{"title":"调查老年人抑郁症的内在和情景预测因素:对 CHARLS 数据库的分析。","authors":"Yafei Wu , Chongtao Wei , Yaheng Zhang , Chenming Gu , Ya Fang","doi":"10.1016/j.ajp.2024.104279","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>This study aimed to investigate the intrinsic and situational predictors of depression under the health ecological model.</div></div><div><h3>Methods</h3><div>Two waves (2011 and 2013) of survey data were collected from the CHARLS. A total of 5845 older adults (≧60) were included, and depression was defined as CESD-10 score ≧10. Random forest combined with interpretable methods were utilized to select important predictors of depression. Multilevel logit model was used to examine the associations of intrinsic and situational predictors with depression.</div></div><div><h3>Results</h3><div>After a 2-year follow up, 1822 individuals (31.17 %) developed depression. Interpretable analyses showed that both intrinsic and situational variables were predictive for depression. Multilevel logit model showed that age, gender, number of chronic diseases, number of pain areas, life satisfaction, and toilet distance were significantly associated with depression.</div></div><div><h3>Conclusion</h3><div>Both intrinsic and situational factors were found to be associated with depression among community older population, highlighting their significance for early prevention from the perspective of public health.</div></div>","PeriodicalId":8543,"journal":{"name":"Asian journal of psychiatry","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Investigating intrinsic and situational predictors of depression among older adults: An analysis of the CHARLS database\",\"authors\":\"Yafei Wu , Chongtao Wei , Yaheng Zhang , Chenming Gu , Ya Fang\",\"doi\":\"10.1016/j.ajp.2024.104279\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>This study aimed to investigate the intrinsic and situational predictors of depression under the health ecological model.</div></div><div><h3>Methods</h3><div>Two waves (2011 and 2013) of survey data were collected from the CHARLS. A total of 5845 older adults (≧60) were included, and depression was defined as CESD-10 score ≧10. Random forest combined with interpretable methods were utilized to select important predictors of depression. Multilevel logit model was used to examine the associations of intrinsic and situational predictors with depression.</div></div><div><h3>Results</h3><div>After a 2-year follow up, 1822 individuals (31.17 %) developed depression. Interpretable analyses showed that both intrinsic and situational variables were predictive for depression. Multilevel logit model showed that age, gender, number of chronic diseases, number of pain areas, life satisfaction, and toilet distance were significantly associated with depression.</div></div><div><h3>Conclusion</h3><div>Both intrinsic and situational factors were found to be associated with depression among community older population, highlighting their significance for early prevention from the perspective of public health.</div></div>\",\"PeriodicalId\":8543,\"journal\":{\"name\":\"Asian journal of psychiatry\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-10-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Asian journal of psychiatry\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1876201824003721\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHIATRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asian journal of psychiatry","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1876201824003721","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHIATRY","Score":null,"Total":0}
Investigating intrinsic and situational predictors of depression among older adults: An analysis of the CHARLS database
Background
This study aimed to investigate the intrinsic and situational predictors of depression under the health ecological model.
Methods
Two waves (2011 and 2013) of survey data were collected from the CHARLS. A total of 5845 older adults (≧60) were included, and depression was defined as CESD-10 score ≧10. Random forest combined with interpretable methods were utilized to select important predictors of depression. Multilevel logit model was used to examine the associations of intrinsic and situational predictors with depression.
Results
After a 2-year follow up, 1822 individuals (31.17 %) developed depression. Interpretable analyses showed that both intrinsic and situational variables were predictive for depression. Multilevel logit model showed that age, gender, number of chronic diseases, number of pain areas, life satisfaction, and toilet distance were significantly associated with depression.
Conclusion
Both intrinsic and situational factors were found to be associated with depression among community older population, highlighting their significance for early prevention from the perspective of public health.
期刊介绍:
The Asian Journal of Psychiatry serves as a comprehensive resource for psychiatrists, mental health clinicians, neurologists, physicians, mental health students, and policymakers. Its goal is to facilitate the exchange of research findings and clinical practices between Asia and the global community. The journal focuses on psychiatric research relevant to Asia, covering preclinical, clinical, service system, and policy development topics. It also highlights the socio-cultural diversity of the region in relation to mental health.