Insights on multimorbidity and associated health service use and costs from three population-based studies of older adults in Ontario with diabetes, dementia and stroke
Notice bibliographique
Résumé
BACKGROUND: Most studies that examine comorbidity and its impact on health service utilization focus on a single index-condition and are published in disease-specific journals, which limit opportunities to identify patterns across conditions/disciplines. These comparisons are further complicated by the impact of using different study designs, multimorbidity definitions and data sources. The aim of this paper is to share insights on multimorbidity and associated health services use and costs by reflecting on the common patterns across 3 parallel studies in distinct disease cohorts (diabetes, dementia, and stroke) that used the same study design and were conducted in the same health jurisdiction over the same time period. METHODS: We present findings that lend to broader Insights regarding multimorbidity based on the relationship between comorbidity and health service use and costs seen across three distinct disease cohorts. These cohorts were originally created using multiple linked administrative databases to identify community-dwelling residents of Ontario, Canada with one of diabetes, dementia, or stroke in 2008 and each was followed for health service use and associated costs. RESULTS: We identified 376,434 indviduals wtih diabetes, 95,399 wtih dementia, and 29,671 with stroke. Four broad insights were identified from considering the similarity in comorbidity, utilization and cost patterns across the three cohorts: 1) the most prevalent comorbidity types were hypertension and arthritis, which accounted for over 75% of comorbidity in each cohort; 2) overall utilization increased consistently with the number of comorbidities, with the vast majority of services attributed to comorbidity rather than the index conditions; 3) the biggest driver of costs for those with lower levels of comorbidity was community-based care, e.g., home care, GP visits, but at higher levels of comorbidity the driver was acute care services; 4) service-specific comorbidity and age patterns were consistent across the three cohorts. CONCLUSIONS: Despite the differences in population demographics and prevalence of the three index conditions, there are common patterns with respect to comorbidity, utilization, and costs. These common patterns may illustrate underlying needs of people with multimorbidity that are often obscured in literature that is still single disease-focused.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| 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 ».