Food Insecurity*
The University of Wisconsin Population Health Institute Model of Health
Use the model to explore the measures of how long and how well we live and what shapes these trends. Select a measure in the diagram or browse the list of all measures.
Measures marked with an asterisk (*) are not included in summary calculations for Population Health and Well-being or Community Conditions.
About
Percentage of population who lack adequate access to food. The 2025 Annual Data Release used data from 2022 for this measure.
Lacking consistent access to food is related to negative health outcomes such as weight gain and premature mortality.1,2 In addition to asking about having a constant food supply in the past year, the measure also addresses the ability of individuals and families to provide balanced meals, including fruits and vegetables, further addressing barriers to healthy eating.
The University of Wisconsin Population Health Institute Model of Health
Use the model to explore the measures of how long and how well we live and what shapes these trends. Select a measure in the diagram or browse the list of all measures.
Measures marked with an asterisk (*) are not included in summary calculations for Population Health and Well-being or Community Conditions.
Data and methods
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Data Source
Map the Meal Gap
Feeding America first published the Map the Meal Gap project in early 2011, with the generous support of the Howard G. Buffett Foundation and The Nielsen Company, to learn more about the face of hunger at the local level. In August 2011, with the support of the ConAgra Foods Foundation, child food insecurity data was added to the project.
Citation:
Gundersen, C., Strayer, M., Dewey, A., Hake, M., & Engelhard, E. (2021). Map the Meal Gap 2021: An Analysis of County and Congressional District Food Insecurity and County Food Cost in the United States in 2019. Feeding America.
Key Measure Methods
Food Insecurity is a percentage
Food Insecurity estimates the percentage of the population who did not have access to a reliable source of food during the past year.
The method for calculating Food Insecurity has changed
The current estimates should not be compared to previous years due to changes in methodology. Updates have been made to the model for the data included in the 2021 Annual Data Release, where estimates now account for disability status and reflect a refined definition of poverty.
Food Insecurity is created using statistical modeling
The Food Insecurity measure is modeled with the Core Food Insecurity Model which uses information from the Community Population Survey, Bureau of Labor Statistics, and American Community Survey.
Modeling generates more stable estimates for places with small numbers of residents or survey responses. However, there are also drawbacks to using modeled data. The smaller the population or sample size of a county, the more the estimates are derived from the model itself and the less they are based on survey responses. Models make statistical assumptions about relationships that may not hold in all cases. Finally, there is no perfect model, and each model generally has limitations specific to their methods.
Caution should be used when comparing these estimates across years
Data prior to the 2021 Annual Data Release should not be compared to data from more recent years due to changes in the methods described in the “The method for calculating Food Insecurity has changed” section. Additionally, state values from the 2023 Annual Data Release should not be compared to state values from other years due to pandemic impacts that affected these state level data.
Numerator
The numerator is the population with a lack of access, at times, to enough food for an active, healthy life or with uncertain availability of nutritionally adequate foods.
Denominator
The denominator is the total county population.
Can This Measure Be Used to Track Progress
This measure can be used to measure progress with some caveats. Methodological changes, which are discussed above and were introducd with the 2021 Annual Data Release, make comparisons with estimates prior to that release year difficult. Additionally, modeled data are not particularly good at incorporating the effects of local conditions, such as health promotion policies or unique population characteristics, into their estimates. Counties trying to measure the effects of programs and policies on the data should use great caution when using modeled estimates. In order to better understand and validate modeled estimates, confirming this data with additional sources of data at the local level is particularly valuable.
Finding More Data
Disaggregation means breaking data down into smaller, meaningful subgroups. Disaggregated data are often broken down by characteristics of people or where they live. Disaggregated data can reveal inequalities that are otherwise hidden. These data can be disaggregated by:
- Age
- Race
- Subcounty Area
The USDA produces two excellent resources for communities looking to explore their food environment: the Food Access Research Atlas and the Food Environment Atlas. Both resources are regularly updated and can provide you with information on where there are healthy food options and where people live with few easy options for finding healthy foods.
In addition, Feeding America’s Map the Meal Gap program creates the annual estimates of food insecurity. They also provide a measure of child food insecurity.
References
- Brownson RC, Haire-Joshu D, Luke DA. Shaping the context of health: A review of environmental and policy approaches in the prevention of chronic diseases. Annual Review of Public Health. 2006;27:341-370.
- Dhurandhar EJ. The food-insecurity obesity paradox: A resource scarcity hypothesis. Physiology & Behavior. 2016;162:88-92.