS. The FBS framework provides a snapshot of the agricultural supply situation at the national level, and allows for a cross-referenced structure whereby data, official or estimated/imputed, may be further analyzed and validated (e.g. animal numbers may result as being under-reported/estimated). </p>\n<p>The main result of the compilation of the FBS is the calculation of the Dietary Energy Supply (DES) in kilocalories per person (based on population figures) in a given year (quantities resulting as available for human consumption are converted into their caloric equivalents by using appropriate nutritive conversion factors by commodity). The DES, in the absence of direct consumption data from household surveys, is one of the key components in the calculation of the Prevalence of Undernourishment (PoU). FAO is presently embarking on a more focused program of providing FBS capacity to countries, including an updated compilation tool. </p>\n<p>FAO obtains crop/livestock primary/processed production data, and principal utilization thereof, through country-tailored questionnaires that are dispatched to all countries annually. Official country trade statistics are obtained annually through bulk downloads of the United Nations trade database (countries are expected to report to UNSD annually). In some cases, when available, national FBS data are also used. These datasets are then validated and form inputs in the country FBS which FAO compiles. It should be noted that when data are not officially reported/available (as is frequently the case with commodity utilization data), and hence it is necessary to resort to imputations to fill the data gaps. </p>\n<p>The new FBS Guidelines for national compilation (completed recently in collaboration with the Global Strategy) and new compilation tool (R-based &#x2018;shiny&#x2019; application). </p>\n<p>Detail on FBS methodology: <a href=\"http://www.fao.org/economic/ess/fbs/ess-fbs02/en/\">http://www.fao.org/economic/ess/fbs/ess-fbs02/en/</a>. </p>\n<p>The FBS Handbook shown here should not be confused with the recently completed FBS Guidelines. The Handbook is of a more technical nature and explains the methodology followed by FAO in compiling country FBS. The Guidelines on the other hand, while based on the Handbook, provide countries with a more revised and practical guidance and recommendations for compilation at the national level. </p>\n<p>Some FBS background text also available on FAOSTAT: <a href=\"http://www.fao.org/faostat/en/#data/FBS\">http://www.fao.org/faostat/en/#data/FBS</a>. </p>",
            "old": "<p>The main three sources of data at national level are: </p>\n<p>a) Official reports on the production, trade and utilization of the major food crop and livestock productions. </p>\n<p>b) Household survey data on food consumption </p>\n<p>c) Demographic characteristics of the national population </p>\n<p>Data sources for agricultural production are usually national surveys that are conducted by the Ministry of Agricultural/Livestock and/or the National Statistical Office. The surveys are usually annual, and in the absence of direct measurements, use information on areas/animal numbers and crop yields/carcass weights to calculate crop or livestock product quantities. Agricultural censuses, which FAO recommends conducting every ten years, may complement these surveys by providing more updated measured data on crops and livestock, and thus enable more precise projections/revisions. </p>\n<p>The data source for agricultural and food trade is almost exclusively the national customs office (with few exceptions where data may be obtained from the Central Bank). Countries often prepare these trade reports following international standard formats (commodity/country classifications, units of measurement, trading partner detail). While such trade data may be considered quite reliable, being the result of direct measurement/reporting by/to the customs office, issues of unreported border trade (and animal movement), misclassification of commodities, confidentiality, time-lag, to name a few, may necessitate some data analysis and validation (often by referring to &#x2018; mirror&#x2019; trade statistics to cross-check quantities and values). </p>\n<p>Data on the utilization of primary and processed crops and livestock may be obtained through specialized surveys (supplemented by research) through the national agri-food industry system. Utilizations of interest here are those quantities destined for, among others, animal feed, for industrial uses (e.g. biofuel production), for national/enterprise/farm stocks, for seed (sowing for the successive agricultural cycle) &#x2013; to enable as accurate an assessment as possible of the quantities destined/available for potential human consumption. </p>\n<p>These datasets (production, trade and utilizations), once cross-checked and validated, form the basis for the compilation of the Food Balance Sheets (FBS). The FBS are an accounting framework whereby supply (production + imports + stock withdrawals) should equal utilization (export + food processing + feed + seed + industrial use, etc.). It should be noted that, within the FBS framework, post-harvest/slaughter losses (up to the retail level) are considered as utilization, and thus a component in the balancing of the FBS. The FBS framework provides a snapshot of the agricultural supply situation at the national level, and allows for a cross-referenced structure whereby data, official or estimated/imputed, may be further analyzed and validated (e.g. animal numbers may result as being under-reported/estimated). The main result of the compilation of the FBS is the calculation of the Dietary Energy Supply (DES) in kilocalories per person (based on population figures) in a given year (quantities resulting as available for human consumption are converted into their caloric equivalents by using appropriate nutritive conversion factors by commodity). The DES, in the absence of direct consumption data from household surveys, is one of the key components in the calculation of the Prevalence of Undernourishment (PoU). FAO is presently embarking on a more focused program of providing FBS capacity to countries, including an updated compilation tool. </p>\n<p>FAO obtains crop/livestock primary/processed production data, and principal utilization thereof, through country-tailored questionnaires that are dispatched to all countries annually. Official country trade statistics are obtained annually through bulk downloads of the United Nations trade database (countries are expected to report to UNSD annually). In some cases, when available, national FBS data are also used. These datasets are then validated and form inputs in the country FBS which FAO compiles. It should be noted that when data are not officially reported/available (as is frequently the case with commodity utilization data), and hence it is necessary to resort to imputations to fill the data gaps. </p>\n<p>The new FBS Guidelines for national compilation (completed recently in collaboration with the Global Strategy) and new compilation tool (R-based &#x2018;shiny&#x2019; application). </p>\n<p>Detail on FBS methodology: <a href=\"http://www.fao.org/economic/ess/fbs/ess-fbs02/en/\">http://www.fao.org/economic/ess/fbs/ess-fbs02/en/</a>. </p>\n<p>The FBS Handbook shown here should not be confused with the recently completed FBS Guidelines. The Handbook is of a more technical nature and explains the methodology followed by FAO in compiling country FBS. The Guidelines on the other hand, while based on the Handbook, provide countries with a more revised and practical guidance and recommendations for compilation at the national level. </p>\n<p>Some FBS background text also available on FAOSTAT: <a href=\"http://www.fao.org/faostat/en/#data/FBS\">http://www.fao.org/faostat/en/#data/FBS</a>. </p>",
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            "target": "<h2>Agregados regionales: </h2>\n<p>Los agregados regionales y globales del PoU se calculan como: </p>\n<p>PoU_REG = (_i PoU_i &#xD7; N_i) / (_i N_i) </p>\n<p>donde PoU_i son los valores de PoU estimados para todos los países de las regiones para los que los datos disponibles permiten calcular una estimación fiable, y N_i el tamaño de la población correspondiente. </p>",
            "old": "<h2>Agregados regionales: </h2>\n<p>Los agregados regionales y globales del PoU se calculan como: </p>\n<p>PoU_REG = (_i PoU_i &#xD7; N_i) / (_i N_i) </p>\n<p>donde PoU_i son los valores de PoU estimados para todos los países de las regiones para los que los datos disponibles permiten calcular una estimación fiable, y N_i el tamaño de la población correspondiente. </p>",
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                "source": "<p>Regional and global aggregates of the PoU are computed as: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>P</mi>\n        <mi>o</mi>\n        <mi>U</mi>\n      </mrow>\n      <mrow>\n        <mi>R</mi>\n        <mi>E</mi>\n        <mi>G</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mrow>\n          <munder>\n            <mo stretchy=\"false\">&#x2211;</mo>\n            <mrow>\n              <mi>i</mi>\n            </mrow>\n          </munder>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>P</mi>\n                <mi>o</mi>\n                <mi>U</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n            <mi>&amp;nbsp;</mi>\n            <mo>&#xD7;</mo>\n            <mi>&amp;nbsp;</mi>\n            <msub>\n              <mrow>\n                <mi>N</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mrow>\n      </mrow>\n      <mrow>\n        <mrow>\n          <munder>\n            <mo stretchy=\"false\">&#x2211;</mo>\n            <mrow>\n              <mi>i</mi>\n            </mrow>\n          </munder>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>N</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mrow>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>where PoU<sub>i</sub> are the values of PoU estimated for all countries (i) in the aggregate for which available data allow to compute a reliable estimate, and N<sub>i</sub> the corresponding population size. </p>",
                "old_state": 10
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            "action": 30,
            "target": "<p>Regional and global aggregates of the PoU are computed as: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>P</mi>\n        <mi>o</mi>\n        <mi>U</mi>\n      </mrow>\n      <mrow>\n        <mi>R</mi>\n        <mi>E</mi>\n        <mi>G</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mrow>\n          <munder>\n            <mo stretchy=\"false\">&#x2211;</mo>\n            <mrow>\n              <mi>i</mi>\n            </mrow>\n          </munder>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>P</mi>\n                <mi>o</mi>\n                <mi>U</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n            <mi>&amp;nbsp;</mi>\n            <mo>&#xD7;</mo>\n            <mi>&amp;nbsp;</mi>\n            <msub>\n              <mrow>\n                <mi>N</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mrow>\n      </mrow>\n      <mrow>\n        <mrow>\n          <munder>\n            <mo stretchy=\"false\">&#x2211;</mo>\n            <mrow>\n              <mi>i</mi>\n            </mrow>\n          </munder>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>N</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mrow>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>where PoU<sub>i</sub> are the values of PoU estimated for all countries (i) in the aggregate for which available data allow to compute a reliable estimate, and N<sub>i</sub> the corresponding population size. </p>",
            "old": "<p>Regional and global aggregates of the PoU are computed as: </p>\n<p>PoU_REG = (_i PoU_i &#xD7; N_i) / (_i N_i) </p>\n<p>where PoU_i are the values of PoU estimated for all countries in the regions for which available data allow to compute a reliable estimate, and N_i the corresponding population size. </p>\n<p> </p>",
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            "action": 59,
            "target": "<h1>Metodología </h1>\n<h2>Método de cálculo: </h2>\n<p>El indicador se calcula a nivel de la población. Para ello, la población está representada por un individuo &#x201C;medio&#x201D; para el que se modela una distribución de probabilidad de los niveles de ingesta energética diaria habitual a través de una función de densidad de probabilidad (fdp) paramétrica. </p>\n<p> Una vez caracterizada la función de densidad de probabilidad, el indicador se obtiene como la probabilidad acumulada de que la ingesta diaria habitual de energía en la dieta (x) esté por debajo del límite inferior del rango de necesidades normales de energía en la dieta para ese individuo representativo o medio (MDER), como en la fórmula siguiente: </p>\n<p>PoU= &#x222B;_(x&lt;MDER) f(x | CEA; CV; Skew) dx </p>\n<p>donde CEA, CV y Skew son la media, el coeficiente de variación y la asimetría que caracterizan la distribución de los niveles de consumo energético habitual en la población. </p>\n<p>Hasta 2012, la distribución de probabilidad f(x) se modelaba como una fdp Log-normal, informada por sólo dos parámetros: media y coeficiente de variación. En su formulación más reciente, se modela como una fdp de tres parámetros, capaz de representar diferentes grados de asimetría, que van desde la de una distribución Normal simétrica a la de la distribución Log-normal positivamente asimétrica. La flexibilidad en la captura de diferentes grados de asimetría es necesaria para tener en cuenta el hecho de que los niveles de consumo de energía humana están naturalmente acotados por límites fisiológicos. Por lo tanto, es concebible que, a medida que aumentan los niveles medios de consumo, la asimetría de la distribución disminuya, pasando gradualmente de las distribuciones Log-normal (positivamente asimétricas), típicas de las poblaciones en las que el consumo medio de alimentos es relativamente bajo, a las distribuciones Normal (simétricas). Las familias de distribuciones skew-normal y skew-lognormal permiten caracterizar todos los posibles grados intermedios de asimetría positiva. (Véase <a href=\"http://www.fao.org/3/a-i4046e.pdf%20\">http://www.fao.org/3/a-i4046e.pdf</a> para una descripción detallada) </p>\n<p>La División de Estadística de la FAO dispone de una función R personalizada para calcular el PoU, dados los cuatro parámetros CEA, CV, Skew y MDER. </p>\n<p>Se pueden utilizar diferentes fuentes de datos para estimar los diferentes parámetros del modelo. </p>\n<p><strong><em>CEA</em></strong></p>\n<p>La media de la distribución de los niveles de consumo de energía alimentaria para el individuo medio de una población (CEA) corresponde, por definición, al nivel de consumo de alimentos medio diario per cápita de la población. </p>\n<p> La CEA puede estimarse a partir de datos sobre el consumo de alimentos obtenidos mediante encuestas que sean representativas de la población de interés. Dependiendo del diseño de la encuesta, pueden utilizarse para estimar la CEA a nivel nacional y subnacional, ya sea por áreas geográficas o por grupos socioeconómicos de la población. Lamentablemente, aunque la situación está mejorando rápidamente, todavía no se dispone de encuestas representativas que recojan datos sobre el consumo de alimentos para todos los países y todos los años. </p>\n<p> Para la población nacional solamente, la CEA puede estimarse también a partir de las cuentas del suministro y utilización total de todos los productos alimentarios en un país, donde la contribución de cada producto a la disponibilidad de alimentos para el consumo humano se expresa en su contenido de energía dietética, y su total se divide por el tamaño de la población. La principal fuente de datos sobre los balances alimentarios nacionales son las Hojas de Balance de Alimentos (FBS) que mantiene la FAO para la mayoría de los países del mundo (véase <a href=\"http://www.fao.org/economic/ess/fbs/en/\">http://www.fao.org/economic/ess/fbs/en/</a>), informadas por los datos oficiales comunicados por los países miembros, y difundidas a través de FAOSTAT (<a href=\"http://faostat3.fao.org/download/FB/*/E\">http://faostat3.fao.org/download/FB/*/E</a>) </p>\n<p><strong><em>CV </em></strong></p>\n<p>Las encuestas que contienen información sobre el consumo de alimentos a nivel individual o familiar son la única fuente disponible para estimar directamente el CV del consumo habitual de alimentos para el individuo representativo de la población. Lamentablemente, los datos de las encuestas sobre el consumo de alimentos están plagados de muchos problemas que complican la estimación fiable del CV. </p>\n<p> En principio, se necesitarían observaciones repetidas del consumo diario de cada individuo de una muestra para estimar los niveles de consumo habitual y controlar los errores de medición. Además, los datos deberían recogerse en diferentes períodos del año en los mismos individuos u hogares para tener en cuenta la posible variación estacional de los niveles de consumo de energía alimentaria. Debido a su coste, las encuestas de ingesta dietética individual representativas a nivel nacional con estas características son muy raras, y prácticamente inexistentes en la mayoría de los países en desarrollo. En consecuencia, las fuentes de datos más comunes para estimar el CV son las encuestas de hogares con fines múltiples, como las Encuestas de Medición del Nivel de Vida, las Encuestas de Ingresos y Gastos de los Hogares (o la Encuesta de Presupuestos Familiares), que recogen también información sobre el consumo de alimentos. Sin embargo, cuando se utilizan datos recopilados a nivel de los hogares, hay que prestar mucha atención a la hora de distinguir los niveles de compras o adquisiciones de alimentos de los niveles de utilización real (consumo y despilfarro) durante el periodo de referencia identificado, así como a la hora de registrar adecuadamente el número de personas que participan en el consumo; además, los datos a nivel de los hogares ocultarán la variabilidad debida a la asignación de alimentos dentro del hogar. </p>\n<p> Por todas estas razones, el coeficiente de variación calculado sobre la serie de niveles de consumo energético dietético diario medio per cápita registrado para cada hogar incluido en una encuesta nunca es una estimación fiable del CV, que debería reflejar la variabilidad en los niveles de consumo energético dietético diario habitual (y no ocasional), a nivel individual (y no de hogar). Las estimaciones empíricas del CV a partir de los datos de las encuestas de hogares están sesgadas al alza debido a la variabilidad espuria inducida por el error de medición, las diferencias entre el consumo ocasional y el habitual, las diferencias entre la adquisición y el consumo real y la estacionalidad; además, no reflejan la variabilidad del consumo de energía alimentaria en la población asociada a las características individuales de los miembros del hogar (como el sexo, la edad, la masa corporal y los niveles de actividad física). </p>\n<p> Por lo tanto, cuando se utilizan datos recogidos a través de encuestas de hogares, la mejor forma de estimar el CV es de forma indirecta, controlando la variabilidad espuria, y ajustándolo para reflejar la variabilidad interindividual (además de la interhogar). La forma más sencilla de proceder es clasificar los hogares en grupos homogéneos y calcular el coeficiente de variación del consumo medio de energía alimentaria per cápita entre los grupos de hogares. De este modo se obtiene una estimación del componente de CV entre hogares, denominado CV_H. Se obtiene una estimación del componente interindividual del CV, etiquetado CV_I, para cada población, a partir de su estructura por sexo, edad y masas corporales, y se combinan los dos componentes para obtener la estimación necesaria como: </p>\n<p>CV^ = v[(CV_H)^2+(CV_I)^2 )]. </p>\n<p> Para los países y años en los que no se dispone de datos de encuestas de hogares, se obtiene una estimación indirecta del CV, CV_IND, a través de una regresión que proyecta los valores del PIB per cápita, el coeficiente de Gini de la renta y un índice del precio relativo de los alimentos (IPRA) sobre el CV, controlando al mismo tiempo un desplazamiento regional (REG). </p>\n<p>CV^_IND=&#xDF;_0+&#xDF;_1 PIB+ &#xDF;_2 GINI+ &#xDF;_3 IPRA+&#xDF;_4 REG. </p>\n<p>Los coeficientes de la regresión se estiman a partir del conjunto de datos y años para los que se dispone de datos sobre la CV, el PIB, el GINI y el IPRA. </p>\n<p><strong><em>Asimetría</em></strong></p>\n<p> Como la asimetría no se ve muy afectada por la presencia de variabilidad espuria, se estima directamente a partir de los datos a nivel de hogar sobre el consumo dietético diario medio, con la única excepción de eliminar los valores extremadamente altos o extremadamente bajos. Si la asimetría estimada empíricamente supera el valor que correspondería a la asimetría de la distribución logarítmica normal con una media y un coeficiente de variación dados, se desprecia el parámetro y se utiliza una distribución lognormal de dos parámetros para f(x). (Véase <a href=\"http://www.fao.org/3/a-i4046e.pdf\">http://www.fao.org/3/a-i4046e.pdf</a> para más detalles). </p>\n<p><strong><em>MDER </em></strong></p>\n<p>Las necesidades energéticas humanas se calculan multiplicando las necesidades normativas de la tasa metabólica básica (TMB, expresada por kg de masa corporal) por el peso ideal de una persona sana de determinada estatura, y luego se multiplican por un coeficiente de nivel de actividad física (CNAF). Así, se calculan rangos de necesidades energéticas normales para cada sexo y grupo de edad de la población, observando que existe toda una gama de valores de Índice de Masa Corporal (IMC) &#x2013; desde 18,5 hasta 25 &#x2013; que son compatibles con la salud. Esto implica que cualquier altura alcanzada puede corresponder a toda una gama de pesos corporales saludables y, por lo tanto, a una gama de valores de necesidades energéticas para la TMB. </p>\n<p> Dada la información sobre la estatura media y la consideración de que el grupo puede contener individuos con diferentes niveles de actividad física, se pueden calcular las necesidades mínimas, medias y máximas de energía dietética para cada sexo y clase de edad, teniendo en cuenta las asignaciones especiales para el crecimiento de los individuos de 0 a 21 años y para el embarazo y la lactancia. </p>\n<p>(Ver <a href=\"ftp://ftp.fao.org/docrep/fao/007/y5686e/y5686e00.pdf\">ftp://ftp.fao.org/docrep/fao/007/y5686e/y5686e00.pdf</a> para más detalles). </p>\n<p> La MDER para un grupo de población determinado, incluso para la población nacional, se obtiene como la media ponderada de los mínimos de los rangos de necesidades energéticas de cada sexo y clase de edad, utilizando el tamaño de la población en cada clase como ponderadores. </p>\n<p> Al calcular la prevalencia de la insuficiencia energética alimentaria en una población, a menudo se ha producido una confusión entre el concepto de MDER y el de la Ingesta Energética Dietética Recomendada, y en relación con el umbral adecuado que debe utilizarse para calcular la probabilidad de insuficiencia. La razón por la que la probabilidad de insuficiencia energética en la dieta debe calcularse con referencia a la MDER y no a la IEDR (que, en cambio, puede utilizarse como estimación del nivel medio de ingesta dietética recomendada para toda la población) es simplemente para reconocer el hecho de que en cualquier población existe un cierto rango de variabilidad normal en las necesidades; utilizar la IEDR como umbral sobrestimaría en gran medida la subalimentación, ya que contaría también la proporción de la población sana que consume menos que la media, simplemente por tener necesidades inferiores a la media. Cuando sea necesario, se debe utilizar la IEDR o el nivel medio de ingesta de energía dietética recomendada en una población para calcular la brecha energética dietética.</p>",
            "old": "<h1>Metodología </h1>\n<h2>Método de cálculo: </h2>\n<p>El indicador se calcula a nivel de la población. Para ello, la población está representada por un individuo &#x201C;medio&#x201D; para el que se modela una distribución de probabilidad de los niveles de ingesta energética diaria habitual a través de una función de densidad de probabilidad (fdp) paramétrica. </p>\n<p> Una vez caracterizada la función de densidad de probabilidad, el indicador se obtiene como la probabilidad acumulada de que la ingesta diaria habitual de energía en la dieta (x) esté por debajo del límite inferior del rango de necesidades normales de energía en la dieta para ese individuo representativo o medio (MDER), como en la fórmula siguiente: </p>\n<p>PoU= &#x222B;_(x&lt;MDER) f(x | CEA; CV; Skew) dx </p>\n<p>donde CEA, CV y Skew son la media, el coeficiente de variación y la asimetría que caracterizan la distribución de los niveles de consumo energético habitual en la población. </p>\n<p>Hasta 2012, la distribución de probabilidad f(x) se modelaba como una fdp Log-normal, informada por sólo dos parámetros: media y coeficiente de variación. En su formulación más reciente, se modela como una fdp de tres parámetros, capaz de representar diferentes grados de asimetría, que van desde la de una distribución Normal simétrica a la de la distribución Log-normal positivamente asimétrica. La flexibilidad en la captura de diferentes grados de asimetría es necesaria para tener en cuenta el hecho de que los niveles de consumo de energía humana están naturalmente acotados por límites fisiológicos. Por lo tanto, es concebible que, a medida que aumentan los niveles medios de consumo, la asimetría de la distribución disminuya, pasando gradualmente de las distribuciones Log-normal (positivamente asimétricas), típicas de las poblaciones en las que el consumo medio de alimentos es relativamente bajo, a las distribuciones Normal (simétricas). Las familias de distribuciones skew-normal y skew-lognormal permiten caracterizar todos los posibles grados intermedios de asimetría positiva. (Véase <a href=\"http://www.fao.org/3/a-i4046e.pdf%20\">http://www.fao.org/3/a-i4046e.pdf</a> para una descripción detallada) </p>\n<p>La División de Estadística de la FAO dispone de una función R personalizada para calcular el PoU, dados los cuatro parámetros CEA, CV, Skew y MDER. </p>\n<p>Se pueden utilizar diferentes fuentes de datos para estimar los diferentes parámetros del modelo. </p>\n<p><strong><em>CEA</em></strong></p>\n<p>La media de la distribución de los niveles de consumo de energía alimentaria para el individuo medio de una población (CEA) corresponde, por definición, al nivel de consumo de alimentos medio diario per cápita de la población. </p>\n<p> La CEA puede estimarse a partir de datos sobre el consumo de alimentos obtenidos mediante encuestas que sean representativas de la población de interés. Dependiendo del diseño de la encuesta, pueden utilizarse para estimar la CEA a nivel nacional y subnacional, ya sea por áreas geográficas o por grupos socioeconómicos de la población. Lamentablemente, aunque la situación está mejorando rápidamente, todavía no se dispone de encuestas representativas que recojan datos sobre el consumo de alimentos para todos los países y todos los años. </p>\n<p> Para la población nacional solamente, la CEA puede estimarse también a partir de las cuentas del suministro y utilización total de todos los productos alimentarios en un país, donde la contribución de cada producto a la disponibilidad de alimentos para el consumo humano se expresa en su contenido de energía dietética, y su total se divide por el tamaño de la población. La principal fuente de datos sobre los balances alimentarios nacionales son las Hojas de Balance de Alimentos (FBS) que mantiene la FAO para la mayoría de los países del mundo (véase <a href=\"http://www.fao.org/economic/ess/fbs/en/\">http://www.fao.org/economic/ess/fbs/en/</a>), informadas por los datos oficiales comunicados por los países miembros, y difundidas a través de FAOSTAT (<a href=\"http://faostat3.fao.org/download/FB/*/E\">http://faostat3.fao.org/download/FB/*/E</a>) </p>\n<p><strong><em>CV </em></strong></p>\n<p>Las encuestas que contienen información sobre el consumo de alimentos a nivel individual o familiar son la única fuente disponible para estimar directamente el CV del consumo habitual de alimentos para el individuo representativo de la población. Lamentablemente, los datos de las encuestas sobre el consumo de alimentos están plagados de muchos problemas que complican la estimación fiable del CV. </p>\n<p> En principio, se necesitarían observaciones repetidas del consumo diario de cada individuo de una muestra para estimar los niveles de consumo habitual y controlar los errores de medición. Además, los datos deberían recogerse en diferentes períodos del año en los mismos individuos u hogares para tener en cuenta la posible variación estacional de los niveles de consumo de energía alimentaria. Debido a su coste, las encuestas de ingesta dietética individual representativas a nivel nacional con estas características son muy raras, y prácticamente inexistentes en la mayoría de los países en desarrollo. En consecuencia, las fuentes de datos más comunes para estimar el CV son las encuestas de hogares con fines múltiples, como las Encuestas de Medición del Nivel de Vida, las Encuestas de Ingresos y Gastos de los Hogares (o la Encuesta de Presupuestos Familiares), que recogen también información sobre el consumo de alimentos. Sin embargo, cuando se utilizan datos recopilados a nivel de los hogares, hay que prestar mucha atención a la hora de distinguir los niveles de compras o adquisiciones de alimentos de los niveles de utilización real (consumo y despilfarro) durante el periodo de referencia identificado, así como a la hora de registrar adecuadamente el número de personas que participan en el consumo; además, los datos a nivel de los hogares ocultarán la variabilidad debida a la asignación de alimentos dentro del hogar. </p>\n<p> Por todas estas razones, el coeficiente de variación calculado sobre la serie de niveles de consumo energético dietético diario medio per cápita registrado para cada hogar incluido en una encuesta nunca es una estimación fiable del CV, que debería reflejar la variabilidad en los niveles de consumo energético dietético diario habitual (y no ocasional), a nivel individual (y no de hogar). Las estimaciones empíricas del CV a partir de los datos de las encuestas de hogares están sesgadas al alza debido a la variabilidad espuria inducida por el error de medición, las diferencias entre el consumo ocasional y el habitual, las diferencias entre la adquisición y el consumo real y la estacionalidad; además, no reflejan la variabilidad del consumo de energía alimentaria en la población asociada a las características individuales de los miembros del hogar (como el sexo, la edad, la masa corporal y los niveles de actividad física). </p>\n<p> Por lo tanto, cuando se utilizan datos recogidos a través de encuestas de hogares, la mejor forma de estimar el CV es de forma indirecta, controlando la variabilidad espuria, y ajustándolo para reflejar la variabilidad interindividual (además de la interhogar). La forma más sencilla de proceder es clasificar los hogares en grupos homogéneos y calcular el coeficiente de variación del consumo medio de energía alimentaria per cápita entre los grupos de hogares. De este modo se obtiene una estimación del componente de CV entre hogares, denominado CV_H. Se obtiene una estimación del componente interindividual del CV, etiquetado CV_I, para cada población, a partir de su estructura por sexo, edad y masas corporales, y se combinan los dos componentes para obtener la estimación necesaria como: </p>\n<p>CV^ = v[(CV_H)^2+(CV_I)^2 )]. </p>\n<p> Para los países y años en los que no se dispone de datos de encuestas de hogares, se obtiene una estimación indirecta del CV, CV_IND, a través de una regresión que proyecta los valores del PIB per cápita, el coeficiente de Gini de la renta y un índice del precio relativo de los alimentos (IPRA) sobre el CV, controlando al mismo tiempo un desplazamiento regional (REG). </p>\n<p>CV^_IND=&#xDF;_0+&#xDF;_1 PIB+ &#xDF;_2 GINI+ &#xDF;_3 IPRA+&#xDF;_4 REG. </p>\n<p>Los coeficientes de la regresión se estiman a partir del conjunto de datos y años para los que se dispone de datos sobre la CV, el PIB, el GINI y el IPRA. </p>\n<p><strong><em>Asimetría</em></strong></p>\n<p> Como la asimetría no se ve muy afectada por la presencia de variabilidad espuria, se estima directamente a partir de los datos a nivel de hogar sobre el consumo dietético diario medio, con la única excepción de eliminar los valores extremadamente altos o extremadamente bajos. Si la asimetría estimada empíricamente supera el valor que correspondería a la asimetría de la distribución logarítmica normal con una media y un coeficiente de variación dados, se desprecia el parámetro y se utiliza una distribución lognormal de dos parámetros para f(x). (Véase <a href=\"http://www.fao.org/3/a-i4046e.pdf\">http://www.fao.org/3/a-i4046e.pdf</a> para más detalles). </p>\n<p><strong><em>MDER </em></strong></p>\n<p>Las necesidades energéticas humanas se calculan multiplicando las necesidades normativas de la tasa metabólica básica (TMB, expresada por kg de masa corporal) por el peso ideal de una persona sana de determinada estatura, y luego se multiplican por un coeficiente de nivel de actividad física (CNAF). Así, se calculan rangos de necesidades energéticas normales para cada sexo y grupo de edad de la población, observando que existe toda una gama de valores de Índice de Masa Corporal (IMC) &#x2013; desde 18,5 hasta 25 &#x2013; que son compatibles con la salud. Esto implica que cualquier altura alcanzada puede corresponder a toda una gama de pesos corporales saludables y, por lo tanto, a una gama de valores de necesidades energéticas para la TMB. </p>\n<p> Dada la información sobre la estatura media y la consideración de que el grupo puede contener individuos con diferentes niveles de actividad física, se pueden calcular las necesidades mínimas, medias y máximas de energía dietética para cada sexo y clase de edad, teniendo en cuenta las asignaciones especiales para el crecimiento de los individuos de 0 a 21 años y para el embarazo y la lactancia. </p>\n<p>(Ver <a href=\"ftp://ftp.fao.org/docrep/fao/007/y5686e/y5686e00.pdf\">ftp://ftp.fao.org/docrep/fao/007/y5686e/y5686e00.pdf</a> para más detalles). </p>\n<p> La MDER para un grupo de población determinado, incluso para la población nacional, se obtiene como la media ponderada de los mínimos de los rangos de necesidades energéticas de cada sexo y clase de edad, utilizando el tamaño de la población en cada clase como ponderadores. </p>\n<p> Al calcular la prevalencia de la insuficiencia energética alimentaria en una población, a menudo se ha producido una confusión entre el concepto de MDER y el de la Ingesta Energética Dietética Recomendada, y en relación con el umbral adecuado que debe utilizarse para calcular la probabilidad de insuficiencia. La razón por la que la probabilidad de insuficiencia energética en la dieta debe calcularse con referencia a la MDER y no a la IEDR (que, en cambio, puede utilizarse como estimación del nivel medio de ingesta dietética recomendada para toda la población) es simplemente para reconocer el hecho de que en cualquier población existe un cierto rango de variabilidad normal en las necesidades; utilizar la IEDR como umbral sobrestimaría en gran medida la subalimentación, ya que contaría también la proporción de la población sana que consume menos que la media, simplemente por tener necesidades inferiores a la media. Cuando sea necesario, se debe utilizar la IEDR o el nivel medio de ingesta de energía dietética recomendada en una población para calcular la brecha energética dietética.</p>",
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                "source": "<p>To compute an estimate of the prevalence of undernourishment in a population, the probability distribution of habitual dietary energy intake levels (expressed in kcal per person per day) for the average individual is modelled as a parametric probability density function (pdf), f(x).</p>\n<p>The indicator is obtained as the cumulative probability that the habitual dietary energy intake (x) is below the minimum dietary energy requirements (MDER) (i.e. the lowest limit of the range of energy requirements for the population&#x2019;s representative average individual) as in the formula below: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>P</mi>\n    <mi>o</mi>\n    <mi>U</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mrow>\n      <msubsup>\n        <mo stretchy=\"false\">&#x222B;</mo>\n        <mrow>\n          <mi>x</mi>\n          <mo>&amp;lt;</mo>\n          <mi>M</mi>\n          <mi>D</mi>\n          <mi>E</mi>\n          <mi>R</mi>\n        </mrow>\n        <mrow>\n          <mi>&amp;nbsp;</mi>\n        </mrow>\n      </msubsup>\n      <mrow>\n        <mi>f</mi>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi>x</mi>\n            <mo>|</mo>\n            <mi>&#x3B8;</mi>\n          </mrow>\n        </mfenced>\n        <mi>d</mi>\n        <mi>x</mi>\n      </mrow>\n    </mrow>\n  </math></p>\n<p>where &#x3B8; is a vector of parameters that characterizes the pdf. The distribution is assumed to be lognormal, and thus fully characterized by only two parameters: the mean dietary energy consumption (DEC), and its coefficient of variation (CV). </p>\n<p>A custom R function is available from the Statistics Division at FAO to compute the PoU, given the three parameters DEC, CV, and MDER. </p>\n<p>Different data sources can be used to estimate the different parameters of the model. </p>\n<p><u>DEC </u></p>\n<p>Ideally, data on food consumption should come from nationally representative household surveys (such as Living Standard Measurement Surveys or Household Incomes and Expenditure Surveys). However, only very few countries conduct such surveys on an annual basis. Thus, in FAO&#x2019;s PoU estimates for global monitoring, DEC values are estimated from the dietary energy supply (DES) reported in the Food Balance Sheets (FBS), compiled by FAO for most countries in the world (<a href=\"https://www.fao.org/faostat/en/#data/FBS\">https://www.fao.org/faostat/en/#data/FBS</a>). </p>\n<p><u>CV </u></p>\n<p>When reliable data on food consumption are available from aforementioned nationally representative household surveys, the CV due to income (CV|y) that describes the distribution of average daily dietary energy requirement in the population can be estimated directly.</p>\n<p>When no suitable survey data are available, FIES data collected by FAO since 2014 are used to project the changes in the CV|y from 2015 (or from the year of the last food consumption survey) up to 2019, based on a smoothed (three-year moving average) trend in severe food insecurity.</p>\n<p>Since 2014, FIES data provide evidence on recent changes in the extent of severe food insecurity that might closely reflect changes in the PoU. To the extent that such changes in PoU are not explained by changes in average food supplies, they can thus be used to infer the likely changes in the CV|y that might have occurred in the most recent year. Analysis of the combined set of historic PoU estimates reveals that, on average, and once differences in DEC and MDER have been controlled for, the CV|y explains about one-third of the differences in PoU across time and space. For each country for which FIES data are available, the CV|y is estimated by the amount that would generate one-third of a percentage point change in the PoU for each observed percentage point change in the prevalence of severe food insecurity. For all other countries, the CV|y is kept constant at the estimated 2017 value.</p>\n<p>In the FAO PoU parametric approach, the CV due to body weight and lifestyle, a.k.a. CV due to requirement (CV|r), represents the variability of the distribution of dietary energy requirements of a hypothetical average individual representative of a healthy population, which is also equal to the CV of the distribution of dietary energy intakes of a hypothetical average individual if the population is perfectly nourished. The distribution of dietary energy requirements of a hypothetical average individual can be assumed to be normal, thus its variability can be estimated if at least two percentiles and their values are known. As a result, given that we are interested in deriving the theoretical distribution of dietary energy requirements for healthy hypothetical average individuals to estimate the CV|r, the MDER and the average dietary energy requirement (ADER) can be used to approximate the 1st percentile and the 50th percentile of the distribution of energy requirements of the hypothetical average individual as they are built on the same principles of a weighted average from sex-age-physiological status groups. Therefore, the value of CV|r is derived as the inverse cumulative standard normal distribution of the difference between the MDER and the ADER. Similar to the MDER, the ADER is estimated using the average of the minimum and the maximum values of the PAL category &#x2018;Active or moderately active lifestyle&#x2019;.</p>\n<p>The total CV is then obtained as the geometric mean of the CV|y and the CV|r:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>C</mi>\n    <mi>V</mi>\n    <mo>=</mo>\n    <msqrt>\n      <mfenced separators=\"|\">\n        <mrow>\n          <msup>\n            <mrow>\n              <mi>C</mi>\n              <mi>V</mi>\n              <mo>|</mo>\n              <mi>y</mi>\n            </mrow>\n            <mrow>\n              <mn>2</mn>\n            </mrow>\n          </msup>\n        </mrow>\n      </mfenced>\n      <mo>+</mo>\n      <mfenced separators=\"|\">\n        <mrow>\n          <msup>\n            <mrow>\n              <mi>C</mi>\n              <mi>V</mi>\n              <mo>|</mo>\n              <mi>r</mi>\n            </mrow>\n            <mrow>\n              <mn>2</mn>\n            </mrow>\n          </msup>\n        </mrow>\n      </mfenced>\n    </msqrt>\n  </math></p>\n<p><strong>Challenges and limitations:</strong> While formally the state of being undernourished or not is a condition that applies to individuals, given the data usually available on a large scale, it is impossible to reliably identify which individuals in a certain group are actually undernourished. Through the statistical model described above, the indicator can only be computed with reference to a population or a group of individuals for which a representative sample is available. The prevalence of undernourishment is thus an estimate of the percentage of individuals in that group that are in such condition and cannot be further disaggregated.</p>\n<p>Due to the probabilistic nature of the inference and the margins of uncertainty associated with estimates of each of the parameters in the model, the precision of the PoU estimates is generally low. While it is not possible to formally compute margins of error around PoU estimates, these are expected to likely exceed 5 percent in most cases. For this reason, FAO does not consider PoU estimates that result to be lower than 2.5 percent as sufficiently reliable to be reported.</p>\n<p><u>MDER </u></p>\n<p>Human energy requirements for an individual in a given sex/age class are determined on the basis of normative requirements for basic metabolic rate (BMR) per kilogram of body mass, multiplied by the ideal weights that a healthy person of that sex/age class may have, given his or her height, and then multiplied by a coefficient of physical activity level (PAL) to take into account physical activity. Given that both healthy BMIs and PALs vary among active and healthy individuals of the same sex and age, a <em>range </em>of energy requirements applies to each sex and age group of the population. The MDER for the average individual in the population, which is the parameter used in the PoU formula, is obtained as the weighted average of the lower bounds of the energy requirement ranges for each sex and age group, using the shares of the population in each sex and age group as weights.</p>\n<p>Information on the population structure by sex and age is available for most countries in the world and for each year from the UN Department of Economic and Social Affairs (DESA) Population Prospects, revised every two years.</p>\n<p>Information on the median height in each sex and age group for a given country is derived from a recent demographic and health survey (DHS) or from other surveys that collect anthropometry data on children and adults. Even if such surveys do not refer to the same year for which the PoU is estimated, the impact of possible small intervening changes in median heights over the years on PoU estimates is expected to be negligible.</p>",
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            "target": "<p>To compute an estimate of the prevalence of undernourishment in a population, the probability distribution of habitual dietary energy intake levels (expressed in kcal per person per day) for the average individual is modelled as a parametric probability density function (pdf), f(x).</p>\n<p>The indicator is obtained as the cumulative probability that the habitual dietary energy intake (x) is below the minimum dietary energy requirements (MDER) (i.e. the lowest limit of the range of energy requirements for the population&#x2019;s representative average individual) as in the formula below: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>P</mi>\n    <mi>o</mi>\n    <mi>U</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mrow>\n      <msubsup>\n        <mo stretchy=\"false\">&#x222B;</mo>\n        <mrow>\n          <mi>x</mi>\n          <mo>&amp;lt;</mo>\n          <mi>M</mi>\n          <mi>D</mi>\n          <mi>E</mi>\n          <mi>R</mi>\n        </mrow>\n        <mrow>\n          <mi>&amp;nbsp;</mi>\n        </mrow>\n      </msubsup>\n      <mrow>\n        <mi>f</mi>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi>x</mi>\n            <mo>|</mo>\n            <mi>&#x3B8;</mi>\n          </mrow>\n        </mfenced>\n        <mi>d</mi>\n        <mi>x</mi>\n      </mrow>\n    </mrow>\n  </math></p>\n<p>where &#x3B8; is a vector of parameters that characterizes the pdf. The distribution is assumed to be lognormal, and thus fully characterized by only two parameters: the mean dietary energy consumption (DEC), and its coefficient of variation (CV). </p>\n<p>A custom R function is available from the Statistics Division at FAO to compute the PoU, given the three parameters DEC, CV, and MDER. </p>\n<p>Different data sources can be used to estimate the different parameters of the model. </p>\n<p><u>DEC </u></p>\n<p>Ideally, data on food consumption should come from nationally representative household surveys (such as Living Standard Measurement Surveys or Household Incomes and Expenditure Surveys). However, only very few countries conduct such surveys on an annual basis. Thus, in FAO&#x2019;s PoU estimates for global monitoring, DEC values are estimated from the dietary energy supply (DES) reported in the Food Balance Sheets (FBS), compiled by FAO for most countries in the world (<a href=\"https://www.fao.org/faostat/en/#data/FBS\">https://www.fao.org/faostat/en/#data/FBS</a>). </p>\n<p><u>CV </u></p>\n<p>When reliable data on food consumption are available from aforementioned nationally representative household surveys, the CV due to income (CV|y) that describes the distribution of average daily dietary energy requirement in the population can be estimated directly.</p>\n<p>When no suitable survey data are available, FIES data collected by FAO since 2014 are used to project the changes in the CV|y from 2015 (or from the year of the last food consumption survey) up to 2019, based on a smoothed (three-year moving average) trend in severe food insecurity.</p>\n<p>Since 2014, FIES data provide evidence on recent changes in the extent of severe food insecurity that might closely reflect changes in the PoU. To the extent that such changes in PoU are not explained by changes in average food supplies, they can thus be used to infer the likely changes in the CV|y that might have occurred in the most recent year. Analysis of the combined set of historic PoU estimates reveals that, on average, and once differences in DEC and MDER have been controlled for, the CV|y explains about one-third of the differences in PoU across time and space. For each country for which FIES data are available, the CV|y is estimated by the amount that would generate one-third of a percentage point change in the PoU for each observed percentage point change in the prevalence of severe food insecurity. For all other countries, the CV|y is kept constant at the estimated 2017 value.</p>\n<p>In the FAO PoU parametric approach, the CV due to body weight and lifestyle, a.k.a. CV due to requirement (CV|r), represents the variability of the distribution of dietary energy requirements of a hypothetical average individual representative of a healthy population, which is also equal to the CV of the distribution of dietary energy intakes of a hypothetical average individual if the population is perfectly nourished. The distribution of dietary energy requirements of a hypothetical average individual can be assumed to be normal, thus its variability can be estimated if at least two percentiles and their values are known. As a result, given that we are interested in deriving the theoretical distribution of dietary energy requirements for healthy hypothetical average individuals to estimate the CV|r, the MDER and the average dietary energy requirement (ADER) can be used to approximate the 1st percentile and the 50th percentile of the distribution of energy requirements of the hypothetical average individual as they are built on the same principles of a weighted average from sex-age-physiological status groups. Therefore, the value of CV|r is derived as the inverse cumulative standard normal distribution of the difference between the MDER and the ADER. Similar to the MDER, the ADER is estimated using the average of the minimum and the maximum values of the PAL category &#x2018;Active or moderately active lifestyle&#x2019;.</p>\n<p>The total CV is then obtained as the geometric mean of the CV|y and the CV|r:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>C</mi>\n    <mi>V</mi>\n    <mo>=</mo>\n    <msqrt>\n      <mfenced separators=\"|\">\n        <mrow>\n          <msup>\n            <mrow>\n              <mi>C</mi>\n              <mi>V</mi>\n              <mo>|</mo>\n              <mi>y</mi>\n            </mrow>\n            <mrow>\n              <mn>2</mn>\n            </mrow>\n          </msup>\n        </mrow>\n      </mfenced>\n      <mo>+</mo>\n      <mfenced separators=\"|\">\n        <mrow>\n          <msup>\n            <mrow>\n              <mi>C</mi>\n              <mi>V</mi>\n              <mo>|</mo>\n              <mi>r</mi>\n            </mrow>\n            <mrow>\n              <mn>2</mn>\n            </mrow>\n          </msup>\n        </mrow>\n      </mfenced>\n    </msqrt>\n  </math></p>\n<p><strong>Challenges and limitations:</strong> While formally the state of being undernourished or not is a condition that applies to individuals, given the data usually available on a large scale, it is impossible to reliably identify which individuals in a certain group are actually undernourished. Through the statistical model described above, the indicator can only be computed with reference to a population or a group of individuals for which a representative sample is available. The prevalence of undernourishment is thus an estimate of the percentage of individuals in that group that are in such condition and cannot be further disaggregated.</p>\n<p>Due to the probabilistic nature of the inference and the margins of uncertainty associated with estimates of each of the parameters in the model, the precision of the PoU estimates is generally low. While it is not possible to formally compute margins of error around PoU estimates, these are expected to likely exceed 5 percent in most cases. For this reason, FAO does not consider PoU estimates that result to be lower than 2.5 percent as sufficiently reliable to be reported.</p>\n<p><u>MDER </u></p>\n<p>Human energy requirements for an individual in a given sex/age class are determined on the basis of normative requirements for basic metabolic rate (BMR) per kilogram of body mass, multiplied by the ideal weights that a healthy person of that sex/age class may have, given his or her height, and then multiplied by a coefficient of physical activity level (PAL) to take into account physical activity. Given that both healthy BMIs and PALs vary among active and healthy individuals of the same sex and age, a <em>range </em>of energy requirements applies to each sex and age group of the population. The MDER for the average individual in the population, which is the parameter used in the PoU formula, is obtained as the weighted average of the lower bounds of the energy requirement ranges for each sex and age group, using the shares of the population in each sex and age group as weights.</p>\n<p>Information on the population structure by sex and age is available for most countries in the world and for each year from the UN Department of Economic and Social Affairs (DESA) Population Prospects, revised every two years.</p>\n<p>Information on the median height in each sex and age group for a given country is derived from a recent demographic and health survey (DHS) or from other surveys that collect anthropometry data on children and adults. Even if such surveys do not refer to the same year for which the PoU is estimated, the impact of possible small intervening changes in median heights over the years on PoU estimates is expected to be negligible.</p>",
            "old": "<p>The indicator is computed at the population level. To this aim, the population is represented by an &#x201C;average&#x201D; individual for which a probability distribution of the habitual daily dietary energy intake levels is modelled through a parametric probability density function (pdf). </p>\n<p>Once the pdf is characterized, the indicator is obtained as the cumulative probability that daily habitual dietary energy intakes (x) are below the lower bound of the range of normal dietary energy requirements for that representative, or average individual (MDER), as in the formula below: </p>\n<p>PoU= &#x222B;_(x&lt;MDER) f(x | DEC; CV) dx </p>\n<p>where DEC and CV are the mean and coefficient of variation that characterize the distribution of habitual dietary energy consumption levels in the population. </p>\n<p>A custom R function is available from the Statistics Division at FAO to compute the PoU, given the three parameters DEC, CV, and MDER. </p>\n<p>Different data sources can be used to estimate the different parameters of the model. </p>\n<p>DEC </p>\n<p>The mean of the distribution of dietary energy consumption levels for the average individual in a population (DEC) corresponds, by definition, to the average, daily per capita food consumption level in the population. </p>\n<p>DEC can be estimated from data on food consumption obtained through surveys that are representative of the population of interest. Depending on the survey design, they can be used to estimate DEC at national and at sub national levels, either by geographic areas or by socio-economic population groups. Unfortunately, though the situation is rapidly improving, representative surveys that collect food consumption data are still not available for every country and every year. </p>\n<p>For the national population only, DEC can be estimated also from accounts of the total supply and utilization of all food commodities in a country, where the contribution of each commodity to the availability of food for human consumption is expressed in their dietary energy content, and their total is divided by the size of the population. The major source of data on national food balances are the Food Balance Sheets (FBS) maintained by FAO for most countries in the world (see http://www.fao.org/economic/ess/fbs/en/), informed by official data reported by member countries, and disseminated through FAOSTAT (http://www.fao.org/faostat/en/#data) </p>\n<p>CV </p>\n<p>Surveys that contain information on food consumption at individual or household level are the only available source to directly estimate the CV of habitual food consumption for the representative individual in the population. Unfortunately, survey data on food consumption are fraught by many problems that complicate the reliable estimation of CV. </p>\n<p>In principle, repeated observations of daily consumption for each individual in a sample would be needed to estimate levels of habitual consumption and to control for measurement errors. Moreover, data should be collected in different periods of the year on the same individuals or households to account for possible seasonal variation in levels of dietary energy consumption. Due to their cost, nationally representative individual dietary intake surveys with such characteristics are very rare, and virtually inexistent for most developing countries. As a consequence, the most common sources of data to estimate CV are multipurpose household surveys, such as Living Standard Measurement Surveys, Household Incomes and Expenditure Surveys (or Household Budgets Survey), that collect also information on food consumption. When using data collected at household level however, careful attention should be taken in distinguishing levels of food purchases or acquisitions from levels of actual utilization (consumption and wastage) during the identified reference period and in properly recording the number of individuals who participate in consumption; moreover, household level data will mask the variability due to intra-household allocation of food. </p>\n<p>For all these reasons, the coefficient of variation calculated on the series of average per capita daily dietary energy consumption levels recorded for each household included in a survey is never a reliable estimate of CV, which should reflect variability in the levels of habitual (and not occasional) daily dietary energy consumption level, at the individual (and not household) level. Empirical estimates of CV from household survey data are upward biased due to the spurious variability induced by measurement error, differences between occasional and habitual consumption, differences between acquisition and actual consumption and seasonality; moreover, they do not reflect the variability in dietary energy consumption in the population associated with individual characteristics of the household members (such as sex, age, body mass and physical activity levels). </p>\n<p>When using data collected through household surveys, CV is thus best estimated indirectly, controlling for spurious variability, and adjusted to reflect inter-individuals (in addition to inter-households) variability. The simplest way to proceed is to classify households into homogeneous groups and to calculate the coefficient of variation of the average per capita dietary energy consumption across household groups. This yields an estimate of the inter-households component of CV, labelled CV_H. An estimate of the inter-individuals component of the CV, labelled CV_I, is obtained, for each population, from its structure by sex, age and body masses, and the two components are combined to obtain the needed estimate as: </p>\n<p>CV^ = v[(CV_H)^2+(CV_I)^2 )]. </p>\n<p>For countries and years when no data from household survey are available, an indirect estimate of the CV, CV_IND, is obtained via a regression that projects the values of per capita GDP, Gini coefficient of income, and an index of the relative price of food (FPI) on the CV, while controlling for a regional shifter (REG). </p>\n<p>CV^_IND=&#xDF;_0+&#xDF;_1 GDP+ &#xDF;_2 GINI+ &#xDF;_3 FPI+&#xDF;_4 REG. </p>\n<p>Coefficients of the regression are estimated from the set of data and years for which data on CV, GDP, GINI and FPI are available. </p>\n<p>MDER </p>\n<p>Human energy requirements are computed by multiplying normative requirements for basic metabolic rate (BMR, expressed per kg of body mass) by the ideal weight of a healthy person of given height, and then multiplied by a coefficient of physical activity level (PAL). Ranges of normal energy requirements are thus computed for each sex and age group of the population, observing that there exist a whole range of Body Mass Index (BMI) values &#x2013; from 18.5 to 25 &#x2013; that are compatible with health. This implies that any given attained height might correspond to a whole range of healthy body weights, and therefore to a range of values for energy requirement for BMR. </p>\n<p>Given information on the median height and the consideration that the group might contain individuals engaged in different levels of physical activity, the minimum, average and maximum dietary energy requirement can be computed for every sex and age class by taking into consideration special allowances for growth in individuals aged 0-21 and for pregnancy and lactation. </p>\n<p>(See ftp://ftp.fao.org/docrep/fao/007/y5686e/y5686e00.pdf for further details). </p>\n<p>The MDER for a given population group, including for the national population, is obtained as the weighted average of the minimums of the energy requirements ranges of each sex and age class, using the population size in each class as weights. </p>\n<p>In computing the prevalence of dietary energy inadequacy in a population there has often been confusion between the concept of MDER and that of the Recommended Dietary Energy Intake, and regarding the appropriate threshold to be used to compute the probability of inadequacy. The reason why the probability of dietary energy inadequacy should be computed with reference to the MDER, and not the ADER (which, instead, can be used as an estimate of the average recommended dietary intake level for the whole population) is simply to recognize the fact that in any population there exists a certain range of normal variability in requirements; using the ADER as a threshold would greatly overestimate undernourishment as it would count also the proportion of the healthy population that consumes less than average, simply because of having less than average requirements. When needed, the ADER, or the average Recommended Dietary Energy Intake level in a population must be used instead to compute the dietary energy gap.&quot; </p>",
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            "target": "<h2>Comentarios y limitaciones: </h2>\n<p>A lo largo de los años, se ha criticado el enfoque paramétrico que informa el cálculo de la PoU, basado en la presunción de que la subalimentación debe evaluarse necesariamente a nivel individual, comparando las necesidades energéticas individuales con las ingestas energéticas individuales. Según este punto de vista, la prevalencia de la subalimentación podría calcularse simplemente contando el número de individuos de una muestra representativa de la población que se clasifica como subalimentada, basándose en una comparación del consumo habitual de alimentos y las necesidades individuales. Lamentablemente, este enfoque no es factible por dos razones: en primer lugar, debido al coste de las encuestas sobre la ingesta alimentaria individual, el consumo individual de alimentos sólo se mide en unos pocos países, cada varios años, en muestras relativamente pequeñas; además, las necesidades energéticas individuales son prácticamente inobservables con los métodos estándar de recolección de datos (hasta el punto que el consumo energético habitual observado de los individuos en un estado saludable sigue siendo la forma preferida de inferir las necesidades energéticas individuales). Esto significa que incluso si fuera posible obtener observaciones precisas del consumo energético dietético individual, esto sería insuficiente para inferir sobre la condición de subnutrición a nivel individual, a menos que se integre por la observación sobre el estado físico (índice de masa corporal) y de su dinámica en el tiempo, del mismo individuo. </p>\n<p> El enfoque basado en un modelo para estimar el PoU desarrollado por la FAO integra la información que está disponible con suficiente regularidad de diferentes fuentes para la mayoría de los países del mundo, de una manera teóricamente consistente, proporcionando así lo que sigue siendo una de las herramientas más fiables para supervisar el progreso hacia la reducción del hambre en el mundo. </p>\n<p><strong><em>Consideraciones específicas adicionales:</em></strong></p>\n<p><em>Factibilidad</em></p>\n<p>La estimación del PoU a nivel nacional es factible para la mayoría de los países del mundo desde 1999. En el peor de los casos, cuando no se dispone de datos sobre el consumo de alimentos procedentes de una encuesta de hogares reciente, la estimación del PoU basada en el modelo se basa en una estimación del nivel medio de consumo de energía alimentaria (CEA) procedente de las Hojas de Balance de Alimentos (FBS, por sus siglas en inglés), una estimación indirecta del coeficiente de variación (CV) basada en la información sobre el PIB del país, el coeficiente de Gini de los ingresos, un índice del precio relativo de los alimentos, u otros indicadores de desarrollo como la tasa de mortalidad de menores de 5 años del país y una estimación de las necesidades mínimas de energía alimentaria (MDER, por sus siglas en inglés) basada en los datos de las Perspectivas de la Población Mundial de la División de Población de las Naciones Unidas. </p>\n<p><em>Fiabilidad</em></p>\n<p>La fiabilidad depende principalmente de la calidad de los datos utilizados para la estimación de los parámetros del modelo. </p>\n<p>La CEA puede estimarse a partir de datos de encuestas o de balances alimentarios. Ninguna de las dos fuentes está exenta de problemas. Cuando se comparan las estimaciones de la CEA nacional a partir de las FBS y de las encuestas, se observan frecuentemente diferencias. </p>\n<p> Las estimaciones de CEA a partir de datos de encuestas pueden verse afectadas por errores de medición sistemáticos debidos a la subnotificación del consumo de alimentos o al registro incompleto de todas las fuentes de consumo de alimentos. Investigaciones recientes demuestran que se puede inducir un sesgo negativo de hasta más de 850 kcal en el consumo calórico diario per cápita estimado por el tipo de módulo de consumo de alimentos elegido para capturar los datos a nivel de hogar. (Véase De Weerdt et al., 2015, cuadro 2, <a href=\"https://feb.kuleuven.be/drc/licos/publications/dp/DP%20365%20Complete.pdf%20\">https://feb.kuleuven.be/drc/licos/publications/dp/DP%20365%20Complete.pdf</a> ). Un análisis detallado de una reciente Encuesta de Presupuestos Familiares en Brasil reveló cómo los alimentos proporcionados gratuitamente a través del programa de comidas escolares y consumidos por los niños mientras están en la escuela, no habían sido contabilizados entre las fuentes de consumo de alimentos de los hogares, lo que explica un sesgo a la baja del consumo medio de energía dietética diaria per cápita de 674 kcal. (Véase Borlizzi, Cafiero &amp; Del Grossi, de próxima aparición). </p>\n<p> Las estimaciones de CEA a partir de las hojas de balance de alimentos también pueden verse afectadas por errores, aunque es difícil establecer la dirección del sesgo inducido. Como la disponibilidad media de alimentos es un residuo en el método de las FBS, cualquier error en la producción, el comercio y las existencias notificadas podría afectar a las estimaciones de la disponibilidad nacional de alimentos. Además, los errores podrían ser inducidos por la dificultad de contabilizar adecuadamente todas las formas de utilización de los productos alimenticios. Sin embargo, en la medida en que todos estos errores no estén correlacionados, el impacto sobre el consumo medio de alimentos estimado será menor de lo que cada uno de los errores, considerado por separado, podría implicar. No obstante, teniendo en cuenta lo problemático que resulta contabilizar con precisión las variaciones de las reservas nacionales de productos alimentarios, para las que los datos oficiales pueden ser poco fiables, se reconoce que la variación anual estimada de las existencias es propensa a una incertidumbre considerable que se trasladaría a la CEA estimada en cada año determinado. </p>\n<p> Para limitar el impacto de tales errores, la FAO ha presentado tradicionalmente las estimaciones de PoU a nivel nacional como promedios de tres años, bajo la presunción de que los errores inducidos por el registro impreciso de las variaciones de las existencias en cada año podrían reducirse en gran medida al considerar un promedio de tres años consecutivos. </p>\n<p>Los datos de encuestas son la única fuente para estimar el CV y la asimetría. Como se describe en la sección de metadatos sobre el método de cálculo, a menos que se obtengan de encuestas de ingesta dietética individual de alta calidad, los datos deben tratarse para reducir el probable sesgo al alza en las estimaciones del CV que sería inducido por la variabilidad espuria debida a errores en la medición de la ingesta energética dietética habitual individual. </p>\n<p><em>Comparabilidad</em></p>\n<p> Si se utiliza el mismo método de cálculo, la comparabilidad en el tiempo y el espacio es relativamente alta, y la única causa potencial de falta de homogeneidad se encuentra en la diferente calidad de los datos de fondo. </p>\n<p><em>Limitaciones</em></p>\n<p>Debido a la naturaleza probabilística de la inferencia y a los márgenes de incertidumbre asociados a las estimaciones de cada uno de los parámetros del modelo, la precisión de las estimaciones del PoU es generalmente baja. Aunque no es posible calcular los márgenes de error teóricos (MdE) para las estimaciones de los PoU, es muy probable que éstos superen más o menos el 2,5% en la mayoría de los casos. Por esta razón, la FAO publica las estimaciones de PoU a nivel nacional sólo cuando son superiores al 2,5%. Esto también sugiere que el 2,5% es el objetivo más bajo factible que puede establecerse para el indicador de PoU, un valor que es insatisfactoriamente grande cuando la ambición es erradicar completamente el flagelo del hambre. </p>\n<p> Si no se dispone de ninguna encuesta que recoja datos sobre el consumo de alimentos y que sea representativa a nivel subnacional, el indicador sólo puede calcularse a nivel nacional.</p>",
            "old": "<h2>Comentarios y limitaciones: </h2>\n<p>A lo largo de los años, se ha criticado el enfoque paramétrico que informa el cálculo de la PoU, basado en la presunción de que la subalimentación debe evaluarse necesariamente a nivel individual, comparando las necesidades energéticas individuales con las ingestas energéticas individuales. Según este punto de vista, la prevalencia de la subalimentación podría calcularse simplemente contando el número de individuos de una muestra representativa de la población que se clasifica como subalimentada, basándose en una comparación del consumo habitual de alimentos y las necesidades individuales. Lamentablemente, este enfoque no es factible por dos razones: en primer lugar, debido al coste de las encuestas sobre la ingesta alimentaria individual, el consumo individual de alimentos sólo se mide en unos pocos países, cada varios años, en muestras relativamente pequeñas; además, las necesidades energéticas individuales son prácticamente inobservables con los métodos estándar de recolección de datos (hasta el punto que el consumo energético habitual observado de los individuos en un estado saludable sigue siendo la forma preferida de inferir las necesidades energéticas individuales). Esto significa que incluso si fuera posible obtener observaciones precisas del consumo energético dietético individual, esto sería insuficiente para inferir sobre la condición de subnutrición a nivel individual, a menos que se integre por la observación sobre el estado físico (índice de masa corporal) y de su dinámica en el tiempo, del mismo individuo. </p>\n<p> El enfoque basado en un modelo para estimar el PoU desarrollado por la FAO integra la información que está disponible con suficiente regularidad de diferentes fuentes para la mayoría de los países del mundo, de una manera teóricamente consistente, proporcionando así lo que sigue siendo una de las herramientas más fiables para supervisar el progreso hacia la reducción del hambre en el mundo. </p>\n<p><strong><em>Consideraciones específicas adicionales:</em></strong></p>\n<p><em>Factibilidad</em></p>\n<p>La estimación del PoU a nivel nacional es factible para la mayoría de los países del mundo desde 1999. En el peor de los casos, cuando no se dispone de datos sobre el consumo de alimentos procedentes de una encuesta de hogares reciente, la estimación del PoU basada en el modelo se basa en una estimación del nivel medio de consumo de energía alimentaria (CEA) procedente de las Hojas de Balance de Alimentos (FBS, por sus siglas en inglés), una estimación indirecta del coeficiente de variación (CV) basada en la información sobre el PIB del país, el coeficiente de Gini de los ingresos, un índice del precio relativo de los alimentos, u otros indicadores de desarrollo como la tasa de mortalidad de menores de 5 años del país y una estimación de las necesidades mínimas de energía alimentaria (MDER, por sus siglas en inglés) basada en los datos de las Perspectivas de la Población Mundial de la División de Población de las Naciones Unidas. </p>\n<p><em>Fiabilidad</em></p>\n<p>La fiabilidad depende principalmente de la calidad de los datos utilizados para la estimación de los parámetros del modelo. </p>\n<p>La CEA puede estimarse a partir de datos de encuestas o de balances alimentarios. Ninguna de las dos fuentes está exenta de problemas. Cuando se comparan las estimaciones de la CEA nacional a partir de las FBS y de las encuestas, se observan frecuentemente diferencias. </p>\n<p> Las estimaciones de CEA a partir de datos de encuestas pueden verse afectadas por errores de medición sistemáticos debidos a la subnotificación del consumo de alimentos o al registro incompleto de todas las fuentes de consumo de alimentos. Investigaciones recientes demuestran que se puede inducir un sesgo negativo de hasta más de 850 kcal en el consumo calórico diario per cápita estimado por el tipo de módulo de consumo de alimentos elegido para capturar los datos a nivel de hogar. (Véase De Weerdt et al., 2015, cuadro 2, <a href=\"https://feb.kuleuven.be/drc/licos/publications/dp/DP%20365%20Complete.pdf%20\">https://feb.kuleuven.be/drc/licos/publications/dp/DP%20365%20Complete.pdf</a> ). Un análisis detallado de una reciente Encuesta de Presupuestos Familiares en Brasil reveló cómo los alimentos proporcionados gratuitamente a través del programa de comidas escolares y consumidos por los niños mientras están en la escuela, no habían sido contabilizados entre las fuentes de consumo de alimentos de los hogares, lo que explica un sesgo a la baja del consumo medio de energía dietética diaria per cápita de 674 kcal. (Véase Borlizzi, Cafiero &amp; Del Grossi, de próxima aparición). </p>\n<p> Las estimaciones de CEA a partir de las hojas de balance de alimentos también pueden verse afectadas por errores, aunque es difícil establecer la dirección del sesgo inducido. Como la disponibilidad media de alimentos es un residuo en el método de las FBS, cualquier error en la producción, el comercio y las existencias notificadas podría afectar a las estimaciones de la disponibilidad nacional de alimentos. Además, los errores podrían ser inducidos por la dificultad de contabilizar adecuadamente todas las formas de utilización de los productos alimenticios. Sin embargo, en la medida en que todos estos errores no estén correlacionados, el impacto sobre el consumo medio de alimentos estimado será menor de lo que cada uno de los errores, considerado por separado, podría implicar. No obstante, teniendo en cuenta lo problemático que resulta contabilizar con precisión las variaciones de las reservas nacionales de productos alimentarios, para las que los datos oficiales pueden ser poco fiables, se reconoce que la variación anual estimada de las existencias es propensa a una incertidumbre considerable que se trasladaría a la CEA estimada en cada año determinado. </p>\n<p> Para limitar el impacto de tales errores, la FAO ha presentado tradicionalmente las estimaciones de PoU a nivel nacional como promedios de tres años, bajo la presunción de que los errores inducidos por el registro impreciso de las variaciones de las existencias en cada año podrían reducirse en gran medida al considerar un promedio de tres años consecutivos. </p>\n<p>Los datos de encuestas son la única fuente para estimar el CV y la asimetría. Como se describe en la sección de metadatos sobre el método de cálculo, a menos que se obtengan de encuestas de ingesta dietética individual de alta calidad, los datos deben tratarse para reducir el probable sesgo al alza en las estimaciones del CV que sería inducido por la variabilidad espuria debida a errores en la medición de la ingesta energética dietética habitual individual. </p>\n<p><em>Comparabilidad</em></p>\n<p> Si se utiliza el mismo método de cálculo, la comparabilidad en el tiempo y el espacio es relativamente alta, y la única causa potencial de falta de homogeneidad se encuentra en la diferente calidad de los datos de fondo. </p>\n<p><em>Limitaciones</em></p>\n<p>Debido a la naturaleza probabilística de la inferencia y a los márgenes de incertidumbre asociados a las estimaciones de cada uno de los parámetros del modelo, la precisión de las estimaciones del PoU es generalmente baja. Aunque no es posible calcular los márgenes de error teóricos (MdE) para las estimaciones de los PoU, es muy probable que éstos superen más o menos el 2,5% en la mayoría de los casos. Por esta razón, la FAO publica las estimaciones de PoU a nivel nacional sólo cuando son superiores al 2,5%. Esto también sugiere que el 2,5% es el objetivo más bajo factible que puede establecerse para el indicador de PoU, un valor que es insatisfactoriamente grande cuando la ambición es erradicar completamente el flagelo del hambre. </p>\n<p> Si no se dispone de ninguna encuesta que recoja datos sobre el consumo de alimentos y que sea representativa a nivel subnacional, el indicador sólo puede calcularse a nivel nacional.</p>",
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                "source": "<p>Over the years, the parametric approach informing the computation of the PoU has been criticized, based on the presumptions that undernourishment should be assessed necessarily starting at the individual level, by comparing individual energy requirements with individual energy intakes. According to such a view, the prevalence of undernourishment could be simply computed by counting the number of individuals in a representative sample of the population that is classified as undernourished, based on a comparison of individual habitual food consumption and requirements. </p>\n<p>Unfortunately, such an approach is not feasible for two reasons: first, due to the cost of individual dietary intake surveys, individual food consumption is measured only in a few countries, every several years, on relatively small samples; moreover, individual energy requirements are practically unobservable with standard data collection methods (to the point that observed habitual energy consumption of individuals in a healthy status is still the preferred way to infer individual energy requirements). This means that even if it were possible to obtain accurate observations of the individual dietary energy consumption, this would be insufficient to infer on the undernourishment condition at individual level, unless integrated by the observation on the physical status (body mass index) and of its dynamic over time, of the same individual. </p>\n<p>The model-based approach to estimate the PoU developed by FAO integrates information that is available with sufficient regularity from different sources for most countries in the world, in a theoretically consistent way, thus providing what is still one of the most reliable tools to monitor progress towards reducing global hunger. </p>\n<p>Further specific consideration </p>\n<p>1. Feasibility </p>\n<p>Estimation of PoU at national level has been feasible for most countries in the world since 1999. In the worst case scenario, when no data on food consumption was available from a recent household survey, the model-based estimate of the PoU is informed by an estimate of mean level of dietary energy consumption (DEC) from Food Balance Sheets (FBS), an indirect estimate of the coefficient of variation (CV) based on information on the country&#x2019;s GDP, Gini coefficient of Income, an index of the relative price of food, or other indicators of development such as country&#x2019;s Under 5 Mortality Rate, and an estimate of the Minimum Dietary Energy Requirement (MDER) based on the UN Population Division&#x2019;s World Population Prospects data. </p>\n<p>2. Reliability </p>\n<p>Reliability mostly depends on the quality of the data used to inform the estimation of the model&#x2019;s parameters. </p>\n<p>DEC could be estimated either from survey data or from food balances. Neither source is devoid of problems. When comparing estimates of national DEC from FBS and from surveys, differences are frequently noted. </p>\n<p>DEC estimates from survey data can be affected by systematic measurement errors due to under-reporting of food consumption, or to incomplete recording of all food consumption sources. Recent research shows that a negative bias of up to more than 850 kcal can be induced on the estimated daily per capita caloric consumption can be induced by the type of food consumption module chosen to capture the data at the household level. (See De Weerdt et al., 2015, Table 2, <a href=\"https://feb.kuleuven.be/drc/licos/publications/dp/DP%20365%20Complete.pdf\">https://feb.kuleuven.be/drc/licos/publications/dp/DP%20365%20Complete.pdf</a>). A detailed analysis of a recent Household Budget Survey in Brazil revealed how food provided for free through the school meals program and consumed by children while at school, had not been accounted among the sources of household food consumption, accounting for a downward bias of the average per capita daily dietary energy consumption of 674 kcal. (See Borlizzi, Cafiero &amp; Del Grossi, forthcoming.) </p>\n<p>DEC estimates from Food Balance Sheets can also be affected by errors, though it is difficult to establish the direction of induced bias. As average food availability is a residual in the FBS method, any errors in reported production, trade, and stocks might affect the estimates of national food availability. Moreover, errors might be induced by the difficulty in properly accounting for all forms of food commodity utilization. To the extent that all these errors are uncorrelated, though, the impact on the estimated average food consumption will be lower than each of the errors, considered separately, might imply. Nevertheless, considering how problematic it is to precisely account for variations in national reserves of food commodities, for which official data may be unreliable, it is recognized that the estimated annual stock variation is prone to considerable uncertainty that would be transferred to the estimated DEC in each given year. </p>\n<p>To limit the impact of such errors, FAO has traditionally presented estimates of PoU at national level as three-year averages, on the presumption that errors induced by imprecise recording of stocks variations in each single year might be highly reduced when considering an average over three consecutive years. </p>\n<p>Survey data are the only source to estimate the CV. As described in the section of metadata on the method of computation, unless obtained from high quality individual dietary intake surveys, data needs to be treated to reduce the likely upward bias in the estimates of the CV that would be induced by the spurious variability due to errors in measuring individual habitual dietary energy intake. </p>\n<p>3. Comparability </p>\n<p>If the same method of computation is used, comparability across time and space is relatively high, with the only potential cause of inhomogeneity found in the different quality of the background data. </p>\n<p>4. Limitations </p>\n<p>Due to the probabilistic nature of the inference and the margins of uncertainty associated with estimates of each of the parameters in the model, the precision of the PoU estimates is generally low. Even though it is not possible to compute theoretical Margins of Error (MoE) for PoU estimates, these would very likely exceed plus or minus 2.5% in most cases. For this reason, FAO publishes national level PoU estimates only when they are larger than 2.5%. This also suggests that 2.5% is the lowest feasible target that can be set for the PoU indicator, a value that is unsatisfactorily large when the ambition is to fully eradicate the scourge of hunger. </p>\n<p>If no survey is available that collects food consumption data and that is representative at subnational level, the indicator can only be computed at national level.&quot;</p>",
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            "target": "<p>Over the years, the parametric approach informing the computation of the PoU has been criticized, based on the presumptions that undernourishment should be assessed necessarily starting at the individual level, by comparing individual energy requirements with individual energy intakes. According to such a view, the prevalence of undernourishment could be simply computed by counting the number of individuals in a representative sample of the population that is classified as undernourished, based on a comparison of individual habitual food consumption and requirements. </p>\n<p>Unfortunately, such an approach is not feasible for two reasons: first, due to the cost of individual dietary intake surveys, individual food consumption is measured only in a few countries, every several years, on relatively small samples; moreover, individual energy requirements are practically unobservable with standard data collection methods (to the point that observed habitual energy consumption of individuals in a healthy status is still the preferred way to infer individual energy requirements). This means that even if it were possible to obtain accurate observations of the individual dietary energy consumption, this would be insufficient to infer on the undernourishment condition at individual level, unless integrated by the observation on the physical status (body mass index) and of its dynamic over time, of the same individual. </p>\n<p>The model-based approach to estimate the PoU developed by FAO integrates information that is available with sufficient regularity from different sources for most countries in the world, in a theoretically consistent way, thus providing what is still one of the most reliable tools to monitor progress towards reducing global hunger. </p>\n<p>Further specific consideration </p>\n<p>1. Feasibility </p>\n<p>Estimation of PoU at national level has been feasible for most countries in the world since 1999. In the worst case scenario, when no data on food consumption was available from a recent household survey, the model-based estimate of the PoU is informed by an estimate of mean level of dietary energy consumption (DEC) from Food Balance Sheets (FBS), an indirect estimate of the coefficient of variation (CV) based on information on the country&#x2019;s GDP, Gini coefficient of Income, an index of the relative price of food, or other indicators of development such as country&#x2019;s Under 5 Mortality Rate, and an estimate of the Minimum Dietary Energy Requirement (MDER) based on the UN Population Division&#x2019;s World Population Prospects data. </p>\n<p>2. Reliability </p>\n<p>Reliability mostly depends on the quality of the data used to inform the estimation of the model&#x2019;s parameters. </p>\n<p>DEC could be estimated either from survey data or from food balances. Neither source is devoid of problems. When comparing estimates of national DEC from FBS and from surveys, differences are frequently noted. </p>\n<p>DEC estimates from survey data can be affected by systematic measurement errors due to under-reporting of food consumption, or to incomplete recording of all food consumption sources. Recent research shows that a negative bias of up to more than 850 kcal can be induced on the estimated daily per capita caloric consumption can be induced by the type of food consumption module chosen to capture the data at the household level. (See De Weerdt et al., 2015, Table 2, <a href=\"https://feb.kuleuven.be/drc/licos/publications/dp/DP%20365%20Complete.pdf\">https://feb.kuleuven.be/drc/licos/publications/dp/DP%20365%20Complete.pdf</a>). A detailed analysis of a recent Household Budget Survey in Brazil revealed how food provided for free through the school meals program and consumed by children while at school, had not been accounted among the sources of household food consumption, accounting for a downward bias of the average per capita daily dietary energy consumption of 674 kcal. (See Borlizzi, Cafiero &amp; Del Grossi, forthcoming.) </p>\n<p>DEC estimates from Food Balance Sheets can also be affected by errors, though it is difficult to establish the direction of induced bias. As average food availability is a residual in the FBS method, any errors in reported production, trade, and stocks might affect the estimates of national food availability. Moreover, errors might be induced by the difficulty in properly accounting for all forms of food commodity utilization. To the extent that all these errors are uncorrelated, though, the impact on the estimated average food consumption will be lower than each of the errors, considered separately, might imply. Nevertheless, considering how problematic it is to precisely account for variations in national reserves of food commodities, for which official data may be unreliable, it is recognized that the estimated annual stock variation is prone to considerable uncertainty that would be transferred to the estimated DEC in each given year. </p>\n<p>To limit the impact of such errors, FAO has traditionally presented estimates of PoU at national level as three-year averages, on the presumption that errors induced by imprecise recording of stocks variations in each single year might be highly reduced when considering an average over three consecutive years. </p>\n<p>Survey data are the only source to estimate the CV. As described in the section of metadata on the method of computation, unless obtained from high quality individual dietary intake surveys, data needs to be treated to reduce the likely upward bias in the estimates of the CV that would be induced by the spurious variability due to errors in measuring individual habitual dietary energy intake. </p>\n<p>3. Comparability </p>\n<p>If the same method of computation is used, comparability across time and space is relatively high, with the only potential cause of inhomogeneity found in the different quality of the background data. </p>\n<p>4. Limitations </p>\n<p>Due to the probabilistic nature of the inference and the margins of uncertainty associated with estimates of each of the parameters in the model, the precision of the PoU estimates is generally low. Even though it is not possible to compute theoretical Margins of Error (MoE) for PoU estimates, these would very likely exceed plus or minus 2.5% in most cases. For this reason, FAO publishes national level PoU estimates only when they are larger than 2.5%. This also suggests that 2.5% is the lowest feasible target that can be set for the PoU indicator, a value that is unsatisfactorily large when the ambition is to fully eradicate the scourge of hunger. </p>\n<p>If no survey is available that collects food consumption data and that is representative at subnational level, the indicator can only be computed at national level.&quot;</p>",
            "old": "<p>Over the years, the parametric approach informing the computation of the PoU has been criticized, based on the presumptions that undernourishment should be assessed necessarily starting at the individual level, by comparing individual energy requirements with individual energy intakes. According to such view, the prevalence of undernourishment could be simply computed by counting the number of individuals in a representative sample of the population that is classified as undernourished, based on a comparison of individual habitual food consumption and requirements. Unfortunately, such approach is not feasible for two reasons: first, due to the cost of individual dietary intake surveys, individual food consumption is measured only in a few countries, every several years, on relatively small samples; moreover, individual energy requirements are practically unobservable with standard data collection methods (to the point that observed habitual energy consumption of individuals in a healthy status is still the preferred way to infer individual energy requirements). This means that even if it were possible to obtain accurate observations of the individual dietary energy consumption, this would be insufficient to infer on the undernourishment condition at individual level, unless integrated by the observation on the physical status (body mass index) and of its dynamic over time, of the same individual. </p>\n<p>The model based approach to estimate the PoU developed by FAO integrates information that is available with sufficient regularity from different sources for most countries in the world, in a theoretically consistent way, thus providing what is still one of the most reliable tools to monitor progress towards reducing global hunger. </p>\n<p>Further specific consideration </p>\n<p>1. Feasibility </p>\n<p>Estimation of PoU at national level has been feasible for most countries in the world since 1999. In the worst case scenario, when no data on food consumption was available from a recent household survey, the model-based estimate of the PoU is informed by an estimate of mean level of dietary energy consumption (DEC) from Food Balance Sheets (FBS), an indirect estimate of the coefficient of variation (CV) based on information on the country&#x2019;s GDP, Gini coefficient of Income, an index of the relative price of food, or other indicators of development such as country&#x2019;s Under 5 Mortality Rate, and an estimate of the Minimum Dietary Energy Requirement (MDER) based on the UN Population Division&#x2019;s World Population Prospects data. </p>\n<p>2. Reliability </p>\n<p>Reliability mostly depends on the quality of the data used to inform the estimation of the model&#x2019;s parameters. </p>\n<p>DEC could be estimated either from survey data or from food balances. Neither source is devoid of problems. When comparing estimates of national DEC from FBS and from surveys, differences are frequently noted. </p>\n<p>DEC estimates from survey data can be affected by systematic measurement errors due to under-reporting of food consumption, or to incomplete recording of all food consumption sources. Recent research shows that a negative bias of up to more than 850 kcal can be induced on the estimated daily per capita caloric consumption can be induced by the type of food consumption module chosen to capture the data at the household level. (See De Weerdt et al., 2015, Table 2, https://feb.kuleuven.be/drc/licos/publications/dp/DP%20365%20Complete.pdf ). A detailed analysis of a recent Household Budget Survey in Brazil revealed how food provided for free through the school meals program and consumed by children while at school, had not been accounted among the sources of household food consumption, accounting for a downward bias of the average per capita daily dietary energy consumption of 674 kcal. (See Borlizzi, Cafiero &amp; Del Grossi, forthcoming.) </p>\n<p>DEC estimates from Food Balance Sheets can also be affected by errors, though it is difficult to establish the direction of induced bias. As average food availability is a residual in the FBS method, any errors in reported production, trade, and stocks might affect the estimates of national food availability. Moreover, errors might be induced by the difficulty in properly accounting for all forms of food commodity utilization. To the extent that all these errors are uncorrelated, though, the impact on the estimated average food consumption will be lower than each of the errors, considered separately, might imply. Nevertheless, considering how problematic it is to precisely account for variations in national reserves of food commodities, for which official data may be unreliable, it is recognized that the estimated annual stock variation is prone to considerable uncertainty that would be transferred to the estimated DEC in each given year. </p>\n<p>To limit the impact of such errors, FAO has traditionally presented estimates of PoU at national level as three-year averages, on the presumption that errors induced by imprecise recording of stocks variations in each single year might be highly reduced when considering an average over three consecutive years. </p>\n<p>Survey data are the only source to estimate the CV. As described in the section of metadata on the method of computation, unless obtained from high quality individual dietary intake surveys, data needs to be treated to reduce the likely upward bias in the estimates of the CV that would be induced by the spurious variability due to errors in measuring individual habitual dietary energy intake. </p>\n<p>3. Comparability </p>\n<p>If the same method of computation is used, comparability across time and space is relatively high, with the only potential cause of inhomogeneity found in the different quality of the background data. </p>\n<p>4. Limitations </p>\n<p>Due to the probabilistic nature of the inference and the margins of uncertainty associated with estimates of each of the parameters in the model, the precision of the PoU estimates is generally low. Even though it is not possible to compute theoretical Margins of Error (MoE) for PoU estimates, these would very likely exceed plus or minus 2.5% in most cases. For this reason, FAO publishes national level PoU estimates only when they are larger than 2.5%. This also suggests that 2.5% is the lowest feasible target that can be set for the PoU indicator, a value that is unsatisfactorily large when the ambition is to fully eradicate the scourge of hunger. </p>\n<p>If no survey is available that collects food consumption data and that is representative at subnational level, the indicator can only be computed at national level.&quot;</p>",
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                "source": "<p>The Office of the Chief Statistician of FAO manages the Interdepartmental Working Group on SDG indicators under the FAO custodianship and identifies a focal point for each of them. The team leader of the Food Security and Nutrition Statistics Team of the Statistics Division is formally appointed as the focal person for the collection, processing, and dissemination of statistics for this indicator. </p>",
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            "target": "<h2> Publicación de datos: </h2>\n<p> Septiembre de 2019 </p>",
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                "source": "<p>Data are released each year alongside the <em>State of Food Security and Nutrition in the World</em> report, usually in mid-July. </p>",
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            "target": "<p>Data are released each year alongside the <em>State of Food Security and Nutrition in the World</em> report, usually in mid-July. </p>",
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            "target": "<h1> Fuentes de datos </h1>\n<h2> Descripción: </h2>\n<p> La fuente ideal de datos para estimar el PoU sería una encuesta de ingesta dietética individual cuidadosamente diseñada y hábilmente realizada, en la que el consumo diario real de alimentos, junto con las alturas y los pesos de cada individuo encuestado, se miden repetidamente en una muestra que es representativa de la población objetivo. Sin embargo, debido a su costo, tales encuestas son raras. </p>\n<p> En principio, una encuesta de hogares bien diseñada que recopile información sobre las adquisiciones de alimentos podría ser suficiente para informar una estimación fiable de la prevalencia de la desnutrición en una población, a un costo razonable y con la periodicidad necesaria para informar el proceso de seguimiento de los ODS, siempre que: </p>\n<p>a) Todas las fuentes de consumo de alimentos para todos los miembros de los hogares se contabilizan adecuadamente, incluidos, en particular, los alimentos que se consumen fuera del hogar; </p>\n<p>b) Se dispone de información suficiente para convertir los datos sobre el consumo de alimentos o sobre los gastos en alimentos en su contribución a la ingesta de energía en la dieta; </p>\n<p>c) Se utilizan los métodos adecuados para calcular el PoU, para controlar el exceso de variabilidad en los niveles estimados de consumo habitual de alimentos entre los hogares, teniendo en cuenta la presencia de variabilidad normal en la distribución del consumo de alimentos entre los individuos, inducida por las diferencias en las necesidades energéticas de los miembros de la población. </p>\n<p> Entre los ejemplos de encuestas que podrían considerarse con este fin figuran las encuestas realizadas para calcular estadísticas económicas y realizar evaluaciones de la pobreza, como las encuestas sobre ingresos y gastos de los hogares, las encuestas sobre el presupuesto de los hogares y las encuestas de medición del nivel de vida. </p>\n<p> En la práctica, sin embargo, a menudo es imposible, y no aconsejable, basarse únicamente en los datos recopilados a través de una encuesta de hogares, ya que la información necesaria para estimar los cuatro parámetros del modelo de PoU es faltante o imprecisa. </p>\n<p> Los datos de consumo de alimentos de las encuestas de hogares a menudo deben integrarse para </p>\n<p>a) Datos sobre la estructura demográfica de la población de interés por sexo y edad; </p>\n<p>b) Datos o información sobre la mediana de la altura de los individuos en cada sexo y clase de edad; </p>\n<p>c) Datos sobre la distribución de los niveles de actividad física en la población; </p>\n<p>d) Datos alternativos sobre las cantidades totales de alimentos disponibles para el consumo humano, para corregir los sesgos en la estimación del consumo medio diario de energía alimentaria nacional en la población. </p>\n<p> Los datos para a), b) y c) podrían estar disponibles a través de la misma encuesta multipropósito que proporciona datos sobre el consumo de alimentos, pero es más probable que estén disponibles de otras fuentes, como las Encuestas Nacionales demográficas y de salud (para a) y b) )y las Encuestas sobre el uso del tiempo (para c) ). </p>\n<p> La corrección del sesgo en el consumo medio diario estimado de energía alimentaria podría tener que basarse en fuentes alternativas en el consumo de alimentos, como las cuentas agregadas de suministro y utilización de alimentos y los balances de alimentos. </p>\n<p> Para fundamentar su estimación del PoU a nivel nacional, regional y mundial, además de todas las encuestas de hogares para las que es posible obtener microdatos sobre el consumo de alimentos, la FAO se basa en: </p>\n<p>a) Perspectivas de la Población Mundial de la División de Población de las Naciones Unidas (<a href=\"https://esa.un.org/unpd/wpp/Download/Standard/Population/\">https://esa.un.org/unpd/wpp/Download/Standard/Population/</a>, que proporcionan estimaciones actualizadas de las estructuras de la población nacional por sexo y edad cada dos años para la mayoría de los países del mundo; </p>\n<p>b) Balances alimentarios de la FAO (<a href=\"http://faostat3.fao.org/download/FB/*/E\">http://faostat3.fao.org/download/FB/*/E</a>), que proporciona estimaciones actualizadas de la disponibilidad nacional de alimentos cada año para la mayoría de los países del mundo. </p>\n<p> Los microdatos de las encuestas de hogares que recopilan datos sobre el consumo de alimentos son extraídos por la FAO directamente a través de los sitios web de las Agencias Nacionales de Estadística, o a través de acuerdos bilaterales específicos. </p>",
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                "source": "<p><strong>Sources of discrepancies: </strong></p>\n<p>Many countries have produced and reported on estimates of the Prevalence of Undernourishment, including in their national MDG Reports, but almost invariably using a different methodology than the one developed by FAO, which makes national figures not comparable to those reported by FAO for global monitoring. </p>\n<p>The most common approach used in preparing national reports has been to calculate the percentage of households for which the average per capita daily dietary energy consumption is found to be below thresholds based on daily Recommended Dietary Intake, usually set at 2,100 kcal, based on household survey data. In some cases, also lower thresholds of around 1,400 kcal have been used, probably as a reaction to the fact that percentages of households reporting average daily consumption of less than 2,100 kcal per capita were implausibly high estimates of the prevalence of undernourishment. </p>\n<p>Almost without exception, no consideration related to the presence of excess variability in the dietary energy consumption data is made, and the reports reveal limited or no progress in the reduction of PoU over time. </p>\n<p>As discussed in the section on the method of computation, the results obtained through these alternative methods are highly unreliable and almost certainly biased toward overestimation. It is therefore advisable that a concerted effort is made to advocate for use of the FAO methods also in preparation of national reports. FAO stands ready to provide all necessary technical support.</p>",
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                "source": "<p><strong>Data availability:</strong></p>\n<p>Since 2017 FAO has reported separate estimates of PoU for 160 countries. </p>\n<p>While country-level estimates are presented as three-year averages, regional and global estimates are yearly estimates. </p>\n<p><strong>Time series: </strong></p>\n<p>2000 - current </p>\n<p><strong>Disaggregation: </strong></p>\n<p>Due to reliance on national Food Balance Sheets data to estimate mean caloric consumption levels in the population, the global monitoring of MDG Target 1C and of the WFS target has been based on estimates of the PoU at national level only. </p>\n<p>In principle, the indicator can be computed for any specific population group, provided sufficient accurate information exists to characterize the model&#x2019;s parameters for that specific group, that is, if data on the group&#x2019;s food consumption levels, age/gender structure and &#x2013; possibly &#x2013; physical activity levels, exist. </p>\n<p>The scope for disaggregation thus crucially depends on the availability of surveys designed to be representative at the level of sub national population groups. Given prevailing practice in the design of national household surveys, sufficient reliable information is seldom available for disaggregation beyond the level of macro area of residence (urban-rural) and of the main Provinces/Divisions in a country. To the extent that most of the used surveys are designed to accurately capture the distribution of income, inference can be drawn on the PoU in different income classes of the population. Gender disaggregation is limited by the possibility to identify and group households by gender-related information (such as sex of the head of the household, or male/female ratio).</p>",
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                "source": "<p>The main three sources of data at national level are: </p>\n<p>a) Official reports on the production, trade and utilization of the major food crop and livestock productions. </p>\n<p>b) Household survey data on food consumption </p>\n<p>c) Demographic characteristics of the national population </p>\n<p>Data sources for agricultural production are usually national surveys that are conducted by the Ministry of Agricultural/Livestock and/or the National Statistical Office. The surveys are usually annual, and in the absence of direct measurements, use information on areas/animal numbers and crop yields/carcass weights to calculate crop or livestock product quantities. Agricultural censuses, which FAO recommends conducting every ten years, may complement these surveys by providing more updated measured data on crops and livestock, and thus enable more precise projections/revisions. </p>\n<p>The data source for agricultural and food trade is almost exclusively the national customs office (with few exceptions where data may be obtained from the Central Bank). Countries often prepare these trade reports following international standard formats (commodity/country classifications, units of measurement, trading partner detail). While such trade data may be considered quite reliable, being the result of direct measurement/reporting by/to the customs office, issues of unreported border trade (and animal movement), misclassification of commodities, confidentiality, time-lag, to name a few, may necessitate some data analysis and validation (often by referring to &#x2018;mirror&#x2019; trade statistics to cross-check quantities and values). </p>\n<p>Data on the utilization of primary and processed crops and livestock may be obtained through specialized surveys (supplemented by research) through the national agri-food industry system. Utilizations of interest here are those quantities destined for, among others, animal feed, for industrial uses (e.g. biofuel production), for national/enterprise/farm stocks, for seed (sowing for the successive agricultural cycle) &#x2013; to enable as accurate an assessment as possible of the quantities destined/available for potential human consumption. </p>\n<p>These datasets (production, trade and utilizations), once cross-checked and validated, form the basis for the compilation of the Food Balance Sheets (FBS). The FBS are an accounting framework whereby supply (production + imports + stock withdrawals) should equal utilization (export + food processing + feed + seed + industrial use, etc.). It should be noted that, within the FBS framework, post-harvest/slaughter losses (up to the retail level) are considered as utilization, and thus a component in the balancing of the FBS. The FBS framework provides a snapshot of the agricultural supply situation at the national level, and allows for a cross-referenced structure whereby data, official or estimated/imputed, may be further analyzed and validated (e.g. animal numbers may result as being under-reported/estimated). </p>\n<p>The main result of the compilation of the FBS is the calculation of the Dietary Energy Supply (DES) in kilocalories per person (based on population figures) in a given year (quantities resulting as available for human consumption are converted into their caloric equivalents by using appropriate nutritive conversion factors by commodity). The DES, in the absence of direct consumption data from household surveys, is one of the key components in the calculation of the Prevalence of Undernourishment (PoU). FAO is presently embarking on a more focused program of providing FBS capacity to countries, including an updated compilation tool. </p>\n<p>FAO obtains crop/livestock primary/processed production data, and principal utilization thereof, through country-tailored questionnaires that are dispatched to all countries annually. Official country trade statistics are obtained annually through bulk downloads of the United Nations trade database (countries are expected to report to UNSD annually). In some cases, when available, national FBS data are also used. These datasets are then validated and form inputs in the country FBS which FAO compiles. It should be noted that when data are not officially reported/available (as is frequently the case with commodity utilization data), and hence it is necessary to resort to imputations to fill the data gaps. </p>\n<p>The new FBS Guidelines for national compilation (completed recently in collaboration with the Global Strategy) and new compilation tool (R-based &#x2018;shiny&#x2019; application). </p>\n<p>Detail on FBS methodology: <a href=\"http://www.fao.org/economic/ess/fbs/ess-fbs02/en/\">http://www.fao.org/economic/ess/fbs/ess-fbs02/en/</a>. </p>\n<p>The FBS Handbook shown here should not be confused with the recently completed FBS Guidelines. The Handbook is of a more technical nature and explains the methodology followed by FAO in compiling country FBS. The Guidelines on the other hand, while based on the Handbook, provide countries with a more revised and practical guidance and recommendations for compilation at the national level. </p>\n<p>Some FBS background text also available on FAOSTAT: <a href=\"http://www.fao.org/faostat/en/#data/FBS\">http://www.fao.org/faostat/en/#data/FBS</a>. </p>",
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                "source": "<p>Regional and global aggregates of the PoU are computed as: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>P</mi>\n        <mi>o</mi>\n        <mi>U</mi>\n      </mrow>\n      <mrow>\n        <mi>R</mi>\n        <mi>E</mi>\n        <mi>G</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mrow>\n          <munder>\n            <mo stretchy=\"false\">&#x2211;</mo>\n            <mrow>\n              <mi>i</mi>\n            </mrow>\n          </munder>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>P</mi>\n                <mi>o</mi>\n                <mi>U</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n            <mi>&amp;nbsp;</mi>\n            <mo>&#xD7;</mo>\n            <mi>&amp;nbsp;</mi>\n            <msub>\n              <mrow>\n                <mi>N</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mrow>\n      </mrow>\n      <mrow>\n        <mrow>\n          <munder>\n            <mo stretchy=\"false\">&#x2211;</mo>\n            <mrow>\n              <mi>i</mi>\n            </mrow>\n          </munder>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>N</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mrow>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>where PoU<sub>i</sub> are the values of PoU estimated for all countries (i) in the aggregate for which available data allow to compute a reliable estimate, and N<sub>i</sub> the corresponding population size. </p>",
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                "source": "<p>To compute an estimate of the prevalence of undernourishment in a population, the probability distribution of habitual dietary energy intake levels (expressed in kcal per person per day) for the average individual is modelled as a parametric probability density function (pdf), f(x).</p>\n<p>The indicator is obtained as the cumulative probability that the habitual dietary energy intake (x) is below the minimum dietary energy requirements (MDER) (i.e. the lowest limit of the range of energy requirements for the population&#x2019;s representative average individual) as in the formula below: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>P</mi>\n    <mi>o</mi>\n    <mi>U</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mrow>\n      <msubsup>\n        <mo stretchy=\"false\">&#x222B;</mo>\n        <mrow>\n          <mi>x</mi>\n          <mo>&amp;lt;</mo>\n          <mi>M</mi>\n          <mi>D</mi>\n          <mi>E</mi>\n          <mi>R</mi>\n        </mrow>\n        <mrow>\n          <mi>&amp;nbsp;</mi>\n        </mrow>\n      </msubsup>\n      <mrow>\n        <mi>f</mi>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi>x</mi>\n            <mo>|</mo>\n            <mi>&#x3B8;</mi>\n          </mrow>\n        </mfenced>\n        <mi>d</mi>\n        <mi>x</mi>\n      </mrow>\n    </mrow>\n  </math></p>\n<p>where &#x3B8; is a vector of parameters that characterizes the pdf. The distribution is assumed to be lognormal, and thus fully characterized by only two parameters: the mean dietary energy consumption (DEC), and its coefficient of variation (CV). </p>\n<p>A custom R function is available from the Statistics Division at FAO to compute the PoU, given the three parameters DEC, CV, and MDER. </p>\n<p>Different data sources can be used to estimate the different parameters of the model. </p>\n<p><u>DEC </u></p>\n<p>Ideally, data on food consumption should come from nationally representative household surveys (such as Living Standard Measurement Surveys or Household Incomes and Expenditure Surveys). However, only very few countries conduct such surveys on an annual basis. Thus, in FAO&#x2019;s PoU estimates for global monitoring, DEC values are estimated from the dietary energy supply (DES) reported in the Food Balance Sheets (FBS), compiled by FAO for most countries in the world (<a href=\"https://www.fao.org/faostat/en/#data/FBS\">https://www.fao.org/faostat/en/#data/FBS</a>). </p>\n<p><u>CV </u></p>\n<p>When reliable data on food consumption are available from aforementioned nationally representative household surveys, the CV due to income (CV|y) that describes the distribution of average daily dietary energy requirement in the population can be estimated directly.</p>\n<p>When no suitable survey data are available, FIES data collected by FAO since 2014 are used to project the changes in the CV|y from 2015 (or from the year of the last food consumption survey) up to 2019, based on a smoothed (three-year moving average) trend in severe food insecurity.</p>\n<p>Since 2014, FIES data provide evidence on recent changes in the extent of severe food insecurity that might closely reflect changes in the PoU. To the extent that such changes in PoU are not explained by changes in average food supplies, they can thus be used to infer the likely changes in the CV|y that might have occurred in the most recent year. Analysis of the combined set of historic PoU estimates reveals that, on average, and once differences in DEC and MDER have been controlled for, the CV|y explains about one-third of the differences in PoU across time and space. For each country for which FIES data are available, the CV|y is estimated by the amount that would generate one-third of a percentage point change in the PoU for each observed percentage point change in the prevalence of severe food insecurity. For all other countries, the CV|y is kept constant at the estimated 2017 value.</p>\n<p>In the FAO PoU parametric approach, the CV due to body weight and lifestyle, a.k.a. CV due to requirement (CV|r), represents the variability of the distribution of dietary energy requirements of a hypothetical average individual representative of a healthy population, which is also equal to the CV of the distribution of dietary energy intakes of a hypothetical average individual if the population is perfectly nourished. The distribution of dietary energy requirements of a hypothetical average individual can be assumed to be normal, thus its variability can be estimated if at least two percentiles and their values are known. As a result, given that we are interested in deriving the theoretical distribution of dietary energy requirements for healthy hypothetical average individuals to estimate the CV|r, the MDER and the average dietary energy requirement (ADER) can be used to approximate the 1st percentile and the 50th percentile of the distribution of energy requirements of the hypothetical average individual as they are built on the same principles of a weighted average from sex-age-physiological status groups. Therefore, the value of CV|r is derived as the inverse cumulative standard normal distribution of the difference between the MDER and the ADER. Similar to the MDER, the ADER is estimated using the average of the minimum and the maximum values of the PAL category &#x2018;Active or moderately active lifestyle&#x2019;.</p>\n<p>The total CV is then obtained as the geometric mean of the CV|y and the CV|r:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>C</mi>\n    <mi>V</mi>\n    <mo>=</mo>\n    <msqrt>\n      <mfenced separators=\"|\">\n        <mrow>\n          <msup>\n            <mrow>\n              <mi>C</mi>\n              <mi>V</mi>\n              <mo>|</mo>\n              <mi>y</mi>\n            </mrow>\n            <mrow>\n              <mn>2</mn>\n            </mrow>\n          </msup>\n        </mrow>\n      </mfenced>\n      <mo>+</mo>\n      <mfenced separators=\"|\">\n        <mrow>\n          <msup>\n            <mrow>\n              <mi>C</mi>\n              <mi>V</mi>\n              <mo>|</mo>\n              <mi>r</mi>\n            </mrow>\n            <mrow>\n              <mn>2</mn>\n            </mrow>\n          </msup>\n        </mrow>\n      </mfenced>\n    </msqrt>\n  </math></p>\n<p><strong>Challenges and limitations:</strong> While formally the state of being undernourished or not is a condition that applies to individuals, given the data usually available on a large scale, it is impossible to reliably identify which individuals in a certain group are actually undernourished. Through the statistical model described above, the indicator can only be computed with reference to a population or a group of individuals for which a representative sample is available. The prevalence of undernourishment is thus an estimate of the percentage of individuals in that group that are in such condition and cannot be further disaggregated.</p>\n<p>Due to the probabilistic nature of the inference and the margins of uncertainty associated with estimates of each of the parameters in the model, the precision of the PoU estimates is generally low. While it is not possible to formally compute margins of error around PoU estimates, these are expected to likely exceed 5 percent in most cases. For this reason, FAO does not consider PoU estimates that result to be lower than 2.5 percent as sufficiently reliable to be reported.</p>\n<p><u>MDER </u></p>\n<p>Human energy requirements for an individual in a given sex/age class are determined on the basis of normative requirements for basic metabolic rate (BMR) per kilogram of body mass, multiplied by the ideal weights that a healthy person of that sex/age class may have, given his or her height, and then multiplied by a coefficient of physical activity level (PAL) to take into account physical activity. Given that both healthy BMIs and PALs vary among active and healthy individuals of the same sex and age, a <em>range </em>of energy requirements applies to each sex and age group of the population. The MDER for the average individual in the population, which is the parameter used in the PoU formula, is obtained as the weighted average of the lower bounds of the energy requirement ranges for each sex and age group, using the shares of the population in each sex and age group as weights.</p>\n<p>Information on the population structure by sex and age is available for most countries in the world and for each year from the UN Department of Economic and Social Affairs (DESA) Population Prospects, revised every two years.</p>\n<p>Information on the median height in each sex and age group for a given country is derived from a recent demographic and health survey (DHS) or from other surveys that collect anthropometry data on children and adults. Even if such surveys do not refer to the same year for which the PoU is estimated, the impact of possible small intervening changes in median heights over the years on PoU estimates is expected to be negligible.</p>",
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                "source": "<p>Over the years, the parametric approach informing the computation of the PoU has been criticized, based on the presumptions that undernourishment should be assessed necessarily starting at the individual level, by comparing individual energy requirements with individual energy intakes. According to such a view, the prevalence of undernourishment could be simply computed by counting the number of individuals in a representative sample of the population that is classified as undernourished, based on a comparison of individual habitual food consumption and requirements. </p>\n<p>Unfortunately, such an approach is not feasible for two reasons: first, due to the cost of individual dietary intake surveys, individual food consumption is measured only in a few countries, every several years, on relatively small samples; moreover, individual energy requirements are practically unobservable with standard data collection methods (to the point that observed habitual energy consumption of individuals in a healthy status is still the preferred way to infer individual energy requirements). This means that even if it were possible to obtain accurate observations of the individual dietary energy consumption, this would be insufficient to infer on the undernourishment condition at individual level, unless integrated by the observation on the physical status (body mass index) and of its dynamic over time, of the same individual. </p>\n<p>The model-based approach to estimate the PoU developed by FAO integrates information that is available with sufficient regularity from different sources for most countries in the world, in a theoretically consistent way, thus providing what is still one of the most reliable tools to monitor progress towards reducing global hunger. </p>\n<p>Further specific consideration </p>\n<p>1. Feasibility </p>\n<p>Estimation of PoU at national level has been feasible for most countries in the world since 1999. In the worst case scenario, when no data on food consumption was available from a recent household survey, the model-based estimate of the PoU is informed by an estimate of mean level of dietary energy consumption (DEC) from Food Balance Sheets (FBS), an indirect estimate of the coefficient of variation (CV) based on information on the country&#x2019;s GDP, Gini coefficient of Income, an index of the relative price of food, or other indicators of development such as country&#x2019;s Under 5 Mortality Rate, and an estimate of the Minimum Dietary Energy Requirement (MDER) based on the UN Population Division&#x2019;s World Population Prospects data. </p>\n<p>2. Reliability </p>\n<p>Reliability mostly depends on the quality of the data used to inform the estimation of the model&#x2019;s parameters. </p>\n<p>DEC could be estimated either from survey data or from food balances. Neither source is devoid of problems. When comparing estimates of national DEC from FBS and from surveys, differences are frequently noted. </p>\n<p>DEC estimates from survey data can be affected by systematic measurement errors due to under-reporting of food consumption, or to incomplete recording of all food consumption sources. Recent research shows that a negative bias of up to more than 850 kcal can be induced on the estimated daily per capita caloric consumption can be induced by the type of food consumption module chosen to capture the data at the household level. (See De Weerdt et al., 2015, Table 2, <a href=\"https://feb.kuleuven.be/drc/licos/publications/dp/DP%20365%20Complete.pdf\">https://feb.kuleuven.be/drc/licos/publications/dp/DP%20365%20Complete.pdf</a>). A detailed analysis of a recent Household Budget Survey in Brazil revealed how food provided for free through the school meals program and consumed by children while at school, had not been accounted among the sources of household food consumption, accounting for a downward bias of the average per capita daily dietary energy consumption of 674 kcal. (See Borlizzi, Cafiero &amp; Del Grossi, forthcoming.) </p>\n<p>DEC estimates from Food Balance Sheets can also be affected by errors, though it is difficult to establish the direction of induced bias. As average food availability is a residual in the FBS method, any errors in reported production, trade, and stocks might affect the estimates of national food availability. Moreover, errors might be induced by the difficulty in properly accounting for all forms of food commodity utilization. To the extent that all these errors are uncorrelated, though, the impact on the estimated average food consumption will be lower than each of the errors, considered separately, might imply. Nevertheless, considering how problematic it is to precisely account for variations in national reserves of food commodities, for which official data may be unreliable, it is recognized that the estimated annual stock variation is prone to considerable uncertainty that would be transferred to the estimated DEC in each given year. </p>\n<p>To limit the impact of such errors, FAO has traditionally presented estimates of PoU at national level as three-year averages, on the presumption that errors induced by imprecise recording of stocks variations in each single year might be highly reduced when considering an average over three consecutive years. </p>\n<p>Survey data are the only source to estimate the CV. As described in the section of metadata on the method of computation, unless obtained from high quality individual dietary intake surveys, data needs to be treated to reduce the likely upward bias in the estimates of the CV that would be induced by the spurious variability due to errors in measuring individual habitual dietary energy intake. </p>\n<p>3. Comparability </p>\n<p>If the same method of computation is used, comparability across time and space is relatively high, with the only potential cause of inhomogeneity found in the different quality of the background data. </p>\n<p>4. Limitations </p>\n<p>Due to the probabilistic nature of the inference and the margins of uncertainty associated with estimates of each of the parameters in the model, the precision of the PoU estimates is generally low. Even though it is not possible to compute theoretical Margins of Error (MoE) for PoU estimates, these would very likely exceed plus or minus 2.5% in most cases. For this reason, FAO publishes national level PoU estimates only when they are larger than 2.5%. This also suggests that 2.5% is the lowest feasible target that can be set for the PoU indicator, a value that is unsatisfactorily large when the ambition is to fully eradicate the scourge of hunger. </p>\n<p>If no survey is available that collects food consumption data and that is representative at subnational level, the indicator can only be computed at national level.&quot;</p>",
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                "state": 0,
                "source": "<p>Data are released each year alongside the <em>State of Food Security and Nutrition in the World</em> report, usually in mid-July. </p>",
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                "source": "<p>The ideal source of data to estimate the PoU would be a carefully designed and skillfully conducted individual dietary intake survey, in which actual daily food consumption, together with heights and weights for each surveyed individual, are repeatedly measured on a sample that is representative of the target population. Due to their cost, however, such surveys are rare.</p>\n<p>In principle, a well-designed household survey that collects information on food acquisitions might be sufficient to inform a reliable estimate of the Prevalence of Undernourishment (PoU) in a population, at a reasonable cost and with the necessary periodicity to inform the SDG monitoring process, provided that: </p>\n<ol>\n  <li>All sources of food consumption for all members of the households are properly accounted for, including, in particular, food that is consumed away from home; </li>\n  <li>Sufficient information is available to convert the data on food consumption or on food expenditures into their contribution to dietary energy intake; </li>\n  <li>The proper methods to compute the PoU are used, to control for excess variability in the estimated levels of habitual food consumption across households, allowing for the presence on normal variability in the distribution of food consumption across individuals, induced by the differences in energy requirements of the members of the population. </li>\n</ol>\n<p>Examples of surveys that could be considered for this purpose include surveys conducted to compute economic statistics and conduct poverty assessments, such as Household Income and Expenditure Surveys, Household Budget Surveys and Living Standard Measurement Surveys. </p>\n<p>In practice, however, it is often impossible, and not advisable, to rely only on data collected through a household survey, as the information needed to estimate the four parameters of the PoU model is either missing or imprecise. </p>\n<p>Household Survey food consumption data often must be integrated by </p>\n<p>a) Data on the demographic structure of the population of interest by sex and age; </p>\n<p>b) Data or information on the median height of individuals in each sex and age class; </p>\n<p>c) Data on the distribution of physical activity levels in the population; </p>\n<p>d) Alternative data on the total amounts of food available for human consumption, to correct for biases in the estimate of the national average daily dietary energy consumption in the population. </p>\n<p>Data for a), b) and c) could be available through the same multipurpose survey that provides food consumption data, but are more likely available from other sources, such as National Demographic and Health Surveys (for a) and b)) and Time Use Surveys (for c)). </p>\n<p>Correcting for bias in the estimated average daily dietary energy consumption might need to be based on alternative sources on food consumption, such as aggregate food supply and utilization accounts and food balance sheets. </p>\n<p>To inform its estimate of PoU at national, regional and global level, in addition to all household surveys for which it is possible to obtain micro data on food consumption, FAO relies on: </p>\n<p>a) UN Population Division&#x2019;s World Population Prospects (https://esa.un.org/unpd/wpp/Download/Standard/Population/), which provide updated estimates of the structures of the national population by sex and age every two years for most countries in the world; </p>\n<p>b) FAO Food Balance Sheets (http://www.fao.org/faostat/en/#data), which provides updated estimates of the national availability of food every year for most countries in the world.</p>\n<p>Micro data from household surveys that collect food consumption data are sourced by FAO directly through the National Statistical Agencies&#x2019; websites, or through specific bilateral agreements. </p>",
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            "details": {
                "state": 0,
                "source": "<p>Prevalence of undernourishment: Percent (%) Number of undernourished people: Millions (of people) </p>",
                "old_state": 0
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            "action": 59,
            "target": "اخر تحديث",
            "old": "اخر تحديث",
            "details": {
                "state": 10,
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            "action": 30,
            "target": "2022-03-31",
            "old": "<p>2021-02-01</p>",
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            "action": 59,
            "target": "<p><strong>URL: </strong></p>\n<p><a href=\"https://www.fao.org/food-agriculture-statistics/statistical-domains/food-security-and-nutrition/en/\">https://www.fao.org/food-agriculture-statistics/statistical-domains/food-security-and-nutrition/en/</a> </p>\n<p><strong>References: </strong></p>\n<p><a href=\"http://www.fao.org/docrep/012/w0931e/w0931e16.pdf\">http://www.fao.org/docrep/012/w0931e/w0931e16.pdf</a></p>\n<p><a href=\"http://www.fao.org/docrep/005/Y4249E/y4249e06.htm#bm06\">http://www.fao.org/docrep/005/Y4249E/y4249e06.htm#bm06</a></p>\n<p><a href=\"http://www.fao.org/3/a-i4060e.pdf\">http://www.fao.org/3/a-i4060e.pdf</a></p>\n<p><a href=\"http://www.fao.org/3/a-i4046e.pdf\">http://www.fao.org/3/a-i4046e.pdf</a></p>",
            "old": "<p><strong>URL: </strong></p>\n<p>http://www.fao.org/economic/ess/ess-fs/en/ </p>\n<p><strong>References: </strong></p>\n<p>http://www.fao.org/docrep/012/w0931e/w0931e16.pdf </p>\n<p>http://www.fao.org/docrep/005/Y4249E/y4249e06.htm#bm06 </p>\n<p>http://www.fao.org/3/a-i4060e.pdf </p>\n<p>http://www.fao.org/3/a-i4046e.pdf </p>",
            "details": {
                "state": 100,
                "source": "<p><strong>URL: </strong></p>\n<p><a href=\"https://www.fao.org/food-agriculture-statistics/statistical-domains/food-security-and-nutrition/en/\">https://www.fao.org/food-agriculture-statistics/statistical-domains/food-security-and-nutrition/en/</a> </p>\n<p><strong>References: </strong></p>\n<p><a href=\"http://www.fao.org/docrep/012/w0931e/w0931e16.pdf\">http://www.fao.org/docrep/012/w0931e/w0931e16.pdf</a></p>\n<p><a href=\"http://www.fao.org/docrep/005/Y4249E/y4249e06.htm#bm06\">http://www.fao.org/docrep/005/Y4249E/y4249e06.htm#bm06</a></p>\n<p><a href=\"http://www.fao.org/3/a-i4060e.pdf\">http://www.fao.org/3/a-i4060e.pdf</a></p>\n<p><a href=\"http://www.fao.org/3/a-i4046e.pdf\">http://www.fao.org/3/a-i4046e.pdf</a></p>",
                "old_state": 100
            },
            "id": 23102117,
            "action_name": "String updated in the repository",
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            "target": "<p><strong>Sources of discrepancies: </strong></p>\n<p>Many countries have produced and reported on estimates of the Prevalence of Undernourishment, including in their national MDG Reports, but almost invariably using a different methodology than the one developed by FAO, which makes national figures not comparable to those reported by FAO for global monitoring. </p>\n<p>The most common approach used in preparing national reports has been to calculate the percentage of households for which the average per capita daily dietary energy consumption is found to be below thresholds based on daily Recommended Dietary Intake, usually set at 2,100 kcal, based on household survey data. In some cases, also lower thresholds of around 1,400 kcal have been used, probably as a reaction to the fact that percentages of households reporting average daily consumption of less than 2,100 kcal per capita were implausibly high estimates of the prevalence of undernourishment. </p>\n<p>Almost without exception, no consideration related to the presence of excess variability in the dietary energy consumption data is made, and the reports reveal limited or no progress in the reduction of PoU over time. </p>\n<p>As discussed in the section on the method of computation, the results obtained through these alternative methods are highly unreliable and almost certainly biased toward overestimation. It is therefore advisable that a concerted effort is made to advocate for use of the FAO methods also in preparation of national reports. FAO stands ready to provide all necessary technical support.</p>",
            "old": "<p><strong>Sources of discrepancies: </strong></p>\n<p>Many countries have produced and reported on estimates of the Prevalence of Undernourishment, including in their national MDG Reports, but almost invariably using a different methodology than the one developed by FAO, which makes national figures not comparable to those reported by FAO for global monitoring. </p>\n<p>The most common approach used in preparing national reports has been to calculate the percentage of households for which the average per capita daily dietary energy consumption is found to be below thresholds based on daily Recommended Dietary Intake, usually set at 2,100.00 kcal, based on household survey data. In some cases, also lower thresholds of around 1,400.00 kcal have been used, probably as a reaction to the fact that percentages of households reporting average daily consumption of less than 2,100.00 kcal per capita were implausibly high estimates of the prevalence of undernourishment. </p>\n<p>Almost without exception, no consideration related to the presence of excess variability in the dietary energy consumption data is made, and the reports reveal limited or no progress in the reduction of PoU over time. </p>\n<p>As discussed in the section on the method of computation, the results obtained through these alternative methods are highly unreliable and almost certainly biased toward overestimation. It is therefore advisable that a concerted effort is made to advocate for use of the FAO methods also in preparation of national reports. FAO stands ready to provide all necessary technical support.</p>",
            "details": {
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                "source": "<p><strong>Sources of discrepancies: </strong></p>\n<p>Many countries have produced and reported on estimates of the Prevalence of Undernourishment, including in their national MDG Reports, but almost invariably using a different methodology than the one developed by FAO, which makes national figures not comparable to those reported by FAO for global monitoring. </p>\n<p>The most common approach used in preparing national reports has been to calculate the percentage of households for which the average per capita daily dietary energy consumption is found to be below thresholds based on daily Recommended Dietary Intake, usually set at 2,100 kcal, based on household survey data. In some cases, also lower thresholds of around 1,400 kcal have been used, probably as a reaction to the fact that percentages of households reporting average daily consumption of less than 2,100 kcal per capita were implausibly high estimates of the prevalence of undernourishment. </p>\n<p>Almost without exception, no consideration related to the presence of excess variability in the dietary energy consumption data is made, and the reports reveal limited or no progress in the reduction of PoU over time. </p>\n<p>As discussed in the section on the method of computation, the results obtained through these alternative methods are highly unreliable and almost certainly biased toward overestimation. It is therefore advisable that a concerted effort is made to advocate for use of the FAO methods also in preparation of national reports. FAO stands ready to provide all necessary technical support.</p>",
                "old_state": 100
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            "id": 23102116,
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            "action": 59,
            "target": "<p><strong>Data availability:</strong></p>\n<p>Since 2017 FAO has reported separate estimates of PoU for 160 countries. </p>\n<p>While country-level estimates are presented as three-year averages, regional and global estimates are yearly estimates. </p>\n<p><strong>Time series: </strong></p>\n<p>2000 - current </p>\n<p><strong>Disaggregation: </strong></p>\n<p>Due to reliance on national Food Balance Sheets data to estimate mean caloric consumption levels in the population, the global monitoring of MDG Target 1C and of the WFS target has been based on estimates of the PoU at national level only. </p>\n<p>In principle, the indicator can be computed for any specific population group, provided sufficient accurate information exists to characterize the model&#x2019;s parameters for that specific group, that is, if data on the group&#x2019;s food consumption levels, age/gender structure and &#x2013; possibly &#x2013; physical activity levels, exist. </p>\n<p>The scope for disaggregation thus crucially depends on the availability of surveys designed to be representative at the level of sub national population groups. Given prevailing practice in the design of national household surveys, sufficient reliable information is seldom available for disaggregation beyond the level of macro area of residence (urban-rural) and of the main Provinces/Divisions in a country. To the extent that most of the used surveys are designed to accurately capture the distribution of income, inference can be drawn on the PoU in different income classes of the population. Gender disaggregation is limited by the possibility to identify and group households by gender-related information (such as sex of the head of the household, or male/female ratio).</p>",
            "old": "<p><strong>Data availability:</strong></p>\n<p>Since 2017 FAO has reported separate estimates of PoU for 170 countries, distributed as follows: </p>\n<p>World 170 </p>\n<p> Africa 37 </p>\n<p> Northern Africa 5 </p>\n<p> Sub-Saharan Africa 32 </p>\n<p> Eastern Africa 8 </p>\n<p> Middle Africa 6 </p>\n<p> Southern Africa 5 </p>\n<p> Western Africa 13 </p>\n<p> Asia 38 </p>\n<p> Central Asia 4 </p>\n<p> Eastern Asia 5 </p>\n<p> Southern Asia 7 </p>\n<p> South-Eastern Asia 9 </p>\n<p> Western Asia 13 </p>\n<p> Latin America and the Caribbean 28 </p>\n<p> Caribbean 8 </p>\n<p> Latin America 20 </p>\n<p> Central America 8 </p>\n<p> South America 12 </p>\n<p> Oceania 9</p>\n<p>Australia and New Zealand 2</p>\n<p>Oceania excluding Australia and New Zealand 7</p>\n<p> Northern America and Europe 42</p>\n<p>Northern America 2</p>\n<p>Europe 38</p>\n<p> Eastern Europe 9</p>\n<p> Northern Europe 10</p>\n<p> Southern Europe 12</p>\n<p> Western Europe 7</p>\n<p>While country-level estimates are presented as three-year averages, regional and global estimates are yearly estimates. </p>\n<p><strong>Time series: </strong></p>\n<p>2000 - current </p>\n<p><strong>Disaggregation: </strong></p>\n<p>Due to reliance on national Food Balance Sheets data to estimate mean caloric consumption levels in the population, the global monitoring of MDG Target 1C and of the WFS target has been based on estimates of the PoU at national level only. </p>\n<p>In principle, the indicator can be computed for any specific population group, provided sufficient accurate information exists to characterize the model&#x2019;s parameters for that specific group, that is, if data on the group&#x2019;s food consumption levels, age/gender structure and &#x2013; possibly &#x2013; physical activity levels, exist. </p>\n<p>The scope for disaggregation thus crucially depends on the availability of surveys designed to be representative at the level of sub national population groups. Given prevailing practice in the design of national household surveys, sufficient reliable information is seldom available for disaggregation beyond the level of macro area of residence (urban-rural) and of the main Provinces/Divisions in a country. To the extent that most of the used surveys are designed to accurately capture the distribution of income, inference can be drawn on the PoU in different income classes of the population. Gender disaggregation is limited by the possibility to identify and group households by gender-related information (such as sex of the head of the household, or male/female ratio).</p>",
            "details": {
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                "source": "<p><strong>Data availability:</strong></p>\n<p>Since 2017 FAO has reported separate estimates of PoU for 160 countries. </p>\n<p>While country-level estimates are presented as three-year averages, regional and global estimates are yearly estimates. </p>\n<p><strong>Time series: </strong></p>\n<p>2000 - current </p>\n<p><strong>Disaggregation: </strong></p>\n<p>Due to reliance on national Food Balance Sheets data to estimate mean caloric consumption levels in the population, the global monitoring of MDG Target 1C and of the WFS target has been based on estimates of the PoU at national level only. </p>\n<p>In principle, the indicator can be computed for any specific population group, provided sufficient accurate information exists to characterize the model&#x2019;s parameters for that specific group, that is, if data on the group&#x2019;s food consumption levels, age/gender structure and &#x2013; possibly &#x2013; physical activity levels, exist. </p>\n<p>The scope for disaggregation thus crucially depends on the availability of surveys designed to be representative at the level of sub national population groups. Given prevailing practice in the design of national household surveys, sufficient reliable information is seldom available for disaggregation beyond the level of macro area of residence (urban-rural) and of the main Provinces/Divisions in a country. To the extent that most of the used surveys are designed to accurately capture the distribution of income, inference can be drawn on the PoU in different income classes of the population. Gender disaggregation is limited by the possibility to identify and group households by gender-related information (such as sex of the head of the household, or male/female ratio).</p>",
                "old_state": 100
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            "action": 59,
            "target": "<p>The main three sources of data at national level are: </p>\n<p>a) Official reports on the production, trade and utilization of the major food crop and livestock productions. </p>\n<p>b) Household survey data on food consumption </p>\n<p>c) Demographic characteristics of the national population </p>\n<p>Data sources for agricultural production are usually national surveys that are conducted by the Ministry of Agricultural/Livestock and/or the National Statistical Office. The surveys are usually annual, and in the absence of direct measurements, use information on areas/animal numbers and crop yields/carcass weights to calculate crop or livestock product quantities. Agricultural censuses, which FAO recommends conducting every ten years, may complement these surveys by providing more updated measured data on crops and livestock, and thus enable more precise projections/revisions. </p>\n<p>The data source for agricultural and food trade is almost exclusively the national customs office (with few exceptions where data may be obtained from the Central Bank). Countries often prepare these trade reports following international standard formats (commodity/country classifications, units of measurement, trading partner detail). While such trade data may be considered quite reliable, being the result of direct measurement/reporting by/to the customs office, issues of unreported border trade (and animal movement), misclassification of commodities, confidentiality, time-lag, to name a few, may necessitate some data analysis and validation (often by referring to &#x2018;mirror&#x2019; trade statistics to cross-check quantities and values). </p>\n<p>Data on the utilization of primary and processed crops and livestock may be obtained through specialized surveys (supplemented by research) through the national agri-food industry system. Utilizations of interest here are those quantities destined for, among others, animal feed, for industrial uses (e.g. biofuel production), for national/enterprise/farm stocks, for seed (sowing for the successive agricultural cycle) &#x2013; to enable as accurate an assessment as possible of the quantities destined/available for potential human consumption. </p>\n<p>These datasets (production, trade and utilizations), once cross-checked and validated, form the basis for the compilation of the Food Balance Sheets (FBS). The FBS are an accounting framework whereby supply (production + imports + stock withdrawals) should equal utilization (export + food processing + feed + seed + industrial use, etc.). It should be noted that, within the FBS framework, post-harvest/slaughter losses (up to the retail level) are considered as utilization, and thus a component in the balancing of the FBS. The FBS framework provides a snapshot of the agricultural supply situation at the national level, and allows for a cross-referenced structure whereby data, official or estimated/imputed, may be further analyzed and validated (e.g. animal numbers may result as being under-reported/estimated). </p>\n<p>The main result of the compilation of the FBS is the calculation of the Dietary Energy Supply (DES) in kilocalories per person (based on population figures) in a given year (quantities resulting as available for human consumption are converted into their caloric equivalents by using appropriate nutritive conversion factors by commodity). The DES, in the absence of direct consumption data from household surveys, is one of the key components in the calculation of the Prevalence of Undernourishment (PoU). FAO is presently embarking on a more focused program of providing FBS capacity to countries, including an updated compilation tool. </p>\n<p>FAO obtains crop/livestock primary/processed production data, and principal utilization thereof, through country-tailored questionnaires that are dispatched to all countries annually. Official country trade statistics are obtained annually through bulk downloads of the United Nations trade database (countries are expected to report to UNSD annually). In some cases, when available, national FBS data are also used. These datasets are then validated and form inputs in the country FBS which FAO compiles. It should be noted that when data are not officially reported/available (as is frequently the case with commodity utilization data), and hence it is necessary to resort to imputations to fill the data gaps. </p>\n<p>The new FBS Guidelines for national compilation (completed recently in collaboration with the Global Strategy) and new compilation tool (R-based &#x2018;shiny&#x2019; application). </p>\n<p>Detail on FBS methodology: <a href=\"http://www.fao.org/economic/ess/fbs/ess-fbs02/en/\">http://www.fao.org/economic/ess/fbs/ess-fbs02/en/</a>. </p>\n<p>The FBS Handbook shown here should not be confused with the recently completed FBS Guidelines. The Handbook is of a more technical nature and explains the methodology followed by FAO in compiling country FBS. The Guidelines on the other hand, while based on the Handbook, provide countries with a more revised and practical guidance and recommendations for compilation at the national level. </p>\n<p>Some FBS background text also available on FAOSTAT: <a href=\"http://www.fao.org/faostat/en/#data/FBS\">http://www.fao.org/faostat/en/#data/FBS</a>. </p>",
            "old": "<p>The main three sources of data at national level are: </p>\n<p>a) Official reports on the production, trade and utilization of the major food crop and livestock productions. </p>\n<p>b) Household survey data on food consumption </p>\n<p>c) Demographic characteristics of the national population </p>\n<p>Data sources for agricultural production are usually national surveys that are conducted by the Ministry of Agricultural/Livestock and/or the National Statistical Office. The surveys are usually annual, and in the absence of direct measurements, use information on areas/animal numbers and crop yields/carcass weights to calculate crop or livestock product quantities. Agricultural censuses, which FAO recommends conducting every ten years, may complement these surveys by providing more updated measured data on crops and livestock, and thus enable more precise projections/revisions. </p>\n<p>The data source for agricultural and food trade is almost exclusively the national customs office (with few exceptions where data may be obtained from the Central Bank). Countries often prepare these trade reports following international standard formats (commodity/country classifications, units of measurement, trading partner detail). While such trade data may be considered quite reliable, being the result of direct measurement/reporting by/to the customs office, issues of unreported border trade (and animal movement), misclassification of commodities, confidentiality, time-lag, to name a few, may necessitate some data analysis and validation (often by referring to &#x2018; mirror&#x2019; trade statistics to cross-check quantities and values). </p>\n<p>Data on the utilization of primary and processed crops and livestock may be obtained through specialized surveys (supplemented by research) through the national agri-food industry system. Utilizations of interest here are those quantities destined for, among others, animal feed, for industrial uses (e.g. biofuel production), for national/enterprise/farm stocks, for seed (sowing for the successive agricultural cycle) &#x2013; to enable as accurate an assessment as possible of the quantities destined/available for potential human consumption. </p>\n<p>These datasets (production, trade and utilizations), once cross-checked and validated, form the basis for the compilation of the Food Balance Sheets (FBS). The FBS are an accounting framework whereby supply (production + imports + stock withdrawals) should equal utilization (export + food processing + feed + seed + industrial use, etc.). It should be noted that, within the FBS framework, post-harvest/slaughter losses (up to the retail level) are considered as utilization, and thus a component in the balancing of the FBS. The FBS framework provides a snapshot of the agricultural supply situation at the national level, and allows for a cross-referenced structure whereby data, official or estimated/imputed, may be further analyzed and validated (e.g. animal numbers may result as being under-reported/estimated). The main result of the compilation of the FBS is the calculation of the Dietary Energy Supply (DES) in kilocalories per person (based on population figures) in a given year (quantities resulting as available for human consumption are converted into their caloric equivalents by using appropriate nutritive conversion factors by commodity). The DES, in the absence of direct consumption data from household surveys, is one of the key components in the calculation of the Prevalence of Undernourishment (PoU). FAO is presently embarking on a more focused program of providing FBS capacity to countries, including an updated compilation tool. </p>\n<p>FAO obtains crop/livestock primary/processed production data, and principal utilization thereof, through country-tailored questionnaires that are dispatched to all countries annually. Official country trade statistics are obtained annually through bulk downloads of the United Nations trade database (countries are expected to report to UNSD annually). In some cases, when available, national FBS data are also used. These datasets are then validated and form inputs in the country FBS which FAO compiles. It should be noted that when data are not officially reported/available (as is frequently the case with commodity utilization data), and hence it is necessary to resort to imputations to fill the data gaps. </p>\n<p>The new FBS Guidelines for national compilation (completed recently in collaboration with the Global Strategy) and new compilation tool (R-based &#x2018;shiny&#x2019; application). </p>\n<p>Detail on FBS methodology: <a href=\"http://www.fao.org/economic/ess/fbs/ess-fbs02/en/\">http://www.fao.org/economic/ess/fbs/ess-fbs02/en/</a>. </p>\n<p>The FBS Handbook shown here should not be confused with the recently completed FBS Guidelines. The Handbook is of a more technical nature and explains the methodology followed by FAO in compiling country FBS. The Guidelines on the other hand, while based on the Handbook, provide countries with a more revised and practical guidance and recommendations for compilation at the national level. </p>\n<p>Some FBS background text also available on FAOSTAT: <a href=\"http://www.fao.org/faostat/en/#data/FBS\">http://www.fao.org/faostat/en/#data/FBS</a>. </p>",
            "details": {
                "state": 100,
                "source": "<p>The main three sources of data at national level are: </p>\n<p>a) Official reports on the production, trade and utilization of the major food crop and livestock productions. </p>\n<p>b) Household survey data on food consumption </p>\n<p>c) Demographic characteristics of the national population </p>\n<p>Data sources for agricultural production are usually national surveys that are conducted by the Ministry of Agricultural/Livestock and/or the National Statistical Office. The surveys are usually annual, and in the absence of direct measurements, use information on areas/animal numbers and crop yields/carcass weights to calculate crop or livestock product quantities. Agricultural censuses, which FAO recommends conducting every ten years, may complement these surveys by providing more updated measured data on crops and livestock, and thus enable more precise projections/revisions. </p>\n<p>The data source for agricultural and food trade is almost exclusively the national customs office (with few exceptions where data may be obtained from the Central Bank). Countries often prepare these trade reports following international standard formats (commodity/country classifications, units of measurement, trading partner detail). While such trade data may be considered quite reliable, being the result of direct measurement/reporting by/to the customs office, issues of unreported border trade (and animal movement), misclassification of commodities, confidentiality, time-lag, to name a few, may necessitate some data analysis and validation (often by referring to &#x2018;mirror&#x2019; trade statistics to cross-check quantities and values). </p>\n<p>Data on the utilization of primary and processed crops and livestock may be obtained through specialized surveys (supplemented by research) through the national agri-food industry system. Utilizations of interest here are those quantities destined for, among others, animal feed, for industrial uses (e.g. biofuel production), for national/enterprise/farm stocks, for seed (sowing for the successive agricultural cycle) &#x2013; to enable as accurate an assessment as possible of the quantities destined/available for potential human consumption. </p>\n<p>These datasets (production, trade and utilizations), once cross-checked and validated, form the basis for the compilation of the Food Balance Sheets (FBS). The FBS are an accounting framework whereby supply (production + imports + stock withdrawals) should equal utilization (export + food processing + feed + seed + industrial use, etc.). It should be noted that, within the FBS framework, post-harvest/slaughter losses (up to the retail level) are considered as utilization, and thus a component in the balancing of the FBS. The FBS framework provides a snapshot of the agricultural supply situation at the national level, and allows for a cross-referenced structure whereby data, official or estimated/imputed, may be further analyzed and validated (e.g. animal numbers may result as being under-reported/estimated). </p>\n<p>The main result of the compilation of the FBS is the calculation of the Dietary Energy Supply (DES) in kilocalories per person (based on population figures) in a given year (quantities resulting as available for human consumption are converted into their caloric equivalents by using appropriate nutritive conversion factors by commodity). The DES, in the absence of direct consumption data from household surveys, is one of the key components in the calculation of the Prevalence of Undernourishment (PoU). FAO is presently embarking on a more focused program of providing FBS capacity to countries, including an updated compilation tool. </p>\n<p>FAO obtains crop/livestock primary/processed production data, and principal utilization thereof, through country-tailored questionnaires that are dispatched to all countries annually. Official country trade statistics are obtained annually through bulk downloads of the United Nations trade database (countries are expected to report to UNSD annually). In some cases, when available, national FBS data are also used. These datasets are then validated and form inputs in the country FBS which FAO compiles. It should be noted that when data are not officially reported/available (as is frequently the case with commodity utilization data), and hence it is necessary to resort to imputations to fill the data gaps. </p>\n<p>The new FBS Guidelines for national compilation (completed recently in collaboration with the Global Strategy) and new compilation tool (R-based &#x2018;shiny&#x2019; application). </p>\n<p>Detail on FBS methodology: <a href=\"http://www.fao.org/economic/ess/fbs/ess-fbs02/en/\">http://www.fao.org/economic/ess/fbs/ess-fbs02/en/</a>. </p>\n<p>The FBS Handbook shown here should not be confused with the recently completed FBS Guidelines. The Handbook is of a more technical nature and explains the methodology followed by FAO in compiling country FBS. The Guidelines on the other hand, while based on the Handbook, provide countries with a more revised and practical guidance and recommendations for compilation at the national level. </p>\n<p>Some FBS background text also available on FAOSTAT: <a href=\"http://www.fao.org/faostat/en/#data/FBS\">http://www.fao.org/faostat/en/#data/FBS</a>. </p>",
                "old_state": 100
            },
            "id": 23102114,
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        {
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            "timestamp": "2022-04-09T01:54:15.101411+02:00",
            "action": 59,
            "target": "<p>Regional and global aggregates of the PoU are computed as: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>P</mi>\n        <mi>o</mi>\n        <mi>U</mi>\n      </mrow>\n      <mrow>\n        <mi>R</mi>\n        <mi>E</mi>\n        <mi>G</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mrow>\n          <munder>\n            <mo stretchy=\"false\">&#x2211;</mo>\n            <mrow>\n              <mi>i</mi>\n            </mrow>\n          </munder>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>P</mi>\n                <mi>o</mi>\n                <mi>U</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n            <mi>&amp;nbsp;</mi>\n            <mo>&#xD7;</mo>\n            <mi>&amp;nbsp;</mi>\n            <msub>\n              <mrow>\n                <mi>N</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mrow>\n      </mrow>\n      <mrow>\n        <mrow>\n          <munder>\n            <mo stretchy=\"false\">&#x2211;</mo>\n            <mrow>\n              <mi>i</mi>\n            </mrow>\n          </munder>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>N</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mrow>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>where PoU<sub>i</sub> are the values of PoU estimated for all countries (i) in the aggregate for which available data allow to compute a reliable estimate, and N<sub>i</sub> the corresponding population size. </p>",
            "old": "<p>Regional and global aggregates of the PoU are computed as: </p>\n<p>PoU_REG = (_i PoU_i &#xD7; N_i) / (_i N_i) </p>\n<p>where PoU_i are the values of PoU estimated for all countries in the regions for which available data allow to compute a reliable estimate, and N_i the corresponding population size. </p>\n<p> </p>",
            "details": {
                "state": 100,
                "source": "<p>Regional and global aggregates of the PoU are computed as: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>P</mi>\n        <mi>o</mi>\n        <mi>U</mi>\n      </mrow>\n      <mrow>\n        <mi>R</mi>\n        <mi>E</mi>\n        <mi>G</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mrow>\n          <munder>\n            <mo stretchy=\"false\">&#x2211;</mo>\n            <mrow>\n              <mi>i</mi>\n            </mrow>\n          </munder>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>P</mi>\n                <mi>o</mi>\n                <mi>U</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n            <mi>&amp;nbsp;</mi>\n            <mo>&#xD7;</mo>\n            <mi>&amp;nbsp;</mi>\n            <msub>\n              <mrow>\n                <mi>N</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mrow>\n      </mrow>\n      <mrow>\n        <mrow>\n          <munder>\n            <mo stretchy=\"false\">&#x2211;</mo>\n            <mrow>\n              <mi>i</mi>\n            </mrow>\n          </munder>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>N</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mrow>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>where PoU<sub>i</sub> are the values of PoU estimated for all countries (i) in the aggregate for which available data allow to compute a reliable estimate, and N<sub>i</sub> the corresponding population size. </p>",
                "old_state": 100
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            "user": null,
            "author": null,
            "timestamp": "2022-04-09T01:54:15.101346+02:00",
            "action": 59,
            "target": "<p>None </p>",
            "old": "<p>None. </p>",
            "details": {
                "state": 100,
                "source": "<p>None </p>",
                "old_state": 100
            },
            "id": 23102112,
            "action_name": "String updated in the repository",
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        {
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            "timestamp": "2022-04-09T01:54:15.101192+02:00",
            "action": 59,
            "target": "<p>To compute an estimate of the prevalence of undernourishment in a population, the probability distribution of habitual dietary energy intake levels (expressed in kcal per person per day) for the average individual is modelled as a parametric probability density function (pdf), f(x).</p>\n<p>The indicator is obtained as the cumulative probability that the habitual dietary energy intake (x) is below the minimum dietary energy requirements (MDER) (i.e. the lowest limit of the range of energy requirements for the population&#x2019;s representative average individual) as in the formula below: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>P</mi>\n    <mi>o</mi>\n    <mi>U</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mrow>\n      <msubsup>\n        <mo stretchy=\"false\">&#x222B;</mo>\n        <mrow>\n          <mi>x</mi>\n          <mo>&amp;lt;</mo>\n          <mi>M</mi>\n          <mi>D</mi>\n          <mi>E</mi>\n          <mi>R</mi>\n        </mrow>\n        <mrow>\n          <mi>&amp;nbsp;</mi>\n        </mrow>\n      </msubsup>\n      <mrow>\n        <mi>f</mi>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi>x</mi>\n            <mo>|</mo>\n            <mi>&#x3B8;</mi>\n          </mrow>\n        </mfenced>\n        <mi>d</mi>\n        <mi>x</mi>\n      </mrow>\n    </mrow>\n  </math></p>\n<p>where &#x3B8; is a vector of parameters that characterizes the pdf. The distribution is assumed to be lognormal, and thus fully characterized by only two parameters: the mean dietary energy consumption (DEC), and its coefficient of variation (CV). </p>\n<p>A custom R function is available from the Statistics Division at FAO to compute the PoU, given the three parameters DEC, CV, and MDER. </p>\n<p>Different data sources can be used to estimate the different parameters of the model. </p>\n<p><u>DEC </u></p>\n<p>Ideally, data on food consumption should come from nationally representative household surveys (such as Living Standard Measurement Surveys or Household Incomes and Expenditure Surveys). However, only very few countries conduct such surveys on an annual basis. Thus, in FAO&#x2019;s PoU estimates for global monitoring, DEC values are estimated from the dietary energy supply (DES) reported in the Food Balance Sheets (FBS), compiled by FAO for most countries in the world (<a href=\"https://www.fao.org/faostat/en/#data/FBS\">https://www.fao.org/faostat/en/#data/FBS</a>). </p>\n<p><u>CV </u></p>\n<p>When reliable data on food consumption are available from aforementioned nationally representative household surveys, the CV due to income (CV|y) that describes the distribution of average daily dietary energy requirement in the population can be estimated directly.</p>\n<p>When no suitable survey data are available, FIES data collected by FAO since 2014 are used to project the changes in the CV|y from 2015 (or from the year of the last food consumption survey) up to 2019, based on a smoothed (three-year moving average) trend in severe food insecurity.</p>\n<p>Since 2014, FIES data provide evidence on recent changes in the extent of severe food insecurity that might closely reflect changes in the PoU. To the extent that such changes in PoU are not explained by changes in average food supplies, they can thus be used to infer the likely changes in the CV|y that might have occurred in the most recent year. Analysis of the combined set of historic PoU estimates reveals that, on average, and once differences in DEC and MDER have been controlled for, the CV|y explains about one-third of the differences in PoU across time and space. For each country for which FIES data are available, the CV|y is estimated by the amount that would generate one-third of a percentage point change in the PoU for each observed percentage point change in the prevalence of severe food insecurity. For all other countries, the CV|y is kept constant at the estimated 2017 value.</p>\n<p>In the FAO PoU parametric approach, the CV due to body weight and lifestyle, a.k.a. CV due to requirement (CV|r), represents the variability of the distribution of dietary energy requirements of a hypothetical average individual representative of a healthy population, which is also equal to the CV of the distribution of dietary energy intakes of a hypothetical average individual if the population is perfectly nourished. The distribution of dietary energy requirements of a hypothetical average individual can be assumed to be normal, thus its variability can be estimated if at least two percentiles and their values are known. As a result, given that we are interested in deriving the theoretical distribution of dietary energy requirements for healthy hypothetical average individuals to estimate the CV|r, the MDER and the average dietary energy requirement (ADER) can be used to approximate the 1st percentile and the 50th percentile of the distribution of energy requirements of the hypothetical average individual as they are built on the same principles of a weighted average from sex-age-physiological status groups. Therefore, the value of CV|r is derived as the inverse cumulative standard normal distribution of the difference between the MDER and the ADER. Similar to the MDER, the ADER is estimated using the average of the minimum and the maximum values of the PAL category &#x2018;Active or moderately active lifestyle&#x2019;.</p>\n<p>The total CV is then obtained as the geometric mean of the CV|y and the CV|r:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>C</mi>\n    <mi>V</mi>\n    <mo>=</mo>\n    <msqrt>\n      <mfenced separators=\"|\">\n        <mrow>\n          <msup>\n            <mrow>\n              <mi>C</mi>\n              <mi>V</mi>\n              <mo>|</mo>\n              <mi>y</mi>\n            </mrow>\n            <mrow>\n              <mn>2</mn>\n            </mrow>\n          </msup>\n        </mrow>\n      </mfenced>\n      <mo>+</mo>\n      <mfenced separators=\"|\">\n        <mrow>\n          <msup>\n            <mrow>\n              <mi>C</mi>\n              <mi>V</mi>\n              <mo>|</mo>\n              <mi>r</mi>\n            </mrow>\n            <mrow>\n              <mn>2</mn>\n            </mrow>\n          </msup>\n        </mrow>\n      </mfenced>\n    </msqrt>\n  </math></p>\n<p><strong>Challenges and limitations:</strong> While formally the state of being undernourished or not is a condition that applies to individuals, given the data usually available on a large scale, it is impossible to reliably identify which individuals in a certain group are actually undernourished. Through the statistical model described above, the indicator can only be computed with reference to a population or a group of individuals for which a representative sample is available. The prevalence of undernourishment is thus an estimate of the percentage of individuals in that group that are in such condition and cannot be further disaggregated.</p>\n<p>Due to the probabilistic nature of the inference and the margins of uncertainty associated with estimates of each of the parameters in the model, the precision of the PoU estimates is generally low. While it is not possible to formally compute margins of error around PoU estimates, these are expected to likely exceed 5 percent in most cases. For this reason, FAO does not consider PoU estimates that result to be lower than 2.5 percent as sufficiently reliable to be reported.</p>\n<p><u>MDER </u></p>\n<p>Human energy requirements for an individual in a given sex/age class are determined on the basis of normative requirements for basic metabolic rate (BMR) per kilogram of body mass, multiplied by the ideal weights that a healthy person of that sex/age class may have, given his or her height, and then multiplied by a coefficient of physical activity level (PAL) to take into account physical activity. Given that both healthy BMIs and PALs vary among active and healthy individuals of the same sex and age, a <em>range </em>of energy requirements applies to each sex and age group of the population. The MDER for the average individual in the population, which is the parameter used in the PoU formula, is obtained as the weighted average of the lower bounds of the energy requirement ranges for each sex and age group, using the shares of the population in each sex and age group as weights.</p>\n<p>Information on the population structure by sex and age is available for most countries in the world and for each year from the UN Department of Economic and Social Affairs (DESA) Population Prospects, revised every two years.</p>\n<p>Information on the median height in each sex and age group for a given country is derived from a recent demographic and health survey (DHS) or from other surveys that collect anthropometry data on children and adults. Even if such surveys do not refer to the same year for which the PoU is estimated, the impact of possible small intervening changes in median heights over the years on PoU estimates is expected to be negligible.</p>",
            "old": "<p>The indicator is computed at the population level. To this aim, the population is represented by an &#x201C;average&#x201D; individual for which a probability distribution of the habitual daily dietary energy intake levels is modelled through a parametric probability density function (pdf). </p>\n<p>Once the pdf is characterized, the indicator is obtained as the cumulative probability that daily habitual dietary energy intakes (x) are below the lower bound of the range of normal dietary energy requirements for that representative, or average individual (MDER), as in the formula below: </p>\n<p>PoU= &#x222B;_(x&lt;MDER) f(x | DEC; CV) dx </p>\n<p>where DEC and CV are the mean and coefficient of variation that characterize the distribution of habitual dietary energy consumption levels in the population. </p>\n<p>A custom R function is available from the Statistics Division at FAO to compute the PoU, given the three parameters DEC, CV, and MDER. </p>\n<p>Different data sources can be used to estimate the different parameters of the model. </p>\n<p>DEC </p>\n<p>The mean of the distribution of dietary energy consumption levels for the average individual in a population (DEC) corresponds, by definition, to the average, daily per capita food consumption level in the population. </p>\n<p>DEC can be estimated from data on food consumption obtained through surveys that are representative of the population of interest. Depending on the survey design, they can be used to estimate DEC at national and at sub national levels, either by geographic areas or by socio-economic population groups. Unfortunately, though the situation is rapidly improving, representative surveys that collect food consumption data are still not available for every country and every year. </p>\n<p>For the national population only, DEC can be estimated also from accounts of the total supply and utilization of all food commodities in a country, where the contribution of each commodity to the availability of food for human consumption is expressed in their dietary energy content, and their total is divided by the size of the population. The major source of data on national food balances are the Food Balance Sheets (FBS) maintained by FAO for most countries in the world (see http://www.fao.org/economic/ess/fbs/en/), informed by official data reported by member countries, and disseminated through FAOSTAT (http://www.fao.org/faostat/en/#data) </p>\n<p>CV </p>\n<p>Surveys that contain information on food consumption at individual or household level are the only available source to directly estimate the CV of habitual food consumption for the representative individual in the population. Unfortunately, survey data on food consumption are fraught by many problems that complicate the reliable estimation of CV. </p>\n<p>In principle, repeated observations of daily consumption for each individual in a sample would be needed to estimate levels of habitual consumption and to control for measurement errors. Moreover, data should be collected in different periods of the year on the same individuals or households to account for possible seasonal variation in levels of dietary energy consumption. Due to their cost, nationally representative individual dietary intake surveys with such characteristics are very rare, and virtually inexistent for most developing countries. As a consequence, the most common sources of data to estimate CV are multipurpose household surveys, such as Living Standard Measurement Surveys, Household Incomes and Expenditure Surveys (or Household Budgets Survey), that collect also information on food consumption. When using data collected at household level however, careful attention should be taken in distinguishing levels of food purchases or acquisitions from levels of actual utilization (consumption and wastage) during the identified reference period and in properly recording the number of individuals who participate in consumption; moreover, household level data will mask the variability due to intra-household allocation of food. </p>\n<p>For all these reasons, the coefficient of variation calculated on the series of average per capita daily dietary energy consumption levels recorded for each household included in a survey is never a reliable estimate of CV, which should reflect variability in the levels of habitual (and not occasional) daily dietary energy consumption level, at the individual (and not household) level. Empirical estimates of CV from household survey data are upward biased due to the spurious variability induced by measurement error, differences between occasional and habitual consumption, differences between acquisition and actual consumption and seasonality; moreover, they do not reflect the variability in dietary energy consumption in the population associated with individual characteristics of the household members (such as sex, age, body mass and physical activity levels). </p>\n<p>When using data collected through household surveys, CV is thus best estimated indirectly, controlling for spurious variability, and adjusted to reflect inter-individuals (in addition to inter-households) variability. The simplest way to proceed is to classify households into homogeneous groups and to calculate the coefficient of variation of the average per capita dietary energy consumption across household groups. This yields an estimate of the inter-households component of CV, labelled CV_H. An estimate of the inter-individuals component of the CV, labelled CV_I, is obtained, for each population, from its structure by sex, age and body masses, and the two components are combined to obtain the needed estimate as: </p>\n<p>CV^ = v[(CV_H)^2+(CV_I)^2 )]. </p>\n<p>For countries and years when no data from household survey are available, an indirect estimate of the CV, CV_IND, is obtained via a regression that projects the values of per capita GDP, Gini coefficient of income, and an index of the relative price of food (FPI) on the CV, while controlling for a regional shifter (REG). </p>\n<p>CV^_IND=&#xDF;_0+&#xDF;_1 GDP+ &#xDF;_2 GINI+ &#xDF;_3 FPI+&#xDF;_4 REG. </p>\n<p>Coefficients of the regression are estimated from the set of data and years for which data on CV, GDP, GINI and FPI are available. </p>\n<p>MDER </p>\n<p>Human energy requirements are computed by multiplying normative requirements for basic metabolic rate (BMR, expressed per kg of body mass) by the ideal weight of a healthy person of given height, and then multiplied by a coefficient of physical activity level (PAL). Ranges of normal energy requirements are thus computed for each sex and age group of the population, observing that there exist a whole range of Body Mass Index (BMI) values &#x2013; from 18.5 to 25 &#x2013; that are compatible with health. This implies that any given attained height might correspond to a whole range of healthy body weights, and therefore to a range of values for energy requirement for BMR. </p>\n<p>Given information on the median height and the consideration that the group might contain individuals engaged in different levels of physical activity, the minimum, average and maximum dietary energy requirement can be computed for every sex and age class by taking into consideration special allowances for growth in individuals aged 0-21 and for pregnancy and lactation. </p>\n<p>(See ftp://ftp.fao.org/docrep/fao/007/y5686e/y5686e00.pdf for further details). </p>\n<p>The MDER for a given population group, including for the national population, is obtained as the weighted average of the minimums of the energy requirements ranges of each sex and age class, using the population size in each class as weights. </p>\n<p>In computing the prevalence of dietary energy inadequacy in a population there has often been confusion between the concept of MDER and that of the Recommended Dietary Energy Intake, and regarding the appropriate threshold to be used to compute the probability of inadequacy. The reason why the probability of dietary energy inadequacy should be computed with reference to the MDER, and not the ADER (which, instead, can be used as an estimate of the average recommended dietary intake level for the whole population) is simply to recognize the fact that in any population there exists a certain range of normal variability in requirements; using the ADER as a threshold would greatly overestimate undernourishment as it would count also the proportion of the healthy population that consumes less than average, simply because of having less than average requirements. When needed, the ADER, or the average Recommended Dietary Energy Intake level in a population must be used instead to compute the dietary energy gap.&quot; </p>",
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                "source": "<p>To compute an estimate of the prevalence of undernourishment in a population, the probability distribution of habitual dietary energy intake levels (expressed in kcal per person per day) for the average individual is modelled as a parametric probability density function (pdf), f(x).</p>\n<p>The indicator is obtained as the cumulative probability that the habitual dietary energy intake (x) is below the minimum dietary energy requirements (MDER) (i.e. the lowest limit of the range of energy requirements for the population&#x2019;s representative average individual) as in the formula below: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>P</mi>\n    <mi>o</mi>\n    <mi>U</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mrow>\n      <msubsup>\n        <mo stretchy=\"false\">&#x222B;</mo>\n        <mrow>\n          <mi>x</mi>\n          <mo>&amp;lt;</mo>\n          <mi>M</mi>\n          <mi>D</mi>\n          <mi>E</mi>\n          <mi>R</mi>\n        </mrow>\n        <mrow>\n          <mi>&amp;nbsp;</mi>\n        </mrow>\n      </msubsup>\n      <mrow>\n        <mi>f</mi>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi>x</mi>\n            <mo>|</mo>\n            <mi>&#x3B8;</mi>\n          </mrow>\n        </mfenced>\n        <mi>d</mi>\n        <mi>x</mi>\n      </mrow>\n    </mrow>\n  </math></p>\n<p>where &#x3B8; is a vector of parameters that characterizes the pdf. The distribution is assumed to be lognormal, and thus fully characterized by only two parameters: the mean dietary energy consumption (DEC), and its coefficient of variation (CV). </p>\n<p>A custom R function is available from the Statistics Division at FAO to compute the PoU, given the three parameters DEC, CV, and MDER. </p>\n<p>Different data sources can be used to estimate the different parameters of the model. </p>\n<p><u>DEC </u></p>\n<p>Ideally, data on food consumption should come from nationally representative household surveys (such as Living Standard Measurement Surveys or Household Incomes and Expenditure Surveys). However, only very few countries conduct such surveys on an annual basis. Thus, in FAO&#x2019;s PoU estimates for global monitoring, DEC values are estimated from the dietary energy supply (DES) reported in the Food Balance Sheets (FBS), compiled by FAO for most countries in the world (<a href=\"https://www.fao.org/faostat/en/#data/FBS\">https://www.fao.org/faostat/en/#data/FBS</a>). </p>\n<p><u>CV </u></p>\n<p>When reliable data on food consumption are available from aforementioned nationally representative household surveys, the CV due to income (CV|y) that describes the distribution of average daily dietary energy requirement in the population can be estimated directly.</p>\n<p>When no suitable survey data are available, FIES data collected by FAO since 2014 are used to project the changes in the CV|y from 2015 (or from the year of the last food consumption survey) up to 2019, based on a smoothed (three-year moving average) trend in severe food insecurity.</p>\n<p>Since 2014, FIES data provide evidence on recent changes in the extent of severe food insecurity that might closely reflect changes in the PoU. To the extent that such changes in PoU are not explained by changes in average food supplies, they can thus be used to infer the likely changes in the CV|y that might have occurred in the most recent year. Analysis of the combined set of historic PoU estimates reveals that, on average, and once differences in DEC and MDER have been controlled for, the CV|y explains about one-third of the differences in PoU across time and space. For each country for which FIES data are available, the CV|y is estimated by the amount that would generate one-third of a percentage point change in the PoU for each observed percentage point change in the prevalence of severe food insecurity. For all other countries, the CV|y is kept constant at the estimated 2017 value.</p>\n<p>In the FAO PoU parametric approach, the CV due to body weight and lifestyle, a.k.a. CV due to requirement (CV|r), represents the variability of the distribution of dietary energy requirements of a hypothetical average individual representative of a healthy population, which is also equal to the CV of the distribution of dietary energy intakes of a hypothetical average individual if the population is perfectly nourished. The distribution of dietary energy requirements of a hypothetical average individual can be assumed to be normal, thus its variability can be estimated if at least two percentiles and their values are known. As a result, given that we are interested in deriving the theoretical distribution of dietary energy requirements for healthy hypothetical average individuals to estimate the CV|r, the MDER and the average dietary energy requirement (ADER) can be used to approximate the 1st percentile and the 50th percentile of the distribution of energy requirements of the hypothetical average individual as they are built on the same principles of a weighted average from sex-age-physiological status groups. Therefore, the value of CV|r is derived as the inverse cumulative standard normal distribution of the difference between the MDER and the ADER. Similar to the MDER, the ADER is estimated using the average of the minimum and the maximum values of the PAL category &#x2018;Active or moderately active lifestyle&#x2019;.</p>\n<p>The total CV is then obtained as the geometric mean of the CV|y and the CV|r:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>C</mi>\n    <mi>V</mi>\n    <mo>=</mo>\n    <msqrt>\n      <mfenced separators=\"|\">\n        <mrow>\n          <msup>\n            <mrow>\n              <mi>C</mi>\n              <mi>V</mi>\n              <mo>|</mo>\n              <mi>y</mi>\n            </mrow>\n            <mrow>\n              <mn>2</mn>\n            </mrow>\n          </msup>\n        </mrow>\n      </mfenced>\n      <mo>+</mo>\n      <mfenced separators=\"|\">\n        <mrow>\n          <msup>\n            <mrow>\n              <mi>C</mi>\n              <mi>V</mi>\n              <mo>|</mo>\n              <mi>r</mi>\n            </mrow>\n            <mrow>\n              <mn>2</mn>\n            </mrow>\n          </msup>\n        </mrow>\n      </mfenced>\n    </msqrt>\n  </math></p>\n<p><strong>Challenges and limitations:</strong> While formally the state of being undernourished or not is a condition that applies to individuals, given the data usually available on a large scale, it is impossible to reliably identify which individuals in a certain group are actually undernourished. Through the statistical model described above, the indicator can only be computed with reference to a population or a group of individuals for which a representative sample is available. The prevalence of undernourishment is thus an estimate of the percentage of individuals in that group that are in such condition and cannot be further disaggregated.</p>\n<p>Due to the probabilistic nature of the inference and the margins of uncertainty associated with estimates of each of the parameters in the model, the precision of the PoU estimates is generally low. While it is not possible to formally compute margins of error around PoU estimates, these are expected to likely exceed 5 percent in most cases. For this reason, FAO does not consider PoU estimates that result to be lower than 2.5 percent as sufficiently reliable to be reported.</p>\n<p><u>MDER </u></p>\n<p>Human energy requirements for an individual in a given sex/age class are determined on the basis of normative requirements for basic metabolic rate (BMR) per kilogram of body mass, multiplied by the ideal weights that a healthy person of that sex/age class may have, given his or her height, and then multiplied by a coefficient of physical activity level (PAL) to take into account physical activity. Given that both healthy BMIs and PALs vary among active and healthy individuals of the same sex and age, a <em>range </em>of energy requirements applies to each sex and age group of the population. The MDER for the average individual in the population, which is the parameter used in the PoU formula, is obtained as the weighted average of the lower bounds of the energy requirement ranges for each sex and age group, using the shares of the population in each sex and age group as weights.</p>\n<p>Information on the population structure by sex and age is available for most countries in the world and for each year from the UN Department of Economic and Social Affairs (DESA) Population Prospects, revised every two years.</p>\n<p>Information on the median height in each sex and age group for a given country is derived from a recent demographic and health survey (DHS) or from other surveys that collect anthropometry data on children and adults. Even if such surveys do not refer to the same year for which the PoU is estimated, the impact of possible small intervening changes in median heights over the years on PoU estimates is expected to be negligible.</p>",
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            "action": 59,
            "target": "<p>Over the years, the parametric approach informing the computation of the PoU has been criticized, based on the presumptions that undernourishment should be assessed necessarily starting at the individual level, by comparing individual energy requirements with individual energy intakes. According to such a view, the prevalence of undernourishment could be simply computed by counting the number of individuals in a representative sample of the population that is classified as undernourished, based on a comparison of individual habitual food consumption and requirements. </p>\n<p>Unfortunately, such an approach is not feasible for two reasons: first, due to the cost of individual dietary intake surveys, individual food consumption is measured only in a few countries, every several years, on relatively small samples; moreover, individual energy requirements are practically unobservable with standard data collection methods (to the point that observed habitual energy consumption of individuals in a healthy status is still the preferred way to infer individual energy requirements). This means that even if it were possible to obtain accurate observations of the individual dietary energy consumption, this would be insufficient to infer on the undernourishment condition at individual level, unless integrated by the observation on the physical status (body mass index) and of its dynamic over time, of the same individual. </p>\n<p>The model-based approach to estimate the PoU developed by FAO integrates information that is available with sufficient regularity from different sources for most countries in the world, in a theoretically consistent way, thus providing what is still one of the most reliable tools to monitor progress towards reducing global hunger. </p>\n<p>Further specific consideration </p>\n<p>1. Feasibility </p>\n<p>Estimation of PoU at national level has been feasible for most countries in the world since 1999. In the worst case scenario, when no data on food consumption was available from a recent household survey, the model-based estimate of the PoU is informed by an estimate of mean level of dietary energy consumption (DEC) from Food Balance Sheets (FBS), an indirect estimate of the coefficient of variation (CV) based on information on the country&#x2019;s GDP, Gini coefficient of Income, an index of the relative price of food, or other indicators of development such as country&#x2019;s Under 5 Mortality Rate, and an estimate of the Minimum Dietary Energy Requirement (MDER) based on the UN Population Division&#x2019;s World Population Prospects data. </p>\n<p>2. Reliability </p>\n<p>Reliability mostly depends on the quality of the data used to inform the estimation of the model&#x2019;s parameters. </p>\n<p>DEC could be estimated either from survey data or from food balances. Neither source is devoid of problems. When comparing estimates of national DEC from FBS and from surveys, differences are frequently noted. </p>\n<p>DEC estimates from survey data can be affected by systematic measurement errors due to under-reporting of food consumption, or to incomplete recording of all food consumption sources. Recent research shows that a negative bias of up to more than 850 kcal can be induced on the estimated daily per capita caloric consumption can be induced by the type of food consumption module chosen to capture the data at the household level. (See De Weerdt et al., 2015, Table 2, <a href=\"https://feb.kuleuven.be/drc/licos/publications/dp/DP%20365%20Complete.pdf\">https://feb.kuleuven.be/drc/licos/publications/dp/DP%20365%20Complete.pdf</a>). A detailed analysis of a recent Household Budget Survey in Brazil revealed how food provided for free through the school meals program and consumed by children while at school, had not been accounted among the sources of household food consumption, accounting for a downward bias of the average per capita daily dietary energy consumption of 674 kcal. (See Borlizzi, Cafiero &amp; Del Grossi, forthcoming.) </p>\n<p>DEC estimates from Food Balance Sheets can also be affected by errors, though it is difficult to establish the direction of induced bias. As average food availability is a residual in the FBS method, any errors in reported production, trade, and stocks might affect the estimates of national food availability. Moreover, errors might be induced by the difficulty in properly accounting for all forms of food commodity utilization. To the extent that all these errors are uncorrelated, though, the impact on the estimated average food consumption will be lower than each of the errors, considered separately, might imply. Nevertheless, considering how problematic it is to precisely account for variations in national reserves of food commodities, for which official data may be unreliable, it is recognized that the estimated annual stock variation is prone to considerable uncertainty that would be transferred to the estimated DEC in each given year. </p>\n<p>To limit the impact of such errors, FAO has traditionally presented estimates of PoU at national level as three-year averages, on the presumption that errors induced by imprecise recording of stocks variations in each single year might be highly reduced when considering an average over three consecutive years. </p>\n<p>Survey data are the only source to estimate the CV. As described in the section of metadata on the method of computation, unless obtained from high quality individual dietary intake surveys, data needs to be treated to reduce the likely upward bias in the estimates of the CV that would be induced by the spurious variability due to errors in measuring individual habitual dietary energy intake. </p>\n<p>3. Comparability </p>\n<p>If the same method of computation is used, comparability across time and space is relatively high, with the only potential cause of inhomogeneity found in the different quality of the background data. </p>\n<p>4. Limitations </p>\n<p>Due to the probabilistic nature of the inference and the margins of uncertainty associated with estimates of each of the parameters in the model, the precision of the PoU estimates is generally low. Even though it is not possible to compute theoretical Margins of Error (MoE) for PoU estimates, these would very likely exceed plus or minus 2.5% in most cases. For this reason, FAO publishes national level PoU estimates only when they are larger than 2.5%. This also suggests that 2.5% is the lowest feasible target that can be set for the PoU indicator, a value that is unsatisfactorily large when the ambition is to fully eradicate the scourge of hunger. </p>\n<p>If no survey is available that collects food consumption data and that is representative at subnational level, the indicator can only be computed at national level.&quot;</p>",
            "old": "<p>Over the years, the parametric approach informing the computation of the PoU has been criticized, based on the presumptions that undernourishment should be assessed necessarily starting at the individual level, by comparing individual energy requirements with individual energy intakes. According to such view, the prevalence of undernourishment could be simply computed by counting the number of individuals in a representative sample of the population that is classified as undernourished, based on a comparison of individual habitual food consumption and requirements. Unfortunately, such approach is not feasible for two reasons: first, due to the cost of individual dietary intake surveys, individual food consumption is measured only in a few countries, every several years, on relatively small samples; moreover, individual energy requirements are practically unobservable with standard data collection methods (to the point that observed habitual energy consumption of individuals in a healthy status is still the preferred way to infer individual energy requirements). This means that even if it were possible to obtain accurate observations of the individual dietary energy consumption, this would be insufficient to infer on the undernourishment condition at individual level, unless integrated by the observation on the physical status (body mass index) and of its dynamic over time, of the same individual. </p>\n<p>The model based approach to estimate the PoU developed by FAO integrates information that is available with sufficient regularity from different sources for most countries in the world, in a theoretically consistent way, thus providing what is still one of the most reliable tools to monitor progress towards reducing global hunger. </p>\n<p>Further specific consideration </p>\n<p>1. Feasibility </p>\n<p>Estimation of PoU at national level has been feasible for most countries in the world since 1999. In the worst case scenario, when no data on food consumption was available from a recent household survey, the model-based estimate of the PoU is informed by an estimate of mean level of dietary energy consumption (DEC) from Food Balance Sheets (FBS), an indirect estimate of the coefficient of variation (CV) based on information on the country&#x2019;s GDP, Gini coefficient of Income, an index of the relative price of food, or other indicators of development such as country&#x2019;s Under 5 Mortality Rate, and an estimate of the Minimum Dietary Energy Requirement (MDER) based on the UN Population Division&#x2019;s World Population Prospects data. </p>\n<p>2. Reliability </p>\n<p>Reliability mostly depends on the quality of the data used to inform the estimation of the model&#x2019;s parameters. </p>\n<p>DEC could be estimated either from survey data or from food balances. Neither source is devoid of problems. When comparing estimates of national DEC from FBS and from surveys, differences are frequently noted. </p>\n<p>DEC estimates from survey data can be affected by systematic measurement errors due to under-reporting of food consumption, or to incomplete recording of all food consumption sources. Recent research shows that a negative bias of up to more than 850 kcal can be induced on the estimated daily per capita caloric consumption can be induced by the type of food consumption module chosen to capture the data at the household level. (See De Weerdt et al., 2015, Table 2, https://feb.kuleuven.be/drc/licos/publications/dp/DP%20365%20Complete.pdf ). A detailed analysis of a recent Household Budget Survey in Brazil revealed how food provided for free through the school meals program and consumed by children while at school, had not been accounted among the sources of household food consumption, accounting for a downward bias of the average per capita daily dietary energy consumption of 674 kcal. (See Borlizzi, Cafiero &amp; Del Grossi, forthcoming.) </p>\n<p>DEC estimates from Food Balance Sheets can also be affected by errors, though it is difficult to establish the direction of induced bias. As average food availability is a residual in the FBS method, any errors in reported production, trade, and stocks might affect the estimates of national food availability. Moreover, errors might be induced by the difficulty in properly accounting for all forms of food commodity utilization. To the extent that all these errors are uncorrelated, though, the impact on the estimated average food consumption will be lower than each of the errors, considered separately, might imply. Nevertheless, considering how problematic it is to precisely account for variations in national reserves of food commodities, for which official data may be unreliable, it is recognized that the estimated annual stock variation is prone to considerable uncertainty that would be transferred to the estimated DEC in each given year. </p>\n<p>To limit the impact of such errors, FAO has traditionally presented estimates of PoU at national level as three-year averages, on the presumption that errors induced by imprecise recording of stocks variations in each single year might be highly reduced when considering an average over three consecutive years. </p>\n<p>Survey data are the only source to estimate the CV. As described in the section of metadata on the method of computation, unless obtained from high quality individual dietary intake surveys, data needs to be treated to reduce the likely upward bias in the estimates of the CV that would be induced by the spurious variability due to errors in measuring individual habitual dietary energy intake. </p>\n<p>3. Comparability </p>\n<p>If the same method of computation is used, comparability across time and space is relatively high, with the only potential cause of inhomogeneity found in the different quality of the background data. </p>\n<p>4. Limitations </p>\n<p>Due to the probabilistic nature of the inference and the margins of uncertainty associated with estimates of each of the parameters in the model, the precision of the PoU estimates is generally low. Even though it is not possible to compute theoretical Margins of Error (MoE) for PoU estimates, these would very likely exceed plus or minus 2.5% in most cases. For this reason, FAO publishes national level PoU estimates only when they are larger than 2.5%. This also suggests that 2.5% is the lowest feasible target that can be set for the PoU indicator, a value that is unsatisfactorily large when the ambition is to fully eradicate the scourge of hunger. </p>\n<p>If no survey is available that collects food consumption data and that is representative at subnational level, the indicator can only be computed at national level.&quot;</p>",
            "details": {
                "state": 100,
                "source": "<p>Over the years, the parametric approach informing the computation of the PoU has been criticized, based on the presumptions that undernourishment should be assessed necessarily starting at the individual level, by comparing individual energy requirements with individual energy intakes. According to such a view, the prevalence of undernourishment could be simply computed by counting the number of individuals in a representative sample of the population that is classified as undernourished, based on a comparison of individual habitual food consumption and requirements. </p>\n<p>Unfortunately, such an approach is not feasible for two reasons: first, due to the cost of individual dietary intake surveys, individual food consumption is measured only in a few countries, every several years, on relatively small samples; moreover, individual energy requirements are practically unobservable with standard data collection methods (to the point that observed habitual energy consumption of individuals in a healthy status is still the preferred way to infer individual energy requirements). This means that even if it were possible to obtain accurate observations of the individual dietary energy consumption, this would be insufficient to infer on the undernourishment condition at individual level, unless integrated by the observation on the physical status (body mass index) and of its dynamic over time, of the same individual. </p>\n<p>The model-based approach to estimate the PoU developed by FAO integrates information that is available with sufficient regularity from different sources for most countries in the world, in a theoretically consistent way, thus providing what is still one of the most reliable tools to monitor progress towards reducing global hunger. </p>\n<p>Further specific consideration </p>\n<p>1. Feasibility </p>\n<p>Estimation of PoU at national level has been feasible for most countries in the world since 1999. In the worst case scenario, when no data on food consumption was available from a recent household survey, the model-based estimate of the PoU is informed by an estimate of mean level of dietary energy consumption (DEC) from Food Balance Sheets (FBS), an indirect estimate of the coefficient of variation (CV) based on information on the country&#x2019;s GDP, Gini coefficient of Income, an index of the relative price of food, or other indicators of development such as country&#x2019;s Under 5 Mortality Rate, and an estimate of the Minimum Dietary Energy Requirement (MDER) based on the UN Population Division&#x2019;s World Population Prospects data. </p>\n<p>2. Reliability </p>\n<p>Reliability mostly depends on the quality of the data used to inform the estimation of the model&#x2019;s parameters. </p>\n<p>DEC could be estimated either from survey data or from food balances. Neither source is devoid of problems. When comparing estimates of national DEC from FBS and from surveys, differences are frequently noted. </p>\n<p>DEC estimates from survey data can be affected by systematic measurement errors due to under-reporting of food consumption, or to incomplete recording of all food consumption sources. Recent research shows that a negative bias of up to more than 850 kcal can be induced on the estimated daily per capita caloric consumption can be induced by the type of food consumption module chosen to capture the data at the household level. (See De Weerdt et al., 2015, Table 2, <a href=\"https://feb.kuleuven.be/drc/licos/publications/dp/DP%20365%20Complete.pdf\">https://feb.kuleuven.be/drc/licos/publications/dp/DP%20365%20Complete.pdf</a>). A detailed analysis of a recent Household Budget Survey in Brazil revealed how food provided for free through the school meals program and consumed by children while at school, had not been accounted among the sources of household food consumption, accounting for a downward bias of the average per capita daily dietary energy consumption of 674 kcal. (See Borlizzi, Cafiero &amp; Del Grossi, forthcoming.) </p>\n<p>DEC estimates from Food Balance Sheets can also be affected by errors, though it is difficult to establish the direction of induced bias. As average food availability is a residual in the FBS method, any errors in reported production, trade, and stocks might affect the estimates of national food availability. Moreover, errors might be induced by the difficulty in properly accounting for all forms of food commodity utilization. To the extent that all these errors are uncorrelated, though, the impact on the estimated average food consumption will be lower than each of the errors, considered separately, might imply. Nevertheless, considering how problematic it is to precisely account for variations in national reserves of food commodities, for which official data may be unreliable, it is recognized that the estimated annual stock variation is prone to considerable uncertainty that would be transferred to the estimated DEC in each given year. </p>\n<p>To limit the impact of such errors, FAO has traditionally presented estimates of PoU at national level as three-year averages, on the presumption that errors induced by imprecise recording of stocks variations in each single year might be highly reduced when considering an average over three consecutive years. </p>\n<p>Survey data are the only source to estimate the CV. As described in the section of metadata on the method of computation, unless obtained from high quality individual dietary intake surveys, data needs to be treated to reduce the likely upward bias in the estimates of the CV that would be induced by the spurious variability due to errors in measuring individual habitual dietary energy intake. </p>\n<p>3. Comparability </p>\n<p>If the same method of computation is used, comparability across time and space is relatively high, with the only potential cause of inhomogeneity found in the different quality of the background data. </p>\n<p>4. Limitations </p>\n<p>Due to the probabilistic nature of the inference and the margins of uncertainty associated with estimates of each of the parameters in the model, the precision of the PoU estimates is generally low. Even though it is not possible to compute theoretical Margins of Error (MoE) for PoU estimates, these would very likely exceed plus or minus 2.5% in most cases. For this reason, FAO publishes national level PoU estimates only when they are larger than 2.5%. This also suggests that 2.5% is the lowest feasible target that can be set for the PoU indicator, a value that is unsatisfactorily large when the ambition is to fully eradicate the scourge of hunger. </p>\n<p>If no survey is available that collects food consumption data and that is representative at subnational level, the indicator can only be computed at national level.&quot;</p>",
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            "target": "<p>The Office of the Chief Statistician of FAO manages the Interdepartmental Working Group on SDG indicators under the FAO custodianship and identifies a focal point for each of them. The team leader of the Food Security and Nutrition Statistics Team of the Statistics Division is formally appointed as the focal person for the collection, processing, and dissemination of statistics for this indicator. </p>",
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            "target": "<p>Data are released each year alongside the <em>State of Food Security and Nutrition in the World</em> report, usually in mid-July. </p>",
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            "target": "<p>The ideal source of data to estimate the PoU would be a carefully designed and skillfully conducted individual dietary intake survey, in which actual daily food consumption, together with heights and weights for each surveyed individual, are repeatedly measured on a sample that is representative of the target population. Due to their cost, however, such surveys are rare.</p>\n<p>In principle, a well-designed household survey that collects information on food acquisitions might be sufficient to inform a reliable estimate of the Prevalence of Undernourishment (PoU) in a population, at a reasonable cost and with the necessary periodicity to inform the SDG monitoring process, provided that: </p>\n<ol>\n  <li>All sources of food consumption for all members of the households are properly accounted for, including, in particular, food that is consumed away from home; </li>\n  <li>Sufficient information is available to convert the data on food consumption or on food expenditures into their contribution to dietary energy intake; </li>\n  <li>The proper methods to compute the PoU are used, to control for excess variability in the estimated levels of habitual food consumption across households, allowing for the presence on normal variability in the distribution of food consumption across individuals, induced by the differences in energy requirements of the members of the population. </li>\n</ol>\n<p>Examples of surveys that could be considered for this purpose include surveys conducted to compute economic statistics and conduct poverty assessments, such as Household Income and Expenditure Surveys, Household Budget Surveys and Living Standard Measurement Surveys. </p>\n<p>In practice, however, it is often impossible, and not advisable, to rely only on data collected through a household survey, as the information needed to estimate the four parameters of the PoU model is either missing or imprecise. </p>\n<p>Household Survey food consumption data often must be integrated by </p>\n<p>a) Data on the demographic structure of the population of interest by sex and age; </p>\n<p>b) Data or information on the median height of individuals in each sex and age class; </p>\n<p>c) Data on the distribution of physical activity levels in the population; </p>\n<p>d) Alternative data on the total amounts of food available for human consumption, to correct for biases in the estimate of the national average daily dietary energy consumption in the population. </p>\n<p>Data for a), b) and c) could be available through the same multipurpose survey that provides food consumption data, but are more likely available from other sources, such as National Demographic and Health Surveys (for a) and b)) and Time Use Surveys (for c)). </p>\n<p>Correcting for bias in the estimated average daily dietary energy consumption might need to be based on alternative sources on food consumption, such as aggregate food supply and utilization accounts and food balance sheets. </p>\n<p>To inform its estimate of PoU at national, regional and global level, in addition to all household surveys for which it is possible to obtain micro data on food consumption, FAO relies on: </p>\n<p>a) UN Population Division&#x2019;s World Population Prospects (https://esa.un.org/unpd/wpp/Download/Standard/Population/), which provide updated estimates of the structures of the national population by sex and age every two years for most countries in the world; </p>\n<p>b) FAO Food Balance Sheets (http://www.fao.org/faostat/en/#data), which provides updated estimates of the national availability of food every year for most countries in the world.</p>\n<p>Micro data from household surveys that collect food consumption data are sourced by FAO directly through the National Statistical Agencies&#x2019; websites, or through specific bilateral agreements. </p>",
            "old": "<p>The ideal source of data to estimate the PoU would be a carefully designed and skillfully conducted individual dietary intake survey, in which actual daily food consumption, together with heights and weights for each surveyed individual, are repeatedly measured on a sample that is representative of the target population. Due to their cost, however, such surveys are rare.</p>\n<p>In principle, a well-designed household survey that collects information on food acquisitions might be sufficient to inform a reliable estimate of the Prevalence of Undernourishment in a population, at a reasonable cost and with the necessary periodicity to inform the SDG monitoring process, provided that: </p>\n<ol>\n  <li>All sources of food consumption for all members of the households are properly accounted for, including, in particular, food that is consumed away from home; </li>\n  <li>Sufficient information is available to convert the data on food consumption or on food expenditures into their contribution to dietary energy intake; </li>\n  <li>The proper methods to compute the PoU are used, to control for excess variability in the estimated levels of habitual food consumption across households, allowing for the presence on normal variability in the distribution of food consumption across individuals, induced by the differences in energy requirements of the members of the population. </li>\n</ol>\n<p>Examples of surveys that could be considered for this purpose include surveys conducted to compute economic statistics and conduct poverty assessments, such as Household Income and Expenditure Surveys, Household Budget Surveys and Living Standard Measurement Surveys. </p>\n<p>In practice, however, it is often impossible, and not advisable, to rely only on data collected through a household survey, as the information needed to estimate the four parameters of the PoU model is either missing or imprecise. </p>\n<p>Household Survey food consumption data often must be integrated by </p>\n<p>a) Data on the demographic structure of the population of interest by sex and age; </p>\n<p>b) Data or information on the median height of individuals in each sex and age class; </p>\n<p>c) Data on the distribution of physical activity levels in the population; </p>\n<p>d) Alternative data on the total amounts of food available for human consumption, to correct for biases in the estimate of the national average daily dietary energy consumption in the population. </p>\n<p>Data for a), b) and c) could be available through the same multipurpose survey that provides food consumption data, but are more likely available from other sources, such as National Demographic and Health Surveys (for a) and b) ) and Time Use Surveys (for c) ). </p>\n<p>Correcting for bias in the estimated average daily dietary energy consumption might need to be based on alternative sources on food consumption, such as aggregate food supply and utilization accounts and food balance sheets. </p>\n<p>To inform its estimate of PoU at national, regional and global level, in addition to all household surveys for which it is possible to obtain micro data on food consumption, FAO relies on: </p>\n<p>a) UN Population Division&#x2019;s World Population Prospects (https://esa.un.org/unpd/wpp/Download/Standard/Population/), which provide updated estimates of the structures of the national population by sex and age every two years for most countries in the world; </p>\n<p>b) FAO Food Balance Sheets (http://www.fao.org/faostat/en/#data), which provides updated estimates of the national availability of food every year for most countries in the world.</p>\n<p>Micro data from household surveys that collect food consumption data are sourced by FAO directly through the National Statistical Agencies&#x2019; websites, or through specific bilateral agreements.&quot; </p>",
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                "source": "<p>The ideal source of data to estimate the PoU would be a carefully designed and skillfully conducted individual dietary intake survey, in which actual daily food consumption, together with heights and weights for each surveyed individual, are repeatedly measured on a sample that is representative of the target population. Due to their cost, however, such surveys are rare.</p>\n<p>In principle, a well-designed household survey that collects information on food acquisitions might be sufficient to inform a reliable estimate of the Prevalence of Undernourishment (PoU) in a population, at a reasonable cost and with the necessary periodicity to inform the SDG monitoring process, provided that: </p>\n<ol>\n  <li>All sources of food consumption for all members of the households are properly accounted for, including, in particular, food that is consumed away from home; </li>\n  <li>Sufficient information is available to convert the data on food consumption or on food expenditures into their contribution to dietary energy intake; </li>\n  <li>The proper methods to compute the PoU are used, to control for excess variability in the estimated levels of habitual food consumption across households, allowing for the presence on normal variability in the distribution of food consumption across individuals, induced by the differences in energy requirements of the members of the population. </li>\n</ol>\n<p>Examples of surveys that could be considered for this purpose include surveys conducted to compute economic statistics and conduct poverty assessments, such as Household Income and Expenditure Surveys, Household Budget Surveys and Living Standard Measurement Surveys. </p>\n<p>In practice, however, it is often impossible, and not advisable, to rely only on data collected through a household survey, as the information needed to estimate the four parameters of the PoU model is either missing or imprecise. </p>\n<p>Household Survey food consumption data often must be integrated by </p>\n<p>a) Data on the demographic structure of the population of interest by sex and age; </p>\n<p>b) Data or information on the median height of individuals in each sex and age class; </p>\n<p>c) Data on the distribution of physical activity levels in the population; </p>\n<p>d) Alternative data on the total amounts of food available for human consumption, to correct for biases in the estimate of the national average daily dietary energy consumption in the population. </p>\n<p>Data for a), b) and c) could be available through the same multipurpose survey that provides food consumption data, but are more likely available from other sources, such as National Demographic and Health Surveys (for a) and b)) and Time Use Surveys (for c)). </p>\n<p>Correcting for bias in the estimated average daily dietary energy consumption might need to be based on alternative sources on food consumption, such as aggregate food supply and utilization accounts and food balance sheets. </p>\n<p>To inform its estimate of PoU at national, regional and global level, in addition to all household surveys for which it is possible to obtain micro data on food consumption, FAO relies on: </p>\n<p>a) UN Population Division&#x2019;s World Population Prospects (https://esa.un.org/unpd/wpp/Download/Standard/Population/), which provide updated estimates of the structures of the national population by sex and age every two years for most countries in the world; </p>\n<p>b) FAO Food Balance Sheets (http://www.fao.org/faostat/en/#data), which provides updated estimates of the national availability of food every year for most countries in the world.</p>\n<p>Micro data from household surveys that collect food consumption data are sourced by FAO directly through the National Statistical Agencies&#x2019; websites, or through specific bilateral agreements. </p>",
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