Objective: To facilitate the introduction of food insecurity screening into clinical settings, we examined the test performance of 2-item screening questions for food insecurity against the US Department of Agriculture's Core Food Security Module.Design: We examined sensitivity, specificity, and accuracy of various 2-item combinations of questions assessing food insecurity in the general population and high-risk population subgroups.Setting: 2013 Current Population Survey December Supplement, a population-based US survey Subjects: All survey participants from the general population and high-risk subgroups Results:The test characteristics of multiple 2-item combinations of questions assessing food insecurity have adequate sensitivity (>97%) and specificity (>70%) for widespread adoption as clinical screening measures. Conclusions:We recommend two specific items for clinical screening programs based on their widespread current use and high sensitivity for detecting food insecurity. These items query how often the household "worried whether food would run out before we got money to buy more" and how often "the food that we bought just didn't last and we didn't have money to get more." The recommended items have sensitivity across high-risk population sub-groups of ≥97% and a specificity ≥74% for food insecurity.
For a decade, Feeding America's Map the Meal Gap (MMG) has provided sub‐state‐level estimates of food insecurity for both the full‐population and for children. Along with being extensively used by food banks, it is widely used by state‐ and local‐governments to help plan responses to food insecurity in their communities. In this paper, we describe the methods underpinning MMG, detail the approach Feeding America has used to make projections about the geography of food insecurity in 2020, and how food insecurity rates may have changed due to COVID‐19 since 2018. We project an increase of 17 million Americans who are food insecure in 2020 but this aggregate increase masks substantial geographic variation found in MMG.
The burgeoning food insecurity literature in the United States has provided a portrait of the causes and consequences of food insecurity. One underexplored aspect is the spatial diversity in food insecurity across the United States. In response, Feeding America has been releasing annual county‐level food insecurity estimates since 2010. In this article, we describe the methods underlying these estimates, followed by answers to the following: What are the state‐level determinants of food insecurity? What is the distribution of food insecurity across counties in the United States? How do the county‐level food insecurity estimates generated in Map the Meal Gap compare with other sources?
Limited access to healthy food caused by food insecurity makes diabetes mellitus (DM) self-management more challenging. Using data from Hunger in America 2014 (n = 60,122 US food pantry users), we sought to understand food preferences and coping strategy utilization (e.g. choosing between paying for food and medical care) among households seeking assistance from US food pantries with and without DM members. The prevalence of wanting and not obtaining fruits, vegetables, dairy, and protein was high among all households. After adjusting for sociodemographic characteristics, households with DM members were more likely to want and not obtain fruits, vegetables, and dairy, and were also more likely to use several coping strategies to increase food access, compared to households without DM members. These results highlight the high demand for healthy food items among clients from US food pantries, particularly among households with DM, as well as the extra burden DM may place on food insecure households.
The majority of poor households in the United States are food secure which is both surprising and not well-understood. We increase our understanding by looking at a particularly vulnerable group-people visiting food pantries-through the use of the Hunger in America (HIA) 2014 data set. Along with providing information on households often overlooked in other data sets, HIA has a wide array of questions. We find, in comparison to food secure households, that food insecure households are more likely to have challenges paying their bills and are more likely to use coping mechanisms. These effects are large, especially when compared to the standard set of explanatory variables. We also find that the impact of the standard set of covariates change dramatically when we include variables that are often unobserved, suggesting that there may be biases in some of the central findings on the determinants of the food insecurity.
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