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Having enough to eat is one of the fundamental basic human needs. Hunger – or, more formally, undernourishment – is defined as eating less than the energy required to maintain an active and healthy life.

The share of undernourished people is the leading indicator for food security and nutrition used by the Food and Agriculture Organization of the United Nations.

The fight against hunger focuses on a sufficient energy intake – enough calories per person per day. But it is not the only factor that matters for a healthy diet. Sufficient protein, fats, and micronutrients are also essential, and we cover this in our topic page on micronutrient deficiencies.

Undernourishment in mothers and children is a leading risk factor for death and other poor health outcomes.

The UN has set a global target as part of the Sustainable Development Goals to “end hunger by 2030“. While the world has progressed in past decades, we are far from reaching this target.

On this page, you can find our data, visualizations, and writing on hunger and undernourishment. It looks at how many people are undernourished, where they are, and other metrics used to track food security.

Hunger – also known as undernourishment – is defined as not consuming enough calories to maintain a normal, active, healthy life.

The world has made much progress in reducing global hunger in recent decades — we will see this in the following key insight. But we are still far away from an end to hunger. Tragically, nearly one-in-ten people still do not get enough food to eat.

The share of the undernourished population is shown globally and by region in the chart.

You can see that rates of hunger are highest in Sub-Saharan Africa. South Asia has much higher rates than the Americas and East Asia. Rates in North America and Europe are below 2.5%. However, the FAO shows this as “2.5%” rather than the specific point estimate.

The age-specific mortality rate of nutritional deficiencies at all ages in Canada declined to *** deaths in 2023. Nevertheless, the last two years recorded a significantly higher age-specific mortality rate than the preceding years.

In 2021, Quezon had the highest number of deaths by malnutrition in the Philippines' CALABARZON region, accounting for *** out of 1,078 total cases in this region. On the other hand, Trece Martires City had only *** death in that year.

In 2021, Nueva Ecija had the highest number of deaths by malnutrition in the Philippines' Central Luzon region, accounting for *** out of total ***** cases in this region. In comparison, the City of Balanga only reported *** deaths in that year.

This statistic depicts key data on children's deaths due to undernourishment in Colombia between 2012 and 2016. Within this period, nearly ** percent of child deaths due to undernourishment were caused by severe malnutrition. Up to ** percent of the children that died as a result of malnutrition were under *** year old, while over ** percent belonged to an indigenous community.

Poland Deaths: Rural: Female: EN: ow Nutritional Marasmus, Unspecified Severe Protein Energy Malnutrition data was reported at 22.000 Person in 2023. This records a decrease from the previous number of 39.000 Person for 2022. Poland Deaths: Rural: Female: EN: ow Nutritional Marasmus, Unspecified Severe Protein Energy Malnutrition data is updated yearly, averaging 17.000 Person from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 50.000 Person in 2017 and a record low of 1.000 Person in 2007. Poland Deaths: Rural: Female: EN: ow Nutritional Marasmus, Unspecified Severe Protein Energy Malnutrition data remains active status in CEIC and is reported by Statistics Poland. The data is categorized under Global Database’s Poland – Table PL.G006: Deaths: By Cause.

BackgroundRecent data suggest that case fatality from severe acute malnutrition (SAM) in India may be lower than the 10%–20% estimated by the World Health Organization (WHO). A contemporary quantification of mortality and recovery from acute malnutrition in Indian community settings is essential to inform policy regarding the benefits of scaling up prevention and treatment programmes.Methods and findingsWe conducted a cohort study using data collected during a recently completed cluster-randomised controlled trial in 120 geographical clusters with a total population of 121,531 in rural Jharkhand and Odisha, eastern India. Children born between October 1, 2013, and February 10, 2015, and alive at 6 months of age were followed up at 9, 12, and 18 months. We measured the children’s anthropometry and asked caregivers whether children had been referred to services for malnutrition in the past 3 months. We determined the incidence and prevalence of moderate acute malnutrition (MAM) and SAM, as well as mortality and recovery at each follow-up. We then used Cox-proportional models to estimate mortality hazard ratios (HRs) for MAM and SAM. In total, 2,869 children were eligible for follow-up at 6 months of age. We knew the vital status of 93% of children (2,669/2,869) at 18 months. There were 2,704 children-years of follow-up time. The incidence of MAM by weight-for-length z score (WLZ) and/or mid-upper arm circumference (MUAC) was 406 (1,098/2,704) per 1,000 children-years. The incidence of SAM by WLZ, MUAC, or oedema was 190 (513/2,704) per 1,000 children-years. There were 36 deaths: 12 among children with MAM and six among children with SAM. Case fatality rates were 1.1% (12/1,098) for MAM and 1.2% (6/513) for SAM. In total, 99% of all children with SAM at 6 months of age (227/230) were alive 3 months later, 40% (92/230) were still SAM, and 18% (41/230) had recovered (WLZ ≥ −2 standard deviation [SD]; MUAC ≥ 12.5; no oedema). The adjusted HRs using all anthropometric indicators were 1.43 (95% CI 0.53–3.87, p = 0.480) for MAM and 2.56 (95% CI 0.99–6.70, p = 0.052) for SAM. Both WLZ < −3 and MUAC ≥ 11.5 and < 12.5 were associated with increased mortality risk (HR: 3.33, 95% CI 1.23–8.99, p = 0.018 and HR: 3.87, 95% CI 1.63–9.18, p = 0.002, respectively). A key limitation of our analysis was missing WLZ or MUAC data at all time points for 2.5% of children, including for two of the 36 children who died.ConclusionsIn rural eastern India, the incidence of acute malnutrition among children older than 6 months was high, but case fatality following SAM was 1.2%, much lower than the 10%–20% estimated by WHO. Case fatality rates below 6% have now been recorded in three other Indian studies. Community treatment using ready-to-use therapeutic food may not avert a substantial number of SAM-related deaths in children aged over 6 months, as mortality in this group is lower than expected. Our findings strengthen the case for prioritising prevention through known health, nutrition, and multisectoral interventions in the first 1,000 days of life, while ensuring access to treatment when prevention fails.

In 2021, Iligan city had the highest number of deaths from malnutrition in the Philippines' Northern Mindanao region, accounting for ** out of 337 total deaths across the region. On the other hand, the city of Valencia reported *** death caused by malnutrition in the same year.

Background and AimsMalnutrition is a well known risk factor for adverse outcomes in patients with cancer, cardiovascular disease (CVD) and chronic kidney disease, but epidemiological evidence on its relationship with the long-term risk of all-cause mortality and cardiovascular death is limited.MethodsA total of 20,116 adults from the United States National Health and Nutrition Examination Survey 2007–2014 were enrolled. The Geriatric Nutritional Risk Index (GNRI), Prognostic Nutritional Index (PNI), Controlling Nutritional Status (CONUT) score, and Triglycerides (TG) × Total Cholesterol (TC) × Body Weight (BW) Index (TCBI) were calculated at baseline. Cox regression and the Kaplan–Meier analysis were conducted when participants were divided into three groups according to the tertiles of objective nutritional scores. Restricted cubic spline was performed to further explore the shape of the relationship between all-cause mortality, cardiovascular death, and nutritional scores. In addition, the area under the curve (AUC), continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were conducted to assess which nutritional scores have the greatest predictive value for all-cause death and cardiovascular death in the general population.ResultsThe cumulative incidence of all-cause death and cardiovascular death was significantly higher in participants with a higher CONUT score, lower GNRI, and lower PNI. TCBI showed the worst performance on grading and risk assessment. After adjusting confounding factors, the lowest PNI and GNRI tertile and highest COUNT score were independently and significantly associated with increased risk of all-cause death (all P < 0.01) and cardiovascular death (all P < 0.05) analyzed by a multivariate Cox regression model. An L-shaped association between the HR (hazard ratio) of all-cause mortality and nutritional scores (GNRI, PNI and TCBI) was observed in the overall populations. In addition, the PNI had the highest predictive value for all-cause mortality [AUC: 0.684, 95% confidence interval (CI): 0.667–0.701] and cardiovascular death (AUC: 0.710, 95% CI: 0.672–0.749) in the general population compared with other nutritional scores.ConclusionThe poorer the nutritional status of the general population, the higher the all-cause mortality and cardiovascular mortality. The PNI score may provide more useful predictive values than other nutritional scores.

🔗 Check out my notebook here: Link

This dataset includes malnutrition indicators and some of the features that might impact malnutrition. The detailed description of the dataset is given below:

• Percentage-of-underweight-children-data: Percentage of children aged 5 years or below who are underweight by country.

• Prevalence of Underweight among Female Adults (Age Standardized Estimate): Percentage of female adults whos BMI is less than 18.

• GDP per capita (constant 2015 US$): GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2015 U.S. dollars.

• Domestic general government health expenditure (% of GDP): Public expenditure on health from domestic sources as a share of the economy as measured by GDP.

• Maternal mortality ratio (modeled estimate, per 100,000 live births): Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP measured using purchasing power parities (PPPs).

• Mean-age-at-first-birth-of-women-aged-20-50-data: Average age at which women of age 20-50 years have their first child.

• School enrollment, secondary, female (% gross): Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers.

*For the 7 deaths without verbal autopsy information, deaths were confirmed by relatives or neighbors when given home address was visited, but the family had moved house and were unable to be contacted or declined further contact with the study team.Characteristics of post-discharge deaths: timing of death and verbal autopsy findings.

Background and aimsMaternal malnutrition is a major global public health problem that can lead to serious maternal diseases. This study aimed to analyze and predict the spatio-temporal trends in the burden of maternal disorders attributable to malnutrition, and to provide a basis for scientific improvement of maternal malnutrition and targeted prevention of maternal disorders.MethodsData on maternal disorders attributable to malnutrition, including number of deaths, disability-adjusted life years (DALYs), population attributable fractions (PAFs), age-standardized mortality rates (ASMRs), and age-standardized DALY rates (ASDRs) were obtained from the Global Burden of Disease Study 2019 to describe their epidemiological characteristics by age, region, year, and type of disease. A log-linear regression model was used to calculate the annual percentage change (AAPC) of ASMR or ASDR to reflect their temporal trends. Bayesian age-period-cohort model was used to predict the number of deaths and mortality rates to 2035.ResultsGlobal number of deaths and DALYs for maternal disorders attributable to malnutrition declined by 42.35 and 41.61% from 1990 to 2019, with an AAPC of –3.09 (95% CI: −3.31, −2.88) and –2.98 (95% CI: −3.20, −2.77) for ASMR and ASDR, respectively. The burden was higher among younger pregnant women (20–29 years) in low and low-middle socio-demographic index (SDI) regions, whereas it was higher among older pregnant women (30–39 years) in high SDI region. Both ASMR and ASDR showed a significant decreasing trend with increasing SDI. Maternal hemorrhage had the highest burden of all diseases. Global deaths are predicted to decline from 42,350 in 2019 to 38,461 in 2035, with the ASMR declining from 1.08 (95% UI: 0.38, 1.79) to 0.89 (95% UI: 0.47, 1.31).ConclusionMaternal malnutrition is improving globally, but in the context of the global food crisis, attention needs to be paid to malnutrition in low SDI regions, especially among young pregnant women, and corresponding measures need to be taken to effectively reduce the burden of disease.

Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions (% of total) in World was reported at 18.41 % in 2019, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions (% of total) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

ABSTRACT Objective To determine the association of a scored patient-generated Subjective Global Assessment with mortality and length of hospital stay in cancer patients. Methods Cross-sectional study carried out between July and September 2014 using secondary data collection using data from 366 medical records of patients admitted to a hospital recognized as a cancer center of excellence. The present study included patients with hospital stay over than or equal three days and minimum age of 20 years. The patient-generated Subjective Global Assessment scores were calculated and compared with the patients’ clinical and anthropometric characteristics and outcomes (death and long length of stay in hospital). Results Of the 366 patients evaluated, 36.0% were malnourished. The presence of malnutrition, according to the scored patient-generated Subjective Global Assessment, was statistically associated with the presence of metastasis (52.4%). On the other hand, malnutrition, according to the body mass index in adults (55.8%) and in older elderly patients (54.2%), was associated with death (55.0%). The adjusted logistic regression model showed that the following factors were associated with prolonged hospitalization: early nutritional screening, presence of severe malnutrition, radiotherapy and chemotherapy, and surgical procedures. As for mortality, the associated factors were: male reproductive system tumor, presence of metastasis, clinical treatment, prolonged hospitalization, and the presence of some degree of malnutrition. Conclusion The patient-generated Subjective Global Assessment score is an important risk marker of prolonged hospitalization and mortality rates. It is a useful tool capable of circumventing significant biases in the nutritional evaluation of cancer patients.

Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions (% of total) in Japan was reported at 10.3 % in 2019, according to the World Bank collection of development indicators, compiled from officially recognized sources. Japan - Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions (% of total) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Background: Under-five mortality remains concentrated in resource-poor countries. Post-discharge mortality is becoming increasingly recognized as a significant contributor to overall child mortality. With a substantial recent expansion of research and novel data synthesis methods, this study aims to update the current evidence base by providing a more nuanced understanding of the burden and associated risk factors of pediatric post-discharge mortality after acute illness. Methods: Eligible studies published between January 1, 2017 and January 31, 2023, were retrieved using MEDLINE, Embase, and CINAHL databases. Studies published before 2017 were identified in a previous review and added to the total pool of studies. Only studies from countries with low or low-middle Socio-Demographic Index with a post-discharge observation period greater than seven days were included. Risk of bias was assessed using a modified version of the Joanna Briggs Institute critical appraisal tool for prevalence studies. Studies were grouped by patient population, and 6-month post-discharge mortality rates were quantified by random-effects meta-analysis. Secondary outcomes included post-discharge mortality relative to in-hospital mortality, pooled risk factor estimates, and pooled post-discharge Kaplan–Meier survival curves. PROSPERO study registration: #CRD42022350975. Findings: Of 1963 articles screened, 42 eligible articles were identified and combined with 22 articles identified in the previous review, resulting in 64 total articles. These articles represented 46 unique patient cohorts and included a total of 105,560 children. For children admitted with a general acute illness, the pooled risk of mortality six months post-discharge was 4.4% (95% CI: 3.5%–5.4%, I2 = 94.2%, n = 11 studies, 34,457 children), and the pooled in-hospital mortality rate was 5.9% (95% CI: 4.2%–7.7%, I2 = 98.7%, n = 12 studies, 63,307 children). Among disease subgroups, severe malnutrition (12.2%, 95% CI: 6.2%–19.7%, I2 = 98.2%, n = 10 studies, 7760 children) and severe anemia (6.4%, 95% CI: 4.2%–9.1%, I2 = 93.3%, n = 9 studies, 7806 children) demonstrated the highest 6-month post-discharge mortality estimates. Diarrhea demonstrated the shortest median time to death (3.3 weeks) and anemia the longest (8.9 weeks). Most significant risk factors for post-discharge mortality included unplanned discharges, severe malnutrition, and HIV seropositivity. Interpretation: Pediatric post-discharge mortality rates remain high in resource-poor settings, especially among children admitted with malnutrition or anemia. Global health strategies must prioritize this health issue by dedicating resources to research and policy innovation. Data Processing Methods: Data were extracted using a standard data extraction form developed by the review authors. Kaplan–Meier survival curves, where provided, were extracted using a plot digitizer. The data extraction file, “PDMSR2024_DataExtraction_Dataset_SD” was generated as described above and analyzed as is. Co-ordinates were extracted from the survival curves in their original, published form, using a plot digitizer (https://automeris.io/WebPlotDigitizer/). The co-ordinates for each survival curve were then cleaned up to: 1. Re-scale the time points to weeks 2. Curves which reported % mortality were converted to % survival (1 – mortality) 3. First co-ordinate was set to (0, 1), i.e., survival is 100% at time-point 0 4. Include the numbers at risk (if reported), primary reference, and subgroup information Using these cleaned co-ordinates, individual-level patient data were extracted (see Guyot et al, 2012, doi.org/10.1186/1471-2288-12-9) and the survival curves re-constructed to obtain the survival and number at risk at specified time-points (0-52 weeks). Where possible, disease and age subgroups were combined to create all admissions curves by combining the individual-level patient data from multiple curves in the same study. Additional data from the survival curves were extracted to produce the “PDMSR2024_AdditionalDataSurvivalCurves6M_Dataset_SD” and “PDMSR2024_AdditionalDataSurvivalCurves12M_Dataset_SD” files by extracting the survival rate at 6 and 12 months. Previously unpublished hazards ratios were extracted from the dataset used in the Wiens et al (2015) study on post-discharge mortality (doi:10.1136/bmjopen-2015-009449) to produce the “PDMSR2024_Wiens2015HazardsRatios_Dataset_SD.xlsx” file. These original data are published on Dataverse at: doi.org/10.5683/SP2/VBPLRM Analyses were in R version 4.3.0 (R Foundation for Statistical Computing, Vienna, Austria), and RStudio version 2023.6.1 (RStudio, Boston, MA). Additional Files: Survival curves in their original, published form, as well as survival curve coordinates files can be made available by request. NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business...

In 2021, General Santos City had the highest number of deaths from malnutrition in the Philippines' SOCCSKSARGEN region, accounting for ** out of 295 total deaths across the region. On the other hand, the city of Kidapawan reported only ** deaths caused by malnutrition in the same year.

Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions (% of total) in Chad was reported at 63.45 % in 2019, according to the World Bank collection of development indicators, compiled from officially recognized sources. Chad - Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions (% of total) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

This is a replication dataset for the manuscript titled: "Replication Data for: Intestinal Disturbances Associated with Mortality of Children with Complicated Severe Malnutrition." This is data is from a case-control study nested within the F75 intervention trial. By comparing the fecal metabolomic profiles and enteropathy markers between cases (children who died, n=68) and controls (children who were discharged alive, n=68), this study aimed to understand the association between intestinal disturbances wnad mortality among children with severe malnutrition admitted to hospitals in Kenya and Malawi. Targeted fecal metabolomics were performed using nuclear magnetic resonance spectroscopy. A total 68 metabolites were targeted. Four enteropathy markers, myeloperoxidase, calprotectin, alpha-1-antitrypsin, and intestinal fatty acid binding protein were quantified by ELISA. Lyophilization was used to determine water content of fecal samples.

Analysis of ‘Death Cause by Country’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/majyhain/death-cause-by-country on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

Across low- and middle-income countries, mortality from infectious disease, malnutrition, nutritional deficiencies, neonatal and maternal deaths are common – and in some cases, dominant. In Kenya, for example, diarrheal infections are still the primary cause of death. HIV/AIDS is the major cause of death in South Africa and Botswana. However, in high-income countries, the proportion of deaths due by these causes is quite low.

Content

The dataset contains thirty two columns and contains the death causes by All Genders (Male, Female) and by all age group.

Acknowledgements

Users are allowed to use, copy, distribute and cite the dataset as follows: “Majyhain, Death Causes by Country, Kaggle Dataset, February 04, 2022.”

Inspiration

The ideas for this data is to: • The amount of people dying by various diseases.

• What is the death cause reasons by country.

• Number of People dying by various diseases.

• Which disease is causing more deaths by country.

• Which disease is causing more deaths by world.

References:

The Data is collected from the following sites:

https://www.who.int/

--- Original source retains full ownership of the source dataset ---