Homeownership status and risk of food insecurity: examining the role of housing debt, housing expenditure and housing asset using a cross-sectional population-based survey of Canadian households
Notice bibliographique
Résumé
BACKGROUND: Household food insecurity is a potent marker of material deprivation with adverse health consequences. Studies have repeatedly found a strong, independent relationship between owning a home and lower vulnerability to food insecurity in Canada and elsewhere, but the reasons for this relationship are poorly understood. We aimed to examine the influence of housing asset, housing debt and housing expenditure on the relationship between homeownership status and food insecurity in Canada. METHODS: Cross-sectional data on food insecurity, housing tenure and expenditures, home value, income and sociodemographic characteristics were derived from the 2010 Survey of Household Spending, a population-based survey. Multivariable logistic regression models were conducted to estimate odds ratios of food insecurity among households of all incomes (n = 10,815) and those with lower incomes (n = 5547). RESULTS: Food insecurity prevalence was highest among market renters (28.5%), followed by homeowners with a mortgage (11.6%) and mortgage-free homeowners (4.3%). Homeowners with a mortgage (OR: 0.51, 95% CI: 0.39-0.68) and those without a mortgage (OR: 0.23, 95% CI: 0.16-0.35) had substantially lower adjusted odds of food insecurity than market renters, and accounting for the burden of housing cost had minimal impact on the association. Mortgage-free homeowners had lower adjusted odds ratios of food insecurity compared to homeowners with a mortgage, but differences in the burden of housing cost fully accounted for the association. When stratifying homeowners based on presence of mortgage and housing asset level, the adjusted odds ratios of food insecurity for market renters were not significant when compared to mortgage holders with low housing asset. Mortgage-free owners with higher housing asset were least vulnerable to food insecurity (adjusted OR: 0.18, 95% CI: 0.11-0.27). CONCLUSIONS: Substantial disparities in food insecurity exist between households with different homeownership status and housing asset level. Housing policies that support homeownership while ensuring affordable mortgages may be important to mitigate food insecurity, but policy actions are required to address renters' high vulnerability to food insecurity.
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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 | 0,004 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 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 ».