目的： 分析空巢老人抑郁症状影响因素,构建并验证空巢老人抑郁症状风险预测模型。
方法： 基于2020年中国健康与养老追踪调查（China Health and Retirement Longitudinal Study,CHARLS）数据库,纳入3 139名空巢老人,采用抑郁自评量表（CESD-10）评估抑郁症状,运用χ2检验及多因素Logistic回归分析得出空巢老人抑郁症状的独立影响因素,随后构建并验证风险预测模型。
结果： 空巢老人抑郁症状发生率为37.4%。Logistic回归分析显示,空巢老人抑郁症状独立影响因素为性别、居住地、受教育水平、是否患有慢性病、生活自理能力和与子女关系满意程度。风险预测模型受试者工作特征曲线（receiver operating characteristic curve,ROC曲线）的曲线下面积（area under the curve,AUC）为0.727（95%CI 0.709~0.745）。采用bootstrap进行1 000次抽样,得到平均AUC也为0.727。
结论： 构建的空巢老人抑郁症状风险预测模型有良好的区分度和稳定性,可为空巢老人抑郁症状的评估和预防提供一定的参考。
Objective: To analyze the factors affecting depressive symptoms in empty nesters and to construct and validate a risk prediction model.
Methods: Based on the 2020 China Health and Retirement Longitudinal Study (CHARLS) database, 3,139 empty nesters were include. “The Center for Epidemiological Studies Depression Scale-10 was used to assess depressive symptoms.” between included and independent influencing factors were identified using the χ² test and multivariate logistic regression analysis. A risk prediction model was then constructed and validated.
Results: The prevalence of depressive symptoms among empty nesters was 37.4%, The logistic regression analysis revealed that factors such as gender, place of residence, education level, chronic disease status, ability to live independently, and satisfaction with relationships with children had a significant impact on the likelihood of depressive symptoms. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.727 (95% CI: 0.709-0.745). The bootstrap method was used to resample the data 1,000 times, yielding a mean AUC of 0.727.
Conclusion: Constructing risk prediction models with good discrimination and calibration can provide some guidance for the assessment and prevention of depressive symptoms in empty nesters.
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