Development of a risk prediction model and a sample risk chart for long-term care certification based on the functional health of older adults

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  • NOFUJI Yu
    Research Team for Social Participation and Community Health, Tokyo Metropolitan Institute of Gerontology
  • ABE Takumi
    Integrated Research Initiative for Living Well with Dementia, Tokyo Metropolitan Institute of Gerontology
  • SEINO Satoshi
    Research Team for Social Participation and Community Health, Tokyo Metropolitan Institute of Gerontology
  • YOKOYAMA Yuri
    Research Team for Social Participation and Community Health, Tokyo Metropolitan Institute of Gerontology
  • AMANO Hidenori
    Research Team for Social Participation and Community Health, Tokyo Metropolitan Institute of Gerontology
  • MURAYAMA Hiroshi
    Research Team for Social Participation and Community Health, Tokyo Metropolitan Institute of Gerontology
  • YOSHIDA Yuka
    Health and Welfare, Yabu City, Hyogo
  • SHINKAI Shoji
    Integrated Research Initiative for Living Well with Dementia, Tokyo Metropolitan Institute of Gerontology Kagawa Nutrition University
  • FUJIWARA Yoshinori
    Research Team for Social Participation and Community Health, Tokyo Metropolitan Institute of Gerontology
  • KITAMURA Akihiko
    Research Team for Social Participation and Community Health, Tokyo Metropolitan Institute of Gerontology Integrated Research Initiative for Living Well with Dementia, Tokyo Metropolitan Institute of Gerontology

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Other Title
  • 高齢者の機能的健康度の評価に基づく要介護発生リスク予測モデルおよびリスクチャート（試作版）の開発
  • コウレイシャ ノ キノウテキ ケンコウド ノ ヒョウカ ニ モトズク ヨウカイゴハッセイ リスク ヨソク モデル オヨビ リスクチャート(シサクバン)ノ カイハツ

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Abstract

<p>Objectives　The first aim of this study was to develop risk prediction models based on age, sex, and functional health to estimate the absolute risk of the 3-year incidence of long-term care certification and to evaluate its performance. The second aim was to produce risk charts showing the probability of the incident long-term care certification as a tool for prompting older adults to engage in healthy behaviors.</p><p>Methods　This study's data was obtained from older adults, aged ≥65 years, without any disability (i.e., they did not certify≥care level 1) and residing in Yabu, Hyogo Prefecture, Japan (n=5,964). A risk prediction model was developed using a logistic regression model that incorporated age and the Kihon Checklist (KCL) score or the Kaigo-Yobo Checklist (KYCL) score for each sex. The 3-year absolute risk of incidence of the long-term care certification (here defined as≥care level 1) was then calculated. We evaluated the model's discrimination and calibration abilities using the area under the receiver operating characteristic curves (AUC) and the Hosmer-Lemeshow goodness-of-fit test, respectively. For internal validity, the mean AUC was calculated using a 5-fold cross-validation method.</p><p>Results　After excluding participants with missing KCL (n=4) or KYCL (n=1,516) data, we included 5,960 for the KCL analysis and 4,448 for the KYCL analysis. We identified incident long-term care certification for men and women during the follow-up period: 207 (8.2%) and 390 (11.3%) for KCL analysis and 128 (6.6%) and 256 (10.2%) for KYCL analysis, respectively. For calibration, the χ2 statistic for the risk prediction model using KCL and KYCL was: P=0.26 and P=0.44 in men and P=0.75 and P=0.20 in women, respectively. The AUC (mean AUC) in the KCL model was 0.86 (0.86) in men and 0.83 (0.83) in women. In the KYCL model, the AUC was 0.86 (0.85) in men and 0.85 (0.85) in women. The risk charts had six different colors, suggesting the predicted probability of incident long-term care certification.</p><p>Conclusions　The risk prediction model demonstrated good discrimination, calibration, and internal validity. The risk charts proposed in our study are easy to use and may help older adults in recognizing their disability risk. These charts may also support health promotion activities by facilitating the assessment and modification of the daily behaviors of older adults in community settings. Further studies with larger sample size and external validity verification are needed to promote the widespread use of risk charts.</p>

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