Avoidable hospital use in community-dwelling persons living with dementia: impact of health service interventions, and primary care continuity
Notice bibliographique
Résumé
Persons with dementia have twice the acute hospital use (Emergency Department (ED) visits and hospital admissions) as older persons without dementia. This hospital use dramatically impacts their health and quality of life. A share of this hospital use might be avoidable with appropriate ambulatory care. However, to date, we do not know how to reduce these potentially avoidable hospital use. The aim of this PhD thesis was to investigate how avoidable hospital use of community-dwelling persons with dementia could be reduced, especially by measuring the impact of health service interventions or primary care continuity on potentially avoidable hospital use. Addressing this overarching aim was accomplished in four articles. The first article aimed at measuring the impact of health service interventions on potentially avoidable hospital use in community-dwelling persons with dementia. I conducted a systematic literature review and meta-analysis to synthesize available evidence on the impact of health service interventions on hospital use in dementia compared to usual care. Despite a comprehensive systematic literature review and meta-analysis, including predominantly unpublished data, no health service intervention beyond usual care was found to reduce hospital use in community-dwelling persons with dementia. The three remaining articles aimed at measuring the impact of primary care continuity on potentially avoidable hospital use in community-dwelling persons with dementia. In the second article, I conducted a descriptive study of hospital use of community-dwelling persons living with dementia in Quebec, over the last 15 years using the Quebec provincial administrative database. I estimated that around 40 and 60 per 100 person-year of community-dwelling persons with dementia had at least one hospitalization and one ED visit during the year of diagnosis, respectively. Between 20 and 30% of those hospitalized, depending on the indicator, had a potentially avoidable hospital use, with average length of Alternate Level of Care (ALC) stay of more than 4.5 months. Most indicators remained constant over the 15 years. In the third article, I described in a Method Brief, for a non-expert audience how advanced statistical methods can be used to strengthen causal inference from observational data, especially with propensity scores; the method I am using in the fourth article. In the fourth article, I measured the association between high primary care continuity and potentially avoidable hospital use in community-dwelling persons with dementia in Quebec. I estimated, using an observational retrospective cohort, with inverse probability of treatment weighting using the propensity score, that high continuity with a primary care physician was significantly associated with fewer potentially avoidable hospitalizations (Ambulatory Care Sensitive Conditions (ACSC) hospitalization and 30-day readmission). In addition, high primary care continuity was significantly associated with fewer ED visits and hospitalizations. The relative risk reduction for Ambulatory Care Sensitive Condition hospitalization (general population definition) in those exposed to high primary care continuity was 0.82 (95% confidence Interval (CI) [0.72;0.94]; P=.004) compared to the unexposed. The relative risk reduction for Ambulatory Care Sensitive Condition hospitalization (older population definition) was 0.87 (CI [0.79;0.95]; P=.002). The relative risk reduction for 30-day hospital readmission was 0.81 (CI [0.72;0.92]; P<.001). The relative risk reduction for hospitalization and Emergency Department visits were 0.90 (CI [0.86;0.94]; P<.001), and 0.92 (CI [0.90;0.95]; P<.001), respectively. In this PhD thesis, I generated evidence that could ultimately inform healthcare policies aiming at reducing avoidable hospital use in community-dwelling persons with dementia
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,008 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».