ICES Reports: Increasing Longevity and Future Hospital Use
Notice bibliographique
Résumé
The implications of an aging population are now a part of longterm hospital planning.David Foot's Boom, Bust & Echo guides you through the process of multiplying the number of soon-to-be-older baby boomers with the high hospitalization rates of the elderly.The inevitable result is a higher demand for future hospital services, but the number of older people is not the only issue.Increasing longevity will also affect future hospital use.Although longevity and the aging of populations are terms that are linked, there are important differences and each has distinct implications that merit consideration.The aging of a community is affected by three factors: the rate of birth, migration, and death.The high birth rate during the post-war years created the baby boom generation -and, in large part, the impending increase in the Canadian aged population.Canada's implicit policy is to encourage the immigration of young people.This mitigates the effects of an aging population by lowering the dependency ratio (the ratio of people of working age to the young and elderly).The increasing likelihood that people survive longer leads to a decline in the death rate and contributes to an older age structure of the population.Longevity, on the other hand, is affected by only the death rate of a community.The lower the death rate, the higher the life expectancy.In a recent ICES Atlas Report, Adding Years to Life and Life to Years in Ontario, we examined the potential impact of a decrease in the death rate on hospital use (See: www.ices.on.caPublications: Atlas Reports Series).At first glance, you might think that a drop in the death rate is synonymous with better health and reduced hospital use.However, we know that health is more than longevity.If death rates decrease without improvements in the degree of disability there will be a greater number of people (mostly elderly) living with chronic conditions.The probable result will be an increase in hospital use.In the 1980s, Fries coined the phrase, "expansion and contraction of morbidity" to describe this changing pattern of disease.He argued that lifestyle improvements would not only reduce death rates but would also lead to an increase in the amount of life lived in a healthy state, or what he called a "compression of morbidity."Other authors take the view that increased medical care will lead to an "expansion of morbidity," and a further increase in hospital use.Health expectancy measures have been developed to measure whether, in addition to "years of life," we are also adding "life to years."These measures combine life expectancy with measures of health-related quality of life.This year, Statistics Canada will publish disability-free life expectancy estimates for all Canadian health planning regions.We reported good news in our Atlas Report.We calculated several health expectancy measures and found that, along with an important gain in life expectancy, there has also been a small
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 | 0,002 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,021 | 0,002 |
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 ».