Income, education, and hospitalization in Canada: results from linked census and administrative data
Notice bibliographique
Résumé
Abstract Background Addressing population health inequities begins with quantifying how social factors affect the health and health care utilization of individuals. Such quantification relies on the availability of detailed health and demographic data. Unfortunately, administrative health care data rarely includes detailed demographic information. Data linkage, which combines administrative health data with national-level census or survey data, enables researchers to examine socio-economic inequalities in health care utilization in greater detail. Data and methods With access to a unique Canadian dataset linking data from the Hospital Discharge Abstract Database (DAD) from 2006 to 2007 with detailed individual-level socio-demographic data from the 2006 Canadian Census, we are able to examine the patterning of hospitalization in Canada in the early 2000s across a variety of socio-demographic variables. We examine the association of education and income, controlling for immigration status, rural residence, marital status and ethnicity, with hospitalization rates for both ambulatory care sensitive conditions (ACSCs) and non-ambulatory care sensitive conditions (non-ACSCs) for children and youth, working-age adults, and older adults, in models stratified by sex. Results Age standardized hospitalization rates show that there is a clear socio-economic gradient in hospitalization in Canada in the 2000s. Education and income are independently, inversely associated with hospitalization for males and females across three broad age groups. These associations are stronger for ACSCs than non-ACSCs. The association of other socio-demographic variables, such as immigrant status, and rural residence is also stronger for hospitalization for ACSCs. The association of socio-economic status with hospitalization for ACSCs is strongest for working age women and men, and is somewhat attenuated for older adults. Conclusions Lower socio-economic status is associated with a higher likelihood of hospitalization for men and women in Canada across three broad age groups in the 2000s. These associations are stronger for ACSCs, suggesting that in addition to increased likelihood of disease, decreased access to preventative care may be driving up hospitalization rates for marginalized groups. We conclude with the recommendation that in order to track progress in reducing health inequities, health systems should either collect detailed individual-level socio-demographic data or link their administrative health data to existing demographic data sets.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,002 | 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 | 0,003 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».