Size and Composition of Caregiver Networks Who Manage Medications for Persons Living With Dementia: Cross-Sectional Analysis of the 2011-2022 National Health and Aging Trends Study
Notice bibliographique
Résumé
Background: Family caregivers commonly help manage medications taken by persons living with dementia. Recent work has highlighted the importance of caregiver networks, which are multiple caregivers managing care for a single person, on managing care for persons living with dementia, especially medication management. However, less is known about the composition of caregiver networks. Objective: The objective of this analysis was to describe the composition of caregiver networks that manage medications, the factors associated with helping with medications within caregiver networks, and whether racial or ethnic differences exist in caregiver network composition. Methods: This cross-sectional secondary analysis used data from the National Health and Aging Trends Study (NHATS) "other person" files from 2011 to 2022. Descriptive statistics were calculated for caregivers who were identified as helping manage medications for a person with dementia. Mixed-effect logistic regression was used to determine factors associated with helping with medications among caregiver networks, with odds ratios converted to predicted probabilities using marginal standardization. A P value of .05 or less was considered statistically significant. Secondary analysis was stratified by race and ethnicity due to identified cultural differences in living situation and overall caregiver network composition. Results: A total of 15,809 caregivers were analyzed. Of those, 3048 (19.2%) managed medications for persons living with dementia. Caregiver networks that manage medications tend to include a spouse or partner and child, at least one of whom has a college degree. Every person with dementia reported at least 1 person who managed their medications. White persons with dementia had an average of 2.4 (range 1-9) people who managed medications, while Black or African American persons with dementia had an average of 2.8 (range 1-9) and Hispanic or Latino persons with dementia had an average of 2.9 (range 1-8) people who managed medications. Spouses were most likely to manage medications across all racial and ethnic groups. In regression modeling, female gender (predicted probability [PP] 15%, 95% CI 13%-17%; P<.001), Black or African American race (PP 7%, 95% CI 4%-10%; P<.001), and Hispanic ethnicity (PP 4%, 95% CI 1%-9%; P=.04) were associated with an increased probability of helping with medications. Conclusions: The size and composition of caregiver networks that manage medications for persons living with dementia differ by race and ethnicity but typically includes at least 2 people, one of whom has a college degree. Helping with medications was more likely among non-White family caregivers, while White patients with dementia were more likely to use paid help to manage medications.
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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 | 0,006 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 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 | 0,001 |
| 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 ».