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Enregistrement W4388560633 · doi:10.1111/jgs.18649

The prevalence and regional variability of diabetes among nursing home residents in Ontario

2023· article· en· W4388560633 sur OpenAlexafffundabout
Armin Farahvash, Lisa McCarthy, Wade Thompson, Sho Podolsky, Iliana C. Lega

Notice bibliographique

RevueJournal of the American Geriatrics Society · 2023
Typearticle
Langueen
DomaineHealth Professions
ThématiqueGeriatric Care and Nursing Homes
Établissements canadiensInstitute for Clinical Evaluative SciencesiCo Therapeutics (Canada)University of British ColumbiaWomen's College HospitalTrillium Health CentreUniversity of Toronto
Organismes subventionnairesCanadian Institutes of Health ResearchOntario Ministry of Health and Long-Term Care
Mots-clésMedicineGerontologyPopulationDiabetes mellitusNursing homesCohortLong-term carePrevalenceHealth careDemographyEnvironmental healthNursing

Résumé

récupéré en direct d'OpenAlex

The prevalence of diabetes mellitus (DM) increases with age and is a major contributor to morbidity and mortality.1, 2 Due to frailty, multiple comorbidities, and functional and cognitive impairment, residents of nursing homes with DM have increased care needs and are at risk of both DM and treatment-related adverse events.2, 3 While the prevalence of DM in nursing homes in Ontario was previously reported at 25% in the early 2000s, DM rates in nursing homes may have increased given the overall increasing rates of DM in the general population.4-6 This study examined the contemporary prevalence of DM among nursing home residents in Ontario, Canada, including regional variations in prevalence and resident characteristics. We conducted a population-based longitudinal descriptive study of nursing home residents in Ontario, Canada, using linked Institute for Clinical Evaluative Sciences (ICES) administrative databases. We identified all nursing home residents between April 1, 2017–March 31, 2022 who were over 66 years of age. Resident characteristics and comorbidities were identified from the Resident Assessment Instrument (RAI) as a part of the Continuing Care Reporting System for Long-Term Care (CCRS-LTC) database. To calculate the prevalence of DM in our cohort, we used the Ontario Diabetes Database (ODD) which is a validated algorithm for identifying DM.7 For each fiscal year between 2017 and 2022, we calculated the prevalence of DM in the entire province and according to geographical regions, based on Ontario Local Health Integration Networks (LHINs). For the most recent fiscal year (April 1, 2021–March 31, 2022), we also measured resident demographic characteristics and comorbidities. We described characteristics of residents overall and compared those with DM to those without using chi-squared test for categorical variables and one-way analysis of variance for continuous variables. The prevalence of DM among nursing home residents in Ontario ranged from 35.5% to 36.7% between 2017 and 2022 (Figure 1, see Supplemental Material Table S1). The prevalence of DM varied geographically, with the highest rates observed in Central West, Central, North East and Toronto Central LHINs where the prevalence of DM ranged from 38.5% to 42.1% (see Supplemental Material Figure S1, Table S2). While the lowest rates were observed in North Simcoe Muskoka, North West and South West LHINs (see Supplemental Material Figure S1, Table S2). As shown in Table 1, nursing home residents with DM were younger than residents without DM (84.1 ± 8.1 vs 85.7 ± 8.3 years, p < 0.001) and the prevalence of DM was higher among men compared to women (42.1% vs 34.2%, p < 0.001). Residents with DM had shorter length of stay in nursing homes (1.74 ± 2.44 vs 2.04 ± 2.73 years, p < 0.001) and a higher proportion were classified as obese based on body mass index (27.1% vs 18.6%, p < 0.001) (Table 1). We report a DM prevalence of 35% to 37% in Ontario nursing homes between 2017 and 2022. This is higher than the previously reported DM prevalence of 25%.4, 5 These rates are in keeping with the increasing overall prevalence of DM among adults, and with recent studies from nursing homes in the United States that have reported DM prevalence of as high as 34%.6, 8, 9 Our study evaluated DM prevalence rates over a 5 year period (which included the years of the COVID-19 pandemic) and did not find any substantial variation in prevalence rates during this time overall or by sex (Figure 1), despite a significant decline in nursing home occupancy likely due to high mortality during the peaks of waves of the pandemic. However, we observed regional variability in the prevalence of DM in Ontario nursing homes that is consistent with higher rates in more central, urban regions which also have higher rates of ethnic diversity (see Supplemental Material Figure S1). Our finding that those with DM were younger and had lower rates of cognitive impairment may be due to individuals with DM having an earlier mortality.10 Residents with DM also had higher BMIs, as they were more frequently in the overweight and obese categories. This is consistent with the expected association between obesity and DM.10 This study demonstrates a higher rate of DM in Ontario nursing home residents than previously reported. Identifying such a high prevalence of DM in nursing homes underscores the importance of rethinking DM management in nursing homes in order to optimize quality of care and redistribute necessary resources accordingly. Armin Farahvash, Lisa M. McCarthy, Wade Thompson, Sho Podolsky, and Iliana C. Lega contributed equally in study design, data analysis, and preparation of the manuscript. This study was supported by ICES, an independent, non-profit research institute funded by an annual grant from the MOH and MLTC. This study was supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health (MOH) and the Ministry of Long-Term Care (MLTC). Parts of this material are based on data and information compiled and provided by: CCRS-LTC, ODD. These datasets were linked using unique encoded identifiers and analyzed at ICES. The analyses, conclusions, opinions, and statements expressed herein are solely those of the authors and do not reflect those of the funding or data sources; no endorsement is intended or should be inferred. This work was supported by the Canadian Institute of Health Research (CIHR) (Grant number: PJT 159472). The authors declare no conflicts of interest. None. Table S1. Prevalence of DM in nursing home residents by sex per year 2017–2022. Table S2. Prevalence of DM in nursing home residents in each LHIN per year 2017–2022. Figure S1. Distribution of DM prevalence across Ontario in long term care residents in 2021 fiscal year. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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)0,001
score de la tête « metaresearch » (Gemma)0,004
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: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,023
Score d'incertitude au seuil0,097

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,004
Études des sciences et des technologies0,0010,001
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,027
Tête enseignante GPT0,339
Écart entre enseignants0,312 · 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
GenreEmpirique

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

En bref

Citations4
Publié2023
Routes d'admission3
Résumé présentoui

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