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<p>This study aimed to explore influencing factors for respite services among family caregivers in disabled elderly individuals, and develop a nomogram model to rank these factors. 356 family caregivers of disabled elderly individuals were collected and divided into a training set (n=249) and a validation set (n=107) in a 7:3 ratio. Univariate and multivariate logistic regression analyses were performed to identify risk factors, and a nomogram model was constructed in the training set. The predictive performance was evaluated using receiver operating characteristic (ROC) curves and calibration curves. Decision curve analysis (DCA) was used to assess the clinical utility.&#xa0;In the training set, 131 (52.61%) family caregivers showed a demand for respite services, while 56 (52.34%) patients showed a demand for respite services in the validation set. Multivariate logistic regression revealed that caregiver age, household income, caregiving duration, caregiving frequency, self-care ability, and community support were independent influencing factors for respite services (all <i>P</i>&lt;0.05). The nomogram model demonstrated good calibration and predictive performance in both training and validation sets, with C-index values of 0.883 and 0.823, respectively. The areas under ROC curve (AUC) were 0.859 (95%CI：0.805-0.912) and 0.894 (95%CI：0.820-0.969), with sensitivity and specificity values of 0.943, 0.783 and 0.907, 0.871, respectively.&#xa0;This study identified key influencing factors for respite services in family caregivers of disabled elderly individuals. The predictive model exhibited strong predictive performance and clinical applicability, aiding relevant authorities in accurately identifying caregivers in need.</p>

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Determinant prioritization and predictive modeling of respite service demand among disabled elderly caregivers

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月29日

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  • Yiqiang An,
  • Lei Cao,
  • Zhiwen Li,
  • Qingqing Cai,
  • Shihong Zhao,
  • Xiangcheng Li

摘要

This study aimed to explore influencing factors for respite services among family caregivers in disabled elderly individuals, and develop a nomogram model to rank these factors. 356 family caregivers of disabled elderly individuals were collected and divided into a training set (n=249) and a validation set (n=107) in a 7:3 ratio. Univariate and multivariate logistic regression analyses were performed to identify risk factors, and a nomogram model was constructed in the training set. The predictive performance was evaluated using receiver operating characteristic (ROC) curves and calibration curves. Decision curve analysis (DCA) was used to assess the clinical utility. In the training set, 131 (52.61%) family caregivers showed a demand for respite services, while 56 (52.34%) patients showed a demand for respite services in the validation set. Multivariate logistic regression revealed that caregiver age, household income, caregiving duration, caregiving frequency, self-care ability, and community support were independent influencing factors for respite services (all P<0.05). The nomogram model demonstrated good calibration and predictive performance in both training and validation sets, with C-index values of 0.883 and 0.823, respectively. The areas under ROC curve (AUC) were 0.859 (95%CI：0.805-0.912) and 0.894 (95%CI：0.820-0.969), with sensitivity and specificity values of 0.943, 0.783 and 0.907, 0.871, respectively. This study identified key influencing factors for respite services in family caregivers of disabled elderly individuals. The predictive model exhibited strong predictive performance and clinical applicability, aiding relevant authorities in accurately identifying caregivers in need.