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Research article
Shuichi Obuchi
Tokyo Metropolitan Institute of Gerontology
Corresponding Author
ORCiD: https://orcid.org/0000-0003-0187-9275
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Predicting incidence of long-term care insurance (LTCI) certification in the short term is of increasing importance in Japan. The present study examined whether the Kihon Checklist (KCL) can be used to predict incidence of LTCI certification (care level 1 or higher) in the short term among older Japanese persons.
Methods: In 2015, the local government in Tokyo, Japan, distributed the KCL to all individuals older than 65 years who had not been certified as having a disability or who had already been certified as requiring support level 1–2 according to LTCI system. We also collected LTCI certification data within the 3 months after collecting the KCL data. The data of 17785 respondents were analyzed. First, we selected KCL items strongly associated with incidence of LTCI certification, using stepwise forward-selection multiple logistic regression. Second, we conducted receiver operating characteristic (ROC) analyses for three conditions (1: Selected KCL items, 2: The main 20 KCL items (nos. 1–20), 3: All 25 KCL items). Third, we estimated specificity and sensitivity for each condition.
Results: During a 3-month follow-up, 81 (0.5%) individuals required new LTCI certification. Eight KCL items were selected by multiple logistic regression as predictive of certification. The area under the ROC curve in the three conditions was 0.92–0.93, and specificity and sensitivity for all conditions were greater than 80%.
Conclusions: Three KCL conditions predicted short-term incidence of LTCI certification. This suggests that KCL items may be used to screen for the risk of incident LTCI certification.
Keywords
older persons, Kihon Checklist, long-term care insurance certification, risk prediction, screening
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CURRENT STATUS: Published
View the published article at BMC Geriatrics.
Version 3
Posted 30 Dec, 2020
No community comments so far
Editorial decision: Accept
On 15 Dec, 2020
Editor assigned
On 14 Dec, 2020
Submission checks complete
On 14 Dec, 2020
Editor invited
On 14 Dec, 2020
No comments provided
Editorial decision: Minor revision
On 01 Dec, 2020
Review #1 received
Received 26 Nov, 2020
Review #2 received
Received 24 Nov, 2020
Reviewer #2 agreed
On 12 Nov, 2020
Reviewers invited
Invitations sent on 09 Nov, 2020
Reviewer #1 agreed
On 09 Nov, 2020
Editor assigned
On 29 Oct, 2020
Submission checks complete
On 29 Oct, 2020
Editor invited
On 29 Oct, 2020
No comments provided
Review #2 received
Received 02 Oct, 2020
Editorial decision: Major revision
On 02 Oct, 2020
Review #1 received
Received 20 Sep, 2020
Reviewer #2 agreed
On 10 Sep, 2020
Reviewer #1 agreed
On 03 Sep, 2020
Editor assigned
On 25 Aug, 2020
Reviewers invited
Invitations sent on 25 Aug, 2020
Submission checks complete
On 20 Aug, 2020
Editor invited
On 20 Aug, 2020
First submitted
On 14 Aug, 2020
Metrics
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Subject Areas
Geriatrics & Gerontology
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A new preprint design is coming!
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See the published version of this preprint at BMC Geriatrics.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Shuichi Obuchi
Tokyo Metropolitan Institute of Gerontology
Corresponding Author
ORCiD: https://orcid.org/0000-0003-0187-9275
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Geriatrics.
Version 3
Posted 30 Dec, 2020
No community comments so far
Editorial decision: Accept
On 15 Dec, 2020
Editor assigned
On 14 Dec, 2020
Submission checks complete
On 14 Dec, 2020
Editor invited
On 14 Dec, 2020
No comments provided
Editorial decision: Minor revision
On 01 Dec, 2020
Review #1 received
Received 26 Nov, 2020
Review #2 received
Received 24 Nov, 2020
Reviewer #2 agreed
On 12 Nov, 2020
Reviewers invited
Invitations sent on 09 Nov, 2020
Reviewer #1 agreed
On 09 Nov, 2020
Editor assigned
On 29 Oct, 2020
Submission checks complete
On 29 Oct, 2020
Editor invited
On 29 Oct, 2020
No comments provided
Review #2 received
Received 02 Oct, 2020
Editorial decision: Major revision
On 02 Oct, 2020
Review #1 received
Received 20 Sep, 2020
Reviewer #2 agreed
On 10 Sep, 2020
Reviewer #1 agreed
On 03 Sep, 2020
Editor assigned
On 25 Aug, 2020
Reviewers invited
Invitations sent on 25 Aug, 2020
Submission checks complete
On 20 Aug, 2020
Editor invited
On 20 Aug, 2020
First submitted
On 14 Aug, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Geriatrics & Gerontology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Geriatrics.
Background: Predicting incidence of long-term care insurance (LTCI) certification in the short term is of increasing importance in Japan. The present study examined whether the Kihon Checklist (KCL) can be used to predict incidence of LTCI certification (care level 1 or higher) in the short term among older Japanese persons.
Methods: In 2015, the local government in Tokyo, Japan, distributed the KCL to all individuals older than 65 years who had not been certified as having a disability or who had already been certified as requiring support level 1–2 according to LTCI system. We also collected LTCI certification data within the 3 months after collecting the KCL data. The data of 17785 respondents were analyzed. First, we selected KCL items strongly associated with incidence of LTCI certification, using stepwise forward-selection multiple logistic regression. Second, we conducted receiver operating characteristic (ROC) analyses for three conditions (1: Selected KCL items, 2: The main 20 KCL items (nos. 1–20), 3: All 25 KCL items). Third, we estimated specificity and sensitivity for each condition.
Results: During a 3-month follow-up, 81 (0.5%) individuals required new LTCI certification. Eight KCL items were selected by multiple logistic regression as predictive of certification. The area under the ROC curve in the three conditions was 0.92–0.93, and specificity and sensitivity for all conditions were greater than 80%.
Conclusions: Three KCL conditions predicted short-term incidence of LTCI certification. This suggests that KCL items may be used to screen for the risk of incident LTCI certification.
Figure 1
This is a list of supplementary files associated with this preprint. Click to download.
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