In 2022, 11 percent of the global population experienced severe food insecurity, an increase from 7.7 percent in 2014. The share of people suffering from hunger increased in almost all of the world regions, but Africa is by far the region the most affected by severe food insecurity. There, prevalence of severe food insecurity increased by almost five percentage points from 2018 to 2022.

The share of population who experience severe food insecurity in Guatemala was estimated at 21.1 percent between 2021 and 2023. There was an increase in moderate and severe food insecurity in Guatemala during the four time periods displayed here. According to the source, a person is considered food insecure when they lack regular access to enough safe and nutritious food to lead an active and healthy life.

Sweden SE: Prevalence of Severe Food Insecurity in the Population: % of population data was reported at 1.300 % in 2020. This records an increase from the previous number of 1.200 % for 2019. Sweden SE: Prevalence of Severe Food Insecurity in the Population: % of population data is updated yearly, averaging 1.100 % from Dec 2015 (Median) to 2020, with 6 observations. The data reached an all-time high of 1.300 % in 2020 and a record low of 0.800 % in 2015. Sweden SE: Prevalence of Severe Food Insecurity in the Population: % of population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sweden – Table SE.World Bank.WDI: Social: Health Statistics. The percentage of people in the population who live in households classified as severely food insecure. A household is classified as severely food insecure when at least one adult in the household has reported to have been exposed, at times during the year, to several of the most severe experiences described in the FIES questions, such as to have been forced to reduce the quantity of the food, to have skipped meals, having gone hungry, or having to go for a whole day without eating because of a lack of money or other resources.;Food and Agriculture Organization of the United Nations (FAO);;

United States US: Prevalence of Severe Food Insecurity in the Population: % of population data was reported at 0.800 % in 2022. This records an increase from the previous number of 0.700 % for 2021. United States US: Prevalence of Severe Food Insecurity in the Population: % of population data is updated yearly, averaging 0.800 % from Dec 2015 (Median) to 2022, with 8 observations. The data reached an all-time high of 1.100 % in 2015 and a record low of 0.700 % in 2021. United States US: Prevalence of Severe Food Insecurity in the Population: % of population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Social: Health Statistics. The percentage of people in the population who live in households classified as severely food insecure. A household is classified as severely food insecure when at least one adult in the household has reported to have been exposed, at times during the year, to several of the most severe experiences described in the FIES questions, such as to have been forced to reduce the quantity of the food, to have skipped meals, having gone hungry, or having to go for a whole day without eating because of a lack of money or other resources.;Food and Agriculture Organization of the United Nations (FAO);;

Japan JP: Prevalence of Severe Food Insecurity in the Population: % of population data was reported at 0.900 % in 2020. This records an increase from the previous number of 0.700 % for 2019. Japan JP: Prevalence of Severe Food Insecurity in the Population: % of population data is updated yearly, averaging 0.600 % from Dec 2015 (Median) to 2020, with 6 observations. The data reached an all-time high of 0.900 % in 2020 and a record low of 0.000 % in 2016. Japan JP: Prevalence of Severe Food Insecurity in the Population: % of population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank.WDI: Social: Health Statistics. The percentage of people in the population who live in households classified as severely food insecure. A household is classified as severely food insecure when at least one adult in the household has reported to have been exposed, at times during the year, to several of the most severe experiences described in the FIES questions, such as to have been forced to reduce the quantity of the food, to have skipped meals, having gone hungry, or having to go for a whole day without eating because of a lack of money or other resources.;Food and Agriculture Organization of the United Nations (FAO);;

Jamaica JM: Prevalence of Severe Food Insecurity in the Population: % of population data was reported at 26.600 % in 2022. This records an increase from the previous number of 25.600 % for 2021. Jamaica JM: Prevalence of Severe Food Insecurity in the Population: % of population data is updated yearly, averaging 24.650 % from Dec 2015 (Median) to 2022, with 8 observations. The data reached an all-time high of 26.600 % in 2022 and a record low of 23.000 % in 2018. Jamaica JM: Prevalence of Severe Food Insecurity in the Population: % of population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Jamaica – Table JM.World Bank.WDI: Social: Health Statistics. The percentage of people in the population who live in households classified as severely food insecure. A household is classified as severely food insecure when at least one adult in the household has reported to have been exposed, at times during the year, to several of the most severe experiences described in the FIES questions, such as to have been forced to reduce the quantity of the food, to have skipped meals, having gone hungry, or having to go for a whole day without eating because of a lack of money or other resources.;Food and Agriculture Organization of the United Nations (FAO);;

In 2023, 19.1 percent of the population in South Asia was exposed to severe food insecurity. This was a slight increase from the previous year, when 19 percent of South Asia's population was subject to severe food insecurity. Severe food insecurity in South Asia Food insecurity describes the situation when a person lacks consistent access to an adequate supply of safe and nourishing food for normal growth or a healthy lifestyle. Meanwhile, severe food insecurity refers to extreme conditions when the person has no food for an entire day or longer. South Asia comprises some of the poorest economies in the Asia-Pacific region. Except for the Maldives, the gross domestic product per person of South Asian countries was below five thousand U.S. dollars. A combination of economic challenges, land degradation, climate change effects, and structural dependence on the global north partly induced by globalization, among other reasons, has resulted in the highest prevalence of severe food insecurity in the Asia-Pacific region for South Asia, with almost a fifth of the population experiencing severe food insecurity. India’s food insecurity and lifestyle diseases South Asia is home to about a quarter of the world’s population, with India making up more than seventy percent of it. The prevalence of chronic hunger, moderate and severe food insecurity in India between 2015 and 2021 continuously increased. The Indian government has deployed several public initiatives to ensure nutrition and food security in the country. Nevertheless, the Global Hunger Index has classified India among the countries that are most affected by hunger and malnutrition, with an index of 28.7. The situations that pose health risks not only stem from malnourishment but from lifestyle as well. For instance, lifestyle shifts towards more modern ones have led to an increase in common lifestyle diseases among Indians.

Switzerland Prevalence of Severe Food Insecurity in the Population: % of population data was reported at 0.600 % in 2021. This records an increase from the previous number of 0.000 % for 2020. Switzerland Prevalence of Severe Food Insecurity in the Population: % of population data is updated yearly, averaging 0.700 % from Dec 2015 (Median) to 2021, with 7 observations. The data reached an all-time high of 1.500 % in 2015 and a record low of 0.000 % in 2020. Switzerland Prevalence of Severe Food Insecurity in the Population: % of population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Switzerland – Table CH.World Bank.WDI: Social: Health Statistics. The percentage of people in the population who live in households classified as severely food insecure. A household is classified as severely food insecure when at least one adult in the household has reported to have been exposed, at times during the year, to several of the most severe experiences described in the FIES questions, such as to have been forced to reduce the quantity of the food, to have skipped meals, having gone hungry, or having to go for a whole day without eating because of a lack of money or other resources.;Food and Agriculture Organization of the United Nations (FAO);;

Abstract

In this report, we present data from the emergency response survey conducted via telephone among households in three conflict affected regions of Nigeria, North East, North Central and South South between August-September 2017. This round is the second round of telephone data collected from a subsample of households in the Nigeria General Household Survey (GHS). The first round collected data on conflict exposure.

The purpose of this second round of data collection was to understand food insecurity in conflict affected regions. Armed conflict can have a detrimental effect on food security. This might be due to for example reduced agricultural production, or price increases due to malfunctioning markets. Food insecurity might be permanent, such that a household living below the poverty line has a constant struggle to acquire food from the market or produce food for their own use. In situations such as armed conflict, also better endowed households might be temporarily food insecure.

In this report, we find that food insecurity is a major concern in all the three regions studied:

· The mean household in all the three regions is “highly food insecure” · North East of Nigeria is the most food insecure of the three regions · Reducing meals or portion size is the most important coping strategy in all three regions · Food prices are the most important source of food insecurity in all three regions · A large majority of households rely on the market as the main source of food in all regions. Price concerns should therefore be taken very seriously by policy makers. · Households in all three regions do not report there being an inadequate supply of food in the market.

Geographic coverage

National Coverage Households

Analysis unit

Households

Universe

The Survey covered all household members. The questionnaire was administered to only one respondent per household - most often a male household head.

Kind of data

Sample survey data [ssd]

Sampling procedure

The food security survey was a telephone based survey conducted between August 15th and September 8th 2017. The interview was the second round of a telephone survey using a sub-set of the sample of GHS (General Household Survey) households. The first round of the telephone interview was administered during spring 2017 with 717 completed interviews with the following geographical distribution: 175 interviews in the North East, 276 in North Central and 266 in South South. The first round was focused on conflict exposure, while the second round discussed in this report focused on food insecurity in conflict affected regions.

In the three conflict affected geographical zones comprising of 16 states of Nigeria, households from LGS's that had high conflict exposure were oversampled chosen for a pilot sample, conducted before the telephone surveys. These LGS's were chosen based on the following criteria: The oversampled LGS's needed to have over 10 conflict events during 2012-14 recorded in the Armed Conflict Location & Event Data Project (ACLED) database.

The first round of the telephone survey (which took place after the pilot) first attempted to reach 742 households from the GHS panel, of which 529 could be reached and interviewed. The rest did not have phone numbers or functioning phone numbers (only 2.7 percent refused to answer). In order to increase the sample size to a level that was considered adequate for the survey, an additional 288 replacement households were included in the sample also from the GHS panel. Out of these replacement households 188 could be interviewed. Therefore altogether 1030 households were attempted to be reached, with a final sample size of 717 completed interviews.

Conflict affected areas were oversampled in order to have a large enough sample of households that in fact experienced conflict events in order to shed light on the type of events that have happened. A random sample of the zones might have given too small sample of conflict affected households and therefore restricted the analysis of the various types of conflict events. Due to the oversampling however, the sample drawn was not representative at the level of the geographical zone, as is the case in the GHS. Therefore in the analysis we use sampling weights that adjust for the propensity of being in a conflict affected LGA in order to ensure that the sample is representative at the level of the geographical zone.

During the second round of the survey 582 of the 717 households were re-interviewed on food security related issues (only the 717 were attempted to be reached). Of the 582 households, 147 in the North East, 219 in North Central, and 216 in South South were interviewed. The attrition rates in our sample from round one to round two are hence 16 percent, 21 percent, and 19 percent for North East, North Central and South South, respectively. The attrition from the conflict survey round was mostly due to not being able to reach the respondents possibly due to non-functioning phone numbers. Only 3 percent of respondents refused to answer.

Similar telephone-based surveys are being conducted in six countries in Sub-Saharan Africa under the World Bank project "Listening to Africa". As a comparison, a mobile phone survey in Tanzania (see Croke et al. 2012 for details), had a high drop-out rate between the very first rounds from 550 to 458 respondents, but very low attrition for the subsequent rounds for the 458 respondents, who could reliably be reached by a mobile phone. In light of this reference point and also considering the fact that the households interviewed live in conflict affected regions, our attrition rates seem to be within reasonable limits.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Response rate

The first round of the telephone survey (which took place after the pilot), first attempted to reach 742 households from the GHS panel, of which 529 could be reached and interviewed. The rest did not have phone numbers or functioning phone numbers (only 2.7 per cent refused to answer). In order to increase the sample size to a level that was considered adequate for the survey, an additional 288 replacement households were included in the sample also from the GHS panel. Out of these replacement households 188 could be interviewed. Therefore altogether 1030 households were attempted to be reached, with a final sample size of 717 completed interviews. The response rate is 96%

Data appraisal

Limitations Recall Bias In the pilot data collection, respondents were asked to report on conflict events that had taken place in their family and their community over the last six years. This extremely long recall period must be considered when drawing inferences from the data. People are likely to under-report less severe (and therefore less memorable) events, particularly those that happened to community members in larger communities. Respondents are also more likely to recall events that happened to family members than those that happened to community members. Other biases may also be at play - for example, those who have been most highly affected by conflict over the last six years may have moved to another community. These factors demonstrate the importance of implementing a regular data collection schedule, which would allow far more accurate data to be collected.

Sampling Bias The GHS is a panel survey taking place over multiple rounds through a period of time. Therefore, households that are more mobile or households that are nomadic are less likely to be represented in this sample. This may be particularly relevant in circumstances where nomadic groups are named as perpetrators of conflict events.

Power Dynamics There are some disadvantages to the phone system, and for this reason it should be supplemented by additional types of data collection wherever possible. In a mobile phone survey, the respondent is the person who owns a mobile phone. In many areas, particularly those highly affected by poverty and those located in rural areas, only one family member owns a mobile phone. This is generally the household head, who is most likely male. Furthermore, in many of these communities, women are not allowed to have access to mobile phones and are forbidden from speaking to outsiders, which can prohibit mobile phone-based data collection.

Gender Dynamics The questionnaire was administered to only one respondent per household - most often a male household head. This means that crimes that carry stigma, especially sexual violence, are less likely to be reported. In this dataset, no sexual assault was reported despite data collected elsewhere that indicate that rape was used as a weapon by Boko Haram and elsewhere. This also means that violence that affects members of the household with less power (such as women, children, and employees), is less likely to be reported. This may be particularly important when considering violence not related to ongoing external conflict, such as domestic violence.

Slovakia SK: Prevalence of Moderate or Severe Food Insecurity in the Population: % of population data was reported at 9.000 % in 2022. This records an increase from the previous number of 8.300 % for 2021. Slovakia SK: Prevalence of Moderate or Severe Food Insecurity in the Population: % of population data is updated yearly, averaging 6.100 % from Dec 2015 (Median) to 2022, with 8 observations. The data reached an all-time high of 9.000 % in 2022 and a record low of 5.000 % in 2018. Slovakia SK: Prevalence of Moderate or Severe Food Insecurity in the Population: % of population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Slovakia – Table SK.World Bank.WDI: Social: Health Statistics. The percentage of people in the population who live in households classified as moderately or severely food insecure. A household is classified as moderately or severely food insecure when at least one adult in the household has reported to have been exposed, at times during the year, to low quality diets and might have been forced to also reduce the quantity of food they would normally eat because of a lack of money or other resources.;Food and Agriculture Organization of the United Nations (FAO);;

In 2023, around 19.2 percent of all children lived in households that were classified as food insecure in the United States. This is a slight increase from the previous year, when 18.5 percent of children were in food-insecure households.

Abstract

Sustainable Development Goal (SDG) target 2.1 commits countries to end hunger, ensure access by all people to safe, nutritious and sufficient food all year around. Indicator 2.1.2, “Prevalence of moderate or severe food insecurity based on the Food Insecurity Experience Scale (FIES)”, provides internationally-comparable estimates of the proportion of the population facing difficulties in accessing food. More detailed background information is available at http://www.fao.org/in-action/voices-of-the-hungry/fies/en/ .

The FIES-based indicators are compiled using the FIES survey module, containing 8 questions. Two indicators can be computed:
1. The proportion of the population experiencing moderate or severe food insecurity (SDG indicator 2.1.2), 2. The proportion of the population experiencing severe food insecurity.

These data were collected by FAO through the Gallup World Poll. General information on the methodology can be found here: https://www.gallup.com/178667/gallup-world-poll-work.aspx. National institutions can also collect FIES data by including the FIES survey module in nationally representative surveys.

Microdata can be used to calculate the indicator 2.1.2 at national level. Instructions for computing this indicator are described in the methodological document available in the documentations tab. Disaggregating results at sub-national level is not encouraged because estimates will suffer from substantial sampling and measurement error.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

Individuals of 15 years or older with access to landline and/or mobile phones.

Kind of data

Sample survey data [ssd]

Sampling procedure

A simple stratified sample design was used for selection of mobile phone samples. Within each explicit stratum (service provider), sample of specified size was drawn using pure Random Digit Dial (RDD) procedures. Sampling was done independently within each stratum. All sampled numbers were pre-screened for working status. For respondents contacted by mobile phone, random respondent selection within the household was done by selecting one adult at random that has the next birthday. Selection of adults in household reached via mobile was primarily to increase coverage and representation of those hard to access via mobile. For the purpose of data collection, the total initial sample was split into random subsamples (replicate samples) and released sequentially based on the progress of interviewing in different strata. The goal was to release an optimum amount of sample each time to achieve a high response rate while completing the targeted number of interviews within the field period. Exclusions: NA Design effect: 2.78

Mode of data collection

Computer Assisted Telephone Interview [cati]

Cleaning operations

Statistical validation assesses the quality of the FIES data collected by testing their consistency with the assumptions of the Rasch model. This analysis involves the interpretation of several statistics that reveal 1) items that do not perform well in a given context, 2) cases with highly erratic response patterns, 3) pairs of items that may be redundant, and 4) the proportion of total variance in the population that is accounted for by the measurement model.

Sampling error estimates

The margin of error is estimated as 5.2. This is calculated around a proportion at the 95% confidence level. The maximum margin of error was calculated assuming a reported percentage of 50% and takes into account the design effect.

Abstract

Sustainable Development Goal (SDG) target 2.1 commits countries to end hunger, ensure access by all people to safe, nutritious and sufficient food all year around. Indicator 2.1.2, “Prevalence of moderate or severe food insecurity based on the Food Insecurity Experience Scale (FIES)”, provides internationally-comparable estimates of the proportion of the population facing difficulties in accessing food. More detailed background information is available at http://www.fao.org/in-action/voices-of-the-hungry/fies/en/ .

The FIES-based indicators are compiled using the FIES survey module, containing 8 questions. Two indicators can be computed:
1. The proportion of the population experiencing moderate or severe food insecurity (SDG indicator 2.1.2), 2. The proportion of the population experiencing severe food insecurity.

These data were collected by FAO through the Gallup World Poll. General information on the methodology can be found here: https://www.gallup.com/178667/gallup-world-poll-work.aspx. National institutions can also collect FIES data by including the FIES survey module in nationally representative surveys.

Microdata can be used to calculate the indicator 2.1.2 at national level. Instructions for computing this indicator are described in the methodological document available in the documentations tab. Disaggregating results at sub-national level is not encouraged because estimates will suffer from substantial sampling and measurement error.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

Individuals of 15 years or older with access to landline and/or mobile phones.

Kind of data

Sample survey data [ssd]

Sampling procedure

A simple stratified sample design was used for selection of landline phone samples. Within each explicit stratum (province) in the case of landline, sample of specified size was drawn using pure Random Digit Dial (RDD) procedures. Sampling was done independently within each stratum. In the case of mobile, sample was specified size was drawn using pure RDD procedures independently from within each stratum. All sampled landline and mobile phone numbers were pre-screened for working status.

For respondents contacted by both landline and mobile telephone, random respondent selection within the household was done by enumerating all adults 15 or older in the household and randomly selecting one. Selection of adults in households reached via mobile was primarily to increase coverage and representation of those hard to access via mobile.

For the purpose of data collection, the total initial sample was split into random subsamples (replicate samples) and released sequentially based on the progress of interviewing in different strata. The goal was to release an optimum amount of sample each time to achieve a high response rate while completing the targeted number of interviews within the field period. Exclusions: NA Design effect: 2.02

Mode of data collection

Other [oth]

Cleaning operations

Statistical validation assesses the quality of the FIES data collected by testing their consistency with the assumptions of the Rasch model. This analysis involves the interpretation of several statistics that reveal 1) items that do not perform well in a given context, 2) cases with highly erratic response patterns, 3) pairs of items that may be redundant, and 4) the proportion of total variance in the population that is accounted for by the measurement model.

Sampling error estimates

The margin of error is estimated as 4.3. This is calculated around a proportion at the 95% confidence level. The maximum margin of error was calculated assuming a reported percentage of 50% and takes into account the design effect.

Data appraisal

The variable WHLDAY was not considered in the computation of the published FAO food insecurity indicator based on FIES due to the results of the validation process.

Currently, 45% of the rural population is in Crisis or Emergency (IPC Phase 3 and 4) while 29% is Stressed (IPC Phase 2). This is a deterioration from the last analysis conducted in June 2019, when 38% of the total population was in IPC Phase 3 and higher. The increase in the number of acutely food insecure population is primarily due to the lean season expected to extend until June. Review of the severity of the drivers of food insecurity in Zimbabwe shows that more households would likely be in a more challenging food security situation in the absence of a large-scale humanitarian food assistance programme ongoing in the country. The Government and partners are reaching large numbers of food insecure households, and genuine efforts need to continue to reach the most vulnerable households and to provide them food, cash and livelihood assistance. For more visit: http://www.ipcinfo.org/ipc-country-analysis/details-map/en/c/1152562/?iso3=ZWE

Austria AT: Prevalence of Moderate or Severe Food Insecurity in the Population: % of population data was reported at 4.300 % in 2021. This records an increase from the previous number of 3.300 % for 2020. Austria AT: Prevalence of Moderate or Severe Food Insecurity in the Population: % of population data is updated yearly, averaging 4.300 % from Dec 2015 (Median) to 2021, with 7 observations. The data reached an all-time high of 5.500 % in 2015 and a record low of 3.000 % in 2019. Austria AT: Prevalence of Moderate or Severe Food Insecurity in the Population: % of population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Austria – Table AT.World Bank.WDI: Social: Health Statistics. The percentage of people in the population who live in households classified as moderately or severely food insecure. A household is classified as moderately or severely food insecure when at least one adult in the household has reported to have been exposed, at times during the year, to low quality diets and might have been forced to also reduce the quantity of food they would normally eat because of a lack of money or other resources.;Food and Agriculture Organization of the United Nations (FAO);;

Abstract

Sustainable Development Goal (SDG) target 2.1 commits countries to end hunger, ensure access by all people to safe, nutritious and sufficient food all year around. Indicator 2.1.2, “Prevalence of moderate or severe food insecurity based on the Food Insecurity Experience Scale (FIES)”, provides internationally-comparable estimates of the proportion of the population facing difficulties in accessing food. More detailed background information is available at http://www.fao.org/in-action/voices-of-the-hungry/fies/en/ .

The FIES-based indicators are compiled using the FIES survey module, containing 8 questions. Two indicators can be computed: 1. The proportion of the population experiencing moderate or severe food insecurity (SDG indicator 2.1.2), 2. The proportion of the population experiencing severe food insecurity.

These data were collected by FAO through the Gallup World Poll. General information on the methodology can be found here: https://www.gallup.com/178667/gallup-world-poll-work.aspx. National institutions can also collect FIES data by including the FIES survey module in nationally representative surveys.

Microdata can be used to calculate the indicator 2.1.2 at national level. Instructions for computing this indicator are described in the methodological document available in the documentations tab. Disaggregating results at sub-national level is not encouraged because estimates will suffer from substantial sampling and measurement error.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

Individuals of 15 years or older with access to landline and/or mobile phones.

Kind of data

Sample survey data [ssd]

Sampling procedure

A simple stratified sample design was used for selection of landline phone samples. Within each explicit stratum (Governorate) in the case of landline, sample of specified size was drawn using list-assisted Random Digit Dial (RDD) procedures. In the case of mobile, within each explicit stratum, sample of specified size was drawn using pure RDD procedure. Sampling was done independently within each stratum. All sampled phone numbers were pre-screened for working status. For respondents contacted by landline telephone, random respondent selection within the household was performed by enumerating the persons in the household aged 15 and older and selecting one at random. For respondents contacted by mobile phone, random respondent selection within the household was done by choosing the adult who had the most recent birthday. Selection of adults in household reached via mobile was primarily to increase coverage and representation of those hard to access via mobile. For the purpose of data collection, the total initial sample was split into random subsamples (replicate samples) and released sequentially based on the progress of interviewing in different strata. The goal was to release an optimum amount of sample each time to achieve a high response rate while completing the targeted number of interviews within the field period. Exclusions: NA Design effect: 1.98

Mode of data collection

Computer Assisted Telephone Interview [cati]

Cleaning operations

Statistical validation assesses the quality of the FIES data collected by testing their consistency with the assumptions of the Rasch model. This analysis involves the interpretation of several statistics that reveal 1) items that do not perform well in a given context, 2) cases with highly erratic response patterns, 3) pairs of items that may be redundant, and 4) the proportion of total variance in the population that is accounted for by the measurement model.

Sampling error estimates

The margin of error is estimated as 4.3. This is calculated around a proportion at the 95% confidence level. The maximum margin of error was calculated assuming a reported percentage of 50% and takes into account the design effect.

Data appraisal

The variable WHLDAY was not considered in the computation of the published FAO food insecurity indicator based on FIES due to the results of the validation process.

In 2023, approximately 2.9 percent of the population in Southeast Asia were exposed to severe food insecurity. This was an increase from 2014, when around 2.4 percent of Southeast Asia's population were subject to severe food insecurity.

The number of people experiencing severe food insecurity in Latin America was estimated at 70.1 million in 2023. In comparison to the previous year, this represents a considerable decrease of severely food insecure people. From 2018 until 2022, there has been a continuous increase of Latin American people experiencing severe food insecurity. According to the source, a person is considered food insecure when they lack regular access to enough safe and nutritious food to lead an active and healthy life.

GUI43 - Food insecurity in the last 12 months of Respondents aged 25 years. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Food insecurity in the last 12 months of Respondents aged 25 years...

The COVID-19 pandemic was projected to increase the food insecurity in East Africa by 73 percent. Before the coronavirus outbreak, there were 24 million food insecure people in the region. With the health crisis, the number is expected to rise to 41.5 million, from which 14 million are living in urban areas.