Evidence map›Paper›PMID 34675496›Full record

ArticleClinical interventions in aging2021

From Disability to Death: A 20-Year Follow-Up from the Taiwan Longitudinal Study on Aging.

Ching-Ju Chiu, Min-Chia Yang, Chi-Chang Huang, Chia-Ming Chang

Abstract read
In one paragraph

Article in Clinical interventions in aging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ching-Ju ChiuInstitute of Gerontology, College of Medicine, National Cheng Kung University, Tainan, 70101, Taiwan.ORCID 0000-0002-2352-9105
Min-Chia YangDepartment of Medicine, College of Medicine, National Cheng Kung University, Tainan, 70101, Taiwan.
Chi-Chang HuangDivision of Geriatrics and Gerontology, Department of Internal Medicine, National Cheng Kung University Hospital, Tainan, Taiwan.
Chia-Ming ChangInstitute of Gerontology, College of Medicine, National Cheng Kung University, Tainan, 70101, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn this study, factors associated with the duration of a disability before death in older adults who are moderately to severely disabled in Taiwan are investigated.

methodsA nationally representative sample of older adults (65+) in 1996 who died before 2016 (n = 1139) were analyzed to calculate their disability status and the length of time they were disabled before death.

resultsThe mean period during which the participants experienced moderate to severe disability before death for older adults in Taiwan was 5.53 years (SD = 3.15). Men who were overweight had an average of 1.17 more survival years (β

conclusionDisability distribution at various time points before death among the elderly in Taiwan was revealed in the study. At 10 years before death, 93% of the elderly were free from any ADL disabilities, and only 4% reported more than three ADL disabilities. At 6 years before death, an average of 10% of the participants had more than three ADL disabilities, and at one year before death, moderate to severe disability increased to 38%. Factors associated with the survival years among those who were moderately to severely disabled showed distinct gender differences.

Indexed as

Activities of Daily LivingPersons with DisabilitiesAgedAgingDisability EvaluationFemaleFollow-Up StudiesHumansLongitudinal StudiesMaleTaiwandisabilityduration before deathelderlyrisk factor

Identifiers

PMID34675496
PMCPMC8517422

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.