Predicting the risk of nursing home placement of elderly persons using a population-based stratification score

IF 3.9 3区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Public Health Pub Date : 2024-09-13 DOI:10.1016/j.puhe.2024.08.030

Giovanni Corrao , Matteo Franchi , Gloria Porcu , Alina Tratsevich , Andrea Stella Bonaugurio , Giulio Zucca , Danilo Cereda , Olivia Leoni , Guido Bertolaso

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引用次数: 0

Abstract

Objective

To develop and validate a novel score predictive of nursing home placement in elderly.

Study design

Population-based case-control study based on healthcare utilization databases of Lombardy, a region of Northern Italy.

Methods

The 2.4 million citizens aged ≥65 years who on January 1, 2018 lived outside nursing home formed the target population. Cases were citizens who experienced nursing home admission (the outcome of interest) until December 31, 2019. Cases were matched 1:1 by gender, age, and municipality of residence to one control. Conditional logistic regression was fitted to select candidate predictors (the exposure to 69 clinical conditions and 11 social and healthcare services) independently associated with the outcome. The model was built from the 26,156 cases, and as many controls (training set), and applied to a validation set (15,807 case-control couples). Predictive performance was assessed by discrimination and calibration.

Results

Twenty-one factors were identified as predictive of nursing home admission and were included in the “Elderly Nursing Home Placement” (ENHP) score. Mental health disorders and chronic neurological illnesses contributed most to prediction of nursing home admission. ENHP performance showed an area under the receiver operating characteristic curve of 0.77 and a remarkable calibration of observed and predicted outcome risk.

Conclusions

A simple score derived from data used for public health management may reliably predict the risk of nursing home placement in elderly. Its use by healthcare decision makers allows to accurately identify high-risk individuals who need home services, thereby avoiding admission to nursing homes.

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利用基于人口的分层评分预测老年人入住养老院的风险

研究设计基于意大利北部伦巴第大区医疗保健利用率数据库的人群病例对照研究方法目标人群为 2018 年 1 月 1 日在养老院外居住的 240 万年龄≥65 岁的公民。病例为截至 2019 年 12 月 31 日入住养老院（相关结果）的公民。根据性别、年龄和居住城市，病例与一名对照进行 1:1 匹配。通过条件逻辑回归筛选出与结果独立相关的候选预测因子（69 种临床症状和 11 种社会和医疗保健服务）。该模型由 26156 个病例和相同数量的对照（训练集）建立，并应用于验证集（15807 对病例对照夫妇）。结果有 21 个因素被确定为预测入住养老院的因素，并被纳入 "老年人养老院安置"（ENHP）评分中。精神疾病和慢性神经疾病对入住养老院的预测作用最大。ENHP 的表现显示，接收者操作特征曲线下的面积为 0.77，观察到的结果风险与预测的结果风险之间的校准效果显著。医疗决策者使用它可以准确识别需要居家服务的高风险人群，从而避免入住养老院。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Public Health 医学-公共卫生、环境卫生与职业卫生

CiteScore

7.60

自引率

0.00%

发文量

280

审稿时长

37 days

期刊介绍： Public Health is an international, multidisciplinary peer-reviewed journal. It publishes original papers, reviews and short reports on all aspects of the science, philosophy, and practice of public health.