合肥市女性居民抑郁症状流行现状及其相关因素研究

Prevalence of Depressive Symptoms and Related Factors among Female Residents in Hefei City

目的了解现代生活方式下城市女性居民抑郁症状流行现状并探讨其相关因素。方法选取5822名合肥市女性居民作为调查对象。通过自行设计问卷,收集其社会人口学特征、日常生活状况、自我健康感知状况和社会支持获得情况四个方面的信息,并采用9条目患者健康问卷进行抑郁症状筛查。通过多因素Logistic回归模型分析调查对象抑郁症状的相关因素,并利用BP神经网络模型对其相关因素进行重要性排序。结果合肥市女性居民抑郁症状检出率为34.8%。多因素Logistic回归模型共筛选出影响女性居民抑郁症状的9个相关因素。BP神经网络模型显示这些相关因素的重要性排序依次为,社会支持获得情况(100.0%)、自评睡眠状况(88.8%)、自觉工作压力(72.4%)、自评健康状况(59.0%)、婚姻状况(41.8%)、手机使用情况(38.5%)、运动习惯(35.9%)、受教育程度(30.9%)和阅读习惯(26.7%)。结论合肥市女性居民抑郁症状检出率较高。在抑郁症的早期防治中,应重点关注女性居民的社会支持获得情况、自评睡眠状况、自觉工作压力以及自评健康状况等因素。

Objective To investigate the prevalence of depressive symptoms in urban female residents and explore its related factors under modern lifestyles.Methods Totally 5822 female residents were selected.A self-designed questionnaire was used to collect the information of the subjects,including sociodemographic characteristics,daily li-ving conditions,self-perceived health status and social support,and patient health questionnaire(PHQ-9)was used for depressive symptoms screening.Multivariate Logistic regression analysis was used to analyze the related factors of depressive symptoms among female residents in Hefei.Back Propagation(BP)neural network model was established to examine the rank of importance of those related factors.Results The prevalence of depressive symptoms among female residents in Hefei was 34.8%.Multivariate Logistic regression model screened out 9 related factors affecting the depressive symptoms.The results of BP neural network model showed that the order of importance of these related factors was,access to social support(100.0%),self-rated sleep status(88.8%),self-rated work or study pressure(72.4%),self-rated health status(59.0%),marital status(41.8%),mobile phone use(38.5%),exercise habits(35.9%),education level(30.9%),and reading habits(26.7%).Conclusion The prevalence of depressive symptoms is high among female residents in Hefei.Health authorities should focus on the prevention and treatment of depressive symptoms among female residents based on their access to social support,self-rated sleep status,self-rated work pressure and self-rated health status.