Evidence map›Paper›PMID 33838644›Full record

ArticleEnvironmental health and preventive medicine2021

Prediction of 11-year incidence of psychophysically dependent status or death among community-dwelling younger elderlies: from an age-specified community-based cohort study (the NISSIN project).

Satoe Okabayashi, Takashi Kawamura, Hisashi Noma, Kenji Wakai, Masahiko Ando, Kazuyo Tsushita, Hideki Ohira, Shigekazu Ukawa, Akiko Tamakoshi

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Environmental health and preventive medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 39% of its field
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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 6 institutions in 1 country.

Satoe OkabayashiKyoto University Health Service, Yoshida-Honmachi, Sakyo-ku, Kyoto, 606-8501, Japan. okabayashi.satoe.8c@kyoto-u.ac.jp.ORCID http://orcid.org/0000-0001-9919-6768
Takashi KawamuraKyoto University Health Service, Yoshida-Honmachi, Sakyo-ku, Kyoto, 606-8501, Japan.
Hisashi NomaDepartment of Data Science, The Institute of Statistical Mathematics, 10-3 Midori-cho, Tachikawa, Tokyo, 190-8562, Japan.
Kenji WakaiDepartment of Preventive Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan.
Masahiko AndoCenter for Advanced Medicine and Clinical Research, Nagoya University Hospital, 65, Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan.
Kazuyo TsushitaKagawa Nutrition University, 3-9-21 Chiyoda, Sakado city, Saitama, 350-0288, Japan.
Hideki OhiraDepartment of Psychology, Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan.
Shigekazu UkawaResearch Unit of Advanced Interdisciplinary Care Science, Graduate School of Human Life Science, Osaka City University, 3-3-138, Sugimoto, Osaka, Sumiyoshi-ku, 558-8585, Japan.
Akiko TamakoshiDepartment of Public Health, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, North 15, West 7, Kita-ku, Sapporo, 060-8638, Japan.
Hokkaido University · JPKyoto University · JPNagoya University · JPKagawa Nutrition University · JPNagoya University Hospital · JPThe Institute of Statistical Mathematics · JP

Funding

Japan Society for the Promotion of Science JP15390197Japan Society for the Promotion of Science JP20K02392Japan Society for the Promotion of Science JP26520105
6 · The paper itself

Abstract

backgroundPredicting adverse health events and implementing preventative measures are a necessary challenge. It is important for healthcare planners and policymakers to allocate the limited resource to high-risk persons. Prediction is also important for older individuals, their family members, and clinicians to prepare mentally and financially. The aim of this study is to develop a prediction model for within 11-year dependent status requiring long-term nursing care or death in older adults for each sex.

methodsWe carried out age-specified cohort study of community dwellers in Nisshin City, Japan. The older adults aged 64 years who underwent medical check-up between 1996 and 2000 were included in the study. The primary outcome was the incidence of the psychophysically dependent status or death or by the end of the year of age 75 years. Univariable logistic regression analyses were performed to assess the associations between candidate predictors and the outcome. Using the variables with p-values less than 0.1, multivariable logistic regression analyses were then performed with backward stepwise elimination to determine the final predictors for the model.

resultsOf the 1525 female participants at baseline, 105 had an incidence of the study outcome. The final prediction model consisted of 15 variables, and the c-statistics for predicting the outcome was 0.763 (95% confidence interval [CI] 0.714-0.813). Of the 1548 male participants at baseline, 211 had incidence of the study outcome. The final prediction model consisted of 16 variables, and the c-statistics for predicting the outcome was 0.735 (95% CI 0.699-0.771).

conclusionsWe developed a prediction model for older adults to forecast 11-year incidence of dependent status requiring nursing care or death in each sex. The predictability was fair, but we could not evaluate the external validity of this model. It could be of some help for healthcare planners, policy makers, clinicians, older individuals, and their family members to weigh the priority of support.

Indexed as

AgedCohort StudiesFemaleGeriatric AssessmentHumansIndependent LivingJapanMaleMiddle AgedNursing HomesRisk AssessmentRisk FactorsAgedCohort studiesDeathForecastingNursing care

Identifiers

PMID33838644
PMCPMC8035719
OpenAlexW3154890558

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Registered trials

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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.