背景:痴呆症是老年人制度化的主要因素。非正式护理人员希望将其痴呆症(PwD)的护理接受者制度化(DI)是制度化的主要预测因素。本研究旨在通过挖掘高患病率痴呆症国家的eHealth平台的数据,为护理人员DI开发预测模型。
方法:从在isupport-Portugal注册的照顾者收集横断面数据。pt.104名护理人员完成了制度化愿望量表(DIS),并分为DI(DIS评分≥1)和无DI(DIS评分=0)。参与者完成了一套全面的社会人口统计学,临床,和心理社会措施,关于护理人员和PwD,它们被视为模型预测因子。选择的模型是分类树,实现预测规则的可视化。
结果:护理人员,主要是女性(82.5%),PwD的后代(70.2),受雇(65.4%),和受过高等教育(M15年的学校教育),提供重症监护(每周24小时),中位病程为2.8年。三分之二(66.3%)认可了DIS(DI组)上的至少一项。模型,照顾者感知的压力是分类树的根(在Zarit负担访谈中分为28.5点),包括照顾者的年龄和PwD(分为46岁和88岁,分别),以及同居,采用五条规则来预测DI。在负担和照顾88岁以下的PwD方面得分在28.5及以上的照顾者比照顾年龄较大的PwD(规则1-2)更容易发生DI,表明期望对护理持续时间的影响。该模型表现出高精度(0.83,95CI0.75,0.89),灵敏度(0.88,95CI0.81,0.95),和良好的特异性(0.71,95CI0.56,0.86)。
结论:这项研究将一系列可修改和不可修改的变量提炼成简化的变量,可解释,和准确的模型,在识别具有实际DI的护理人员时特别有用。考虑到预测规则中变量的性质,该模型有望应用于其他现有数据集,并作为实际制度化的代理。预测普华永道的机构安置对于干预作为照顾者负担的可改变因素至关重要,以及护理计划和融资。
BACKGROUND: Dementia is a leading factor in the
institutionalization of older adults. Informal caregivers\' desire to institutionalize (DI) their care recipient with dementia (PwD) is a primary predictor of
institutionalization. This study aims to develop a prediction model for caregivers\' DI by mining data from an eHealth platform in a high-prevalence dementia country.
METHODS: Cross-sectional data were collected from caregivers registering on isupport-portugal.pt. One hundred and four caregivers completed the Desire to Institutionalize Scale (DIS) and were grouped into DI (DIS score ≥ 1) and no DI (DIS score = 0). Participants completed a comprehensive set of sociodemographic, clinical, and psychosocial measures, pertaining to the caregiver and the PwD, which were accounted as model predictors. The selected model was a classification tree, enabling the visualization of rules for predictions.
RESULTS: Caregivers, mostly female (82.5%), offspring of the PwD (70.2), employed (65.4%), and highly educated (M 15 years of schooling), provided intensive care (Mdn 24 h. week) over a median course of 2.8 years. Two-thirds (66.3%) endorsed at least one item on the DIS (DI group). The model, with caregivers\' perceived stress as the root of the classification tree (split at 28.5 points on the Zarit Burden Interview) and including the ages of caregivers and PwD (split at 46 and 88 years, respectively), as well as cohabitation, employed five rules to predict DI. Caregivers scoring 28.5 and above on burden and caring for PwD under 88 are more prone to DI than those caring for older PwD (rules 1-2), suggesting the influence of expectations on caregiving duration. The model demonstrated high accuracy (0.83, 95%CI 0.75, 0.89), sensitivity (0.88, 95%CI 0.81, 0.95), and good specificity (0.71, 95%CI 0.56, 0.86).
CONCLUSIONS: This study distilled a comprehensive range of modifiable and non-modifiable variables into a simplified, interpretable, and accurate model, particularly useful at identifying caregivers with actual DI. Considering the nature of variables within the prediction rules, this model holds promise for application to other existing datasets and as a proxy for actual
institutionalization. Predicting the institutional placement of PwD is crucial for intervening on modifiable factors as caregiver burden, and for care planning and financing.