Household Food Insecurity in Middle- and High-Income Countries Before and During the COVID-19 Pandemic
Bibliographic record
Abstract
The impact of the pandemic on the prevalence of food insecurity is unclear given the potentially contradictory effects of shocks such as lockdowns versus stabilizers such as income supplements. We examined changes in the prevalence of household food insecurity in five countries from before (2019) to during the pandemic (2020). Data were drawn from cross-sectional surveys conducted in Australia, Canada, Mexico, the United Kingdom (UK), and the United States (US) in November/December of 2018,2019, and 2020. Adults aged 18–99 years were surveyed annually (2018: n = 22,731,2019: n = 19,274,2020: n = 21,323). Household food insecurity in the past 12 months was assessed using the Household Food Security Survey Module. Weighted logit models examined changes in the prevalence of living in households affected by food insecurity from 2019 to 2020, adjusting for the prevalence in 2018 and demographic characteristics. Weighted generalized logit models examined changes in the severity of food insecurity. Adults in Mexico had a higher probability of living in food-insecure households in 2020 compared to 2019 (β = 0.14, p = 0.02). In contrast, decreases in the probability of living in food-insecure households in 2020 compared to 2019 were observed in Australia (β = −0.21, p < .001) and Canada (β = −0.14, p = 0.03). In the UK and the US, no important changes in the prevalence of food insecurity were observed (UK: β = −0.11, p = 0.11, US: β = 0.05, p = 0.42). Changes in the severity of food insecurity within countries are also evident. Changes in the prevalence of household food insecurity during the pandemic appear to differ across countries. Further analyses will contextualize these differences in relation to varied policy responses to the pandemic, as well as changes in prevalence among subgroups such as those with low incomes. A. Pepetone received a stipend from a Canadian Institutes of Health Research (CIHR)/Natural Sciences and Engineering Research Council/Social Sciences and Humanities Research Council Healthy Cities Research Training Platform. Funding for the International Food Policy Study was provided by a CIHR Project Grant, with additional support from Health Canada, the Public Health Agency of Canada (PHAC), and a CIHR-PHAC Applied Public Health Chair held by D. Hammond.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".