| Abstract |
[Abstract] Objective To study retrospectively the psychological status of elderly people in communities and the related factors affecting their anxiety and depression. Methods From December 2022 to December 2023, 80 elderly patients with established resident health records in a community were selected for retrospective analysis. According to the evaluation results of Zung Self-rating Anxiety Scale (SAS) and Zung Self-rating Depression Scale (SDS) in the archives, anxiety and depression were divided into occurrence group and non-occurrence group. The data of the elderly in the two groups of communities were collected and compared. Logistic regression model was used to analyze the relevant factors affecting their mental state for the indicators with differences, and a risk prediction model was constructed. The value of the prediction model was evaluated by receiver operating characteristic curve (ROC curve), and calibration curve and decision curve were constructed. Results There were significant differences in marital status, monthly income, chronic disease, living alone, PSQI score and GSES score between the two groups (P < 0.05). Marital status, monthly income, chronic disease, living alone, PSQI score and GSES score were all risk factors for anxiety and depression (OR value > 1). By constructing a nomogram to verify the accuracy of the nomogram prediction effect, ROC curve was drawn, and it was found that AUC was 0.769 and 95%CI was (0.666~0.871). The calibration curve and reference curve of the risk prediction model are similar, which proves that there is a high consistency between the predicted risk and the actual risk of anxiety and depression in the community elderly. At the same time, the net benefit rate of the prediction model in the threshold range is higher, which proves that the applicability of the prediction model is better. Conclusion The elderly in the community will have a certain degree of anxiety and depression. Marital status, monthly income, chronic disease, living alone, sleep and self-efficacy are all influencing factors of anxiety and depression in the elderly in the community. Therefore, the Logistic regression risk prediction model is constructed, which has high prediction efficiency and good applicability. To provide guidance for early detection and formulation of corresponding intervention measures.
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