According to the Global Hunger Index 2024, which was adopted by the International Food Policy Research Institute, Somalia was the most affected by hunger and malnutrition, with an index of 44.1. Yemen and Chad followed behind. The World Hunger Index combines three indicators: undernourishment, child underweight, and child mortality. Sub-Saharan Africa most affected The index is dominated by countries in Sub-Saharan Africa. In the region, more than one fifth of the population is undernourished . In terms of individuals, however, South Asia has the highest number of undernourished people. Globally, there are 735 million people that are considered undernourished or starving. A lack of food is increasing in over 20 countries worldwide. Undernourishment worldwide The term malnutrition includes both undernutrition and overnutrition. Undernutrition occurs when an individual cannot maintain normal bodily functions such as growth, recovering from disease, and both learning and physical work. Some conditions such as diarrhea, malaria, and HIV/AIDS can all have a negative impact on undernutrition. Rural and agricultural communities can be especially susceptible to hunger during certain seasons. The annual hunger gap occurs when a family’s food supply may run out before the next season’s harvest is available and can result in malnutrition. Nevertheless, the prevalence of people worldwide that are undernourished has decreased over the last decades, from 18.7 percent in 1990-92 to 9.2 percent in 2022, but it has slightly increased since the outbreak of COVID-19. According to the Global Hunger Index, the reduction of global hunger has stagnated over the past decade.

The average for 2021 based on 167 countries was 10.78 percent. The highest value was in Madagascar: 51 percent and the lowest value was in Algeria: 2.5 percent. The indicator is available from 2001 to 2021. Below is a chart for all countries where data are available.

The Global Hunger Index (GHI) is a tool designed to comprehensively measure and track hunger globally, regionally, and by country. Each year, the International Food Policy Research Institute (IFPRI) calculates GHI scores in order to assess progress, or the lack thereof, in decreasing hunger. The GHI is designed to raise awareness and understanding of regional and country differences in the struggle against hunger.This year, GHI scores have been calculated using a revised and improved formula. The revision replaces child underweight, previously the sole indicator of child undernutrition, with two indicators of child undernutrition—child wasting and child stunting—which are equally weighted in the GHI calculation. The revised formula also standardizes each of the component indicators to balance their contribution to the overall index and to changes in the GHI scores over time.The 2015 GHI has been calculated for 117 countries for which data on the four component indicators are available and where measuring hunger is considered most relevant. GHI scores are not calculated for some higher income countries where the prevalence of hunger is very low. The GHI is only as current as the data for its four component indicators.This year's GHI reflects the most recent available country-level data and projections available between 2010 and 2016. It therefore reflects the hunger levels during this period rather than solely capturing conditions in 2015. The 1990, 1995, 2000, 2005, and 2015 GHI scores reflect the latest revised data for the four component indicators of the GHI. Where original source data were not available, the estimates of the GHI component indicators were based on the most recent data available.The four component indicators used to calculate the GHI scores draw upon data from the following sources:1. Undernourishment: Updated data from the Food and Agriculture Organization of the United Nations (FAO) were used for the 1990, 1995, 2000, 2005, and 2015 GHI scores. Undernourishment data and projections for the 2015 GHI are for 2014-2016.2. Child wasting and stunting: The child undernutrition indicators of the GHI—child wasting and child stunting—include data from the joint database of United Nations Children's Fund (UNICEF), the World Health Organization (WHO), and the World Bank, and additional data from WHO's continuously updated Global Database on Child Growth and Malnutrition; the most recent Demographic and Health Survey (DHS) and Multiple Indicator Cluster Survey (MICS) reports; statistical tables from UNICEF; and the latest national survey data for India from UNICEF India. For the 2015 GHI, data on child wasting and child stunting are for the latest year for which data are available in the period 2010-2014.3. Child mortality: Updated data from the UN Inter-agency Group for Child Mortality Estimation were used for the 1990, 1995, 2000, and 2005, and 2015 GHI scores. For the 2015 GHI, data on child mortality are for 2013.

According to the Global Hunger Index, India had an index value of **** in 2023. The composition of the index was a combination of different indicators such as undernourishment, child underweight, and child mortality. India's score indicates a serious level of hunger crisis, placing the country at a position of ***** out of 121 countries that year. However, the country had improved the situation from ** index points falling in the category of alarming level in 2000.

Nigeria Undernourished Population was up 4.6% in 2019, from a year earlier.

Central African Republic jumped by 2.8points of Prevalence of Undernourishment in 2019, compared to the previous year.

According to the Global Hunger Index 2024, hunger worldwide decreased since 2000, but the pace of the reduction has slowed since 2016. In the Middle East and North Africa, for instance, the hunger index value was the same in 2024 as in 2016, and it had even increased marginally in Latin America and the Caribbean. In 2024, Somalia had the highest index score worldwide, meaning it was the country where hunger was most prevalent. The World Hunger Index combines four indicators: undernourishment, child stunting, child wasting, and child mortality.

The average for 2021 based on 9 countries was 9.44 percent. The highest value was in Papua New Guinea: 23.4 percent and the lowest value was in Australia: 2.5 percent. The indicator is available from 2001 to 2021. Below is a chart for all countries where data are available.

The average for 2021 based on 19 countries was 10.61 percent. The highest value was in Haiti: 45 percent and the lowest value was in Chile: 2.5 percent. The indicator is available from 2001 to 2021. Below is a chart for all countries where data are available.

The zipped CSV files details Global Acute Malnutrition (GAM) prevalence data resulting from Round V of the Nutrition and Food Security SMART survey conducted at the Local Government (LGA) Level in all three crisis-affected states of north east Nigeria, July 2018. Also featured is data on Moderate Acute Malnutrition (MAM), Severe Acute Malnutrition (SAM), and MAM prevalence among beneficiaries 15-49 years, including the respective severity ranking.

Supplementary files for article Maternal height-standardized prevalence of stunting in 67 low- and middle-income countries.Background: Prevalence of stunting is frequently used as a marker of population-level child undernutrition. Parental height varies widely in low- and middle-income countries (LMIC) and is also a major determinant of stunting. While stunting is a useful measure of child health, with multiple causal components, removing the component attributable to parental height may in some cases be helpful to identify shortcoming in current environments.Methods: We estimated maternal height-standardized prevalence of stunting (SPS) in 67 LMICs and parental height-SPS in 20 LMICs and compared with crude prevalence of stunting (CPS) using data on 575,767 children under-five from 67 Demographic and Health Surveys (DHS). We supplemented the DHS with population-level measures of other child health outcomes from the World Health Organization’s (WHO) Global Health Observatory and the United Nations’ Inter-Agency Group for Child Mortality Estimation. Prevalence of stunting was defined as percentage of children with height-for-age falling below −2 z-scores from the median of the 2006 WHO growth standard.Results: The average CPS across countries was 27.8% (95% confidence interval [CI], 27.5–28.1%) and the average SPS was 23.3% (95% CI, 23.0–23.6%). The rank of countries according to SPS differed substantially from the rank according to CPS. Guatemala, Bangladesh, and Nepal had the biggest improvement in ranking according to SPS compared to CPS, while Gambia, Mali, and Senegal had the biggest decline in ranking. Guatemala had the largest difference between CPS and SPS with a CPS of 45.2 (95% CI, 43.7–46.9%) and SPS of 14.1 (95% CI, 12.6–15.8%). Senegal had the largest increase in the prevalence after standardizing maternal height, with a CPS of 28.0% (95% CI, 25.8–30.2%) and SPS of 31.6% (95% CI, 29.5–33.8%). SPS correlated better than CPS with other population-level measures of child health.Conclusions: Our study suggests that CPS is sensitive to adjustment for maternal height. Maternal height, while a strong predictor of child stunting, is not amenable to policy interventions. We showed the plausibility of SPS in capturing current exposures to undernutrition and infections in children.

Additional file 1. Consists of 9 worksheets. NFHS-3 scores & ranks (State). Calculation and Tabulation for computing Linear Aggregation (LA), Geometric Mean (GM) and MANUSH scores and Ranks using NFHS-3 data for States in India, 2005–06. NFHS-4 scores & ranks (State). Calculation and Tabulation for computing Linear Aggregation (LA), Geometric Mean (GM) and MANUSH scores and Ranks using NFHS-4 data for States in India, 2015–16. CNNS scores & ranks (State). Calculation and Tabulation for computing Linear Aggregation (LA), Geometric Mean (GM) and MANUSH scores and Ranks using CNNS data for States in India, 2016–18. NFHS-4 scores & ranks (Districts). Calculation and Tabulation for computing Linear Aggregation (LA), Geometric Mean (GM) and MANUSH scores and Ranks using NFHS-4 data for Districts in India, 2016–18. Monotonicity Cases. Examples explaining Monotonicity axiom. Uniformity Cases. Examples explaining Uniformity axiom. Shortfall Sensitivity Cases. Examples explaining Shortfall Sensitivity axiom. Hiatus Sensitivity Cases. Examples explaining Hiatus Sensitivity to Level. Districts under NNM and MANUSH. List and ranking of districts phased under National Nutrition Mission (NNM) and its priority categorisation based on MANUSH scores.

In 2024, Guatemala and El Salvador ranked with the highest severe food insecurity in Central America, both with over **** percent and **** percent, respectively. According to the source, an individual is deemed food insecure when they lack regular access to enough safe and nutritious food to lead an active and healthy life.

Introduction: The synergistic impact of coexistent malnutrition and sarcopenia on morality in hospitalized patients with decompensated cirrhosis remains elusive. This prospective cohort study aimed to delineate the prevalence concerning coexistence of malnutrition and sarcopenia and the prognosticating role on long-term mortality among cirrhosis. Methods: Adult cirrhotic patients with decompensated episodes between 2019 and 2021 were consecutively enrolled. Malnutrition and sarcopenia were diagnosed according to the Global Leadership Initiative on Malnutrition (GLIM) criteria and the European Working Group on Sarcopenia in Older People (EWGSOP2) algorithm, respectively. The entire cohort was divided into three groups: non-malnutrition and non-sarcopenia (NN), malnutrition or sarcopenia and coexistent malnutrition and sarcopenia (MS). Log-rank test and multivariate Cox regression model were utilized to evaluate survival status and independent risk factors for mortality, respectively. Results: Our findings indicated that malnutrition manifested 44.6% of inpatients with decompensated cirrhosis, while sarcopenia presented in 16.4% of the entire cohort, indicative of a prevalence of 14.7% regarding coexistent malnutrition and sarcopenia. The Kaplan-Meier graphic demonstrated a significant difference regarding survival curves among the three groups, referring to the MS group presented with the lowest survival rate (log-rank test: P <0.001). Moreover, coexistent malnutrition and sarcopenia were associated with nearly 4 times higher mortality risk (model 1: HR=3.31, 95% CI: 1.20-9.13, P=0.020; model 2: HR=4.34, 95% CI: 1.52-12.4, P=0.006) in comparison with patients without any condition (NN group). Conclusions: Malnutrition and sarcopenia had superimposed negative impacts on inpatients with decompensated cirrhosis. It is imperative to identify these vulnerable subset to provide prompt therapeutic intervention for better prognosis.

The Global Hunger Index ranks countries on a 100 point scale, with 0 being the best score ("no hunger") and 100 being the worst, though neither of these extremes is achieved in practice. The higher the score, the worse the food situation of a country. The GHI combines three equally weighted indicators: 1) the proportion of the undernourished as a percentage of the population; 2) the prevalence of underweight children under the age of five; 3) the mortality rate of children under the age of five.

Abstract

Background: Acute malnutrition in infants and children less than 5 years is persistent in the arid and semi-arid lands (ASALs) of East Africa and the Sahel region despite years of investment. In the ASALs of Kenya, the situation is exacerbated by deep-rooted poverty and unequal access to basic services, sustained community conflicts, migration, poor seasonal rainfall/drought and other shocks. Nutrition specific and nutrition sensitive national and county level programs have either not been developed or not implemented effectively.

Objectives: To understand and map immediate, underlying, basic and systemic drivers of acute malnutrition for the development of overarching as well as micro-solutions for the sustainable reduction of persistent acute malnutrition (PAM) and inform pilot studies and Phase 2 (second phase of NAWIRI project implementation) activities in Turkana and Samburu Counties.

Methods: This longitudinal mixed-methods observational cohort study of children less than 3 years and their mothers and/or caregivers in Samburu and Turkana counties. The longitudinal study is collecting data every 4 months over a 2-year period for a total of 6 waves across seasons. The study sample was population-based, with stratification by sub-county. Wave 2 data collection was carried out from November 15 to December 3, 2021 in Samburu and from October 25 to November 23, 2021 in Turkana. Wave 2 anthropometric data were collected from one sampled child per household and the primary caregiver of the child in the sampled household.

Study outcomes: To develop new interventions, and to adapt and contextualize existing interventions to prevent global acute malnutrition (GAM); strengthen social and behavior change (SBC) strategies around maternal, infant and young child nutrition (MIYCN), water and sanitation (WASH), community health systems, gender dynamics, livelihoods and resilience, and to inform improvements of the current nutrition surveillance system.

Geographic coverage

Turkana and Samburu Counties.

Analysis unit

Mothers and/or caregivers with at least one child less than 3 years of age at enrollment.

Universe

The survey covered household with children less than 3 years and their mothers and/or caregivers in Samburu and Turkana Counties

Sampling procedure

SAMBURU

The study sample was population-based, with stratification by sub-counties grouped into three survey zones (Central, North, and East) reflecting administrative sub-counties used in the Samburu Standardized Monitoring and Assessment of Relief and Transitions (SMART) Surveys. Stratification by livelihood zones was done through post-stratification analysis. We analyzed the data by livelihood zone because it was hypothesized that undernutrition might be more related to a household's livelihood than to its physical location.

As noted, the study used mixed-method techniques with quantitative and qualitative data collection. The quantitative component included a household survey and a caregiver survey and covered 699 households. The qualitative data collection activities yielded rich and in-depth insights that will be triangulated with the quantitative survey findings in a companion report. Therefore, this report focuses only on findings from the quantitative survey component. Results are reported for global acute malnutrition (GAM), stunting, and underweight. However, the discussion focuses only on GAM because the purpose of the Nawiri program is to reduce persistent acute malnutrition.

The baseline data collection was carried out in June and July 2021 following a full household listing operation in the county to establish the sampling frame of households with children under 3 years. Subsequent data collection waves are planned for November-December 2021 (Wave 2), March-April 2022 (Wave 3), September-October 2022 (Wave 4), March-April 2023 (Wave 5), and August-September 2023 (Wave 6).

TURKANA

The study sample was population-based, with stratification by sub-counties grouped into four survey zones (Central, North, West, and South) reflecting administrative sub-counties used in the Turkana Standardized Monitoring and Assessment of Relief and Transitions (SMART) Surveys. Stratification by livelihood zones was done through post-stratification analysis. We analyzed the data by livelihood zone because it was hypothesized that undernutrition might be more related to a household's livelihood than to its physical location.
As noted, the study used mixed-method techniques with quantitative and qualitative data collection. The quantitative component included a household survey and a caregiver survey and covered 1,211 households. The qualitative data collection activities yielded rich and in-depth insights that will be triangulated with the quantitative survey findings in a companion report. Therefore, this report focuses only on findings from the quantitative survey component. Results are reported for global acute malnutrition (GAM), stunting, and underweight. However, the discussion focuses only on GAM because the purpose of the Nawiri program is to reduce persistent acute malnutrition.

The baseline data collection was carried out in May and June 2021 following a full household listing operation in the county to establish the sampling frame of households with children under 3 years. Anthropometric data were collected from all under-5 children in the sampled households. Subsequent data collection waves are planned for October-November 2021 (Wave 2), March-April 2022 (Wave 3), September-October 2022 (Wave 4), March-April 2023 (Wave 5), and August-September 2023 (Wave 6).

Sampling deviation

na

Mode of data collection

Face-to-face [f2f]

Research instrument

Women/caregiver questionnaire: background, informed consent, household demographics, poverty probability index (pp1), household wealth ranking (perception)), food consumption, water, hygiene and sanitation (wash) (water access, availability and seasonality, household water insecurity experiences (hwise) scale, hygiene and sanitation), household shocks experienced, social safety nets and economic safety guards, mother's/caregivers information, pregnancy and antenatal care, family planning, infant and young child feeding practices, supplementation and consumption of iron rich or iron fortified foods, maternal knowledge and attitude, on infant and young child feeding practices, caregiving practices, child feeding utensils hygiene, food safety, hygiene, and sanitation practices, child immunization, health and health seeking practices, acute malnutrition screening (community health volunteers), womens minimum dietary diversity, food insecurity experience scale (hfies), gender, women empowerment, violence and community conflict, psychological wellbeing, anthropometric measurements

Cleaning operations

Data quality monitoring processes and checks were implemented throughout the data collection process, during the time of developing the data collection tools (through built-in quality control in the tablet-based platform), during training of fieldworkers, in real time during data collection (routine monitoring by the research team and periodic cross-checks against the protocols), and during the data cleaning process. During fieldwork, data quality was enhanced through regular spot checks and sit-ins by supervisors to verify the authenticity of data collected. Data were then reviewed and certified by the field coordinator before they were transferred to the server.

The quantitative data were collected using SurveyCTO, a survey platform for electronic data collection that has in-built skips and quality checks. Using this software increased efficiency and reduced the time needed for cleaning the data. In addition, the platform supported offline data capturing for regions with slow or no internet connectivity and data transmission when the internet became available. Fieldwork was conducted by trained fieldworkers using digital tablets with the questionnaire loaded in SurveyCTO. The questionnaire included the following modules: (1) identification and tracking, (2) demographics and household composition, (3) anthropometry of children <5 years and mothers, (4) socioeconomics, (5) household food security, (6) WASH, (7) health-seeking behavior, (8) MIYCN, (9) shock experience/exposure, and (10) shock preparedness and response. Data were uploaded from the tablets onto a secure African Population and Health Research Center (APHRC) server after each day of data collection. Data were synchronized automatically to a server when the tablet was in a location with network coverage. The uploaded data were then checked for quality daily by a data manager and a team dedicated to coordinate field procedures and at the APHRC head office in Nairobi.

Response rate

na

Sampling error estimates

na

Proposed Composite Index of Anthropometric Failure Categories.

Abstract

Background: Acute malnutrition in infants and children less than 5 years is persistent in the arid and semi-arid lands (ASALs) of East Africa and the Sahel region despite years of investment. In the ASALs of Kenya, the situation is exacerbated by deep-rooted poverty and unequal access to basic services, sustained community conflicts, migration, poor seasonal rainfall/drought and other shocks. Nutrition specific and nutrition sensitive national and county level programs have either not been developed or not implemented effectively.

Objectives: To understand and map immediate, underlying, basic and systemic drivers of acute malnutrition for the development of overarching as well as micro-solutions for the sustainable reduction of persistent acute malnutrition (PAM) and inform pilot studies and Phase 2 (second phase of NAWIRI project implementation) activities in Turkana and Samburu Counties.

Methods: This study will be a longitudinal mixed-methods observational cohort study of children less than 3 years and their mothers and/or caregivers in Samburu and Turkana Counties. Both quantitative and qualitative methods will be utilized in the data collection processes. Data collection is scheduled to begin in January 2021. Data analysis and learning and adapting will be ongoing so that results can inform pilots, theory of change (ToC) review and Phase 2 activities throughout the study.

Study outcomes: To develop new interventions, and to adapt and contextualize existing interventions to prevent global acute malnutrition (GAM); strengthen social and behavior change (SBC) strategies around maternal, infant and young child nutrition (MIYCN), water and sanitation (WASH), community health systems, gender dynamics, livelihoods and resilience, and to inform improvements of the current nutrition surveillance system.

Geographic coverage

Turkana and Samburu Counties.

Analysis unit

Children less than 3 years and their mothers and/or caregivers

Universe

The survey covered household with children less than 3 years and their mothers and/or caregivers in Samburu and Turkana Counties

Sampling procedure

SAMBURU

The study sample was population-based, with stratification by sub-counties grouped into three survey zones (Central, North, and East) reflecting administrative sub-counties used in the Samburu Standardized Monitoring and Assessment of Relief and Transitions (SMART) Surveys. Stratification by livelihood zones was done through post-stratification analysis. We analyzed the data by livelihood zone because it was hypothesized that undernutrition might be more related to a household's livelihood than to its physical location.

As noted, the study used mixed-method techniques with quantitative and qualitative data collection. The quantitative component included a household survey and a caregiver survey and covered 699 households. The qualitative data collection activities yielded rich and in-depth insights that will be triangulated with the quantitative survey findings in a companion report. Therefore, this report focuses only on findings from the quantitative survey component. Results are reported for global acute malnutrition (GAM), stunting, and underweight. However, the discussion focuses only on GAM because the purpose of the Nawiri program is to reduce persistent acute malnutrition.

The baseline data collection was carried out in June and July 2021 following a full household listing operation in the county to establish the sampling frame of households with children under 3 years. Subsequent data collection waves are planned for November-December 2021 (Wave 2), March-April 2022 (Wave 3), September-October 2022 (Wave 4), March-April 2023 (Wave 5), and August-September 2023 (Wave 6).

TURKANA

The study sample was population-based, with stratification by sub-counties grouped into four survey zones (Central, North, West, and South) reflecting administrative sub-counties used in the Turkana Standardized Monitoring and Assessment of Relief and Transitions (SMART) Surveys. Stratification by livelihood zones was done through post-stratification analysis. We analyzed the data by livelihood zone because it was hypothesized that undernutrition might be more related to a household's livelihood than to its physical location.
As noted, the study used mixed-method techniques with quantitative and qualitative data collection. The quantitative component included a household survey and a caregiver survey and covered 1,211 households. The qualitative data collection activities yielded rich and in-depth insights that will be triangulated with the quantitative survey findings in a companion report. Therefore, this report focuses only on findings from the quantitative survey component. Results are reported for global acute malnutrition (GAM), stunting, and underweight. However, the discussion focuses only on GAM because the purpose of the Nawiri program is to reduce persistent acute malnutrition.

The baseline data collection was carried out in May and June 2021 following a full household listing operation in the county to establish the sampling frame of households with children under 3 years. Anthropometric data were collected from all under-5 children in the sampled households. Subsequent data collection waves are planned for October-November 2021 (Wave 2), March-April 2022 (Wave 3), September-October 2022 (Wave 4), March-April 2023 (Wave 5), and August-September 2023 (Wave 6).

Sampling deviation

na

Mode of data collection

Face-to-face [f2f]

Research instrument

HOUSEHOLD QUESTIONNAIRE:background, informed consent, household schedule/roster, household demographics, household characteristics, socio-economic characteristics (socio-economic characteristics, POVERTY PROBABILITY INDEX (PP1), HOUSEHOLD WEALTH RANKING (PERCEPTION)), food consumption, water, hygiene and sanitation (wash) (water access, availability and seasonality, household water insecurity experiences (hwise) scale, hygiene and sanitation), household shocks experienced, social safety nets and economic safety guards.

WOMEN/CAREGIVER QUESTIONNAIRE:background, informed consent, mother's/caregivers information, births / pregnancy history, pregnancy and antenatal care, family planning, infant and young child feeding practices, supplementation and consumption of iron rich or iron fortified foods, maternal knowledge and attitude, on infant and young child feeding practices, caregiving practices, child feeding utensils hygiene, food safety, hygiene, and sanitation practices, child immunization, health and health seeking practices, acute malnutrition screening (community health volunteers), womens minimum dietary diversity, food insecurity experience scale (hfies), gender, women empowerment, violence and community conflict, psychological wellbeing, anthropometric measurements

Cleaning operations

Data quality monitoring processes and checks were implemented throughout the data collection process, during the time of developing the data collection tools (through built-in quality control in the tablet-based platform), during training of fieldworkers, in real time during data collection (routine monitoring by the research team and periodic cross-checks against the protocols), and during the data cleaning process. During fieldwork, data quality was enhanced through regular spot checks and sit-ins by supervisors to verify the authenticity of data collected. Data were then reviewed and certified by the field coordinator before they were transferred to the server.

The quantitative data were collected using SurveyCTO, a survey platform for electronic data collection that has in-built skips and quality checks. Using this software increased efficiency and reduced the time needed for cleaning the data. In addition, the platform supported offline data capturing for regions with slow or no internet connectivity and data transmission when the internet became available. Fieldwork was conducted by trained fieldworkers using digital tablets with the questionnaire loaded in SurveyCTO. The questionnaire included the following modules: (1) identification and tracking, (2) demographics and household composition, (3) anthropometry of children <5 years and mothers, (4) socioeconomics, (5) household food security, (6) WASH, (7) health-seeking behavior, (8) MIYCN, (9) shock experience/exposure, and (10) shock preparedness and response. Data were uploaded from the tablets onto a secure African Population and Health Research Center (APHRC) server after each day of data collection. Data were synchronized automatically to a server when the tablet was in a location with network coverage. The uploaded data were then checked for quality daily by a data manager and a team dedicated to coordinate field procedures and at the APHRC head office in Nairobi.

Response rate

na

State Table or Frequency of Transition at End of Study.

In 2022, about *** percent of the population in the Philippines were undernourished. Over the last ten years, the proportion of undernourished people significantly dropped from its peak share of **** percent. According to the WHO, malnutrition refers to deficiencies, excesses, or imbalances in a person’s intake of energy and nutrients.