Predictors of households at risk for food insecurity in the United States during the COVID-19 pandemic

  1. Brianna N Lauren
  2. Elisabeth R Silver
  3. Adam S Faye
  4. Alexandra M Rogers
  5. Jennifer A Woo-Baidal
  6. Elissa M Ozanne
  7. Chin Hur

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Abstract

Objective:

To examine associations between sociodemographic and mental health characteristics with household risk for food insecurity during the COVID-19 outbreak.

Design:

Cross-sectional online survey analysed using univariable tests and a multivariable logistic regression model.

Setting:

The United States during the week of 30 March 2020.

Participants:

A convenience sample of 1965 American adults using Amazon’s Mechanical Turk platform. Participants reporting household food insecurity prior to the pandemic were excluded from analyses.

Results:

One thousand two hundred and fifty participants reported household food security before the COVID-19 outbreak. Among this subset, 41 % were identified as at risk for food insecurity after COVID-19, 55 % were women and 73 % were white. On a multivariable analysis, race, income, relationship status, living situation, anxiety and depression were significantly associated with an incident risk for food insecurity. Black, Asian and Hispanic/Latino respondents, respondents with an annual income <$100 000 and those living with children or others were significantly more likely to be newly at risk for food insecurity. Individuals at risk for food insecurity were 2·60 (95 % CI 1·91, 3·55) times more likely to screen positively for anxiety and 1·71 (95 % CI 1·21, 2·42) times more likely to screen positively for depression.

Conclusions:

An increased risk for food insecurity during the COVID-19 pandemic is common, and certain populations are particularly vulnerable. There are strong associations between being at risk for food insecurity and anxiety/depression. Interventions to increase access to healthful foods, especially among minority and low-income individuals, and ease the socioemotional effects of the outbreak are crucial to relieving the economic stress of this pandemic.

Article activity feed

  1. Version published to 10.1017/s1368980021000355
  2. ScreenIT

    SciScore for 10.1101/2020.06.10.20122275: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Measures: Analysis:
    Measures
    suggested: None

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our study has limitations. Firstly, our cross-sectional approach precludes causality inferences, and relies on retrospective reports of food insecurity prior to the COVID-19 outbreak. Secondly, we collected data through Amazon’s Mechanical Turk, potentially creating a response bias and limiting the generalizability of our findings to the larger US population. Finally, our data were collected in late March, when the economic impacts of the outbreak were just beginning to affect many nationwide. We hypothesize that this may partially explain the unexpected result that unemployment was not a predictor of incident food insecurity. Although the validated food insecurity screening questions that we used in our study specifically referenced an ability to afford food (rather than an inability to buy food due to shortages), our finding of increased risk of food insecurity among those with annual income between $50,000-$100,000 is also surprising. Because we did not specify a time frame for these earnings, participants’ responses may reflect historical income rather than concurrent changes in annual income resulting from the pandemic. Continued research on the effects of the pandemic on food insecurity, particularly in larger community-based samples, is necessary to fully understand its impacts. Our study quantifies the effects of the COVID-19 pandemic on household food insecurity in the US and identifies associated risk factors. Our results suggest that the economic fallout of the pan...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.06.10.20122275v1 on medRxiv