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.

In 2023, the rate of undernourishment worldwide was 9.1 percent. The region with the largest share of undernourished people was Sub-Saharan Africa, with 23.2 percent. Undernourished people worldwideSouthern Asia and Sub-Saharan Africa have some of the highest numbers of undernourished people in the world, totaling 281 million and 278 million, respectively in 2023. Based on the World Hunger Index 2024, Somalia and Yemen were among the most affected countries by hunger and malnutrition. Worldwide, about 733.4 million people were suffering from malnutrition in 2023. MalnutritionMalnourishment occurs when a person’s diet consists of too little or too much of certain nutrients. Undernutrition occurs when a person does not intake enough calories, protein, or micronutrients. One of the primary causes of malnutrition is due to limited or lack of accessibility to affordable nutritious foods. Malnutrition is considered to contribute to over a third of child deaths globally. In Asia, an estimated 77 million cases of stunting, which is the primary effect of malnutrition, were recorded for children under the age of five in 2022. The FAO reports that food security and nutrition commitments by national governments are essential in eradicating the world hunger problem. Agricultural productivity, accessibility to land, services, and markets, rural development strategies, and social protection are needed to ensure a reduction in malnutrition.

This is the raw data of a population study in Oeolo, East Nusa Tenggara, Indonesia. The data includes the demographic characteristics and the nutrition of status of the children under five in that area.Categories for weight-age (WHO and Indonesian):1=severely underweight2=underweight3=normalCategories for height-age (WHO and Indonesian):1=severely stunted2=stunted3=normal

This graph shows the 15 countries in the world that have made the fastest progress in the time from 1990 to 2010 against child malnutrition and consequently child stunting. Measurements displayed are the annual average percentage change in child stunting rates. China has, on average, seen a 6.3 percent decrease in the rate of child stunting from 1990 to 2010.

This graph shows the 15 countries in the world that have made the least progress in the time from 1990 to 2010 against child malnutrition and consequently child stunting. Measurements displayed are the annual average percentage change in child stunting rates. Somalia has, on average, seen a 6.3 percent increase in the rate of child stunting from 1990 to 2010.

Prevalence of undernourishment (% of population) in Japan was reported at 3.4 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Japan - Prevalence of undernourishment (% of population) - actual values, historical data, forecasts and projections were sourced from the World Bank on May of 2025.

Dataset

This dataset was created by Felix Konrad

Contents

The Current Population Survey Food Security Supplement (CPS-FSS) is the source of national and State-level statistics on food insecurity used in USDA's annual reports on household food security. The CPS is a monthly labor force survey of about 50,000 households conducted by the Census Bureau for the Bureau of Labor Statistics. Once each year, after answering the labor force questions, the same households are asked a series of questions (the Food Security Supplement) about food security, food expenditures, and use of food and nutrition assistance programs. Food security data have been collected by the CPS-FSS each year since 1995. Four data sets that complement those available from the Census Bureau are available for download on the ERS website. These are available as ASCII uncompressed or zipped files. The purpose and appropriate use of these additional data files are described below: 1) CPS 1995 Revised Food Security Status data--This file provides household food security scores and food security status categories that are consistent with procedures and variable naming conventions introduced in 1996. This includes the "common screen" variables to facilitate comparisons of prevalence rates across years. This file must be matched to the 1995 CPS Food Security Supplement public-use data file. 2) CPS 1998 Children's and 30-day Food Security data--Subsequent to the release of the April 1999 CPS-FSS public-use data file, USDA developed two additional food security scales to describe aspects of food security conditions in interviewed households not captured by the 12-month household food security scale. This file provides three food security variables (categorical, raw score, and scale score) for each of these scales along with household identification variables to allow the user to match this supplementary data file to the CPS-FSS April 1998 data file. 3) CPS 1999 Children's and 30-day Food Security data--Subsequent to the release of the April 1999 CPS-FSS public-use data file, USDA developed two additional food security scales to describe aspects of food security conditions in interviewed households not captured by the 12-month household food security scale. This file provides three food security variables (categorical, raw score, and scale score) for each of these scales along with household identification variables to allow the user to match this supplementary data file to the CPS-FSS April 1999 data file. 4) CPS 2000 30-day Food Security data--Subsequent to the release of the September 2000 CPS-FSS public-use data file, USDA developed a revised 30-day CPS Food Security Scale. This file provides three food security variables (categorical, raw score, and scale score) for the 30-day scale along with household identification variables to allow the user to match this supplementary data file to the CPS-FSS September 2000 data file. Food security is measured at the household level in three categories: food secure, low food security and very low food security. Each category is measured by a total count and as a percent of the total population. Categories and measurements are broken down further based on the following demographic characteristics: household composition, race/ethnicity, metro/nonmetro area of residence, and geographic region. The food security scale includes questions about households and their ability to purchase enough food and balanced meals, questions about adult meals and their size, frequency skipped, weight lost, days gone without eating, questions about children meals, including diversity, balanced meals, size of meals, skipped meals and hunger. Questions are also asked about the use of public assistance and supplemental food assistance. The food security scale is 18 items that measure insecurity. A score of 0-2 means a house is food secure, from 3-7 indicates low food security, and 8-18 means very low food security. The scale and the data also report the frequency with which each item is experienced. Data are available as .dat files which may be processed in statistical software or through the United State Census Bureau's DataFerret http://dataferrett.census.gov/. Data from 2010 onwards is available below and online. Data from 1995-2009 must be accessed through DataFerrett. DataFerrett is a data analysis and extraction tool to customize federal, state, and local data to suit your requirements. Through DataFerrett, the user can develop an unlimited array of customized spreadsheets that are as versatile and complex as your usage demands then turn those spreadsheets into graphs and maps without any additional software. Resources in this dataset:Resource Title: December 2014 Food Security CPS Supplement. File Name: dec14pub.zipResource Title: December 2013 Food Security CPS Supplement. File Name: dec13pub.zipResource Title: December 2012 Food Security CPS Supplement. File Name: dec12pub.zipResource Title: December 2011 Food Security CPS Supplement. File Name: dec11pub.zipResource Title: December 2010 Food Security CPS Supplement. File Name: dec10pub.zip

Prevalence of undernourishment (% of population) in Sweden was reported at 2.5 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Sweden - Prevalence of undernourishment (% of population) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

The graph database built and used in the ABCkb paper. The scripts used to generate and run this database are contained in the following github repository. https://github.com/atrautm1/ABCkb

Factors associated with stunted, underweight, and wasted status.

Background: Improved health care and rising life expectancy are creating a growing pool of old age people all over the world, including Africa. Malnutrition in the old age people is associated with both short- and long-term negative health outcomes. However, the reported burdens of malnutrition are fragmented and inconsistent, where more compiled evidence is warranted to aid decision-makers. Hence, this paper is aimed to estimate the pooled prevalence of malnutrition among old age people in Africa.Methods: A systematic search for research reporting the prevalence of malnutrition among old age people (aged above 60 years) was conducted from HINARI/PubMed and Google Scholar databases using combination keywords. Published articles in English language starting from January 2000 to October 2021 were screened. We presented the results based on the standard for reporting systematic review and meta-analysis of observational studies. A random-effect meta-analysis was done to estimate the prevalence of malnutrition along with the 95% confidence intervals. The publication bias was assessed using the funnel plot.Results: A total of 1,442 studies were retrieved based on the search strategy, where only 36 studies (n = 15,266 participants) reported from 11 African countries were included for meta-analysis. The reported prevalence of malnutrition ranges from 2.2 to 77.3% across Africa. Overall, the pooled prevalence of malnutrition was 18% (95% CI: 15-22; I2 = 98.1; p < 0.001). The prevalence is higher in the Central Africa (3.8%; 95% CI: 3.2-4.4), in the community (3.1%; 95% CI: 2.7-3.7), and among advanced age (3.5%; 95% CI: 2.3-5.4).Conclusion: The prevalence of malnutrition in African old age people is high and differs by setting, assessment tool, and country of residence. Hence, due attention to geriatric nutrition is mandatory, and the need for a valid, reliable, and simple screening tool should be thought of.

Graph and download economic data for SNAP Benefits Recipients in Seneca County, NY (CBR36099NYA647NCEN) from 1989 to 2022 about Seneca County, NY; SNAP; nutrition; food stamps; benefits; NY; food; and USA.

Prevalence of undernourishment (% of population) in Switzerland was reported at 2.5 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Switzerland - Prevalence of undernourishment (% of population) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

BackgroundMalnutrition is prevalent in patients with pulmonary tuberculosis (PTB) and is associated with a poor prognosis.ObjectiveThis study aims to assess the prevalence and risk factors of malnutrition in patients with PTB.MethodsStudies related to the prevalence and risk factors of malnutrition in patients with PTB were searched through PubMed, Embase, Web of Science, and Cochrane Library databases from January 1990 to August 2022, and two researchers screened the literature, evaluated the quality, and extracted data independently. A random-effects model was used to pool the effect sizes and 95% confidence intervals. Subgroup analysis, meta-regression analysis, and sensitivity analysis were further performed to identify sources of heterogeneity and evaluate the stability of the results. Publication bias was assessed by Doi plot, Luis Furuya-Kanamori (LFK) asymmetry index, funnel plot, and Egger's tests.ResultsA total of 53 studies involving 48, 598 participants were identified in this study. The prevalence of malnutrition was 48.0% (95% CI, 40.9–55.2%). Subgroup analysis revealed that malnutrition was more common among male gender (52.3%), bacterial positivity (55.9%), family size over 4 (54.5%), drug resistance (44.1%), residing in rural areas (51.2%), HIV infection (51.5%), Asian (51.5%), and African (54.5%) background. The prevalence of mild, moderate, and severe malnutrition was 21.4%, 14.0%, and 29.4%, respectively. Bacterial positivity (OR = 2.08, 95% CI 1.26–3.41), low income (OR = 1.44, 95% CI 1.11–1.86), and residing in rural areas (OR = 1.51, 95% CI 1.20–1.89) were risk factors of malnutrition in patients with PTB. However, male (OR = 1.04, 95% CI 0.85–1.26) and drinking (OR = 1.17, 95% CI 0.81–1.69) were not risk factors for malnutrition in patients with PTB. Due to the instability of sensitivity analysis, HIV infection, age, family size, smoking, and pulmonary cavity need to be reevaluated. Meta-regression suggested that sample size was a source of heterogeneity of prevalence. The Doi plot and LFK asymmetry index (LFK = 3.87) indicated the presence of publication bias for prevalence, and the funnel plot and Egger's test showed no publication bias for risk factors.ConclusionThis meta-analysis indicated that malnutrition was prevalent in patients with PTB, and bacterial positivity, low income, and those residing in rural areas were risk factors for malnutrition. Therefore, clinical workers should pay attention to screening the nutritional status of patients with PTB and identifying the risk factors to reduce the incidence of malnutrition and provide nutritional interventions early to improve the prognosis in patients with PTB.

Graph and download economic data for SNAP Benefits Recipients in Mills County, TX (CBR48333TXA647NCEN) from 1989 to 2022 about Mills County, TX; SNAP; nutrition; food stamps; benefits; food; TX; and USA.

Dataset

This dataset was created by Jayesh Sunil Rajbhar

Released under Apache 2.0

Contents

Comparison among groups (Chi2).

Prevalence of undernourishment (% of population) in France was reported at 2.5 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. France - Prevalence of undernourishment (% of population) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Abstract

The UNHCR Standardized Expanded Nutrition Surveys (SENS) provide regular nutrition data that play a key role in delivering effective and timely interventions to ensure good nutritional outcomes among populations affected by forced displacement. This survey took place in the four Ivorian refugee camps (Bahn, PTP, Solo, and Little Wlebo) in Nimba, Grand Gedeh and Maryland Counties along the border with Cote d'Ivoire from November to December 2013. UNHCR and the World Food Programme (WFP) organized the survey in close collaboration with partners including Africa Humanitarian Action (AHA), International Refugee Committee (IRC), Merlin, Danish Refugee Council (DRC), CARE, and the Country Health Teams / Ministry of Health and Social Welfare of the Government of the Republic of Liberia. This was the second SENS to be conducted in the camps since the Ivorian refugee emergency in 2010 in Liberia; the first was in 2012.

Geographic coverage

Maryland county (Little Wlebo refugee camp), Grand Gedeh county (Solo and PTP refugee camps), and Nimba county (Bahn refugee camp)

Analysis unit

Households Children 0-23 months Children 6-59 months Women 15-49 years

Universe

children 0-59 months women 15-49 years refugee households

Kind of data

Sample survey data [ssd]

Sampling procedure

The UNHCR SENS guidelines for refugee populations and the Standardized Monitoring and Assessment of Relief and Transitions (SMART) methodology version 2 were used to calculate the required sample size. Information on nutritional status obtained from the 2012 nutrition survey was used for sample size estimation. The systematic random sampling methodology without list was used during sample size estimation since in each block households are well arranged in rows and columns.

From the 2012 survey, the upper limit of the global acute malnutrition according to WHO 2006 growth standards was 5.4%, which for the purpose of the 2013 survey the figure was rounded to 6% and assumed as an expected prevalence of acute malnutrition. Precision of 3% was desired and 10% to take care of non-response and absentees. The non-response rate was taken across in all camps due to the fact that the mobility among refugees in the amps is very high. The under-five population was below 10,000 and thus the need for adjustment to small population.

Based on November 2013's ProGres data, the estimated sample size was automatically generated from emergency nutrition assessment (ENA) software.

Sampling deviation

During the survey, the sample size was adjusted in Little Wlebo and Bahn camps due to the fact that the non-response rate was higher than estimated.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Household size above 10 in household-level modules top-coded to 10.