Profiles of Met and Unmet Care Needs in the Oldest Old Primary Care Patients with Cognitive Disorders and Dementia: Results of the AgeCoDe and AgeQualiDe Study
Notice bibliographique
Résumé
INTRODUCTION: The prevalence of mild cognitive impairment (MCI) and dementia is increasing as the oldest old population grows, requiring a nuanced understanding of their care needs. Few studies have examined need profiles of oldest old patients with MCI or dementia. Therefore, this study aimed to identify patients' need profiles. METHODS: The data analysis included cross-sectional baseline data from N = 716 primary care patients without cognitive impairment (n = 575), with MCI (n = 97), and with dementia (n = 44) aged 85+ years from the multicenter cohort AgeQualiDe study "needs, health service use, costs and health-related quality of life in a large sample of oldest old primary care patients [85+]". Patients' needs were assessed using the Camberwell Assessment of Needs for the Elderly (CANE), and latent class analysis identified need profiles. Multinomial logistic regression analyzed the association of MCI and dementia with need profiles, adjusting for sociodemographic factors, social network (Lubben Social Network Scale [LSNS-6]), and frailty (Canadian Study of Health and Aging-Clinical Frailty Scale [CSHA-CFS]). RESULTS: Results indicated three profiles: "no needs," "met physical and environmental needs," and "unmet physical and environmental needs." MCI was associated with the met and unmet physical and environmental needs profiles; dementia was associated with the unmet physical and environmental needs profile. Patients without MCI or dementia had larger social networks (LSNS-6). Frailty was associated with dementia. CONCLUSIONS: Integrated care should address the needs of the oldest old and support social networks for people with MCI or dementia. Assessing frailty can help clinicians to identify the most vulnerable patients and develop beneficial interventions for cognitive disorders. INTRODUCTION: The prevalence of mild cognitive impairment (MCI) and dementia is increasing as the oldest old population grows, requiring a nuanced understanding of their care needs. Few studies have examined need profiles of oldest old patients with MCI or dementia. Therefore, this study aimed to identify patients' need profiles. METHODS: The data analysis included cross-sectional baseline data from N = 716 primary care patients without cognitive impairment (n = 575), with MCI (n = 97), and with dementia (n = 44) aged 85+ years from the multicenter cohort AgeQualiDe study "needs, health service use, costs and health-related quality of life in a large sample of oldest old primary care patients [85+]". Patients' needs were assessed using the Camberwell Assessment of Needs for the Elderly (CANE), and latent class analysis identified need profiles. Multinomial logistic regression analyzed the association of MCI and dementia with need profiles, adjusting for sociodemographic factors, social network (Lubben Social Network Scale [LSNS-6]), and frailty (Canadian Study of Health and Aging-Clinical Frailty Scale [CSHA-CFS]). RESULTS: Results indicated three profiles: "no needs," "met physical and environmental needs," and "unmet physical and environmental needs." MCI was associated with the met and unmet physical and environmental needs profiles; dementia was associated with the unmet physical and environmental needs profile. Patients without MCI or dementia had larger social networks (LSNS-6). Frailty was associated with dementia. CONCLUSIONS: Integrated care should address the needs of the oldest old and support social networks for people with MCI or dementia. Assessing frailty can help clinicians to identify the most vulnerable patients and develop beneficial interventions for cognitive disorders.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| 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 ».