Criterion validity of the health assessment questionnaire for the national screening program for older adults in Japan: The SONIC study

Multidimensional insights about healthy aging from the cohort study for community-dwelling older adults: The SONIC study

The Septuagenarian, Octogenarian, Nonagenarian Investigation with Centenarian (SONIC) study was established considering population trends and targeting the oldest-old population. This study is unique in its narrow age range, consisting of individuals aged in their 70s, 80s and 90s, and is carried out as a longitudinal cohort study with follow ups every 3 years in urban and rural areas of eastern and western Japan. The aims of the SONIC study are primarily to clarify aging-related changes in multiple domains of human functioning, explore the dynamics of interactions among these domains and identify factors influencing healthy longevity, including psychological well-being. Investigations spanning medical, dental, nutritional, psychological and sociological fields were carried out by specialists, yielding important results. Findings from the SONIC study in Japan, a super-aged society, will provide valuable information for addressing the global aging trend. This review introduces the results from the SONIC study, and explains factors contributing to healthy longevity and happy aging. Geriatr Gerontol Int 2025; 25: 346-355.

Clarifying the Actual Situation of Old-Old Adults with Unknown Health Conditions and Those Indifferent to Health Using the National Health Insurance Database (KDB) System

Background/Objectives: This study aimed to investigate the actual situation of individuals with unknown health conditions (UHCs) and those indifferent to health (IH) among old-old adults (OOAs) aged 75 years and above using the National Health Insurance Database (KDB) system. Methods: A total of 102 individuals with no history of medical examinations were selected from the KDB system in a city in Japan. Data were collected through home visit interviews and blood pressure monitors distributed by public health nurses (PHNs) from Community Comprehensive Support Centers (CCSCs). The collected data included personal attributes, health concern levels, and responses to a 15-item OOA questionnaire. Semi-structured interviews were conducted with seven PHNs. The control group consisted of 76 users of the "Kayoinoba" service (Kayoinoba users: KUs). Results: Of the 83 individuals who could be interviewed, 50 (49.0%) were classified as UHCs and 11 (10.8%) were classified as IH, including 5 from the low health concern group and 6 who refused to participate. In the word cloud generated from the PHNs' interviews, the words and phrases "community welfare commissioner", "community development", "blood pressure monitor", "troublesome", "suspicious", and "young" were highlighted. In the comparison of health assessments between UHCs and KUs, "body weight loss" and "cognitive function" were more prevalent among KUs, and "smoking" and "social participation" were more prevalent among UHCs. Conclusions: The home visit activities of CCSCs utilizing the KDB system may contribute to an understanding of the actual situation of UHCs, including IHs, among OOAs. UHCs (including patients with IH status) had a higher proportion of risk factors related to smoking and lower social participation than KUs.

Artificial Intelligence (AI)-Driven Frailty Prediction Using Electronic Health Records in Hospitalized Patients With Cardiovascular Disease

Background

This study aimed to create a deep learning model for predicting phenotypic physical frailty from electronic medical record information in patients with cardiovascular disease.

Methods and Results

This single-center retrospective study enrolled patients who could be assessed for physical frailty according to cardiovascular health study criteria (25.5% [691/2,705] of the patients were frail). Patients were randomly separated for training (Train set: 80%) and validation (Test set: 20%) of the deep learning model. Multiple models were created using LightGBM, random forest, and logistic regression for deep learning, and their predictive abilities were compared. The LightGBM model had the highest accuracy (in a Test set: F1 score 0.561; accuracy 0.726; area under the curve of the receiver operating characteristics [AUC] 0.804). These results using only commonly used blood biochemistry test indices (in a Test set: F1 score 0.551; accuracy 0.721; AUC 0.793) were similar. The created models were consistently and strongly associated with physical functions at hospital discharge, all-cause death, and heart failure-related readmission.

Conclusions

Deep learning models derived from large sample sizes of phenotypic physical frailty have shown good accuracy and consistent associations with prognosis and physical functions.

Predictive validity of the Questionnaire for Medical Checkup of Old-Old for all-cause mortality and disability incidence

AIM

The Questionnaire for Medical Checkup of Old-Old (QMCOO) has been used nationwide in Japan as part of the health checkup for latter-stage older adults since the financial year 2020. Although the QMCOO is useful in screening for frailty, its cutoff values for predicting adverse health outcomes have rarely been assessed. Therefore, this study aimed to calculate the cutoff values for predicting all-cause mortality and disability incidence and to evaluate their predictive validity.

METHODS

This study included 3837 health checkup recipients aged ≥75 years residing in Higashiura Town, Aichi Prefecture, Japan. The health checkup was conducted from June 1, 2020, to January 31, 2021. Mortality and disability incidence were analyzed separately. For the latter, participants with a disability history were excluded, leaving 3040 available for analysis.

RESULTS

During the follow-up of 11 191 and 8550 person-years, 276 died and 438 developed a disability, respectively. The areas under the receiver operating characteristic curve (95% confidence intervals) for all-cause mortality and disability incidence were 0.68 (0.65-0.71) and 0.66 (0.63-0.68), respectively, with QMCOO cutoff values at 3/4 and 2/3 points. Cox proportional hazard models adjusted for age, sex, body mass index, self-reported diseases, and frequency of alcohol consumption demonstrated the predictive validity of those cutoff values.

CONCLUSIONS

The optimal cutoff value of the QMCOO for predicting all-cause mortality was 3/4, while the optimal cutoff value for predicting disability incidence was 2/3 among an older Japanese population. Geriatr Gerontol Int 2024; 24: 1203-1209.

Questionnaire for Medical Checkup of Old-Old is non-inferior to the Kihon Checklist in screening frailty among independent older adults aged 75 years and older: The Itabashi Longitudinal Study on Aging

AIM

The Questionnaire for Medical Checkup of Old-Old (QMCOO) is a 15-item dichotomous questionnaire developed for the early detection and intervention of frailty in a nationwide health checkup program targeting the old-old (i.e. aged ≥75 years). The Kihon Checklist (KCL) is a 25-item questionnaire widely used for screening and self-monitoring frailty status in administrative settings. With fewer items than the KCL, the QMCOO might expedite the frailty screening process. This study tested whether the QMCOO shows noninferiority in detecting frailty compared with the KCL.

METHODS

Overall, 645 participants aged ≥75 years in the Itabashi Longitudinal Study on Aging were assessed for their frailty status according to the revised Japanese version of the Cardiovascular Health Study criteria. They also completed the QMCOO and the KCL simultaneously. We compared the discriminative performance of the two questionnaires using non-inferiority testing with an operationally defined non-inferiority margin of 10% of the area under the receiver operating characteristic curve computed from the KCL.

RESULTS

The prevalence of frailty was 8.8%. The area under the receiver operating characteristic curve for the QMCOO in determining frailty was 0.76 (95% CI 0.70, 0.82), and the corresponding area under the receiver operating characteristic curve for the KCL was 0.77 (95% CI 0.69, 0.84). The QMCOO was not inferior to the KCL for frailty discrimination (P for non-inferiority = 0.006).

CONCLUSIONS

The accuracy of the QMCOO for determining frailty was not inferior to that of the KCL. The QMCOO might be more acceptable and useful, as it can be applied in a shorter time with fewer questions than the KCL. Geriatr Gerontol Int 2024; 24: 176-181.

Development of a Preventive Health Screening Procedure Enabling Supportive Service Planning for Home-Dwelling Older Adults (PORI75): Protocol for an Action Research Study

BACKGROUND

In Finland, at least 1 in 4 residents will be >75 years of age in 2030. The national aging policy has emphasized the need to improve supportive services to enable older people to live in their own homes for as long as possible.

OBJECTIVE

This study aimed to develop a preventive health screening procedure for home-dwelling older adults aged 75 years to enable the use of clinical patient data for purposes of strategic planning of supportive services in primary care.

METHODS

The action research method was applied to develop the health screening procedure with selected validated health measures in cooperation with the local practicing interprofessional health care teams from 10 primary care centers in the Social Security Center of Pori, Western Finland (99,485 residents, n=11,938, 12% of them >75 years). The selection of evidence-based validated health measures was based on the national guide to screen factors increasing fall risk and the national functioning measures database. The cut-off points of the selected health measures and laboratory tests were determined in consecutive consensus meetings with the local primary care physicians, with decisions based on internationally validated measures, national current care guidelines, and local policies in clinical practice.

RESULTS

The health screening procedure for 75-year-old residents comprised 30 measures divided into three categories: (1) validated self-assessments (9 measures), (2) nurse-conducted screenings (14 measures), and (3) laboratory tests (7 measures). The procedure development process comprised the following steps: (1) inventory and selection of the validated health measures and laboratory tests, (2) training of practical nurses to perform screenings for the segment of 75-year-old residents and to guide them to possible further medical actions, (3) creation of research data from clinical patient data for secondary use purposes, (4) secondary data analysis, and (5) consensus meeting after the pilot test of the health screening procedure for 75-year-old residents procedure in 2019 based on the experiences of health care professionals and collected research data.

CONCLUSIONS

The developed preventive health screening procedure for 75-year-old residents enables the use of clinical patient data for purposes of strategic planning of supportive services in primary care if the potential bias by a low participation rate is controlled.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/48753.