The Joint Impact of Age at Death and Dementia on Long-Term Care Use in the Last Years of Life: Changes From 1996 to 2013 in Finland
Bibliographic record
Abstract
Welfare states increasingly rely on aging in place policies and have cut back on institutional long-term care (LTC) provision. Simultaneously, the major determinants of LTC use, that is, dementia and living to very old age, are increasing. We investigated how increasing longevity and concomitant dementia were associated with changes in round-the-clock LTC use in the last 5 years of life between 1996 and 2013. Retrospective data drawn from national registers included all those who died aged 70+ in 2007 and 2013, plus a 40% random sample from 2001 ( N = 86,554). A generalized estimating equations (GEE) were used to estimate the association of dementia and age with LTC use during three study periods 1996-2001, 2002-2007, and 2008-2013. Between the study periods, the total number of days spent in LTC increased by around 2 months. Higher ages at death and the increased number of persons with dementia contributed to this increase. The group of the most frequent LTC users, that is, people aged 90+ with or without dementia, grew the most in size, yet their LTC use decreased. The implications of very old age and concomitant dementia for care needs must be acknowledged to guarantee an adequate quantity and quality of care.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".