Using linked health survey and Census data to understand transitions to instutional care among Canadian seniors
Notice bibliographique
Résumé
ABSTRACT ObjectivesWhile existing data sources, such as the 2011 Census, provide an accurate count of who is currently institutionalized, there is a significant gap in terms of our understanding of who is at risk for institutionalization and what the future demand for care will be. The objective of this study is to use linked national health survey, the 2005 Canadian Community health Survey (CCHS), to the 2011 Census to identify factors associated with transitions from private households to alternative living arrangements, specifically long-term care (nursing homes), and retirement homes among Canadians 55 years of age and older. ApproachHierarchical deterministic methods were used to link the 2005 CCHS (n=114,000) to the 2011 Census (n=35 million) using identifying variables common to both data sets (i.e. name, birthdate, sex, postal code, social insurance number). Sex specific multivariate regression models with multiple outcomes were used to assess the impact of a comprehensive set of factors (i.e. demographic, socio-economic, health status, chronic conditions and marital status) available in the CCHS on the likelihood of residing in three possible home environments, relative to living in a private dwelling (PD): long-term care (LTC), retirement homes (RH) or private dwelling with support (PDS) as identified in the 2011 Census. Analyses were adjusted for mortality. ResultsOver 85% of CCHS records were linked to the 2011 Census (n=92,849). Among those 55 years of age and older (n=29,934), approximately 2.0% and 1.6% were living in LTC and RH respectively: an additional 7.2% were living in PDS. Results of the regression analyses, revealed that those with Alzheimer’s disease were at highest risk of transition to LTC (OR=11.7 females; 6.8 males). Losing a spouse was significantly associated with transitions to LTC, RH and PDS for both men and women. Being an immigrant was protective, with immigrant seniors less likely to transition to LTC and RH. Other factors significantly associated with transitions to LTC included low income, poor mental health (women only), and assistance with activities of daily living (meal preparation for women, finances for men). Regional variations were also noted. ConclusionNewly linked health survey and census data provide a unique opportunity to take a comprehensive look at those most at risk for institutionalization.
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 | 0,003 | 0,003 |
| 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 | 0,006 | 0,000 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,003 | 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 ».