Analysis of the prevalence and influencing factors of anxiety and depression in the Chinese population: A cross-sectional survey

Xinyi Kong; Yibo Wu; Xinpei Wang; Yike Sun; Ke Chen; Qiyu Li; Jie Li

doi:10.1016/j.heliyon.2023.e15889

Analysis of the prevalence and influencing factors of anxiety and depression in the Chinese population: A cross-sectional survey

Heliyon. 2023 Apr 28;9(5):e15889. doi: 10.1016/j.heliyon.2023.e15889. eCollection 2023 May.

Authors

Xinyi Kong¹, Yibo Wu², Xinpei Wang³, Yike Sun⁴, Ke Chen⁵, Qiyu Li⁶, Jie Li⁷

Affiliations

¹ Department of Cardiovascular Intervention, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, China.
² School of Public Health, Peking University, Beijing, 100191, China.
³ Medical Equipment Department, Peking University First Hospital, Beijing, 100043, China.
⁴ College of Integrated Traditional Chinese and Western Medicine, Jining Medical University, Jining, 272111, China.
⁵ Department of Psychology, Anhui University, Anhui Province, Hefei, China.
⁶ School of Humanities and Health Management, Jinzhou Medical University, Jinzhou, 121000, China.
⁷ Department of Internal Medicine, Jiangxi Chest Hospital, Nanchang, 330006, China.

Abstract

To explore the prevalence and influencing factors of anxiety and depression symptoms among Chinese people in 2021. Investigation teams were recruited in 120 cities across the country. Based on the data from "the Seventh National Population Census in 2021″, quota sampling was conducted on the residents of these cities to obtain samples that conformed to population characteristics. Next, baseline information on research objects was collected, and the questionnaire survey was conducted through the online questionnaire Wenjuanxing platform. The Patient Health Questionnaire-9 (PHQ-9) rating scale was used to evaluate the mental state of the subjects. The correlation between baseline information and different PHQ-9 risk intervals was analyzed using the Chi-square test and Logit model. The impact of relevant risk factors on PHQ-9 scores was analyzed using the decision tree. The Chi-square test results revealed that place of residence (p = 0.438) and obesity (p = 0.443) was not significantly correlated with PHQ-9 risk intervals. According to Logit model analysis, age (p = 0.001, 95%CI 0.84-0.96), marital status (p < 0.001, 95%CI 0.71-0.89), drinking (p < 0.001, 95%CI 1.07-1.18), diabetes or hypertension (p = 0.001, 95%CI 1.11-1.47), health care (p < 0.001, 95%CI 0.53-0.66), economic welfare (p = 0.022, 95%CI 0.85-0.99), COVID-19 vaccine (p < 0.001, 95%CI 1.28-1.72), and HPV vaccine (p < 0.001, 95%CI 0.46-0.57) were potential influencing factors of PHQ-9 risk intervals. Decision tree analysis results showed that the grouping strategy in the PHQ-9 two-side groups had a better classification effect on the questionnaire population according to the PHQ-9 score characteristics. The prevalence rate of moderate to severe depression among Chinese people was about 8.29%. Age, marital status, drinking, diabetes or hypertension, health care, economic well, COVID-19 vaccine, and HPV vaccine were potential influencing factors of anxiety and depression symptoms in Chinese people.

Keywords: Anxiety; Chinese population; Depression; Influencing factors; Prevalence.