Examining Associations between Sociodemographic, Behavioural, Environmental, Risk Determinants and Multimorbidity
Notice bibliographique
Résumé
Multimorbidity, defined as having 2 or more chronic conditions, is a global phenomenon with high prevalence in Canada and the province of Ontario. Multimorbidity has been associated with various social, health and economic costs. While research has been conducted determining key indicators and their associations with multimorbidity; studies using a comprehensive list of determinants, including environmental factors have been far fewer. The overall aim of this thesis was to evaluate potential relationships between sociodemographic, lifestyle behavioural, environmental determinants, and risk conditions, identified by the Chronic Disease Indicator Framework. The first study examined the association between various dimensions of marginalization and multimorbidity. The second study examined multiple indicator variables and their associations with multimorbidity and healthcare utilization costs. The third study examined neighbourhood walkability as part of the built environment, and impact of various exposures, in a time to event analysis (becoming multimorbid) for a period of 16 years. Study findings suggest that nearly a third of the Ontario population are multimorbid. Of the four dimensions identified by the Ontario Marginalization Index, material deprivation was highly correlated with multimorbidity and higher cost chronic conditions. Out of all determinant variables, age, self-perceived health, body mass index and income, were significantly associated with multimorbidity and showed the largest magnitudes. Cost of care has risen by 21% during the study period, with greater morbidity, number of conditions, and healthcare utilization costs, associated with the lowest income populations. There was an observed relationship between most walkable neighbourhoods and lower body mass index. The least walkable neighbourhoods had significantly higher risk for multimorbidity. The high-risk approach currently adopted by the health establishment, which targets patients with health risks and markers for future illness, has not been effective at slowing multimorbidity in the population. A more progressive population strategy is needed, that features more radical measures of prevention to help reduce the rise in chronic illness across the life course. The built environment may be a valuable part of a wider prevention strategy. Walkable neighbourhoods that promote greater physical activity may aid in prevention, by reducing obesity at the population level to reduce incidence of multimorbidity.
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 | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».