presentation on food insecurity

Food Insecurity

Economic Stability

About This Literature Summary

This summary of the literature on Food Insecurity as a social determinant of health is a narrowly defined examination that is not intended to be exhaustive and may not address all dimensions of the issue. Please note: The terminology used in each summary is consistent with the respective references. For additional information on cross-cutting topics, please see the Access to Foods that Support Healthy Dietary Patterns literature summary.

Related Objectives (4)

Here's a snapshot of the objectives related to topics covered in this literature summary. Browse all objectives .

• Reduce household food insecurity and hunger  — NWS‑01
• Eliminate very low food security in children — NWS‑02
• Increase fruit consumption by people aged 2 years and over — NWS‑06
• Increase vegetable consumption by people aged 2 years and older — NWS‑07

Related Evidence-Based Resources (1)

Here's a snapshot of the evidence-based resources related to topics covered in this literature summary. Browse all evidence-based resources .

• The Role of Law and Policy in Achieving the Healthy People 2020 Nutrition and Weight Status Goals of Increased Fruit and Vegetable Intake in the United States

Literature Summary

Food insecurity is defined as a household-level economic and social condition of limited or uncertain access to adequate food. 1  In 2020, 13.8 million households were food insecure at some time during the year. 2 Food insecurity does not necessarily cause hunger, i but hunger is a possible outcome of food insecurity. 3

The United States Department of Agriculture (USDA) divides food insecurity into the following 2 categories: 1

• Low food security : “Reports of reduced quality, variety, or desirability of diet. Little or no indication of reduced food intake.”
• Very low food security : “Reports of multiple indications of disrupted eating patterns and reduced food intake.”

Food insecurity may be long term or temporary. 4 , 5 , 6  It may be influenced by a number of factors, including income, employment, race/ethnicity, and disability. The risk for food insecurity increases when money to buy food is limited or not available. 7 , 8 , 9 , 10 , 11  In 2020, 28.6 percent of low-income households were food insecure, compared to the national average of 10.5 percent. 2  Unemployment can also negatively affect a household’s food security status. 10  High unemployment rates among low-income populations make it more difficult to meet basic household food needs. 10  In addition, children with unemployed parents have higher rates of food insecurity than children with employed parents. 12  Disabled adults may be at a higher risk for food insecurity due to limited employment opportunities and health care-related expenses that reduce the income available to buy food. 13 , 14  Racial and ethnic disparities exist related to food insecurity. In 2020, Black non-Hispanic households were over 2 times more likely to be food insecure than the national average (21.7 percent versus 10.5 percent, respectively). Among Hispanic households, the prevalence of food insecurity was 17.2 percent compared to the national average of 10.5 percent. 2 Potential factors influencing these disparities may include neighborhood conditions, physical access to food, and lack of transportation.

Neighborhood conditions may affect physical access to food. 15  For example, people living in some urban areas, rural areas, and low-income neighborhoods may have limited access to full-service supermarkets or grocery stores. 16  Predominantly Black and Hispanic neighborhoods may have fewer full-service supermarkets than predominantly White and non-Hispanic neighborhoods. 17  Convenience stores may have higher food prices, lower-quality foods, and less variety of foods than supermarkets or grocery stores. 16 , 18  Access to healthy foods is also affected by lack of transportation and long distances between residences and supermarkets or grocery stores. 16

Residents are at risk for food insecurity in neighborhoods where transportation options are limited, the travel distance to stores is greater, and there are fewer supermarkets. 16  Lack of access to public transportation or a personal vehicle limits access to food. 16  Groups who may lack transportation to healthy food sources include those with chronic diseases or disabilities, residents of rural areas, and some racial/ethnicity groups. 15 , 16 , 19  A study in Detroit found that people living in low-income, predominantly Black neighborhoods travel an average of 1.1 miles farther to the closest supermarket than people living in low-income predominantly White neighborhoods. 20

Adults who are food insecure may be at an increased risk for a variety of negative health outcomes and health disparities. For example, a study found that food-insecure adults may be at an increased risk for obesity. 21  Another study found higher rates of chronic disease in low-income, food-insecure adults between the ages of 18 years and 65 years. 22  Food-insecure children may also be at an increased risk for a variety of negative health outcomes, including obesity. 23 , 24 , 25 They also face a higher risk of developmental problems compared with food-secure children. 12 , 25 , 26  In addition, reduced frequency, quality, variety, and quantity of consumed foods may have a negative effect on children’s mental health. 27

Food assistance programs, such as the National School Lunch Program (NSLP); the Women, Infants, and Children (WIC) program; and the Supplemental Nutrition Assistance Program (SNAP), address barriers to accessing healthy food. 28 , 29 , 30 , 31 Studies show these programs may reduce food insecurity. 29 , 30 , 31  More research is needed to understand food insecurity and its influence on health outcomes and disparities. Future studies should consider characteristics of communities and households that influence food insecurity. 32  This additional evidence will facilitate public health efforts to address food insecurity as a social determinant of health.

i  The term hunger refers to a potential consequence of food insecurity. Hunger is discomfort, illness, weakness, or pain caused by prolonged, involuntary lack of food.

U.S. Department of Agriculture, Economic Research Service. (n.d.). Definitions of food security . Retrieved March 10, 2022, from https://www.ers.usda.gov/topics/food-nutrition-assistance/food-security-in-the-u-s/definitions-of-food-security/

U.S. Department of Agriculture, Economic Research Service. (n.d.). Key statistics & graphics. Retrieved March 10, 2022, from https://www.ers.usda.gov/topics/food-nutrition-assistance/food-security-in-the-us/key-statistics-graphics.aspx

Carlson, S. J., Andrews, M. S., & Bickel, G. W. (1999). Measuring food insecurity and hunger in the United States: Development of a national benchmark measure and prevalence estimates. Journal of Nutrition, 129 (2S Suppl), 510S–516S. doi:  10.1093/jn/129.2.510S

Jones, A. D., Ngure, F. M., Pelto, G., & Young, S. L. (2013). What are we assessing when we measure food security? A compendium and review of current metrics. Advances in Nutrition, 4(5), 481–505.

Food and Agriculture Organization. (2008). An introduction to the basic concepts of food security . Food Security Information for Action Practical Guides. EC–FAO Food Security Programme.

Nord, M., Andrews, M., & Winicki, J. (2002). Frequency and duration of food insecurity and hunger in U.S. households. Journal of Nutrition Education and Behavior, 34 (4), 194–201.

Sharkey, J. R., Johnson, C. M., & Dean, W. R. (2011). Relationship of household food insecurity to health-related quality of life in a large sample of rural and urban women. Women & Health, 51 (5), 442–460.

Seefeldt, K. S., & Castelli, T. (2009). Low-income women’s experiences with food programs, food spending, and food-related hardships (no. 57) . USDA Economic Research Service. https://www.ers.usda.gov/publications/pub-details/?pubid=84306

Nord, M., Andrews, M., & Carlson, S. (2007). Measuring food security in the United States: household food security in the United States, 2001. Economic Research Report (29).

Nord, M. (2007). Characteristics of low-income households with very low food security: An analysis of the USDA GPRA food security indicator. USDA-ERS Economic Information Bulletin (25).

Klesges, L. M., Pahor, M., Shorr, R. I., Wan, J. Y., Williamson, J. D., & Guralnik, J. M. (2001). Financial difficulty in acquiring food among elderly disabled women: Results from the Women’s Health and Aging Study. American Journal of Public Health, 91 (1), 68.

Nord, M. (2009). Food insecurity in households with children: Prevalence, severity, and household characteristics. USDA-ERS Economic Information Bulletin (56).

Coleman-Jensen, A., & Nord, M. (2013). Food insecurity among households with working-age adults with disabilities. USDA-ERS Economic Research Report (144).

Huang, J., Guo, B., & Kim, Y. (2010). Food insecurity and disability: Do economic resources matter? Social Science Research, 39 (1), 111–124.

Zenk, S. N., Schulz, A. J., Israel, B. A., James, S. A., Bao, S., & Wilson, M. L. (2005). Neighborhood racial composition, neighborhood poverty, and the spatial accessibility of supermarkets in metropolitan Detroit. American Journal of Public Health, 95 (4), 660–667.

Ploeg, M. V., Breneman, V., Farrigan, T., Hamrick, K., Hopkins, D., Kaufman, P., Lin, B.-H., Nord, M., Smith, T. A., Williams, R., Kinnison, K., Olander, C., Singh, A., & Tuckermanty, E. (n.d.). Access to affordable and nutritious food-measuring and understanding food deserts and their consequences: Report to congress. Retrieved March 10, 2022, from http://www.ers.usda.gov/publications/pub-details/?pubid=42729

Powell, L. M., Slater, S., Mirtcheva, D., Bao, Y., & Chaloupka, F. J. (2007). Food store availability and neighborhood characteristics in the United States. Preventive Medicine, 44 (3), 189–195.

Crockett, E. G., Clancy, K. L., & Bowering, J. (1992). Comparing the cost of a thrifty food plan market basket in three areas of New York State. Journal of Nutrition Education, 24 (1), 71S–78S.

Seligman, H. K., Laraia, B. A., & Kushel, M. B. (2010). Food insecurity is associated with chronic disease among low-income NHANES participants. Journal of Nutrition, 140 (2), 304–310.

Zenk, S. N., Schulz, A. J., Israel, B. A., James, S. A., Bao, S., & Wilson, M. L. (2005). Neighborhood racial composition, neighborhood poverty, and the spatial accessibility of supermarkets in metropolitan Detroit. American Journal of Public Health , 95(4), 660–667.

Hernandez, D. C., Reesor, L. M., & Murillo, R. (2017). Food insecurity and adult overweight/obesity: Gender and race/ethnic disparities. Appetite, 117, 373–378.

Gregory, C. A., & Coleman-Jensen, A. (n.d.). Food insecurity, chronic disease, and health among working-age adults . Retrieved March 10, 2022, from http://www.ers.usda.gov/publications/pub-details/?pubid=84466

Gundersen, C., & Kreider, B. (2009). Bounding the effects of food insecurity on children’s health outcomes. Journal of Health Economics , 28 (5), 971–983.

Metallinos-Katsaras, E., Must, A., & Gorman, K. (2012). A longitudinal study of food insecurity on obesity in preschool children. Journal of the Academy of Nutrition and Dietetics, 112 (12), 1949–1958.

Cook, J. T., & Frank, D. A. (2008). Food security, poverty, and human development in the United States. Annals of the New York Academy of Sciences, 1136 (1), 193–209.

Cook, J. T. (2013, April). Impacts of child food insecurity and hunger on health and development in children: Implications of measurement approach. In Paper commissioned for the Workshop on Research Gaps and Opportunities on the Causes and Consequences of Child Hunger.

Burke, M. P., Martini, L. H., Çayır, E., Hartline-Grafton, H. L., & Meade, R. L. (2016). Severity of household food insecurity is positively associated with mental disorders among children and adolescents in the United States. Journal of Nutrition , 146(10), 2019–2026.

Bhattarai, G. R., Duffy, P. A., & Raymond, J. (2005). Use of food pantries and food stamps in low‐income households in the United States. Journal of Consumer Affairs , 39(2), 276–298.

Huang, J., & Barnidge, E. (2016). Low-income children's participation in the National School Lunch Program and household food insufficiency. Social Science & Medicine, 150 , 8–14.

Kreider, B., Pepper, J. V., & Roy, M. (2016). Identifying the effects of WIC on food insecurity among infants and children. Southern Economic Journal, 82 (4), 1106–1122.

Ratcliffe, C., McKernan, S. M., & Zhang, S. (2011). How much does the Supplemental Nutrition Assistance Program reduce food insecurity? American Journal of Agricultural Economics, 93 (4), 1082–1098.

Larson, N. I., & Story, M. T. (2011). Food insecurity and weight status among U.S. children and families: A review of the literature. American Journal of Preventive Medicine, 40 (2), 166–173.

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Recognizing and tackling a global food crisis

Globally, over 200 million people are facing emergency and famine conditions.

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This year, acute food insecurity is projected to reach a new peak, surpassing the food crisis experienced in 2007-2008. A combination of factors—including greater poverty and supply chain disruptions in the wake of the COVID-19 pandemic, the war in Ukraine, rising inflation, and high commodity prices—has increased food and nutrition insecurity. This is a multifaceted crisis, affecting access to and availability of food, with long-term consequences for health and productivity. The World Bank has scaled up its efforts to bolster food security, reduce risks, and strengthen food systems over the short and long term. Urgent action is needed across governments and multilateral partners to avert a severe and prolonged food crisis.

Declining food access and availability, with high risks

For most countries, domestic food prices have risen sharply in 2022, compromising access to food—particularly for low-income households, who spend the majority of their incomes on food and are especially vulnerable to food price increases. Higher food inflation followed a sharp spike in global food commodity prices, exacerbated by the war in Ukraine. Average global wheat, maize, and rice prices were respectively 18 percent, 27 percent, and 10 percent higher in October 2022 relative to October 2021.

At the same time, food availability is declining. For the first time in a decade, global cereal production will fall in 2022 relative to 2021. More countries are relying on existing food stocks and reserves to fill the gap, raising the risk if the current crisis persists. And rising energy and fertilizer prices—key inputs to produce food—threaten production for the next season, especially in net fertilizer-importing countries and regions like East Africa.

These trends are already affecting health. Stunting and wasting in children, and anaemia in pregnant women, are increasing as households are less able to include sufficient nutrition in their diets. A recent World Bank survey indicated that 42 percent of households across all countries covered were unable to eat healthy or nutritious food in the previous 30 days. These health effects carry long-term consequences for the ability to learn and work, and therefore escape poverty.

Globally, food security is under threat beyond just the immediate crisis. Growing public debt burdens, currency depreciation, higher inflation, increasing interest rates, and the rising risk of a global recession may compound access to and availability of food, especially for importing countries. At the same time, the agricultural food sector is both vulnerable and a contributor to climate change, responsible for one-third of global greenhouse gas emissions. And agricultural productivity growth is not staying ahead of the impacts of climate change, contributing to more food-related shocks. For example, an unprecedented multi-season drought has worsened food insecurity in the Horn of Africa, with Somalia on the verge of famine.

Managing the crisis and preparing for the future

The World Bank is responding to this escalating crisis with four areas of actions: (i) supporting production and producers, (ii) facilitating increased trade in food and production inputs, (iii) supporting vulnerable households, and (iv) investing in sustainable food security. It has made over $26 billion available for short- and long-term food security interventions in 69 countries, including active interventions in 22 of the 24 hunger hotspots identified as countries with the most pressing needs by the Food and Agriculture Organization and the World Food Programme. Since April 2022, the World Bank has disbursed $8.1 billion, approximately evenly split between crisis response and long-term resilience projects. In the short term, projects like the Emergency Project to Combat the Food Crisis in Cameroon will provide 98,490 beneficiaries with emergency food and nutrition assistance with support from the World Food Programme. In addition to supporting vulnerable households, governments of food-exporting countries can improve global food security by limiting measures like export bans and stockpiling of food. In the longer term, governments can make an enormous difference by repurposing public spending on agricultural policies and support for a more resilient and sustainable food system that directly improves health, economies, and the planet.

These actions and newly released funding underline the scale of the crisis. Timely, coordinated, and sustained action through partnerships such as the Global Alliance on Food Security can maximize the impact of new policies and funding, and mitigate the scale of the crisis. The time to act is now.

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Feeding America Highlights Public/Private Partnership That Helped Mitigate Food Insecurity in 2020

USDA’s Household Food Security Report for 2020 Shows Overall Food Insecurity Remained Steady in 2020, Though Key Population Subgroups Experienced Increases in Hardship

Last year, the COVID-19 pandemic created a hunger crisis that pushed at least 60 million people to turn to food banks, food pantries, and other private charitable food programs for help, according to an estimate by Feeding America. Additionally, bipartisan legislation provided support for federal nutrition programs and food banks. With the release today of the Household Food Security in the United States in 2020 report by the US Department of Agriculture (USDA), which showed that overall food insecurity rates did not increase in 2020 from the previous year, we now know that this massive public/private response to hunger during the pandemic was successful in mitigating food insecurity rates across the country.

“Today’s report from the USDA found that over 38 million people, including over 11 million children, experienced food insecurity in 2020. 38 million people facing hunger is far too many, but without the unprecedented response from food banks and government nutrition programs, that number would have been far larger,” said Claire Babineaux-Fontenot, CEO of Feeding America. “Last year, over 60 million people turned to the charitable food sector for support, and thanks to our community volunteers, donors, and dedicated staff, we distributed over 6 billion meals to people in need.”

While food insecurity levels from 2019 to 2020 remained flat overall, food insecurity improved for some sub-populations while worsening for others. In particular, the report reveals a deepening divide across racial and ethnic lines. An estimated 24.0% of Black individuals experienced food insecurity in 2020, up from 19.2% in 2019. For Latino individuals, there was an increase from 15.8% in 2019 to 19.3% in 2020. Compared to white individuals, Black individuals were 3.2 times more likely and Latino individuals were 2.5 times more likely to experience food insecurity. Additionally, food insecurity in 2020 increased for households with children and for people living in the South. While food insecurity in rural areas in 2020 remains higher than in urban areas, food insecurity levels increased in urban areas, especially in principal cities that were more affected by economic closures in 2020.

“People all over the country, in every county, parish, and borough, make difficult choices when it comes to food insecurity. Often, the choice is between paying for food or buying medicine, keeping the lights on, or even childcare. It’s also true that some communities, including rural communities and communities of color, make those choices and face hunger at disproportionately higher rates. In fact, today’s USDA food security report shows that the disparities are widening,” Babineaux-Fontenot added. “I believe that together we can work to end hunger in this county for everyone. We have the resources and we know what works. Let’s choose an America where no one is hungry.”

As part of the charitable food sector, the Feeding America network of food banks distributed more than six billion meals in 2020, an increase of 44% from the prior year. This record-breaking meal distribution was aided by the federal government through USDA foods, which provided 2.4 billion meals to people served by the Feeding America network in 2021. More importantly, the broader federal resources provided in 2020 through SNAP emergency allotments, child nutrition, and SNAP program waivers and expanded eligibility, Pandemic EBT, and other assistance helped temper food insecurity levels. Still, these measures are temporary; long-term expansions to the federal safety net will require additional action by Congress. 

Babineaux-Fontenot continued, “We are at a pivotal moment in the anti-hunger movement. If we can sustain these efforts, such as the update to the Thrifty Food Plan, and extend certain federal interventions, including the Summer EBT program and the Child Tax Credit, we can continue to be there for our neighbors facing hunger so that one day in the near future, we can achieve food security for everyone in the US.”

To learn more about Feeding America’s hunger-relief efforts, visit feedingamerica.org .

Please contact one of our media representatives or call 800-771-2303

About Feeding America

Feeding America® is the largest hunger-relief organization in the United States. Through a network of more than 200 food banks, 21 statewide food bank associations, and over 60,000 partner agencies, food pantries and meal programs, we helped provide 5.3 billion meals to tens of millions of people in need last year. Feeding America also supports programs that prevent food waste and improve food security among the people we serve; brings attention to the social and systemic barriers that contribute to food insecurity in our nation; and advocates for legislation that protects people from going hungry. Visit www.feedingamerica.org , find us on Facebook or follow us on X.com .

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Food Accessibility, Insecurity and Health Outcomes

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Go to food and nutrition insecurity scientific resources

Go to food accessibility and insecurity as an SDOH

Go to NIMHD food accessibility/insecurity resources

Having access to nutritious food is a basic human need.

• Food security means having access to enough food for an active, healthy life.
• Nutrition security means consistent access, availability, and affordability of foods and beverages that promote well-being, prevent disease, and, if needed, treat disease.

On the other hand, food and nutrition insecurity is an individual-, household-, and neighborhood-level economic and social condition describing limited or uncertain access to adequate and affordable nutritious foods and is a major public health concern.

Food insecurity and the lack of access to affordable nutritious food are associated with increased risk for multiple chronic health conditions such as diabetes , obesity, heart disease, mental health disorders and other chronic diseases . In 2020, almost 15% of U.S. households were considered food insecure at some point in time, meaning not all household members were able to access enough food to support active, healthy lifestyles. In nearly half of these households, children were also food insecure ( see chart above ), which has implications for human development and school experience . Food insecurity disproportionately affects persons from racial and ethnic minority and socioeconomically disadvantaged populations:

• 20% of Black/African American households were food insecure at some point in 2021 , as were 16% of Hispanic/Latino households when compared to 7% of White households.
• Food insecurity for U.S. Hispanic/Latino adults differs by origin. Current national data is not available but from 2011-2014 food insecurity was highest among those identifying from Puerto Rico (25.3%), followed by Mexico (20.8%) Central and South America (20.7%) and Cuba (12.1%).
• In the past 20 years, American Indian/Alaskan Native (AI/AN) households have also been at least twice as likely to have experienced food insecurity when compared with White households, often exceeding rates of 25% across different regions and AI/AN communities.
• Native Hawaiian and Pacific Islander (NHPI) adults also experience a high food insecurity prevalence (20.5%) and had significantly higher odds of experiencing low and very low food security compared with White households.
• While national data on specific Asian American national origin populations is not readily available, among Asian Americans living in California from 2001-2012 , food insecurity was highest among Vietnamese households (16.4%), followed by Filipino (8.3%), Chinese (7.6%), Korean (6.7%), South Asian (3.14%), and Japanese households (2.3%), highlighting considerable variation across Asian American communities.
• Food insecurity is inextricably linked to poverty , with 35.3 % of households with incomes below the federal poverty line being food insecure.
• Although the graph below on national trends in food insecurity does not capture the full impact of the COVID-19 pandemic, food insecurity is likely to increase, and racial and ethnic disparities in food insecurity experiences could worsen.

Healthy food accessibility and insecurity is a social determinant of health.

Food and nutrition insecurity are predominantly influenced by the local environment, including surrounding neighborhood infrastructure, accessibility, and affordability barriers. Access to grocery stores that carry healthy food options (such as fresh fruit, vegetables, low-fat fish and poultry) are not located equitably across residential and regional areas in the United States.

Areas that lack access to affordable, healthy foods are known as food deserts . Food deserts are:

• Found in urban or suburban neighborhoods that lack grocery stores (supermarkets or small grocery stores) that offer healthy food options.
• Found in rural areas and neighborhoods where the nearest grocery stores are too far away to be convenient or accessible.
• More prevalent in neighborhoods that are comprised of a majority of racial or ethnic minority residents or in rural AI/AN communities.
• More likely found in areas with a higher percentage of residents experiencing poverty , regardless of urban or rural designation.

Urban, suburban, and rural areas can also be overwhelmed with stores that sell unhealthy calorie-dense and inexpensive junk foods, including soda, snacks, and other high sugar foods. This is known as a food swamp . Food swamps:

• Reduce access to nutritional foods and provide easier access to unhealthy foods .
• Are a predictor of obesity , particularly in communities where residents have limited access to their own or public transportation and experience the greatest income inequality.

Reducing food and nutrition insecurity in the U.S. will require a multifaceted approach that considers, among other possibilities:

• Strategies that engage communities in local health programs; for example, recruiting community partners to assist in addressing gaps between food access and intake .
• Interventions that utilize federal food and nutritional supplemental programs, including the Supplemental Nutrition Assistance Program ( SNAP ) and the Special Supplemental Nutritional Program for Women, Infants, and Children ( WIC ).
• Leveraging local and federal policies targeting food insecurity; for example, retail store interventions , where healthy food placement, promotion and price influence healthier choices; sweetened beverage taxes to reduce the purchase appeal to consumers; and junk food taxes balanced with removal of taxes on water and fruits and vegetables.

NIMHD is studying and addressing issues related to food and nutrition insecurity through a variety of initiatives:

NIH Publication

Research Opportunities to Address Nutrition Insecurity and Disparities Coauthored by Shannon N. Zenk, Lawrence A. Tabak and Eliseo J. Pérez-Stable, JAMA 2022

NIMHD Events on Food Insecurity

Food Insecurity, Neighborhood Food Environment, and Nutrition Health Disparities: State of the Science NIMHD co-sponsored this September 2021 workshop led by the NIH Office of Nutrition Research and its Nutrition and Health Disparities Implementation Working Group

NIMHD Hosts Senior Research Investigators to Present on Food Insecurity and Related Topics:

NIMHD co-sponsored the November 2020 virtual workshop NIH Rural Health Seminar: Challenges in the Era of COVID-19 . This workshop focused on long-standing health disparities and social inequities experienced by rural populations, and featured experts on food insecurity, including:

• Dr. Brenda Eskenazi , Professor in Maternal and Child Health and Epidemiology, Brian and Jennifer Maxwell Endowed Chair in Public Health and Director of the Center for Environmental Research and Children’s Health, University of California, Berkeley, who spoke on the topic of COVID-19 and the impact on health of Californian farmworkers .
• Dr. Alice Ammerman , Mildred Kaufman Distinguished Professor of Nutrition, Director, Center for Health Promotion and Disease Prevention, University of North Carolina, who spoke on interventions to address job loss and food security in rural communities during COVID .

NIMHD Content on Food Insecurity

Nimhd science visioning and research strategies.

As part of the Scientific Visioning Research Process , NIMHD developed a set of 30 strategies to transform minority health and health disparities research . Several of these strategies focus on issues related to food security and accessibility, including:

• Assessing how environment and neighborhood structures such as areas where people have limited access to a variety of healthy and affordable foods (or food deserts) influence health behaviors.
• Promoting multi-sectoral interventions that address the structural drivers of food deserts.
• Promoting interventions that address the social determinants of health within health care systems, including food insecurity.

Publications

Food Insecurity and Obesity: Research Gaps, Opportunities, and Challenges Dr. Derrick Tabor, NIMHD Program Officer, co-authored “Food insecurity and obesity: research gaps, opportunities” in Translational Behavioral Medicine . This review highlights NIH funding for grants related to food insecurity and obesity, identifies research gaps, and presents upcoming research opportunities to better understand the health impact of food insecurity.

NIMHD Research Framework

Native Hawaiian Health Adaptation The NIMHD Research Framework was adapted by Keawe’aimoku Kaholokula, Ph.D., University of Hawai’i at Mānoa, to reflect social and cultural influences of Native Hawaiian health . Ka Mālama Nohona (nurturing environments) to support Native Hawaiian health include strategic goals of food sovereignty and security to promote a strong foundation for healthy living.

NIMHD Articles

NIMHD Research Features

• The Osage Community Supported Agriculture Program: A Tribal Nation’s Effort Toward Food Security and Food Sovereignty
• The Navajo Nation Junk Food Tax and the Path to Food Sovereignty
• Fighting Cancer—and Reducing Disparities—Through Food Policy
• Fresh Food for the Osage Nation: Researchers and a Native Community Work Toward Improved Food Resources and Food Sovereignty

NIMHD Insights Blog

• Amplifying the Voice of Native Hawaiian and Pacific Islander Communities Amid the COVID-19 Crisis by Joseph Keawe‘aimoku Kaholokula, Ph.D.
• Racism and the Health of Every American by NIMHD Director Eliseo J. Pérez-Stable, M.D.
• The Future of Minority Health and Health Disparities Research by Tany Agurs-Collins, Ph.D., R.D., and Susan Persky, Ph.D.
• Addressing Social Needs and Structural Inequities to Reduce Health Disparities: A Call to Action for Asian American and Pacific Islander Heritage Month by Marshall H. Chin, M.D., M.P.H.
• “Insights” on Simulation Modeling and Systems Science, New Research Funding Opportunity by Xinzhi Zhang, M.D., Ph.D.

NIMHD Funding Resources and Opportunities

Funding opportunity announcements (foas).

NIMHD supports many FOAs that include topics related to food security as an area of research interest:

• Request for Information: Food Is Medicine Research Opportunities
• Notice of Special Interest: Stimulating Research to Understand and Address Hunger, Food and Nutrition Insecurity
• Community Level Interventions to Improve Minority Health and Reduce Health Disparities (R01 Clinical Trial Optional)
• Addressing Health Disparities Among Immigrant Populations through Effective Interventions (R01 Clinical Trial Optional)
• Health Services Research on Minority Health and Health Disparities (R01 Clinical Trial Optional)
• Long-Term Effects of Disasters on Health Care Systems Serving Populations Experiencing Health Disparities (R01 Clinical Trial Optional)
• Please see our list of Active NIMHD Funding Opportunities for more.

NIMHD-Supported Research Projects

See a list of active NIMHD-supported research projects studying food security and related topics .

NIMHD-Supported, NIH-Wide Initiatives

The PhenX Toolkit provides recommended and established data collection protocols for conducting biomedical research. There are PhenX protocols available for assessing and understanding food insecurity and food swamps .

The Strategic Plan for NIH Nutrition Research is the first NIH-wide strategic plan for nutrition research that highlights crosscutting, innovative opportunities to advance nutrition research from basic science to experimental design to research training. The plan emphasizes the need for studies on minority health and nutrition-related health disparities research.

Additional Resources and Data

The Centers for Disease Control’s Healthier Food Environments: Improving Access to Healthier Foods discusses CDC efforts to improve food access within the community.

The United States Department of Agriculture has two databases that compile national data on food accessibility:

• The Food Access Research Atlas provides food access data for populations within census tracts and mapped overviews of food access for low-income communities.
• The Food Environment Atlas assembles statistics on food environment indicators to stimulate research on the determinants of food choice and diet quality.

The Healthy People initiative provides 10-year, measurable public health objectives and useful tools to help track progress. The Healthy People 2030 includes food insecurity as a social determinant of health.

Page updated July 3, 2024

Page updated February 24, 2023

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Global Report on Food Crises (GRFC) 2024

Published by the Food Security Information Network (FSIN) in support of the Global Network against Food Crises (GNAFC), the GRFC 2024 is the reference document for global, regional and country-level acute food insecurity in 2023. The report is the result of a collaborative effort among 16 partners to achieve a consensus-based assessment of acute food insecurity and malnutrition in countries with food crises and aims to inform humanitarian and development action.  

FSIN and Global Network Against Food Crises. 2024. GRFC 2024 . Rome.

When citing this report online please use this link:

https://www.fsinplatform.org/report/global-report-food-crises-2024/

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Food security and nutrition and sustainable agriculture

Related sdgs, end hunger, achieve food security and improve ....

Description

Publications.

As the world population continues to grow, much more effort and innovation will be urgently needed in order to sustainably increase agricultural production, improve the global supply chain, decrease food losses and waste, and ensure that all who are suffering from hunger and malnutrition have access to nutritious food. Many in the international community believe that it is possible to eradicate hunger within the next generation, and are working together to achieve this goal.

World leaders at the 2012 Conference on Sustainable Development (Rio+20) reaffirmed the right of everyone to have access to safe and nutritious food, consistent with the right to adequate food and the fundamental right of everyone to be free from hunger. The UN Secretary-General’s Zero Hunger Challenge launched at Rio+20 called on governments, civil society, faith communities, the private sector, and research institutions to unite to end hunger and eliminate the worst forms of malnutrition.

The Zero Hunger Challenge has since garnered widespread support from many member States and other entities. It calls for:

• Zero stunted children under the age of two
• 100% access to adequate food all year round
• All food systems are sustainable
• 100% increase in smallholder productivity and income
• Zero loss or waste of food

The Sustainable Development Goal to “End hunger, achieve food security and improved nutrition and promote sustainable agriculture” (SDG2) recognizes the inter linkages among supporting sustainable agriculture, empowering small farmers, promoting gender equality, ending rural poverty, ensuring healthy lifestyles, tackling climate change, and other issues addressed within the set of 17 Sustainable Development Goals in the Post-2015 Development Agenda.

Beyond adequate calories intake, proper nutrition has other dimensions that deserve attention, including micronutrient availability and healthy diets. Inadequate micronutrient intake of mothers and infants can have long-term developmental impacts. Unhealthy diets and lifestyles are closely linked to the growing incidence of non-communicable diseases in both developed and developing countries.

Adequate nutrition during the critical 1,000 days from beginning of pregnancy through a child’s second birthday merits a particular focus. The Scaling-Up Nutrition (SUN) Movement has made great progress since its creation five years ago in incorporating strategies that link nutrition to agriculture, clean water, sanitation, education, employment, social protection, health care and support for resilience.

Extreme poverty and hunger are predominantly rural, with smallholder farmers and their families making up a very significant proportion of the poor and hungry. Thus, eradicating poverty and hunger are integrally linked to boosting food production, agricultural productivity and rural incomes.

Agriculture systems worldwide must become more productive and less wasteful. Sustainable agricultural practices and food systems, including both production and consumption, must be pursued from a holistic and integrated perspective.

Land, healthy soils, water and plant genetic resources are key inputs into food production, and their growing scarcity in many parts of the world makes it imperative to use and manage them sustainably. Boosting yields on existing agricultural lands, including restoration of degraded lands, through sustainable agricultural practices would also relieve pressure to clear forests for agricultural production. Wise management of scarce water through improved irrigation and storage technologies, combined with development of new drought-resistant crop varieties, can contribute to sustaining drylands productivity.

Halting and reversing land degradation will also be critical to meeting future food needs. The Rio+20 outcome document calls for achieving a land-degradation-neutral world in the context of sustainable development. Given the current extent of land degradation globally, the potential benefits from land restoration for food security and for mitigating climate change are enormous. However, there is also recognition that scientific understanding of the drivers of desertification, land degradation and drought is still evolving.

There are many elements of traditional farmer knowledge that, enriched by the latest scientific knowledge, can support productive food systems through sound and sustainable soil, land, water, nutrient and pest management, and the more extensive use of organic fertilizers.

An increase in integrated decision-making processes at national and regional levels are needed to achieve synergies and adequately address trade-offs among agriculture, water, energy, land and climate change.

Given expected changes in temperatures, precipitation and pests associated with climate change, the global community is called upon to increase investment in research, development and demonstration of technologies to improve the sustainability of food systems everywhere. Building resilience of local food systems will be critical to averting large-scale future shortages and to ensuring food security and good nutrition for all.

State of Food Security and Nutrition in the World 2020

Updates for many countries have made it possible to estimate hunger in the world with greater accuracy this year. In particular, newly accessible data enabled the revision of the entire series of undernourishment estimates for China back to 2000, resulting in a substantial downward shift of the seri...

Food and Agriculture

Our planet faces multiple and complex challenges in the 21st century. The new 2030 Agenda for Sustainable Development commits the international community to act together to surmount them and transform our world for today’s and future generations....

Food Security and Nutrition in Small Island Developing States (SIDS)

The outcome document of Rio+20, “The Future We Want” (United Nations Conference on Sustainable Development, June 2012) acknowledged that SIDS remains a special case for sustainable development. Building on the Barbados Programme of Action and the Mauritius Strategy, the document calls for the conv...

Global Blue Growth Initiative and Small Island Developing States (SIDS)

Three-quarters of the Earth’s surface is covered by oceans and seas which are an engine for global economic growth and a key source of food security. The global ocean economic activity is estimated to be USD 3–5 trillion. Ninety percent of global trade moves by marine transport. Over 30 percent of g...

FAO Strategy for Partnerships with Civil Society Organizations

The Food and Agriculture Organization of the United Nations (FAO) is convinced that hunger and malnutrition can be eradicated in our lifetime. To meet the Zero Hunger Challenge, political commitment and major alliances with key stakeholders are crucial. Only through effective collaboration with go...

FAO and the 17 Sustainable Development Goals

The Sustainable Development Goals offer a vision of a fairer, more prosperous, peaceful and sustainable world in which no one is left behind. In food - the way it is grown, produced, consumed, traded, transported, stored and marketed - lies the fundamental connection between people and the planet, ...

FAO Strategy for Partnerships with the Private Sector

The fight against hunger can only be won in partnership with governments and other non-state actors, among which the private sector plays a fundamental role. FAO is actively pursuing these partnerships to meet the Zero Hunger Challenge together with UN partners and other committed stakeholders. We ...

Emerging Issues for Small Island Developing States

The 2012 UNEP Foresight Process on Emerging Global Environmental Issues primarily identified emerging environmental issues and possible solutions on a global scale and perspective. In 2013, UNEP carried out a similar exercise to identify priority emerging environmental issues that are of concern to ...

Transforming our World: The 2030 Agenda for Sustainable Development

This Agenda is a plan of action for people, planet and prosperity. It also seeks to strengthen universal peace in larger freedom, We recognize that eradicating poverty in all its forms and dimensions, including extreme poverty, is the greatest global challenge and an indispensable requirement for su...

Farmer’s organizations in Bangladesh: a mapping and capacity assessment

Farmers’ organizations (FOs) in Bangladesh have the potential to be true partners in, rather than “beneficiaries” of, the development process. FOs bring to the table a deep knowledge of the local context, a nuanced understanding of the needs of their communities and strong social capital. Increasing...

Good practices in building innovative rural institutions to increase food security

Continued population growth, urbanization and rising incomes are likely to continue to put pressure on food demand. International prices for most agricultural commodities are set to remain at 2010 levels or higher, at least for the next decade (OECD-FAO, 2010). Small-scale producers in many developi...

The State of Food Insecurity in the World

When the 69th United Nations General Assembly begins its General Debate on 23 September 2014, 464 days will remain to the end of 2015, the target date for achieving the Millennium Development Goals (MDG). A stock-taking of where we stand on reducing hunger and malnutrition shows that progress in hu...

2024 SDG Global Business Forum

 The 2024 SDG Global Business Forum will take place virtually as a special event alongside the 2024 High-Level Political Forum on Sustainable Development (HLPF), the United Nations central platform for the follow-up and review of the SDGs. The Forum will place special emphasis on the SDGs under

Expert Group Meeting on SDG2 and its interlinkages with other SDGs

The theme of the 2024 High-Level Political Forum (HLPF) is “Reinforcing the 2030 Agenda and eradicating poverty in times of multiple crises: the effective delivery of sustainable, resilient and innovative solutions”. The 2024 HLPF will have an in-depth review of Sustainable Development Goa

Expert Group Meetings for 2024 HLPF Thematic Review

The theme of the 2024 High Level Political Forum (HLPF) is “Reinforcing the 2030 Agenda and eradicating poverty in times of multiple crisis: the effective delivery of sustainable, resilient and innovative solutions”. The 2024 HLPF will have an in-depth review of SDG 1 on No Poverty, SDG 2 on Zero Hu

International Workshop on “Applications of Juncao Technology and its contribution to alleviating poverty, promoting employment and protecting the environment”

According to the United Nations Food Systems Summit that was held in 2021, many of the world’s food systems are fragile and not fulfilling the right to adequate food for all. Hunger and malnutrition are on the rise again. According to FAO’s “The State of Food Security and Nutrition in the World 2023

Second Regional Workshop on “Applications of Juncao Technology and its Contribution to the Achievement of Sustainable Agriculture and the Sustainable Development Goals in Africa” 18 - 19 December 2023

Ⅰ. Purpose of the Workshop At the halfway point of the 2030 Agenda for Sustainable Development, the application of science and technology in developing sustainable agricultural practices has the potential to accelerate transformative change in support of the Sustainable Development Goals. In that r

The State of Food Security and Nutrition in the World (SOFI) 2023 Launch

On 12 July 2023 from 10 AM to 12 PM (EDT), FAO and its co-publishing partners will be launching, for the fifth time, the State of Food Security and Nutrition in the World (SOFI) report at a Special Event in the margins of the ECOSOC High-Level Political Forum (HLPF). The 2023 edition

The State of Food Security and Nutrition in the World 2022 (SOFI) Launch

The State of Food Security and Nutrition in the World is an annual flagship report to inform on progress towards ending hunger, achieving food security and improving nutrition and to provide in-depth analysis on key challenges for achieving this goal in the context of the 2030 Agenda for Sustainable

The State of Food Security and Nutrition in the World 2021 (SOFI)

The State of Food Security and Nutrition in the World 2021 (SOFI 2021) report presents the first evidence-based global assessment of chronic food insecurity in the year the COVID-19 pandemic emerged and spread across the globe. The SOFI 2021 report will also focus on complementary food system solu

Committee on World Food Security (CFS 46)

Ministerial meeting on food security and climate adaptation in small island developing states.

The proposed meeting will offer SIDS Ministers and Ambassadors the opportunity to explore the implications of the SAMOA Pathway as it relates to food security and nutrition and climate change adaptation. The ultimate objective is to enhance food security, health and wellbeing in SIDS. Ministers an

• January 2015 SDG 2 SDG2 focuses on ending hunger, achieving food security and improved nutrition and promoting sustainable agriculture. In particular, its targets aims to: end hunger and ensure access by all people, in particular the poor and people in vulnerable situations, including infants, to safe, nutritious and sufficient food all year round by 2030 (2.1); end all forms of malnutrition by 2030, including achieving, by 2025, the internationally agreed targets on stunting and wasting in children under 5 years of age, and address the nutritional needs of adolescent girls, pregnant and lactating women and older persons (2.2.); double,by 2030, double the agricultural productivity and incomes of small-scale food producers, in particular women, indigenous peoples, family farmers, pastoralists and fishers, including through secure and equal access to land, other productive resources and inputs, knowledge, financial services, markets and opportunities for value addition and non-farm employment (2.3); ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production, that help maintain ecosystems, that strengthen capacity for adaptation to climate change, extreme weather, drought, flooding and other disasters and that progressively improve land and soil quality (2.4); by 2020, maintain the genetic diversity of seeds, cultivated plants and farmed and domesticated animals and their related wild species, including through soundly managed and diversified seed and plant banks at the national, regional and international levels, and promote access to and fair and equitable sharing of benefits arising from the utilization of genetic resources and associated traditional knowledge, as internationally agreed (2.5); The alphabetical goals aim to: increase investment in rural infrastructure, agricultural research and extension services, technology development and plant and livestock gene banks , correct and prevent trade restrictions and distortions in world agricultural markets as well as adopt measures to ensure the proper functioning of food commodity markets and their derivatives and facilitate timely access to market information, including on food reserves, in order to help limit extreme food price volatility.
• January 2014 Rome Decl. on Nutrition and Framework for Action The Second International Conference on Nutrition (ICN2) took place at FAO Headquarters, in Rome in November 2014. The Conference resulted in the Rome Declaration on Nutrition and the Framework for Action, a political commitment document and a flexible policy framework, respectively, aimed at addressing the current major nutrition challenges and identifying priorities for enhanced international cooperation on nutrition.
• January 2012 Future We Want (Para 108-118) In Future We Want, Member States reaffirm their commitments regarding "the right of everyone to have access to safe, sufficient and nutritious food, consistent with the right to adequate food and the fundamental right of everyone to be free from hunger". Member States also acknowledge that food security and nutrition has become a pressing global challenge. At Rio +20, the UN Secretary-General’s Zero Hunger Challenge was launched in order to call on governments, civil society, faith communities, the private sector, and research institutions to unite to end hunger and eliminate the worst forms of malnutrition.
• January 2009 UN SG HLTF on Food and Nutrition Security The UN SG HLTF on Food and Nutrition Security was established by the UN SG, Mr Ban Ki-moon in 2008 and since then has aimed at promoting a comprehensive and unified response of the international community to the challenge of achieving global food and nutrition security. It has also been responsible for building joint positions among its members around the five elements of the Zero Hunger Challenge.
• January 2002 Report World Food Summit +5 The World Food Summit +5 adopted a declaration, calling on the international community to fulfill the pledge, made at the original World Food Summit in 1996, to reduce the number of hungry people to about 400 million by 2015.
• January 2000 MDG 1 MDG 1 aims at eradicating extreme poverty and hunger. Its three targets respectively read: halve, between 1990 and 2015, the proportion of people whose income is less than $1.25 a day (1.A), achieve full and productive employment and decent work for all, including women and young people (1.B), halve, between 1990 and 2015, the proportion of people who suffer from hunger (1.C).
• January 1996 Rome Decl. on World Food Security The Summit aimed to reaffirm global commitment, at the highest political level, to eliminate hunger and malnutrition, and to achieve sustainable food security for all. Thank to its high visibility, the Summit contributed to raise further awareness on agriculture capacity, food insecurity and malnutrition among decision-makers in the public and private sectors, in the media and with the public at large. It also set the political, conceptual and technical blueprint for an ongoing effort to eradicate hunger at global level with the target of reducing by half the number of undernourished people by no later than the year 2015. The Rome Declaration defined seven commitments as main pillars for the achievement of sustainable food security for all whereas its Plan of Action identified the objectives and actions relevant for practical implementation of these seven commitments.
• January 1992 1st ICN The first International Conference on Nutrition (ICN) convened at the FAO's Headquarters in Rome to identify common strategies and methods to eradicate hunger and malnutrition. The conference was organized by the Food and Agriculture Organization (FAO) and the World Health Organization (WHO) and was attended by delegations from 159 countries as well as the European Economic Community, 16 United Nations organizations, 11 intergovernmental organizations, and 144 non-governmental organizations.
• January 1986 Creation of AGROSTAT (now FAOSTAT) Since 1986, AGROSTAT, now known as FAOSTAT, has provided cross sectional data relating to food and agriculture as well as time-series for some 200 countries.
• January 1979 1st World Food Day World Food Day is celebrated each year on 16 October to commemorate the day on which FAO was founded in 1945. Established on the occasion of FAO Twentieth General Conference held in November 1979, the first World Food Day was celebrated in 1981 and was devoted to the theme "Food Comes First".

Publications

Flagship publication, the state of food security and nutrition in the world.

JUST RELEASED

The state of food security and nutrition in the world 2024, financing to end hunger, food insecurity and malnutrition in all its forms.

SOFI 2024 provides time-critical recommendations for more efficient use of innovative financing tools, and for reforms to the food security and nutrition financing architecture. Agreement on how food security and nutrition financing is defined, along with methods for tracking, measurement and implementation, is an unmissable first step towards sustainably increasing the financing flows needed to end hunger, food insecurity and all forms of malnutrition, and to ensure access to healthy diets for all. The agendas of the Summit of the Future, scheduled in September 2024, and the Fourth International Conference on Financing for Development, due in June and July 2025, are both informed by the findings of SOFI 2024.

Digital resources

Digital report

Interactive story - Understanding food insecurity

Interactive story - Putting a number on hunger

FAO Hunger Map 2021-2023

Video - SOFI 2024

Video - Interview with FAO's Chief Economist

Previous editions, sofi editions 2023-2021.

SOFI editions 2020-2015

Transforming Food Systems for Affordable Healthy Diets

Safeguarding against economic slowdowns and downturns

Building climate resilience for food security and nutrition

SOFI editions 2014-2010

SOFI editions 2009-2004

SOFI editions 2003-1999

• Agrifood Economics
• Food and Agriculture Statistics
• Urban Food Agenda
• Press release

Technical information: Giovanni Carrasco

Media inquiries: fao newsroom.

• Research article
• Open access
• Published: 15 February 2023

The impact of food insecurity on health outcomes: empirical evidence from sub-Saharan African countries

• Sisay Demissew Beyene   ORCID: orcid.org/0000-0001-7347-4168 1  

BMC Public Health volume  23 , Article number:  338 ( 2023 ) Cite this article

29k Accesses

24 Citations

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Food insecurity adversely affects human health, which means food security and nutrition are crucial to improving people’s health outcomes. Both food insecurity and health outcomes are the policy and agenda of the 2030 Sustainable Development Goals (SDGs). However, there is a lack of macro-level empirical studies (Macro-level study means studies at the broadest level using variables that represent a given country or the whole population of a country or economy as a whole. For example, if the urban population (% of the total population) of XYZ country is 30%, it is used as a proxy variable to represent represent country's urbanization level. Empirical study implies studies that employ the econometrics method, which is the application of math and statistics.) concerning the relationship between food insecurity and health outcomes in sub-Saharan African (SSA) countries though the region is highly affected by food insecurity and its related health problems. Therefore, this study aims to examine the impact of food insecurity on life expectancy and infant mortality in SSA countries.

The study was conducted for the whole population of 31 sampled SSA countries selected based on data availability. The study uses secondary data collected online from the databases of the United Nations Development Programme (UNDP), the Food and Agricultural Organization (FAO), and the World Bank (WB). The study uses yearly balanced data from 2001 to 2018. This study employs a multicountry panel data analysis and several estimation techniques; it employs Driscoll-Kraay standard errors (DKSE), a generalized method of momentum (GMM), fixed effects (FE), and the Granger causality test.

A 1% increment in people’s prevalence for undernourishment reduces their life expectancy by 0.00348 percentage points (PPs). However, life expectancy rises by 0.00317 PPs with every 1% increase in average dietary energy supply. A 1% rise in the prevalence of undernourishment increases infant mortality by 0.0119 PPs. However, a 1% increment in average dietary energy supply reduces infant mortality by 0.0139 PPs.

Conclusions

Food insecurity harms the health status of SSA countries, but food security impacts in the reverse direction. This implies that to meet SDG 3.2, SSA should ensure food security.

Peer Review reports

Food security is essential to people’s health and well-being [ 1 ]. Further, the World Health Organization (WHO) argues that health is wealth and poor health is an integral part of poverty; governments should actively seek to preserve their people’s lives and reduce the incidence of unnecessary mortality and avoidable illnesses [ 2 ]. However, lack of food is one of the factors which affect health outcomes. Concerning this, the Food Research and Action Center noted that the social determinants of health, such as poverty and food insecurity, are associated with some of the most severe and costly health problems in a nation [ 3 ].

According to the FAO, the International Fund for Agricultural Development (IFAD), and the World Food Programme (WFP), food insecurity is defined as "A situation that exists when people lack secure access to sufficient amounts of safe and nutritious food for normal growth and development and an active and healthy life" ([ 4 ]; p50). It is generally believed that food security and nutrition are crucial to improving human health and development. Studies show that millions of people live in food insecurity, which is one of the main risks to human health. Around one in four people globally (1.9 billion people) were moderately or severely food insecure in 2017 and the greatest numbers were in SSA and South Asia. Around 9.2% of the world's population was severely food insecure in 2018. Food insecurity is highest in SSA countries, where nearly one-third are defined as severely insecure [ 5 ]. Similarly, 11% (820 million) of the world's population was undernourished in 2018, and SSA countries still share a substantial amount [ 5 ]. Even though globally the number of people affected by hunger has been decreasing since 1990, in recent years (especially since 2015) the number of people living in food insecurity has increased. It will be a huge challenge to achieve the SDGs of zero hunger by 2030 [ 6 ]. FAO et al. [ 7 ] projected that one in four individuals in SSA were undernourished in 2017. Moreover, FAO et al. [ 8 ] found that, between 2014 and 2018, the prevalence of undernourishment worsened. Twenty percent of the continent's population, or 256 million people, are undernourished today, of which 239 million are in SSA. Hidden hunger is also one of the most severe types of malnutrition (micronutrient deficiencies). One in three persons suffers from inadequacies related to hidden hunger, which impacts two billion people worldwide [ 9 ]. Similarly, SSA has a high prevalence of hidden hunger [ 10 , 11 ].

An important consequence of food insecurity is that around 9 million people die yearly worldwide due to hunger and hunger-related diseases. This is more than from Acquired Immunodeficiency Syndrome (AIDS), malaria, and tuberculosis combined [ 6 ]. Even though the hunger crisis affects many people of all genders and ages, children are particularly affected in Africa. There are too many malnourished children in Africa, and malnutrition is a major factor in the high infant mortality rates and causes physical and mental development delays and disorders in SSA [ 12 ]. According to UN statistics, chronic malnutrition globally accounts for 165 million stunted or underweight children. Around 75% of these kids are from SSA and South Asia. Forty percent of children in SSA are impacted. In SSA, about 3.2 million children under the age of five dies yearly, which is about half of all deaths in this age group worldwide. Malnutrition is responsible for almost one child under the age of five dying every two minutes worldwide. The child mortality rate in the SSA is among the highest in the world, about one in nine children pass away before the age of five [ 12 ].

In addition to the direct impact of food insecurity on health outcomes, it also indirectly contributes to disordered eating patterns, higher or lower blood cholesterol levels, lower serum albumin, lower hemoglobin, vitamin A levels, and poor physical and mental health [ 13 , 14 , 15 ]. Iodine, iron, and zinc deficiency are the most often identified micronutrient deficiencies across all age groups. A deficiency in vitamin A affects an estimated 190 million pre-schoolers and 19 million pregnant women [ 16 ]. Even though it is frequently noted that hidden hunger mostly affects pregnant women, children, and teenagers, it further affects people’s health at all stages of life [ 17 ].

With the above information, researchers and policymakers should focus on the issue of food insecurity and health status. The SDGs that were developed in 2015 intend to end hunger in 2030 as one of its primary targets. However, a growing number of people live with hunger and food insecurity, leading to millions of deaths. Hence, this study questioned what is the impact of food insecurity on people's health outcomes in SSA countries. In addition, despite the evidence implicating food insecurity and poor health status, there is a lack of macro-level empirical studies concerning the impact of food insecurity on people’s health status in SSA countries, which leads to a knowledge (literature) gap. Therefore, this study aims to examine the impact of food insecurity on life expectancy and infant mortality in SSA countries for the period ranging from 2001–2018 using panel mean regression approaches.

Theoretical and conceptual framework

Structural factors, such as climate, socio-economic, social, and local food availability, affect people’s food security. People’s health condition is impacted by food insecurity through nutritional, mental health, and behavioral channels [ 18 ]. Under the nutritional channel, food insecurity has an impact on total caloric intake, diet quality, and nutritional status [ 19 , 20 , 21 ]. Hunger and undernutrition may develop when food supplies are scarce, and these conditions may potentially lead to wasting, stunting, and immunological deficiencies [ 22 ]. However, food insecurity also negatively influences health due to its effects on obesity, women's disordered eating patterns [ 23 ], and poor diet quality [ 24 ].

Under the mental health channel, Whitaker et al. [ 25 ] noted that food insecurity is related to poor mental health conditions (stress, sadness, and anxiety), which have also been linked to obesity and cardiovascular risk [ 26 ]. The effects of food insecurity on mental health can worsen the health of people who are already sick as well as lead to disease acquisition [ 18 ]. Similarly, the behavioral channel argues that there is a connection between food insecurity and health practices that impact disease management, prevention, and treatment. For example, lack of access to household food might force people to make bad decisions that may raise their risk of sickness, such as relying too heavily on cheap, calorically dense, nutrient-poor meals or participating in risky sexual conduct. In addition, food insecurity and other competing demands for survival are linked to poorer access and adherence to general medical treatment in low-income individuals once they become sick [ 27 , 28 , 29 , 30 ]

Food insecurity increases the likelihood of exposure to HIV and worsens the health of HIV-positive individuals [ 18 ]. Weiser et al. [ 31 ] found that food insecurity increases the likelihood of unsafe sexual activities, aggravating the spread of HIV. It can also raise the possibility of transmission through unsafe newborn feeding practices and worsening maternal health [ 32 ]. In addition, food insecurity has been linked to decreased antiretroviral adherence, declines in physical health status, worse immunologic status [ 33 ], decreased viral suppression [ 34 , 35 ], increased incidence of serious illness [ 36 ], and increased mortality [ 37 ] among people living with HIV.

With the above theoretical relationship between target variables and since this study focuses on the impact of food insecurity on health outcomes, and not on the causes, it adopted the conceptual framework of Weiser et al. [ 18 ] and constructed Fig.  1 .

A conceptual framework of food insecurity and health. Source: Modified and constructed by the author using Weiser et al. [ 18 ] conceptual framework. Permission was granted by Taylor & Francis to use their original Figs. (2.2, 2.3, and 2.4); to develop the above figure. Permission number: 1072954

Several findings associate food insecurity with poorer health, worse disease management, and a higher risk of premature mortality even though they used microdata. For instance, Stuff et al. [ 38 ] found that food insecurity is related to poor self-reported health status, obesity [ 39 ], abnormal blood lipids [ 40 ], a rise in diabetes [ 24 , 40 ], increased gestational diabetes[ 41 ], increased perceived stress, depression and anxiety among women [ 25 , 42 ], Human Immunodeficiency Virus (HIV) acquisition risk [ 43 , 44 , 45 ], childhood stunting [ 46 ], poor health [ 47 ], mental health and behavioral problem [ 25 , 48 , 49 ].

The above highlight micro-level empirical studies, and since the scope of this study is macro-level, Table 1 provides only the existing macro-level empirical findings related to the current study.

Empirical findings in Table 1 are a few, implying a limited number of macro-level level empirical findings. Even the existing macro-level studies have several limitations. For instance, most studies either employed conventional estimation techniques or overlooked basic econometric tests; thus, their results and policy implications may mislead policy implementers. Except for Hameed et al. [ 53 ], most studies’ data are either outdated or unbalanced; hence, their results and policy implications may not be valuable in the dynamic world and may not be accurate like balanced data. Besides, some studies used limited (one) sampled countries; however, few sampled countries and observations do not get the asymptotic properties of an estimator [ 56 ]. Therefore, this study tries to fill the existing gaps by employing robust estimation techniques with initial diagnostic and post-estimation tests, basic panel econometric tests and robustness checks, updated data, a large number of samples.

Study setting and participants

According to Smith and Meade [ 57 ], the highest rates of both food insecurity and severe food insecurity were found in Sub-Saharan Africa in 2017 (55 and 28%, respectively), followed by Latin America and the Caribbean (32 and 12%, respectively) and South Asia (30 and 13%). Similarly, SSA countries have worst health outcomes compared to other regions. For instance, in 2020, the region had the lowest life expectancy [ 58 ] and highest infant mortality [ 59 ]. Having the above information, this study's target population are SSA countries chosen purposively. However, even though SSA comprises 49 of Africa's 55 countries that are entirely or partially south of the Sahara Desert. This study is conducted for a sample of 31 SSA countries (Angola, Benin, Botswana, Burkina Faso, Cameroon, Cabo Verde, Chad, Congo Rep., Côte d'Ivoire, Ethiopia, Gabon, The Gambia, Ghana, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Nigeria, Rwanda, Senegal, Sierra Leone, South Africa, Sudan, Tanzania, and Togo). The sampled countries are selected based on data accessibility for each variable included in the empirical models from 2001 to 2018. Since SSA countries suffer from food insecurity and related health problems, this study believes the sampled countries are appropriate and represent the region. Moreover, since this study included a large sample size, it improves the estimator’s precision.

Data type, sources, and scope

This study uses secondary data collected in December 2020 online from the databases of the Food and Agricultural Organization (FAO), the United Nations Development Programme (UNDP), and the World Bank (WB) (see Table 2 ). In addition, the study uses yearly balanced data from 2001 to 2018, which is appropriate because it captures the Millennium Development Goals, SDGs, and other economic conditions, such as the rise of SSA countries’ economies and the global financial crisis of the 2000s. Therefore, this study considers various global development programs and events. Generally, the scope of this study (sampled countries and time) is sufficient to represent SSA countries. In other words, the study has n*T = 558 observations, which fulfills the large sample size criteria recommended by Kennedy [ 56 ].

The empirical model

Model specification is vital to conduct basic panel data econometric tests and estimate the relationship of target variables. Besides social factors, the study includes economic factors determining people's health status. Moreover, it uses two proxies indicators to measure both food insecurity and health status; hence, it specifies the general model as follows:

The study uses four models to analyze the impact of food insecurity on health outcomes.

where LNLEXP and LNINFMOR (dependent variables) refer to the natural logarithm of life expectancy at birth and infant mortality used as proxy variables for health outcomes. Similarly, PRUND and AVRDES are the prevalence of undernourishment and average dietary energy supply adequacy – proxy and predictor variables for food insecurity.

Moreover, to regulate countries’ socio-economic conditions and to account for time-varying bias that can contribute to changes in the dependent variable, the study included control variables, such as GDPPC, GOVEXP, MNSCHOOL, and URBAN. GDPPC is GDP per capita, GOVEXP refers to domestic general government health expenditure, MNSCHOOL is mean years of schooling and URBAN refers to urbanization. Further, n it , v it , ε it , and μ it are the stochastic error terms at period t. The parameters \({\alpha }_{0}, { \beta }_{0}, { \theta }_{0},{ \delta }_{0}\) refer to intercept terms and \({\alpha }_{1}-{\alpha }_{5}, {\beta }_{1}-{\beta }_{5}, { \theta }_{1}-{\theta }_{5}, and {\delta }_{1}-{\delta }_{5}\) are the long-run estimation coefficients. Since health outcomes and food insecurity have two indicators used as proxy variables, this study estimates different alternative models and robustness checks of the main results. Furthermore, the above models did not address heterogeneity problems; hence, this study considers unobserved heterogeneity by introducing cross-section and time heterogeneity in the models. This is accomplished by assuming a two-way error component for the disturbances with:

From Eq.  2 , the unobservable individual (cross-section) and unobservable time heterogeneities are described by \({\delta }_{i} and {\tau }_{t}\) (within components), respectively. Nonetheless, the remaining random error term is \({\gamma }_{it}\) (panel or between components). Therefore, the error terms in model 1A-1D will be substituted by the right-hand side elements of Eq.  2 .

Depending on the presumptions of whether the error elements are fixed or random, the FE and RE models are the two kinds of models that will be evaluated. Equation ( 2 ) yields a two-way FE error component model, or just a FE model if the assumptions are that \({\delta }_{i} and {\tau }_{t}\) are fixed parameters to be estimated and that the random error component, \({\gamma }_{it}\) , is uniformly and independently distributed with zero mean and constant variance (homoscedasticity).

Equation ( 2 ), on the other hand, provides a two-way RE error component model or a RE model if we suppose \({\delta }_{i} and {\tau }_{t}\) are random, just like the random error term, or \({\delta }_{i},{\tau }_{t}, and {\gamma }_{it}\) are all uniformly and independently distributed with zero mean and constant variance, or they are all independent of each other and independent variables [ 60 ].

Rather than considering both error components, \({\delta }_{i}, and {\tau }_{t}\) , we can examine only one of them at a time (fixed or random), yielding a one-way error component model, FE or RE. The stochastic error term \({\varpi }_{it}\) in Eq.  2 will then be:

Statistical analysis

This study conducted descriptive statistics, correlation analysis, and initial diagnosis tests (cross-sectional and time-specific fixed effect, outliers and influential observations, multicollinearity, normality, heteroscedasticity, and serial correlation test). Moreover, it provides basic panel econometric tests and panel data estimation techniques. For consistency, statistical software (STATA) version 15 was used for all analyses.

Descriptive statistics and correlation analysis

Descriptive statistics is essential to know the behavior of the variables in the model. Therefore, it captures information, such as the mean, standard deviation, minimum, maximum, skewness, and kurtosis. Similarly, the study conducted Pearson correlation analysis to assess the degree of relationship between the variables.

Initial diagnosis

Cross-sectional and time-specific fixed effect.

One can anticipate differences arising over time or within the cross-sectional units, given that the panel data set comprises repeated observations over the same units gathered over many periods. Therefore, before estimation, this study considered unexplained heterogeneity in the models. One fundamental limitation of cross-section, panel, and time series data regression is that they do not account for country and time heterogeneity [ 60 ]. These unobserved differences across nations and over time are crucial in how the error term is represented and the model is evaluated. These unobserved heterogeneities, however, may be represented by including both country and time dummies in the regression. However, if the parameters exceed the number of observations, the estimate will fail [ 60 ]. However, in this study, the models can be estimated. If we include both country and time dummies, we may assume that the slope coefficients are constant, but the intercept varies across countries and time, yielding the two-way error components model. As a result, this study examines the null hypothesis that intercepts differ across nations and time in general.

Detecting outliers and influential observations

In regression analysis, outliers and influential observations may provide biased findings. Therefore, the Cooks D outlier and influential observation test was used in the study to handle outliers and influencing observations. To evaluate whether these outliers have a stronger impact on the model to be estimated, each observation in this test was reviewed and compared with Cook’s D statistic [ 61 ]. Cook distance evaluates the extent to which observation impacts the entire model or the projected values. Hence, this study tested the existence of outliers.

Normality, heteroscedasticity, multicollinearity, and serial correlation test

Before the final regression result, the data used for the variables were tested for normality, heteroscedasticity, multicollinearity, and serial correlation to examine the characteristics of the sample.

Regression models should be checked for nonnormal error terms because a lack of Gaussianity (normal distribution) can occasionally compromise the accuracy of estimation and testing techniques. Additionally, the validity of inference techniques, specification tests, and forecasting critically depends on the normalcy assumption [ 62 ]. Similarly, multicollinearity in error terms leads to a dataset being highly sensitive to a minor change, instability in the regression model, and skewed and unreliable results. Therefore, this study conducted the normality using Alejo et al. [ 62 ] proposed command and multicollinearity (using VIF) tests.

Most conventional panel data estimation methods rely on homoscedastic individual error variance and constant serial correlation. Since the error component is typically connected to the variance that is not constant during the observation and is serially linked across periods, these theoretical presumptions have lately reduced the applicability of various panel data models. Serial correlation and heteroskedasticity are two estimate issues frequently connected to cross-sectional and time series data, respectively. Similarly, panel data is not free from these issues because it includes cross-sections and time series, making the estimated parameters ineffective, and rendering conclusions drawn from the estimation incorrect [ 63 ]. Therefore, this study used the Wooldridge [ 63 ] test for serial correlation in linear panel models as well as the modified Wald test for heteroskedasticity.

Basic panel econometric tests

The basic panel data econometric tests are prerequisites for estimating the panel data. The three main basic panel data tests are cross-sectional dependence, unit root, and cointegration.

Cross-sectional dependence (CD)

A growing body of the panel data literature concludes that panel data models are likely to exhibit substantial CD in the errors resulting from frequent shocks, unobserved components, spatial dependence, and idiosyncratic pairwise dependence. Even though the impact of CD in estimation depends on several factors, relative to the static model, the effect of CD in dynamic panel estimators is more severe [ 64 ]. Moreover, Pesaran [ 65 ] notes that recessions and economic or financial crises potentially affect all countries, even though they might start from just one or two countries. These occurrences inevitably introduce cross-sectional interdependencies across the cross-sectional unit, their regressors, and the error terms. Hence, overlooking the CD in panel data leads to biased estimates and spurious results [ 64 , 66 ]. Further, the CD test determines the type of panel unit root and cointegration tests we should apply. Therefore, examining the CD is vital in panel data econometrics.

In the literature, there are several tests for CD, such as the Breusch and Pagan [ 67 ] Lagrange multiplier (LM) test, Pesaran [ 68 ] scaled LM test, Pesaran [ 68 ] CD test, and Baltagi et al. [ 69 ] bias-corrected scaled LM test (for more detail, see Tugcu and Tiwari [ 70 ]). Besides, Friedman [ 71 ] and Frees [ 72 , 73 ] also have other types of CD tests (for more detail, see De Hoyos and Sarafidis [ 64 ]). This study employs Frees [ 72 ] and Pesaran [ 68 ] among the existing CD tests. This is because, unlike the Breusch and Pagan [ 67 ] test, these tests do not require infinite T and fixed N, and are rather applicable for both a large N and T. Additionally, Free’s CD test can overcome the irregular signs associated with correlation. However, it also employs Friedman [ 71 ] CD for mixed results of the above tests.

Unit root test

The panel unit root and cointegration tests are common steps following the CD test. Generally, there are two types of panel unit root tests: (1) the first-generation panel unit root tests, such as Im et al. [ 74 ], Maddala and Wu [ 75 ], Choi [ 76 ], Levin et al. [ 77 ], Breitung [ 78 ] and Hadri [ 79 ], and (2) the second-generation panel unit root tests, such as [ 66 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 ].

The first-generation panel unit root tests have been criticized because they assume cross-sectional independence [ 90 , 91 , 92 , 93 ]. This hypothesis is somewhat restrictive and unrealistic, as macroeconomic time series exhibit significant cross-sectional correlation among countries in a panel [ 92 ], and co-movements of economies are often observed in the majority of macroeconomic applications of unit root tests [ 91 ]. The cross-sectional correlation of errors in panel data applications in economics is likely to be the rule rather than the exception [ 93 ]. Moreover, applying first-generation unit root tests under CD models can generate substantial size distortions [ 90 ], resulting in the null hypothesis of nonstationary being quickly rejected [ 66 , 94 ]. As a result, second-generation panel unit root tests have been proposed to take CD into account. Therefore, among the existing second-generation tests, this study employs Pesaran’s [ 66 ] cross-sectionally augmented panel unit root test (CIPS) for models 1A–1C . The rationale for this is that, unlike other unit root tests that allow CD, such as Bai and Ng [ 80 ], Moon and Perron [ 87 ], and Phillips and Sul [ 84 ], Pesaran’s [ 66 ] test is simple and clear. Besides, Pesaran [ 66 ] is robust when time-series’ heteroscedasticity is observed in the unobserved common factor [ 95 ]. Even though theoretically, Moon and Perron [ 87 ], Choi [ 96 ] and Pesaran [ 66 ] require large N and T, Pesaran [ 66 ] is uniquely robust in small sample sizes [ 97 ]. Therefore, this study employs the CIPS test to take into account CD, and heteroskedasticity in the unobserved common factor and both large and small sample countries. However, since there is no CD in model 1D , this study employs the first-generation unit root tests called Levin, Lin, and Chu (LLC), Im, Pesaran, Shin (IPS) and Fisher augmented Dickey–Fuller (ADF) for model 1D .

Cointegration test

The most common panel cointegration tests when there is CD are Westerlund [ 98 ], Westerlund and Edgerton [ 99 ], Westerlund and Edgerton [ 100 ], Groen and Kleibergen [ 101 ], Westerlund’s [ 102 ] Durbin-Hausman test, Gengenbach et al. [ 103 ] and Banerjee and Carrion-i-Silvestre [ 104 ]. However, except for a few, most tests are not coded in Statistical Software (STATA) and are affected by insufficient observations. The current study primarily uses Westerlund [ 98 ] and Banerjee and Carrion-i-Silvestre [ 104 ] for models 1A–1C . However, to decide uncertain results, it also uses McCoskey and Kao [ 105 ] cointegration tests for model 1C . The rationale for using Westerlund’s [ 98 ] cointegration test is that most panel cointegration has failed to reject the null hypothesis of no cointegration due to the failure of common-factor restriction [ 106 ]. However, Westerlund [ 98 ] does not require any common factor restriction [ 107 ] and allows for a large degree of heterogeneity (e.g., individual-specific short-run dynamics, intercepts, linear trends, and slope parameters) [ 92 , 107 , 108 ]. Besides, its command is coded and readily available in STATA. However, it suffers from insufficient observations, especially when the number of independent variables increases. The present study employs the Banerjee and Carrion-i-Silvestre [ 104 ] and McCoskey and Kao [ 105 ] cointegration tests to overcome this limitation. The two Engle-Granger-based cointegration tests applicable when there is no CD and are widely used and available in STATA are Pedroni [ 109 , 110 ] and Kao [ 111 ]. However, the Pedroni test has two benefits over Kao: it assumes cross-sectional dependency and considers heterogeneity by employing specific parameters [ 112 ]. Hence, this study uses the Pedroni cointegration test for model 1D .

Panel data estimation techniques

The panel data analysis can be conducted using different estimation techniques and is mainly determined by the results of basic panel econometric tests. Thus, this study mainly employs the Driscoll-Kraay [ 113 ] standard error (DKSE) (for models 1A and 1B ), FE (for model 1C ), and two-step GMM (for model 1D ) estimation techniques to examine the impact of food insecurity on health outcomes. It also employs the Granger causality test. However, for robustness checks, it uses fully modified ordinary least squares (FMOLS), panel-corrected standard error (PCSE), and feasible generalized least squares (FGLS) methods (for models 1A and 1B ). Moreover, it uses a random effect (RE) for model 1C and panel dynamic fixed effect (DFE) techniques for model 1D .

Even though several panel estimation techniques allow CD, most of them – such as cross-section augmented autoregressive distributed lag (CS-ARDL), cross-section augmented distributed lag (CS-DL), common correlated effects pooled (CCEP), and common correlated effects mean group (CCEMG) estimators – require a large number of observations over groups and periods. Similarly, the continuously updated fully modified (CUP-FM) and continuously updated bias-corrected (CUP-BC) estimators are not coded in STATA. Others, like the PCSE, FGLS, and seemingly unrelated regression (SUR), are feasible for T (the number of time series) > N (the number of cross-sectional units) [ 114 , 115 ]. However, a DKSE estimate is feasible for N > T [ 114 ]. Therefore, depending on the CD, cointegration test, availability in STATA, and comparing N against T, this study mainly employs the DKSE regression for models 1A and 1B , FE model for model 1C , and GMM for model 1C .

Finally, to check the robustness of the main result, this study employs FMOLS, FGLS, and PCSE estimation techniques for models 1A and 1B . Furthermore, even though the Hausman test confirms that the FE is more efficient, the study employs the RE for model 1C . This is because Firebaugh et al. [ 116 ] note that the RE and FE models perform best in panel data. Besides, unlike FE, RE assumes that individual differences are random. In addition, this study uses panel DFE for model 1D (selected based on the Hausman test). Finally, the robustness check is also conducted using an alternative model (i.e., when a dependent variable is without a natural log and Granger causality test).

Table 3 shows the overall mean of LNLEXP of the region is 4.063 years which indicates that the region can achieve only 57.43 (using ln(x) = 4.063 = loge (x)  = e 4.063 , where e = 2.718) years of life expectancy. This is very low compared to other regions. Besides, the ranges in the value of LNLEXP are between 3.698 and 4.345 or (40–76 years), implying high variation. Similarly, the mean value of LNINFMOR is 3.969; implying SSA countries recorded 52 infants death per 1000. Moreover, the range of LNINFMOR is between 2.525 and 4.919 or (12 – 135 infant death per 1000), implying high variation within the region. The mean value of people’s prevalence for undernourishment is 21.26; indicating 21% of the population is undernourished. However, the mean value of AVRDES is 107.826, which is greater than 100, implying that the calorie supply is adequate for all consumers if the food is distributed according to the requirements of individuals. When we observe the skewness and kurtosis of the variables of the models, except for LNLEXP and LNINFMOR, all variables are positively skewed. In addition, all variables have positive kurtosis with values between 2.202 and 6.092.

Table 3 also shows the degree of relationship between variables, such that most values are below the threshold or rule of thumb (0.7) for a greater association [ 117 ]. However, the association between LNINFMOR and LNLEXP, as well as between PRUNP and AVRDES, is over the threshold and seems to have a multicollinearity issue. Nevertheless, these variables did not exist together in the models, indicating the absence of a multicollinearity problem.

Table 4 shows whether the cross-sectional specific and time-specific FE in extended models ( model 1A-1D plus Eq.  2 ) are valid. The result reveals that the null hypothesis of the captured unobserved heterogeneity is homogenous across the countries, and time is rejected at 1%, implying the extended models are correctly specified. Besides, to check the robustness of the two-way error component model relative to the pooled OLS estimator, this study conducted an additional poolability test. The result shows the null hypothesis that intercepts homogeneity (pooling) is rejected at a 1% level; thus, the FE model is most applicable, but the pooled OLS is biased.

Cooks D is an indicator of high leverage and residuals. The impact is high when D exceeds 4/N, (N = number of observations). A D > 1 implies a significant outlier problem. The Cooks D result of this study confirms the absence of outliers' problem (see supplementary file 1 ).

Normality, heteroscedasticity, serial correlation, and multicollinearity tests

The results in Table 5 indicate that the probability value of the joint test for normality on e and u are above 0.01, implying that the residuals are normally distributed. The heteroscedasticity results show that the probability value of the chi-square statistic is less than 0.01 in all models. Therefore, the null hypothesis of constant variance can be rejected at a 1% level of significance. In other words, the modified Wald test result for Groupwise heteroskedasticity presented in Table 5 , rejects the null hypothesis of Groupwise homoskedasticity observed by the probability value of 0.0000, which implies the presence of heteroscedasticity in the residuals. Similarly, all models suffer from serial correlation since the probability value of 0.0000 rejects the null hypothesis of no first-order serial correlation, indicating the presence of autocorrelation in all panel models. Finally, the multicollinearity test reveals that the models have no multicollinearity problem since the Variance inflation Factors (VIF) values are below 5.

Cross-sectional dependence test

Results in Table 6 strongly reject the null hypothesis of cross-sectional independence for models 1A – 1C . However, for model 1D , the study found mixed results (i.e., Pesaran [ 68 ] fails to reject the null hypothesis of no CD while Frees [ 72 ] strongly rejects it). Thus, to decide, this study employs the Friedman [ 71 ] CD test. The result fails to reject the null hypothesis of cross-sectional independence, implying that two out of three tests fail to reject the null hypothesis of cross-sectional independence in model 1D . Therefore, unlike others, there is no CD in model 1D (see Table 6 ).

Unit root tests

Table 7 shows that all variables are highly (at 1% level) significant either at level (I(0)) or first difference (I(1)), which implies all variables are stationary. In other words, the result fails to reject the null hypothesis of unit root (non-stationary) for all variables at a 1%-significance level, either at levels or the first differences. Thus, we might expect a long-run connection between these variables collectively.

Cointegration tests

The results in Table 8 show that both the Westerlund [ 98 ] and Banerjee and Carrion-i-Silvestre [ 104 ] cointegration tests strongly reject the null hypothesis of no-cointegration in models 1A and 1B . However, model 1C provides a mixed result, i.e. the Banerjee and Carrion-i-Silvestre [ 104 ] test rejects the null hypothesis of no cointegration, yet the reverse is true for the Westerlund [ 98 ] test. Therefore, this study conducted further cointegration tests for model 1C . Even though Westerlund and Edgerton [ 99 ] suffer from insufficient observation, it is based on the McCoskey and Kao [ 105 ] LM test [ 118 ]. Thus, we can use a residual-based cointegration test in the heterogeneous panel framework proposed by McCoskey and Kao [ 105 ]. However, an efficient estimation technique of cointegrated variables is required, and hence the FMOLS and DOLS estimators are recommended. The residuals derived from the FMOLS and DOLS will be tested for stationarity with the null hypothesis of no cointegration amongst the regressors. Since the McCoskey and Kao [ 105 ] test involves averaging the individual LM statistics across the cross-sections, for testing the residuals FMOLS and DOLS stationarity, McCoskey, and Kao [ 105 ] test is in the spirit of IPS (Im et al. [ 74 ]) [ 119 ].

Though FMOLS and DOLS are recommended for the residuals cointegration test, DOLS is better than FMOLS (for more detail, see Kao and Chiang [ 120 ]); therefore, this study uses a residual test derived from DOLS. The result fails to reject the null hypothesis of no cointegration. Two (Banerjee and Carrion-i-Silvestre [ 104 ] and McCoskey and Kao [ 105 ]) out of three tests fail to reject the null hypothesis of no cointegration; hence, we can conclude that there is no long-run relationship among the variables in model 1C .

Unlike other models, since there is CD in model 1D , this study employs the Pedroni [ 109 ] and Kao [ 111 ] cointegration tests for model 1D . The result strongly rejects the null hypothesis of no cointegration, which is similar to models 1A and 1B , that a long-run relationship exists among the variables in model 1D (see Table 5 ).

Panel data estimation results

Table 9 provides long-run regression results of all models employing appropriate estimation techniques such as DKSE, FE, and two-step GMM, along with the Granger causality test. However, the DKSE regression can be estimated in three ways: FE with DKSE, RE with DKSE, and pooled Ordinary Least Squares/Weighted Least Squares (pooled OLS/WLS) regression with DKSE. Hence, we must choose the most efficient model using Hausman and Breusch-Pagan LM for RE tests (see supplementary file 2 ). As a result, this study employed FE with DKSE for models 1A and 1B . Further, due to Hausman's result, absence of cointegration and to deal with heterogeneity and spatial dependence in the dynamic panel, this study employs FE for the model1C (see the supplementary file 2). However, due to the absence of CD, the presence of cointegration, and N > T, this study uses GMM for model 1D . Moreover, according to Roodman [ 121 ], the GMM approach can solve heteroskedasticity and autocorrelation problems. Furthermore, even though two-step GMM produces only short-run results, it is possible to generate long-run coefficients from short-run results [ 122 , 123 ].

The DKSE result of model 1A shows that a 1% increment in people's prevalence for undernourishment reduces their life expectancy by 0.00348 PPs (1 year or 366 days). However, in model 1C, a 1% rise in the prevalence of undernourishment increases infant mortality by 0.0119 PPs (1 year or 369 days). The DKSE estimations in model 1B reveal that people’s life expectancy rises by 0.00317 PPs with every 1% increase in average dietary energy supply. However, the GMM result for model 1D confirms that a 1% incrementin average dietary energy supply reduces infant mortality by 0.0139 PPs. Moreover, this study conducted a panel Granger causality test to confirm whether or not food insecurity has a potential causality to health outcomes. The result demonstrates that the null hypothesis of change in people’s prevalence for undernourishment and average dietary energy supply does not homogeneously cause health outcomes is rejected at 1% significance, implying a change in food insecurity does Granger-cause health outcomes of SSA countries (see Table 9 ).

In addition to the main results, Table 9 also reports some post-estimation statistics to ascertain the consistency of the estimated results. Hence, in the case of DKSE and FE models, the validity of the models is determined by the values of R 2 and the F statistics. For instance, R 2 quantifies the proportion of the variance in the dependent variable explained by the independent variables, representing the model’s quality. The results in Table 9 demonstrate that the explanatory variables explain more than 62% of the variance on the dependent variable. Cohen [ 125 ] classifies the R 2 value of 2% as a moderate influence in social and behavioral sciences, while 13 and 26% are considered medium and large effects, respectively. Therefore, the explanatory variables substantially impact this study's models. Similarly, the F statistics explain all independent variables jointly explain the dependent one. For the two-step system GMM, the result fails to reject the null hypothesis of no first (AR(1)) and second-order (AR(2)) serial correlation, indicating that there is no first and second-order serial correlation. In addition, the Hansen [ 126 ] and Sargan [ 127 ] tests fail to reject the null hypothesis of the overall validity of the instruments used, which implies too many instruments do not weaken the model.

Robustness checks

The author believes the above findings may not be enough for policy recommendations unless robustness checks are undertaken. Hence, the study estimated all models without the natural logarithm of the dependent variables (see Table 10 ). The model 1A result reveals, similar to the above results, individuals’ prevalence for undernourishment significantly reduces their life expectancy in SSA countries. That means a 1% increase in the people's prevalence of undernourishment reduces their life expectancy by 0.1924 PPs. Moreover, in model 1B , life expectancy rises by 0.1763 PPs with every 1% increase in average dietary energy supply. In model 1C , the rise in infants’ prevalence for undernourishment has a positive and significant effect on their mortality rate in SSA countries. The FE result implies that a 1% rise in infants’ prevalence for undernourishment increases their mortality rate by 0.9785 PPs. The GMM result in model 1D indicates that improvement in average dietary energy supply significantly reduces infant mortality. Further, the Granger causality result confirms that the null hypothesis of change in the prevalence of undernourishment and average dietary energy supply does not homogeneously cause health outcomes and is rejected at a 1% level of significance. This implies a change in food insecurity does Granger-cause health outcomes in SSA countries (see Table 10 ).

The study also conducted further robustness checks using the same dependent variables (as Table 9 ) but different estimation techniques. The results confirm that people’s prevalence of undernourishment has a negative and significant effect on their life expectancy, but improvement in average dietary energy supply significantly increases life expectancy in SSA countries. However, the incidence of undernourishment in infants contributes to their mortality; however, progress in average dietary energy supply for infants significantly reduces their mortality (see Table 11 ).

The main objective of this study is to examine the impact of food insecurity on the health outcomes of SSA countries. Accordingly, the DKSE result of model 1A confirms that the rise in people’s prevalence for undernourishment significantly reduces their life expectancy in SSA countries. However, the FE result shows that an increment in the prevalence of undernourishment has a positive and significant impact on infant mortality in model 1C . This indicates that the percentage of the population whose food intake is insufficient to meet dietary energy requirements is high, which leads to reduce life expectancy but increases infant mortality in SSA countries. The reason for this result is linked to the insufficient food supply in SSA due to low production and yields, primitive tools, lack of supporting smallholder farms and investment in infrastructure, and government policies. Besides, even though the food is available, it is not distributed fairly according to the requirements of individuals. Moreover, inadequate access to food, poor nutrition, and chronic illnesses are caused by a lack of well-balanced diets. In addition, many of these countries are impacted by poverty, making it difficult for citizens to afford nutritious food. All these issues combine to create an environment where individuals are more likely to suffer malnutrition-related illnesses, resulting in a lower life expectancy rate. The DKSE estimation result in model 1B reveals that improvement in average dietary energy supply positively impacts people's life expectancy in SSA countries. However, the improvement in average dietary energy supply reduces infant mortality.

Based on the above results, we can conclude that food insecurity harms SSA nations' health outcomes. This is because the prevalence of undernourishment leads to increased infant mortality by reducing the vulnerability, severity, and duration of infectious diseases such as diarrhea, pneumonia, malaria, and measles. Similarly, the prevalence of undernourishment can reduce life expectancy by increasing the vulnerability, severity, and duration of infectious diseases. However, food security improves health outcomes – the rise in average dietary energy supply reduces infant mortality and increases the life expectancy of individuals.

Several facts and theories support the above findings. For instance, similar to the theoretical and conceptual framework section, food insecurity in SSA countries can affect health outcomes in nutritional, mental health, and behavioral channels. According to FAO et al. [ 128 ], the prevalence of undernourishment increased in Africa from 17.6% of the population in 2014 to 19.1% in 2019. This figure is more than twice the global average and the highest of all regions of the world. Similarly, SSA is the world region most at risk of food insecurity [ 129 ]. According to Global Nutrition [ 130 ] report, anemia affects an estimated 39.325% of women of reproductive age. Some 13.825% of infants have a low weight at birth in the SSA region. Excluding middle African countries (due to lack of data), the estimated average prevalence of infants aged 0 to 5 months who are exclusively breastfed is 35.73%, which is lower than the global average of 44.0%. Moreover, SSA Africa still experiences a malnutrition burden among children aged under five years. The average prevalence of overweight is 8.15%, which is higher than the global average of 5.7%. The prevalence of stunting is 30.825%—higher than the worldwide average of 22%. Conversely, the SSA countries’ prevalence of wasting is 5.375%, which is higher than most regions such as Central Asia, Eastern Asia, Western Asia, Latin America and the Caribbean, and North America. The SSA region's adult population also faces a malnutrition burden: an average of 9.375% of adult (aged 18 and over) women live with diabetes, compared to 8.25% of men. Meanwhile, 20.675% of women and 7.85% of men live with obesity.

According to Saltzman et al. [ 17 ], micronutrient deficiencies can affect people’s health throughout their life cycle. For instance, at the baby age, it causes (low birth weight, higher mortality rate, and impaired mental development), child (stunting, reduced mental capacity, frequent infections, reduced learning capacity, higher mortality rate), adolescent (stunting, reduced mental capacity, fatigue, and increased vulnerability to infection), pregnant women (increased mortality and perinatal complications), adult (reduced productivity, poor socio-economic status, malnutrition, and increased risk of chronic disease), elderly (increased morbidity (including osteoporosis and mental impairment), and higher mortality rate).

Though this study attempts to fill the existing gaps, it also has limitations. It examined the impact of food insecurity on infant mortality; however, their association is reflected indirectly through other health outcomes. Hence, future studies can extend this study by examining the indirect effect of food insecurity on infant mortality, which helps to look at in-depth relationships between the variables. Moreover, this study employed infant mortality whose age is below one year; hence, future studies can broaden the scope by decomposing infant mortality into (neonatal and postnatal) and under-five mortality.

Millions of people are dying every year due to hunger and hunger-related diseases worldwide, especially in SSA countries. Currently, the link between food insecurity and health status is on researchers' and policymakers' agendas. However, macro-level findings in this area for most concerned countries like SSA have been given only limited attention. Therefore, this study examined the impact of food insecurity on life expectancy and infant mortality rates. The study mainly employs DKSE, FE, two-step GMM, and Granger causality approaches, along with other estimation techniques for robustness checks for the years between 2001 and 2018. The result confirms that food insecurity harms health outcomes, while food security improves the health status of SSA nations'. That means that a rise in undernourishment increases the infant mortality rate and reduces life expectancy. However, an improvement in the average dietary energy supply reduces infant mortality and increases life expectancy. Therefore, SSA countries need to guarantee their food accessibility both in quality and quantity, which improves health status. Both development experts and political leaders agree that Africa has the potential for agricultural outputs, can feed the continent, and improve socio-economic growth. Besides, more than half of the world's unused arable land is found in Africa. Therefore, effective utilization of natural resources is essential to achieve food security. Moreover, since the majority of the food in SSA is produced by smallholder farmers [ 131 ] while they are the most vulnerable to food insecurity and poverty [ 132 , 133 ]; hence, special focus and support should be given to smallholder farmers that enhance food self-sufficiency. Further, improvement in investment in agricultural research; improvement in markets, infrastructures, and institutions; good macroeconomic policies and political stability; and developing sub-regional strategies based on their agroecological zone are crucial to overcoming food insecurity and improving health status. Finally, filling a stomach is not sufficient; hence, a person's diet needs to be comprehensive and secure, balanced (including all necessary nutrients), and available and accessible. Therefore, SSA countries should ensure availability, accessibility, usability, and sustainability to achieve food and nutrition security.

Availability of data and materials

The datasets used and/or analyzed during the current study are available in supplementary materials.

Abbreviations

Augmented Dickey–Fuller

Acquired Immunodeficiency Syndrome

Average Dietary Energy Supply

Common Correlated Effects Mean Group

Common Correlated Effects Pooled

Cross-Sectional Dependence

Cross-Sectionally Augmented Panel Unit Root Test

Cross-Section Augmented Autoregressive Distributed Lag

Cross-Section Augmented Distributed Lag

Continuously Updated Bias-Corrected

Continuously Updated Full Modified

Dynamic Fixed Effect

Driscoll-Kraay Standard Errors

Dynamic Ordinary Least Square

Error Correction Model

Food and Agricultural Organization

Fixed Effect

Feasible Generalised Least Squares

Fully Modified Ordinary Least Square

Gross Domestic Product (GDP) per capita

Generalised Method of Momentum

Domestic General Government Health Expenditure

Human Immunodeficiency Virus

Integration at First Difference

International Fund for Agricultural Development

Infant Mortality Rate

Im, Pesaran, Shin

Lag of Infant Mortality Rate

Lag of Natural Logarithm of Infant Mortality Rate

Life Expectancy at Birth

Levin, Lin, and Chu

Lagrange Multiplier

Natural Logarithm of Infant Mortality Rate

Natural Logarithm of Life Expectancy at Birth

Mean Years of Schooling

Ordinary Least Squares

Panel-Corrected Standard Error

Pooled Mean Group

Prevalence of Undernourishment

Random Effect

Sustainable Development Goals

Sub-Saharan African

Statistical Software

Seemingly Unrelated Regression

Urbanisation

World Food Programme

World Health Organization

Weighted Least Squares

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The author sincerely thanks the Editor, the three anonymous reviewers, and BalázsKotosz (Adjunct Professor) for their comments and advice.

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  Table S1. Cook’s D results

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  Table S2. Hausman and Breusch-Pagan LM for REtests. 

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Beyene, S.D. The impact of food insecurity on health outcomes: empirical evidence from sub-Saharan African countries. BMC Public Health 23 , 338 (2023). https://doi.org/10.1186/s12889-023-15244-3

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Food Insecurity and Hunger in the United States: An Assessment of the Measure (2006)

Chapter: 3 concepts and definitions, 3 concepts and definitions.

T his chapter discusses the conceptual issues associated with the concepts and definitions of food insecurity and hunger and their applications for measurement in the monitoring of food insecurity in the United States. The chapter also discusses the labeling of the severity levels of food insecurity.

FOOD INSECURITY, HUNGER, MALNUTRITION, AND UNDERNOURISHMENT 1

Food scarcity, with its dangers for survival and serious physical and psychological discomfort, has been part of human experience and human culture from the earliest inception of language and thought. Various concepts have emerged to describe aspects and consequences of food scarcity, although they are often ambiguous in meaning. For example, depending on usage and the user, the concept of hunger covers a spectrum from the short-term physical experience of discomfort to chronic food shortage to severe and life-threatening lack of food.

With the establishment of the modern science of nutrition, the concept of malnutrition as a condition brought about by insufficient intake of nutrients to meet biological requirements became a focal construct. Technically the prefix mal actually refers to both over- and underintake, but the typical

usage—and until recently the bulk of research on malnutrition—has been directed to understanding inadequate intakes of macro- and micronutrients. The measures of central concern are observed through analysis of biological tissues (e.g., serum), observation of well-established physical (e.g., anthropometric) and clinically observable consequences (e.g., blindness), and by inference from data on intake. For example, anthropometric status is commonly used to assess malnutrition of children under age 5 (de Onis, Blösner, Borghi, Frongillo, and Morris, 2004).

As malnutrition acquired a central role in scientific conceptualization, it was often mentioned jointly with the idea of hunger, to the point at which the two often became virtually synonymous. Nutritional scientists as well as social advocates therefore sought to describe the inequalities of access to adequate food and its consumption. One approach was to compare intakes of a nutrient for a given gender and life stage group with an established reference value, such as the Recommended Dietary Allowances (RDAs).

Some problems with using the RDA approach stem, in part, from its conceptual underpinnings. To cover the needs of nearly all of a group, the reference values were set at very high levels. Consequently, a proportion of the population may consume less than the RDAs but still have adequate nutrient intakes. Another problem is purely technical. It is difficult to use a single interview to assess usual nutrient intake in a biologically meaningful fashion. For instance, vitamin A intake varies considerably over time, and only the mean intake over a period of weeks is meaningful nutritionally, because vitamin A is stored and body reserves buffer the variability of intake. Further technical problems relate to the accuracy of reported intake and of the information used to translate food intake into nutrients. As a consequence of these problems, assessment of nutritional adequacy through interviews and analysis of the record in relation to the RDA is no longer considered appropriate (Institute of Medicine, 2000).

The United Nations Food and Agricultural Organization (FAO) took a different biologically based approach to define undernourishment as not ingesting enough food to meet energy needs. Operationally the FAO indicator is calculated from national food energy balance sheets. These balance sheets estimate the total energy available for human consumption nationally by adding total energy produced plus energy imported plus the change in stocks minus energy exported, energy wasted, and energy used for other than human consumption. FAO then creates a synthetic distribution of energy consumption for each country in which the mean is total energy available (from the balance sheets) and the variance is taken from another source, typically an estimate from a nationally representative household expenditure survey that accounts for energy exported and energy used for other than human consumption (Naiken, 2003). The resulting estimated distribution of undernourishment (i.e., food energy consumed) across countries is

highly correlated with the distribution of food energy available for consumption obtained directly from the national food energy balance sheets when national population size is taken into account (Smith, 1998). Thus the two measurements, one from the energy balance sheets and one from the prevalence of undernourishment, are redundant. That is, the FAO method for estimating undernourishment measures only food energy availability, but not consumption of (or access to) food by households.

The discovery that people frequently did not have enough to eat according to accepted cultural norms created a conceptual crisis. Either the food problems of poor people were imaginary, or other concepts were needed to describe and measure them. An intuitively understandable construct was hunger defined as a physical pain. This word has typically and historically been used not only to refer to the physical sensation, but also to a feeling of weakness from not eating. As stated in the previous chapter, beginning in the 1960s, the word hunger began to take on a wider meaning. It was expanded to encompass issues of access to food and socioeconomic deprivation related to food. Perhaps because these expanded referents seemed less compatible with the intuitive meaning of hunger, other constructs were needed. It is in this context that the phrase food insecurity came into use in the United States. Internationally, food insecurity was already current. Originally, it was used to describe the instability of national or regional food supplies over time (Pelletier, Olson, and Frongillo, 2001; Rose, Basiotis, and Klein, 1995). It was then expanded to include a lack of secure provisions at the household and individual levels.

Figure 3-1 depicts the core concepts related to nutritional state that were established at the commencement of the U.S. national nutritional monitoring system (Anderson, 1990).

CONCEPT AND DEFINITION OF FOOD INSECURITY

As described in the previous chapter, the broad conceptual definitions of food security and insecurity developed by the expert panel convened in 1989 by the Life Sciences Research Office (LSRO) have served as the basis for the standardized operational definitions used for estimating food security in the United States. Food security according to the LSRO definition means access to enough food for an active, healthy life. It includes at a minimum (a) the ready availability of nutritionally adequate and safe foods and (b) an assured ability to acquire acceptable foods in socially acceptable ways (e.g., without resorting to emergency food supplies, scavenging, stealing, or other coping strategies). Food insecurity exists whenever the availability of nutritionally adequate and safe foods or the ability to acquire acceptable foods in socially acceptable ways is limited or uncertain.

FIGURE 3-1 Core concepts related to nutritional state.

Food insecurity, as measured in the United States, refers to the social and economic problem of lack of food due to resource or other constraints, not voluntary fasting or dieting, or because of illness, or for other reasons. This definition, supported by the ethnographic research conducted by Radimer et al. (1992); Wolfe, Frongillo, and Valois (2003); Hamelin, Habicht, and Beaudry (1999); Hamelin, Beaudry, and Habicht (2002); Quandt and Rao (1999); Quandt, McDonald, Arcury, Bell, and Vitolins (2000); and Quandt, Arcury, McDonald, Bell, and Vitolins (2001), means that food insecurity is experienced when there is (1) uncertainty about future food availability and access, (2) insufficiency in the amount and kind of food required for a healthy lifestyle, or (3) the need to use socially unacceptable ways to acquire food (see Figure 3-2 ). Although lack of economic resources is the most common constraint, food insecurity can also be experienced when food is available and accessible but cannot be used because of physical or other constraints, such as limited physical functioning by elderly people or those with disabilities (Lee and Frongillo, 2001a, 2001b).

Some closely linked consequences of uncertainty, insufficiency, and social unacceptability are assumed to be part of the experience of food insecurity. Worry and anxiety typically result from uncertainty. Feelings of alienation and deprivation, distress, and adverse changes in family and social interactions also occur (Hamelin et al., 1999, 2002; Frongillo and Horan, 2004). As stated in the previous chapter, hunger and malnutrition are also potential, although not necessary, consequences of food insecurity. Management strategies that people use to prevent or respond to

FIGURE 3-2 Food insecurity, and its determinants and consequences (adapted from Habicht et al., 2004).

the experience of food insecurity are conceptually different from food insecurity but are tied to it.

Food insecurity is measured as a household-level concept that refers to uncertain, insufficient, or unacceptable availability, access, or utilization of food. It is experienced along with some closely linked consequences of it. There is a strong rationale for measuring food insecurity at the household level. It is possible for individuals to be food secure in a food-insecure household, just as it is possible for individuals to not be poor in a poor household, depending on the intrahousehold allocation of resources. It means that we can measure and report the number of people who are in food-insecure households (with not all of them necessarily food insecure themselves). When a household contains one or more food-insecure persons, the household is considered food insecure.

Although food is a fundamental need in that each individual must have access to necessary nutrients to survive and to participate actively in society, food is only one of the needs that people must make efforts to meet. Households often make trade-offs among needs to ensure their long-term viability as units. Households manage the stocks and flows of assets and

cash to meet basic needs, offset risk, ease shocks, and meet contingencies (Pelletier et al., 2001; Rose et al., 1995). For example, people in households may consume less food in the present to preserve assets and future ability to make their living, or people may forgo some food to be able to buy medication to treat illness (Wolfe et al., 2003). A full understanding of food insecurity requires incorporation of the time element—both in the sense of the periodicity of occurrence of various needs and events and in the sense of the frequency and duration of episodes (Maxwell and Frankenberger, 1992). Frequency and duration are therefore important elements for the U.S. Department of Agriculture (USDA) to consider in the operational definition and measurement of household food insecurity and individual hunger. (This issue is discussed further in Chapter 4 .)

ADVERSE OUTCOMES OF FOOD INSECURITY

Research has shown that food insecurity is associated with adverse health and developmental outcomes in children and adults that are both nutritional and nonnutritional in nature. 2 Food insecurity is associated with higher prevalence of inadequate intake of key nutrients (Rose, Habicht, and Devaney, 1998; Casey, Szeto, Lansing, Bogle, and Weber, 2001; Lee and Frongillo, 2001a; Adams, Grummer-Strawn, and Chavez, 2003), risk of overweight in women and some girls (Olson, 1999; Alaimo, Olson, and Frongillo, 2001a; Laitinen, Power, and Javelin, 2001; Townsend, Peerson, Love, Achterberg, and Murphy, 2001), depressive symptoms in adolescents (Alaimo, Olson, and Frongillo, 2002), and academic and social developmental delays in children (Kleinman et al., 1998; Murphy et al., 1998; Alaimo et al., 2001b; Reid, 2001; Stormer and Harrison, 2003; Ashiabi, 2005). Data from a longitudinal study of welfare recipients show that household food insecurity is associated with poor physical and mental health of low-income black and white women (Siefert, Heflin, Corcoran, and Williams, 2004). Food insecurity is also associated with more behavioral problems (Olson, 1999; Shook Slack and Yoo, 2004), poorer school performance (Olson, 1999; Alaimo et al., 2001b; Dunifon and Kowaleski-Jones, 2003), and adverse health outcomes (Alaimo, Olson, Frongillo, and Briefel, 2001c; Cook et al., 2004; Weinreb et al., 2005) in children. Data from the Early Child Longitudinal Study-Kindergarten Class show that reporting at least one indicator of food insecurity was significantly associated with impaired learning in mathematics from fall to spring of the kindergarten year (Winicki and Jemison, 2003) and with impaired learning in reading from kindergarten to third grade (Jyoti, Frongillo, and Jones, 2005).

CONCEPT AND DEFINITION OF HUNGER

The conceptual definition of hunger adopted by the interagency group on the food security is: “The uneasy or painful sensation caused by a lack of food, the recurrent and involuntary lack of food. Hunger may produce malnutrition over time…. Hunger … is a potential, although not necessary, consequence of food insecurity” (Anderson, 1990, pp. 1575, 1576). This language does not provide a clear conceptual basis for what hunger should mean as part of the measurement of food insecurity. The first phrase “the uneasy or painful sensation caused by a lack of food” refers to a possible consequence of food insecurity, as discussed above. The second phrase “the recurrent and involuntary lack of access to food” refers to the whole problem of food insecurity, the social and economic problem of lack of food as defined above.

Holben (2005) 3 has enumerated a large number of definitions of hunger from various sources. Taken together, these definitions fall into four groups regarding the concept of hunger: (1) a motivational drive, need, or craving for food; (2) an uneasy sensation felt when one has not eaten for some time; (3) discomfort, illness, weakness, or pain caused by a prolonged, involuntary lack of food; and (4) the prolonged, involuntary lack of food itself. The first and second of these are not the interest of the household food security survey because they refer to a natural phenomenon that all humans experience on a regular basis. The fourth is also not a useful definition or concept of hunger because it refers to the problem of food insecurity itself. The third provides a starting point for consideration as to what is intended for the Household Food Security Survey Module (HFSSM). It refers to the consequence of food insecurity that, because of a prolonged, involuntary lack of food due to lack of economic resources, results in discomfort, illness, weakness, or pain that goes beyond the usual uneasy sensation.

Available evidence from ethnographic work affirms that this definition of hunger is well understood and is reported in similar terms in the United States (Radimer et al., 1992; Wolfe, Frongillo, and Valois, 2003) and Québec (Hamelin, Beaudry, and Habicht, 2002). There is consensus in U.S. society, supported by this empirical research, that an individual’s report that he or she has experienced hunger because of lack of food provides a straightforward indication that the individual has, indeed, experienced hunger in the sense of the third definition (i.e., discomfort, illness, weakness, or

pain caused by a prolonged, involuntary lack of food). But unlike food insecurity, which is a household-level concept, hunger is an individual-level concept. For purposes of the HFSSM included in the Food Security Supplement to the CPS, the term “hunger” should refer to a potential consequence of food insecurity that, because of prolonged, involuntary lack of food, results in discomfort, illness, weakness, or pain that goes beyond the usual uneasy sensation. Two questions therefore arise. First, can the experience of severe food insecurity with hunger by households be measured and its prevalence estimated? Second, can the experience of hunger by individuals be measured and its prevalence estimated?

The HFSSM is measuring food insecurity at the level of the household; it is not measuring hunger at the individual level. The scale does not give special weight to the hunger questions. The HFSSM does include items that are related to being hungry among food-insecure households. The ethnographic and quantitative evidence discussed earlier has shown that the HFSSM items on hunger are probably appropriate in the food insecurity scale, but these items contribute to the measurement of household food insecurity and not specifically to the measurement of hunger at the individual level.

For the purposes of measuring and estimating the prevalence of hunger among individuals in the population, something that the HFSSM does not do, some of these same items might be used in a measure of hunger among individuals, but it would require a measurement process that is based on the conceptual definition of the condition, as well as a battery of items designed to measure it and a reoriented sampling design that includes the individual as the unit of analysis. This work could be based on the information from such sources as up-to-date ethnographic studies of low-income populations, results of experiments and analysis of surveys, analysis of public opinion and perspectives of user groups, expert assessment, and other relevant information.

The panel therefore concludes that hunger is a concept distinct from food insecurity, which is an indicator and possible consequence of food insecurity, that can be useful in characterizing severity of food insecurity. Hunger itself is an important concept, but it should be measured at the individual level distinct from, but in the context of, food insecurity.

To summarize, the panel’s conclusion is based on the fact that, although a strong theoretical and research base exists for the conceptualization and measurement of food insecurity, we do not have a correspondingly strong base for either the conceptualization of hunger or its measurement. That is, there is now ample theoretical, conceptual, ethnographic, and quantitative work done to justify the measurement of the experience of food insecurity using a questionnaire. For the measurement of the experience of hunger to be equally credible, there needs to be a stronger base than we currently have

in developing clear concepts for how we should think about hunger and in tested means to accurately elicit information from survey respondents about whether they have experienced hunger.

Recommendation 3-1: USDA should continue to measure and monitor food insecurity regularly in a household survey. Given that hunger is a separate concept from food insecurity, USDA should undertake a program to measure hunger, which is an important potential consequence of food insecurity.

Recommendation 3-2: To measure hunger, which is an individual and not a household construct, USDA should develop measures for individuals on the basis of a structured research program, and develop and implement a modified or new data gathering mechanism. The first step should be to develop an operationally feasible concept and definition of hunger.

Recommendation 3-3: USDA should examine in its research program ways to measure other potential, closely linked consequences of food insecurity, in addition to hunger, such as feelings of deprivation and alienation, distress, and adverse family and social interaction.

It took a lot of discussion and conferences for the Food Security Measurement Project to reach a working agreement on the operational definition of food security and insecurity. Hunger is a complex concept, and it should be well thought through to ensure agreement among the key users and then to develop and test the appropriate questions and to identify the survey mechanism and sample design for collecting the needed data. Such an effort will take time.

APPLICATION OF THE CONCEPTS AND DEFINITIONS FOR MEASUREMENT

The broad conceptual definition of household food insecurity includes more elements than are included in the current USDA measure of food insecurity. The current measure of prevalence of household food insecurity obtained through the HFSSM focuses on the uncertainty and insufficiency of food availability and access that are limited by resource constraints, and the worry or anxiety and hunger that may result from it. It does not include questions on nutritional adequacy, safety, or social unacceptability of food access, concepts that are part of the broad conceptual definition.

It also does not include questions on use that may be particularly applicable to elderly people and those with disabilities. The HFSSM covers the core ideas of food being available and accessible but not the ability to be used; measurement of food insecurity is tied to economic constraints but not physical constraints that might affect use of food. Wolfe and colleagues (2003) point out that although economic constraint is a major cause of food insecurity, elderly people sometimes have enough money for food but are not able to access it because of transportation or functional limitations, or they are not able to use the food, that is, not able to prepare or eat available food. However, there is currently no epidemiological evidence demonstrating that incorporating items about the ability to use food will alter the prevalence estimates of food insecurity. Furthermore, there is no evidence to suggest that the original decision made when the measure was developed to focus on food insecurity that arises in the context of economic constraints—deemed to be the food insecurity that is policy-relevent—should be altered.

Furthermore, the HFSSM does not attempt to measure management strategies, although questions are asked in the Food Security Supplement (FSS) that do assess management (i.e., augmentation) strategies, such as getting emergency food from a food pantry, eating meals at a soup kitchen, and borrowing money to buy food. “These coping behavior items were tested for inclusion in the food security scale. However, they were found not to meet the statistical test criteria for inclusion with the measurement scale, even though they correlate closely with the scale. Very few households use these copying behaviors that are not also identified as food insecure by the scaled measure” (Bickel, Nord, Price, Hamilton, and Cook, 2000, p. 43).

As stated in the previous chapter, one of the requirements of the National Nutrition Monitoring and Related Research Act is to recommend a standardized mechanism and instrument for defining and obtaining data on prevalence of food insecurity or food insufficiency at the national and state levels. The Food Security Measurement Project working group reached agreement during the 1994 conference to limit the operational definitions and measurement to only those aspects of food security that can be captured in household-level surveys and to further limit the measure to lack of economic resources to obtain food. The definition does not include the supply of food or nutrition. These additional aspects would require developing measures and fielding separate surveys to measure them. The food supply in the United States is generally regarded as safe relative to some other countries. Nutritional adequacy is already assessed by other elements of the nutrition monitoring system, in particular the continuing National Health and Nutrition Examination Survey.

The panel therefore concludes that it is neither required nor necessarily appropriate for USDA to attempt to measure all elements of the conceptual definition of food insecurity as part of the HFSSM.

LABELS OF FOOD INSECURITY

Since food insecurity is conceptualized by USDA as a continuous scale score, attaching labels to various levels of the score to communicate the results in a simpler manner is a natural and common presentation device. As discussed in Chapter 5 , it is a common practice to present estimates from scale scores by identifying cut points on the scale and characterizing the units between the cut points by a descriptive label. For example, a person scoring above a cut point might be considered to exhibit proficiency in a specific skill in a certification test, or cut points might be assigned that classify students’ performance in mathematics in an educational assessment (e.g., see Adams and Wu, 2002). One goal of the cut points is to form a classification scheme that categorizes individuals (e.g., in a certification test). A second goal is to describe the distribution of the number of persons or households in each category of the classification scheme over the population (e.g., in the National Assessment of Educational Progress, the children assessed are not labeled individually, but the proportion of children at each level is estimated). Methods for establishing cut points to define a classification scheme are discussed in Chapter 5 .

The labels for the categories associated with the units between the cut points are themselves very important because they are the vocabulary used in the discussion of the scores. In fact, the labels are the primary way of identifying the outcomes for many audiences for whom the score itself may be of less interest. For example, journalists, the public, and secondary users of the data typically discuss estimates almost exclusively in terms of the labels. An analogy is the relationship between income, a continuous variable similar to a scale score, and poverty, which is a label, based on a cut point of income (and family size). Many users are almost exclusively interested in the number and characteristics of persons below the “poverty” level.

Because of their importance, the labels should be consistent with the data collected and should communicate a common understanding of what is being measured. The recent report issued by the Committee on Performance Levels for Adult Literacy discusses these as important criteria for labels (National Research Council, 2005). In that report, the committee explicitly recommended not using “proficiency” in any of the labels for the 2003 National Assessment of Adult Literacy (NAAL) assessments, because the test was not intended to measure proficiency, nor can it be revised in a

post hoc manner to do so. 4 The committee concluded that labeling adults as proficient based on the NAAL assessments would be inconsistent with the data collected and the common understanding of the term “proficient.” This recommendation points out the importance of the labels and the requirement that they be appropriate.

The labeling used for the classification of food insecurity is at the heart of the criticism of the current measurement system. In particular, the category “food insecurity with hunger” has come under scrutiny because of disagreement over whether hunger is actually measured. For the current discussion, we ignore the distinction between households with and without children and discuss only the labels for households without children—food secure, food insecure, and food insecure with hunger. A key criticism of the current system is that a household may be labeled “food insecure with hunger” even though the household respondent does not explicitly affirm hunger in the interview. This criticism is discussed earlier in this chapter and is further considered in Chapter 5 .

The rationale for including hunger in the label for the scale is understandable. Hunger is a politically sensitive and evocative label that conjures images of severe food deprivation, and the HFSSM includes some items that are specifically related to hunger. As discussed here and in Chapter 2 , however, the measurement of food insecurity rather than hunger has been the primary focus of the HFSSM since its inception.

A particular concern, which has been raised and discussed earlier in this chapter, is that hunger as experienced by an individual and hunger as experienced by persons in a household may differ. As an indication of the severity of food insecurity, the HFSSM asks the household respondent if in the past 12 months she or he has experienced being hungry because of lack of food due to resource constraints. This is not the same as evaluating all individuals in the household in a survey as to whether or not they have experienced hunger.

A second concern is that, in some households with severe food insecurity, none of the household members may be hungry, while in other households some members will be hungry and some will not. Food insecurity has other consequences, many of which may have effects that are serious and long-lasting.

To illustrate the panel’s concerns, consider the USDA report containing the basic estimates from the 2004 CPS supplement on food security (Nord et al., 2005b). While this report carefully explains the concepts and issues associated with food insecurity and how it is measured, a section (page 7) is

titled “How often were people hungry in households that were food insecure with hunger.” The point of the section is that food insecurity is typically episodic rather than chronic in the United States, and most of the details in the section clearly state that the estimates refer to the classification of households, not hunger in individuals. The title suggests, however, that the survey addresses the question of how often people are hungry, even though the survey does not produce valid estimates of how often people are hungry. Despite careful wording within the section, the title is misleading.

Another example is the USDA report on measuring food security for children (Nord and Bickel, 2002). Again, this report carefully explains the concepts and issues associated with food security and how it is measured and addresses a serious concern about the effect of the classification scheme for a subgroup of great importance, children. Despite the authors’ clear understanding of the key issues and care in presentation, the report discusses the prevalence of hunger among children, even in the abstract. The labeling of the most severe range as food insecure with hunger adds confusion to the reporting.

These two reports are actually among the best in terms of expression of the concepts and using the data appropriately, and yet even they could be easily misinterpreted. Other examples could be cited from documents that are less carefully worded and distort the data from the survey. USDA needs to be more careful in its reports to properly communicate that this third category refers to households with severe food insecurity in which the respondent has either missed meals or was hungry because there wasn’t enough money for food at some time during the year.

With a label that includes the word “hunger,” challenges in communicating an appropriate understanding of food insecurity are inevitable. The challenges are intrinsic, in that the label conveys the idea that severe food insecurity is synonymous with hunger, while this is not necessarily the case. Modifications of the label that still include the word “hunger” do not eliminate the potential for misleading many users, especially the public. Measuring prevalence of household food insecurity with the respondent experiencing hunger is not the same as measuring the prevalence of hunger experienced by individuals. The latter will require a separate research and development process to be implemented in an individual respondent–based survey as opposed to a household respondent–based one.

Alternate labels that may be less problematic could be used. The panel urges USDA to consider alternate labels that may be better and to develop short and appropriate descriptions of the types of households that fall within the cut points associated with the labels. The report of the Committee on Performance Levels for Adult Literacy, referred to above, has given guidance on these types of descriptions that are equally valid for the food insecurity scale.

Recommendation 3-4: USDA should examine alternate labels to convey the severity of food insecurity without the problems inherent in the current labels. Furthermore, USDA should explicitly state in its annual reports that the data presented in the report are estimates of prevalence of household food insecurity and not prevalence of hunger among individuals.

The United States is viewed by the world as a country with plenty of food, yet not all households in America are food secure, meaning access at all times to enough food for an active, healthy life. A proportion of the population experiences food insecurity at some time in a given year because of food deprivation and lack of access to food due to economic resource constraints. Still, food insecurity in the United States is not of the same intensity as in some developing countries. Since 1995 the U.S. Department of Agriculture (USDA) has annually published statistics on the extent of food insecurity and food insecurity with hunger in U.S. households. These estimates are based on a survey measure developed by the U.S. Food Security Measurement Project, an ongoing collaboration among federal agencies, academic researchers, and private organizations.

USDA requested the Committee on National Statistics of the National Academies to convene a panel of experts to undertake a two-year study in two phases to review at this 10-year mark the concepts and methodology for measuring food insecurity and hunger and the uses of the measure. In Phase 2 of the study the panel was to consider in more depth the issues raised in Phase 1 relating to the concepts and methods used to measure food security and make recommendations as appropriate.

The Committee on National Statistics appointed a panel of 10 experts to examine the above issues. In order to provide timely guidance to USDA, the panel issued an interim Phase 1 report, Measuring Food Insecurity and Hunger: Phase 1 Report. That report presented the panel's preliminary assessments of the food security concepts and definitions; the appropriateness of identifying hunger as a severe range of food insecurity in such a survey-based measurement method; questions for measuring these concepts; and the appropriateness of a household survey for regularly monitoring food security in the U.S. population. It provided interim guidance for the continued production of the food security estimates. This final report primarily focuses on the Phase 2 charge. The major findings and conclusions based on the panel's review and deliberations are summarized.

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Food Insecurity

Aug 07, 2013

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Food Insecurity. FCS 3151 M. Burns, PhD, RD. Is food insecurity an issue in America?.

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Food Insecurity FCS 3151 M. Burns, PhD, RD

Is food insecurity an issue in America? “Given the agricultural bounty and wealth of the United States, it is not only inhumane but also unwise and short-sighted, from a human capital perspective, to allow food insecurity and hunger to continue to exist at their present levels.” J Am Diet Assoc. 2002 M. Burns, 2005

A few statistics • Households with children had more than twice the rate of food insecurity as those without children (16.7% v. 8.2%) • Married with children – lowest rate of food insecurity • Food insecure households headed by a single mom (31.7%) • Poverty rate by ethnicity: Black (24.4%), Hispanic (22.5%), and white (10.5%) M. Burns, 2005

Food security is not • While the government is concerned about the ‘security’ of our food supply, food security, for our purposes, deals primarily with food availability. M. Burns, 2005

Movie Fans? • What movie did Julia Roberts win the academy award for best actress in 2000? M. Burns, 2005

Erin Brockovich M. Burns, 2005

Food insecurity is evident when families... • Lack access to food. • Depend on food assistance programs. • Skip meals. • Substitute nutritious foods with less expensive alternatives. • Seek assistance from soup kitchens and food pantries. M. Burns, 2005

How is food security measured? • Food Security Questionnaire • 10-18 indicators (10 in no children) • Used in the Current Population Survey and CSFII • Four minutes to administer • Scoring results in placement into one of three categories M. Burns, 2005

This presentation will probably involve audience discussion, which will create action items. Use PowerPoint to keep track of these action items during your presentation • In Slide Show, click on the right mouse button • Select “Meeting Minder” • Select the “Action Items” tab • Type in action items as they come up • Click OK to dismiss this box • This will automatically create an Action Item slide at the end of your presentation with your points entered. Soup Stop Soup Stop Darlene Riedemann

Soup Stop We are a diverse ecumenical community group who have joined together to provide a nutritional meal in a loving, friendly atmosphere.Without question, we accept all and feed all who are hungry while respecting the dignity of all our guests. Mission Statement

Soup Stop Background • Soup Stop was formed in the Spring of 2001 • A temporary board was set up • Soup Stop is a group of people with a desire to feed hungry people • Milestones • First meal served 06/18/2001 • Contributors (individual, business, and churches) • Volunteers are the backbone of Soup Stop

Soup Stop Temp board goals • Draft a mission statement • Find and approve a location • Determine time table for service and the timeframe for initiation

Soup Stop Activity • Community meeting held - temporary board formed • Mission statement drafted • Drafted a letter of intentions to various local organizations • Identified opportunities for food handler training • Evaluated several potential locations • Procured some service equipment (pro gratis) • Published public information in local newspapers • Stayed within our budget

Soup Stop Possible Initial Locations Eliminated • South Side Café • Northside Baptist • Newman Center • Otterbein • Beaver’s Catering • Eagle’s Club • Laborer’s Hall

Soup Stop We have addressedseveral needs... • Location for food preparation and serving • Health department approval • Sources of funding • “Umbrella” organization for insurance • Licensed food handlers (one needs to be present at all times) • Volunteers, volunteers, volunteers

Soup Stop Status Summary • We are located at First Presbyterian Church in downtown Charleston • Soup Stop is feeding people • More than 50 volunteers are working • Meals are served five days of the week • The current meal is peanut butter sandwich, fruit, a snack, choice of soup and bottled water.

Soup Stop Progress • Soup Stop has developed a dual plan for hot lunches • Microwaveable meals (Mon-Wed-Fri) • Catered portions from What’s Cooking (Tue-Thu) • Soup Stop has served 4,300 meals since 06/18/2001

Soup Stop Attention Areas • It has been a slow course to serving hot meals • Equipment and kitchen limitations • Health Department compliance • Soup Stop has addressed these issues by… • Working with Health Department to identify requirements • Equipment needs were identified • A plan for delivery was developed

Soup Stop * Board member **Director Volunteers • Carl & Nancy Curran • Lenore Thompson • Marylee Coffey • Nell Cunningham • Susanne Applegate • Cathy Babbs • Judy Kline* • MaryAnn & Sam Taber • Lee & Sandy Adams • Velda Hoker • Helen Krebut-Reed • JoAnne Laible • Joyce & Dave Mauer • Marjorie Howard • Wynette Noll* • Miriam Whitlow* • Dorothy Rodgers • John Bennett • Harold Strangeman* • Ed & Delores Ferguson • Linda Simpson • Gail Cox • Phyllis Hackett • Jeanette Scott • Brian Houston • Dorothy Schulke • Barbara Fanello** • Dan & Vicki Bircher • Shirley Stewart* • Christy Pruemer • Megah Ghimire* • Gabby Zimija • Melissa Bailey • Eric Barkley* • Nick Kousma* • Donna Ayers* • Peter Leigh* • Brenda Warren* • Darlene Riedemann* • Jack Cosby*

Food security status M. Burns, 2005

Food secure • All people at all times have access to enough food for an active, healthy life. • This includes the ready availability of nutritionally-adequate, safe foods and the assured ability to acquire them in socially acceptable ways. M. Burns, 2005

Food insecure withouthunger • Limited or uncertain ability to acquire or consume an adequate quality or sufficient quantity of food in socially acceptable ways. M. Burns, 2005

Food insecure with hunger • The uneasy or painful sensation caused by a recurrent or involuntary lack of food, which may produce malnutrition over time. • Moderate – adults • Severe – children, and more severe among adults M. Burns, 2005

Poor Working poor The Young The old Low-income women Ethnic minorities Inner city and rural dwellers Certain southern and western states Farmers Homeless Who is hungry? M. Burns, 2005

M. Burns, 2005

Consequences of Food Insecurity • Physical impairments related to insufficient food • Psychological issues due to lack of access to food • Sociofamilial disturbances M. Burns, 2005

Healthy People 2010 • To increase food security among US households to 94% (from the baseline of 88%) and in so doing reduce hunger M. Burns, 2005

Moment of Reflection How can food insecure individuals living in poverty be overweight? M. Burns, 2005

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Fao: 31.8 million nigerians grappling with food insecurity.

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Ndubuisi Francis and James Emejo in Abuja

The Food and Agriculture Organisation (FAO), yesterday said that over 31.8 million Nigerians were suffering from acute food insecurity, worsened by malnutrition among women and children across the country.

The organisation disclosed this in its 2024 Cadre Harmonise report, published in collaboration with other development partners including GIZ.

The report pointed out that the surge in food commodity prices occasioned by the removal of fuel subsidy, as well as security challenges, had further placed millions of Nigerians in a precarious situation.

During their joint review meeting on the Implementation of the Food Systems in Nigeria, the stakeholders advocated a multi-sectoral approach of collaboration to tackle food insecurity.

They stressed that the Civil Society Organisations (CSOs) and the private sector must play a role to complement nutrition efforts.

The partners further pledged their unalloyed support to transform the food system in Nigeria.

While earlier declaring the meeting open, Permanent Secretary, Ministry of Budget and Economic Planning, Dr. Emeka Obi, said the objective of the meeting was to discuss the status of the implementation and for the Ministries, Departments and Agencies (MDAs) to make presentations in addition to giving updates on the activities implemented for the food transformation pathways, in Nigeria.

In a statement by the Director, Press and Public Relations in the ministry, Julie Osagie-Jacobs , the permanent secretary, appreciated the support of development partners, particularly GIZ and others for their dedication in moving the food system forward in Nigeria while noting that their collective efforts would continually lead to innovative solutions that would strengthen the food systems.

Also, National Convenor of Food Systems in Nigeria/Director, Social Development, Ministry of Budget and Economic Planning, Dr. Sanjo Faniran, hailed all stakeholders for their dedication in moving the food system forward.

He added that the review meeting was also to identify gaps, successes and challenges, offer recommendations as well as peer review, among MDAs. A total of 24 MDAs attended the review meeting

Meanwhile, in a bid to strengthen food security and boost agricultural productivity in the country, the federal government has stepped up moves to foster a global partnership with the United Nations World Food Programme (WFP).

 Minister of Finance and Coordinating Minister of the Economy, Mr. Wale Edun, who disclosed this in Abuja while receiving a high-level WFP delegation led by the newly appointed Regional Director, Mr. Chris Nikoi, stated that the visit was part of ongoing efforts to strengthen food security and boost agricultural productivity in Nigeria.

This, he added, was in line with the Renewed Hope Agenda of the President Bola  Tinubu-led administration.

The Director,  Information and Public Relations,  Ministry of Finance,  Mohammed Manga said in a statement that the meeting also attended by the Minister of Agriculture and Food Security, Senator Abubakar Kyari, explored opportunities for partnership and collaborative initiatives to drive Nigeria’s agricultural growth.

“The engagement further provided an opportunity for the WFP delegation to highlight the numerous opportunities for partnership and also expressed their readiness to support Nigeria’s agricultural sector through collaborative ventures.

“Key among these initiatives are service delivery programmes focused on the provision of fertilizers and seeds, which are essential for enhancing farming output and ensuring food security,” the statement said.

In his remarks, Edun commended the WFP for its proactive engagement and underscored the significance of the interventions in advancing the administration’s reform agenda, particularly in the areas of enhanced food availability and agricultural production.

He assured the delegation that the Ministry of Finance would take the necessary steps to facilitate the implementation of the proposed initiatives.

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