Ming Guan1,2,3. 1. International Issues Center, Xuchang University, Xuchang, Road Bayi 88, Xuchang, Henan, China. gming0604@163.com. 2. Family Issues Center, Xuchang University, Road Bayi 88, Xuchang, Henan, China. gming0604@163.com. 3. School of Business, Xuchang University, Road Bayi 88, Xuchang, Henan, China. gming0604@163.com.
Abstract
BACKGROUND: The focus on child mental health in developing countries was increasing. However, little was known in China. This study aimed to explore the associations between socioprovincial factors and self-reported mental disorders in rural China. METHODS: Data were from a publicly available survey with 54,498 students from Grade 4 to 8 in rural China. Chi-square test was used for descriptive analysis. Self-reported mental disorders included overall mental disorder, study anxiety, personal anxiety, loneliness, guilt, sensitivity, symptomatic psychosis, phobia, and impulsivity. Multiple logistic regressions and errors-in-variables regression models were employed to explore the associations between socioprovincial factors and mental disorders. Poisson regressions and errors-in-variables regression models were adopted to reveal the associations between socioprovincial factors and number of self-reported mental disorders. RESULTS: Descriptive statistics showed that mental health was poor in rural adolescents in China. Logistic regression showed that the odds of overall mental disorder and study anxiety were 189% (AOR = 2.89, 95%CI: 2.76, 3.02) and 92% (OR = 1.92, 95%CI: 1.84, 2.00) in Gansu more than those in Anhui, while the odds of personal anxiety, guilt, sensitivity, symptomatic psychosis, and phobia were 92% (AOR = 0.08, 95%CI: 0.08, 0.09), 71% (AOR = 0.29, 95%CI: 0.27, 0.30), 88% (AOR = 0.12, 95%CI: 0.11, 0.13), 69% (AOR = 0.31, 95%CI: 0.29, 0.32), and 78% (AOR = 0.22, 95%CI: 0.21, 0.23) in Gansu less than those in Anhui. Moreover, Gansu (Poisson regression: IRR =1.45, 95%CI: 1.42-1.47; errors-in-variables regression: Coefficient = 0.26, 95%CI: 0.16, 0.36), Ningxia (Poisson regression: IRR =1.63, 95%CI: 1.60-1.67; errors-in-variables regression: Coefficient = 0.43, 95%CI: 0.32, 0.53), Qinghai (Poisson regression: IRR =1.65, 95%CI: 1.60-1.69; errors-in-variables regression: Coefficient = 0.44, 95%CI: 0.34, 0.55), and Shaanxi (Poisson regression: IRR =1.28, 95%CI: 1.25-1.30; errors-in-variables regression: Coefficient = 0.11, 95%CI: 0.00, 0.21) were significantly associated with the number of self-reported mental disorders. CONCLUSION: Class and provincial disparities in self-reported mental disorders were reported among the students from Grade 4 to 8 in rural China. Mental health care supported by governments and schools could be an effective way to reduce the disparities in mental disorders among the adolescents.
BACKGROUND: The focus on child mental health in developing countries was increasing. However, little was known in China. This study aimed to explore the associations between socioprovincial factors and self-reported mental disorders in rural China. METHODS: Data were from a publicly available survey with 54,498 students from Grade 4 to 8 in rural China. Chi-square test was used for descriptive analysis. Self-reported mental disorders included overall mental disorder, study anxiety, personal anxiety, loneliness, guilt, sensitivity, symptomatic psychosis, phobia, and impulsivity. Multiple logistic regressions and errors-in-variables regression models were employed to explore the associations between socioprovincial factors and mental disorders. Poisson regressions and errors-in-variables regression models were adopted to reveal the associations between socioprovincial factors and number of self-reported mental disorders. RESULTS: Descriptive statistics showed that mental health was poor in rural adolescents in China. Logistic regression showed that the odds of overall mental disorder and study anxiety were 189% (AOR = 2.89, 95%CI: 2.76, 3.02) and 92% (OR = 1.92, 95%CI: 1.84, 2.00) in Gansu more than those in Anhui, while the odds of personal anxiety, guilt, sensitivity, symptomatic psychosis, and phobia were 92% (AOR = 0.08, 95%CI: 0.08, 0.09), 71% (AOR = 0.29, 95%CI: 0.27, 0.30), 88% (AOR = 0.12, 95%CI: 0.11, 0.13), 69% (AOR = 0.31, 95%CI: 0.29, 0.32), and 78% (AOR = 0.22, 95%CI: 0.21, 0.23) in Gansu less than those in Anhui. Moreover, Gansu (Poisson regression: IRR =1.45, 95%CI: 1.42-1.47; errors-in-variables regression: Coefficient = 0.26, 95%CI: 0.16, 0.36), Ningxia (Poisson regression: IRR =1.63, 95%CI: 1.60-1.67; errors-in-variables regression: Coefficient = 0.43, 95%CI: 0.32, 0.53), Qinghai (Poisson regression: IRR =1.65, 95%CI: 1.60-1.69; errors-in-variables regression: Coefficient = 0.44, 95%CI: 0.34, 0.55), and Shaanxi (Poisson regression: IRR =1.28, 95%CI: 1.25-1.30; errors-in-variables regression: Coefficient = 0.11, 95%CI: 0.00, 0.21) were significantly associated with the number of self-reported mental disorders. CONCLUSION: Class and provincial disparities in self-reported mental disorders were reported among the students from Grade 4 to 8 in rural China. Mental health care supported by governments and schools could be an effective way to reduce the disparities in mental disorders among the adolescents.
Entities:
Keywords:
Number of self-reported mental disorders; Rural China; Self-reported mental disorders; Socioprovincial factors; Students from grade 4 to 8
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