Dataset Information

Factors associated with willingness to enter long-term care facilities among older adults in Chengdu, China.

ABSTRACT: OBJECTIVE:To describe Chinese older adults' willingness to enter long-term care facilities and to examine individual factors associated with the willingness of using this growing model of long-term care in China. METHODS:A cross-sectional study involving a random sample of 670 adults aged 60+ in the Hezuo community in Chengdu, China in 2016. Respondents were interviewed by trained staff on socio-demographics, health status, quality of life, social support, and willingness to enter long-term care facilities. RESULTS:Only 11.9% of the respondents were willing to enter long-term care facilities for meeting their medical and social service needs. Multivariable logistic regression analysis showed that willingness to enter long-term care facilities was associated with higher household income (OR = 4.55, 95% CI:1.72-12.00), insurance of Urban Resident Basic Medical Insurance (OR = 4.80, 95% CI:1.17-19.67) and unemployment (OR = 0.48, 95% CI:0.24-0.99). Among those who were willing to enter long-term care facilities, an overwhelming majority (81.2%) would prefer going to a facility within 30-minute walking distance from their current residence, 82.5% indicated the need of nursing care, and 90.0% expected a partnership between the long-term care facility and a large hospital. CONCLUSIONS:A minority of older Chinese were willing to receive long-term care delivered at a facility within walking distance from their current residence. Recent policy aimed at increasing the supply of long-term care facilities may not be consistent with consumer preference for home and community-based care. Balancing investment between home and community-based care, and establishing long-term care insurance remain the top priorities for long-term care research and policy development in China.

SUBMITTER: Huang Z

PROVIDER: S-EPMC6095533 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Factors associated with willingness to enter long-term care facilities among older adults in Chengdu, China.

Huang Ziyue Z Liu Qingyue Q Meng Hongdao H Liu Danping D Dobbs Debra D Hyer Kathryn K Conner Kyaien O KO

PloS one 20180816 8

<h4>Objective</h4>To describe Chinese older adults' willingness to enter long-term care facilities and to examine individual factors associated with the willingness of using this growing model of long-term care in China.<h4>Methods</h4>A cross-sectional study involving a random sample of 670 adults aged 60+ in the Hezuo community in Chengdu, China in 2016. Respondents were interviewed by trained staff on socio-demographics, health status, quality of life, social support, and willingness to enter ...[more]

PMID: 30114274

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Project description:ObjectivesThe integrated model of frailty shifted the physiologically-dominated view to an integrated and multidisciplinary view of frailty and adopted an integrated and holistic approach to the complex challenges facing frail older adults and their caregivers. There is an urgent need for researchers to identify multi-faceted frailty among older adults and to explore contributors to it. We assessed the level of frailty among older residents in long-term care (LTC) facilities in Shanghai, China and explored risk factors of multiple dimensions of frailty among older residents.MethodsIt is a cross-sectional descriptive study. We interviewed 218 older residents from nine LTC facilities. We used Tilburg Frailty Indicator to assess older residents' physiological, psychological, and social frailty. The Geriatric Depression Scale, Mini-Mental state examination, Athens Insomnia Scale, and Activity of Daily Living (ADL) Scale were used to assess their depressive symptoms, cognitive function, sleep quality and ADL abilities, respectively.ResultsOlder residents have a moderate level of frailty in physiological, psychological and social domains. There is a high correlation among dimensions of frailty. Older residents' depressive symptoms have remained positively associated with their overall and each domain of frailty. Older residents' actual activity participation and exercise in LTC facilities are negatively associated with older residents' overall, physiological and social frailty.ConclusionsFrailty is a multi-dimensional concept and is prevalent among older residents in LTC facilities. It is important to take an integrated view on frailty and carry out appropriate and comprehensive interventions to prevent adverse outcomes and provide holistic care for frail older residents in LTC facilities. There is an urgent need to improve the quality and expand the number of activities offered within LTC facilities that would engage older residents in a person-centered way, despite their age, ADL abilities, frailty and functional limitations.

Project description:BackgroundNumerous studies have identified risk factors for physical restraint (PR) use in older adults in long-term care facilities. Nevertheless, there is a lack of predictive tools to identify high-risk individuals.ObjectiveWe aimed to develop machine learning (ML)-based models to predict the risk of PR in older adults.MethodsThis study conducted a cross-sectional secondary data analysis based on 1026 older adults from 6 long-term care facilities in Chongqing, China, from July 2019 to November 2019. The primary outcome was the use of PR (yes or no), identified by 2 collectors' direct observation. A total of 15 candidate predictors (older adults' demographic and clinical factors) that could be commonly and easily collected from clinical practice were used to build 9 independent ML models: Gaussian Naïve Bayesian (GNB), k-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), extreme gradient boosting (XGBoost), and light gradient boosting machine (Lightgbm), as well as stacking ensemble ML. Performance was evaluated using accuracy, precision, recall, an F score, a comprehensive evaluation indicator (CEI) weighed by the above indicators, and the area under the receiver operating characteristic curve (AUC). A net benefit approach using the decision curve analysis (DCA) was performed to evaluate the clinical utility of the best model. Models were tested via 10-fold cross-validation. Feature importance was interpreted using Shapley Additive Explanations (SHAP).ResultsA total of 1026 older adults (mean 83.5, SD 7.6 years; n=586, 57.1% male older adults) and 265 restrained older adults were included in the study. All ML models performed well, with an AUC above 0.905 and an F score above 0.900. The 2 best independent models are RF (AUC 0.938, 95% CI 0.914-0.947) and SVM (AUC 0.949, 95% CI 0.911-0.953). The DCA demonstrated that the RF model displayed better clinical utility than other models. The stacking model combined with SVM, RF, and MLP performed best with AUC (0.950) and CEI (0.943) values, as well as the DCA curve indicated the best clinical utility. The SHAP plots demonstrated that the significant contributors to model performance were related to cognitive impairment, care dependency, mobility decline, physical agitation, and an indwelling tube.ConclusionsThe RF and stacking models had high performance and clinical utility. ML prediction models for predicting the probability of PR in older adults could offer clinical screening and decision support, which could help medical staff in the early identification and PR management of older adults.

Dataset Information

Factors associated with willingness to enter long-term care facilities among older adults in Chengdu, China.

Publications

Factors associated with willingness to enter long-term care facilities among older adults in Chengdu, China.

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