MétaCan
Menu
Retour à la cohorte
Enregistrement W1983952152 · doi:10.1111/j.1532-5415.2006.00928.x

DEPRESSIVE SYMPTOMS IN OLDER PEOPLE PREDICT NURSING HOME ADMISSION

2006· letter· en· W1983952152 sur OpenAlexaffabout
Philip D. St. John, Patrick R. Montgomery

Notice bibliographique

RevueJournal of the American Geriatrics Society · 2006
Typeletter
Langueen
DomaineMedicine
ThématiqueFrailty in Older Adults
Établissements canadiensUniversity of Manitoba
Organismes subventionnairesnon disponible
Mots-clésMedicineActivities of daily livingGerontologyGeriatric Depression ScaleGeriatricsDepression (economics)Mental healthConfoundingMini–Mental State ExaminationIndependent livingCohortPopulationNursing homesCohort studyAging in placeDepressive symptomsCognitionCognitive impairmentPsychiatryEnvironmental healthNursing

Résumé

récupéré en direct d'OpenAlex

To the Editor: In a recent article in the Journal of the American Geriatrics Society, Harris and Copper1 showed that depressive symptoms predict admission to a nursing home (NH). The study had numerous strengths, but there were some limitations. The measure of depressive symptoms was crude, and potential confounding variables such as cognition, social support, and functional status were incompletely measured. Furthermore, this association may be different in other health and social systems, as well as in different uses of NHs in other countries than in the United States. We would like to add information to this topic by presenting data from a population-based study in the Canadian province of Manitoba. The Manitoba Study of Health and Aging (MSHA) is a cohort study conducted in conjunction with the Canadian Study of Health and Aging.2 In 1991/92, 1,751 community-dwelling persons aged 65 and older were interviewed in their homes. Age, sex, education, living arrangement, and the number of persons providing support were all self-reported. Measures included the Center for Epidemiologic Studies Depression Scale (CES-D),3 the Mini-Mental State Examination (MMSE),4 and the Older Americans Resources and Services;5 activities of daily living (ADLs) and instrumental activities of daily living (IADLs) were considered impaired if a participant required assistance or used an assistive device for any ADL or IADL. The CES-D was dichotomized, with a score greater than 15 indicating depression. To investigate gradient versus threshold effects, the CES-D was also divided into categories of 0 to 5, 6 to 10, 11 to 15, and greater than 15. NH admission was determined at the time of follow-up, in 1996/97. Six persons had missing data for NH admission. The process of entering a NH requires review by a panel for eligibility and is registered in a central database. NHs in Manitoba are not used for convalescence or rehabilitation, and temporary admission is highly unusual. Bivariate analyses were conducted using Student t tests for continuous variables and chi-square tests for categorical variables. Logistic regression models were constructed with NH admission over 5 years as the outcome. SPSS version 13 (SPSS Inc., Chicago, IL) was used. There were 1,745 persons interviewed at time 1 with data available on institutionalization; 211 were admitted to a NH over the 5-year interval. The average age was 76.2, 58.5% were female, the mean educational level was 9.3 years, 42.3% lived alone, the median number of persons providing help was 3, 17.0% had an MMSE score of less than 24, 19.9% had impairments in one or more ADLs, 39.3% had impairments in one or more IADLs, and 13.9% had depressive symptoms. Depressive symptoms predicted NH admission; 19.4% of those with depressive symptoms were institutionalized, versus 10.9% of those without (P<.001, chi-square test). The association between CES-D score and NH admission was present in those with and without cognitive impairment. There was a gradient effect in the association between CES-D score and NH admission (Figure 1). In logistic regression models adjusted for age, sex, education, living arrangement, and social support, depressive symptoms predicted NH admission; the adjusted odds ratio (AOR) for NH admission was 1.57 (95% confidence interval (CI)=1.05–2.35). When the MMSE was added into this model, the AOR was 1.52 (95% CI=1.01–2.28). When ADLs and IADLs were considered as well, depressive symptoms no longer predicted NH admission, with an AOR of 1.19 (95% CI=0.78–1.80). Depressive symptoms predicted admission to a nursing home over a 5-year period. This effect is seen in those with and without cognitive impairment. There is a gradient in this association, extending into the normal range of the Center for Epidemiologic Studies Depression Scale (CES-D) score (range 0–60). MMSE=Mini-Mental State Examination (range 0–30, with >23 considered normal). In this sample, depressive symptoms predicted NH admission, but when functional impairment was considered, this association was no longer apparent. This adds information to Harris and Cooper's article.1 The MSHA used the CES-D, a reliable, valid measure of depressive symptoms, which also allows the exploration of gradient effects. In addition, there were data on potential confounders such as cognitive status, functional status, and social support. Furthermore, it is important to note that the association between depressive symptoms and institutionalization is present in a different healthcare system. There are also some limitations to our analysis; a crude measure of social support was used, and the CES-D is not a measure of major depression, which requires a clinical examination. It is possible that functional status acts as a confounding variable, with functionally impaired persons experiencing depressive symptoms before NH admission, but it is also possible that functional impairment is a mediating factor. Depression has been shown to predict functional decline,6 which may in turn lead to NH admission. Whether the effect is causal or not, the observation that depressive symptoms predict NH admission is important for clinicians and policy-makers, and older adults with depressive symptoms should be assessed and monitored. Financial Disclosure: The MSHA was funded primarily by Manitoba Health, with additional funding provided through the Canadian Study of Health and Aging by the Seniors Independence Research Program of the National Health Research and Development Program of Canada (Project no. 6606–3954-MC(S)).The MSHA-2 was funded primarily by Manitoba Health's Health Communities Development Fund with additional funding provided through the Canadian Study of Health and Aging by the Seniors Independence Research Program of the National Health Research and Development Program of Health Canada (Project no. 6606–3954-MC(S)). The results and conclusions are those of the authors, and no official endorsement by Manitoba Health or other funding agencies is intended or should be inferred. Author Contributions: Philip St. John examined participants; gathered data for and was involved with the original data collection for this study; contributed to the concept, interpretation, and manuscript preparation; and analyzed the data. Patrick Montgomery examined participants; gathered data for and was involved with the original data collection for this study; and contributed to the concept, interpretation, and manuscript preparation. Sponsor's Role: The sponsors had no role in the design, methods, subject recruitment, data collection, or analysis of preparation of this study. The results and conclusions are those of the authors, and no official endorsement by Manitoba Health or other funding agencies is intended or should be inferred.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,017
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,013

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,017
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0020,001
Science ouverte0,0020,000
Intégrité de la recherche0,0040,003
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

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.

Tête enseignante Opus0,007
Tête enseignante GPT0,261
Écart entre enseignants0,254 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations11
Publié2006
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueJournal of the American Geriatrics SocietyMême sujetFrailty in Older AdultsTravaux en français237 207