ICES Reports: Increasing Longevity and Future Hospital Use
Bibliographic record
Abstract
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".