Household Food Insecurity in Middle- and High-Income Countries Before and During the COVID-19 Pandemic
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».