Depression among left-behind children in China

Abstract

By retrieving literature published from 2005 to 2015 from Chinese National Knowledge Infrastructure, Wanfang, Vip, PubMed, and Web of Science, we filtered out studies using the Children’s Depression Inventory only and compared left-behind children and non-left-behind children. The methodological quality of the papers was evaluated using the Newcastle–Ottawa Scale. Finally, we included six studies to carry out a meta-analysis. The results showed that the Children’s Depression Inventory scores of left-behind children are significantly higher than those of non-left-behind children (standardized mean difference: −0.233, 95% confidence interval: −0.036 to −0.430, p < 0.05).

Keywords

Children’s Depression Inventory China depression left-behind children meta-analysis

Introduction

With the continuous development of the Chinese economy and the accelerating process of urbanization, a large number of rural surplus labors are leaving their homes and places of employment for better jobs and higher payment (Fan et al., 2010). According to the monitoring report of migrant workers in China in 2015, the number of migrant workers has been increasing continuously in recent years. It reached 277.47 million in 2015, with an increase in 3.52 million compared with the previous year and an increase rate in 1.3 percent (National Bureau of Statistics, 2016). Migration has become a common phenomenon, which has caused issues related to left-behind children all over the rural parts of the country (Beh and Ye, 2012). Left-behind children are defined as children under the age of 18 years who are left behind at the registered household location while one or both parents migrate to other places for work such that the parents and children do not live together for at least 6 months (Duan and Zhou, 2005). The official number of left-behind children in China remains inconclusive. However, according to the estimation of the sixth national population census data, there were 61,025,500 rural left-behind children in 2010, accounting for 37.7 percent of rural children and 21.88 percent of the total number of children in China (Women’s Federation News, 2013). An indisputable fact is that the total number of left-behind children is rapidly expanding, especially in rural areas (Ding and Bao, 2014). Moreover, due to the huge gap in development between urban and rural areas, left-behind children are provided with weaker social services, community care, educational resources, and so on (Ye and Pan, 2011).

Mental illness is a common disorder in China. Various vulnerable groups in China, such as earthquake survivors (Liang, 2015b; Liang and Cao, 2014), rural-to-urban migrants (Liang, 2015a; Liang and Guo, 2015; Liang and Wang, 2013), and landless peasants (Liang and Zhu, 2014) suffered from it. Previous studies have noted that Chinese left-behind children are suffering from significant mental health problems, such as depression, psychosocial dysfunction, emotional dysregulation, and behavior problems (Fan et al., 2010; Wang et al., 2015b). It has been indicated that approximately 30 percent of the left-behind children have a mental illness (Liu et al., 2014) and a lack of a normal family upbringing, care, education (Fan et al., 2010), and parent–child communication (Davison and Birch, 2001; Hahm et al., 2003) are important factors causing the mental illness in left-behind children. A number of studies have revealed that compared with the general population, left-behind children are more likely to exhibit psychological problems, such as low self-esteem, anxiety, loneliness, depression, emotional instability, and social anxiety (Zhao et al., 2015). Depression is one of the most common psychological problems among left-behind children. Due to the long-term separation from their parents, left-behind children cannot communicate well with their parents; therefore, they are very prone to depression. Depressive symptoms are common in children and adolescents, and if they are not discovered promptly and treated with appropriate psychological counseling, they can develop into depression. Recent studies have shown that the incidence of depression in children is rising. Due to the paradigm shift in education, the left-behind children are often unsociable and emotionally fragile and have low self-esteem and other negative psychological symptoms (He et al., 2011).

At present, most studies have shown that compared with non-left-behind children, left-behind children are more likely to be depressed (Guo et al., 2012). However, there are also some studies showing that despite the impact of “risk factors” on the development of left-behind children, they do not significantly differ from non-left-behind children in terms of mental health (Wen and Lin, 2012). Thus, although there are many studies on depression in left-behind children, whether left-behind children are more likely to be depressed than non-left-behind children remains inconclusive. This is because of the use of inconsistent measurement scales and methods and the limited scope of the sampling leading to inconsistent results. In addition, although some studies have conducted a systematic review of depression and anxiety in left-behind children (Cheng and Sun, 2015), they have not used a meta-analysis to carry out quantitative research, and they have not used uniform scales, which has biased the estimations of morbidity. Moreover, some studies used a meta-analysis to study the depression rate of left-behind children and its related influencing factors (Liu et al., 2014). However, similar to previous studies, studies based on the Center for Epidemiologic Studies Depression Scale for Children (CES-DC), Depression Self-Rating Scale for Children (DSRSC), Children’s Depression Inventory (CDI), and other scales have different standards, which might have lead to biased results on the depression rate of left-behind children. Furthermore, the studies have not compared left-behind children and non-left-behind children. Therefore, there is not yet a comprehensive and comparative study on depression in left-behind children, based on a unified scale.

The growth of left-behind children is a family issue. Considering the large and growing population of left-behind children in China, their mental health could affect the future of the country and society. Thus, exploration and examination of the mental health of the left-behind children are necessary. This study aims to use a meta-analysis to study depression in Chinese left-behind children based on the CDI. The CDI is a self-report inventory devised by Kovacs (1992) to measure depression in children and adolescents (Smucker et al., 1986). Existing empirical data prove that the CDI is a multidimensional construct that can overlap with other disorders, especially anxiety (Saylor et al., 1984), and it is useful and effective for screening for potentially emotionally disturbed children (Nelson et al., 1987). Some scholars have suggested applying the scale for the early diagnosis of depressive symptoms and treatment performance management (Sitarenios and Kovacs, 1999). However, a meta-analysis can help us to investigate the overall depression status of Chinese left-behind children from a broader and more comprehensive perspective. This is the first study to use a meta-analysis to compare published studies on depression in left-behind children in China comprehensively and provide evidence for the debate on whether left-behind children are more likely to have symptoms of depression than non-left-behind children. Furthermore, it is the first to provide additional empirical investigations to investigate the mental health of Chinese left-behind children and provide policy recommendations on how to improve their mental health.

Methods

Search strategy

We searched the three main Chinese journal databases (Chinese National Knowledge Infrastructure (CNKI), Wanfang, and Vip) which cover the vast majority of papers in Chinese and two English-language periodical databases (PubMed and Web of Science) for studies published from 2005 to 2015 on the depression status of Chinese left-behind children. The keywords included were as follows: “depression,” “depressive disorder,” or “depressive symptom” and “left-behind,” “left in rural areas,” or “parent absent,” combined with “children” and “China.” The references of identified primary and review articles were also searched in order to ensure that all relevant articles were included. Published, peer-reviewed articles available in English or Chinese were considered for this review.

Study selection

The inclusion criteria for this review were as follows: (1) studies were original research studies published in Chinese or English, (2) studies were observational studies comparing depression based on the CDI in left-behind children with the general population of children in China, and (3) studies had complete original data, including sample size, measurement factor average scores, and standard deviation. Two researchers (Ying Liang and Lei Wang) independently checked the articles. Any disagreements between the two reviewers were resolved through discussion.

Data extraction and quality evaluation

The two researchers used a pre-established data extraction method to extract information included in the literature independently. The extracted content included the following: basic information on the publication, study area, study purpose, study design and method, survey tools, study object, main findings, and conclusions. Then, we checked the extracted data and input the data after reaching an agreement. The two researchers evaluated the quality of the included literature using the Newcastle–Ottawa Scale (NOS). The NOS is applicable to evaluating case–control and cohort studies (Stang, 2010) with three aspects and eight items, including the selection of the study population, comparability, and exposure evaluation or outcome evaluation. The NOS uses the semi-quantification principle of the star-style system to evaluate the quality of the literature out of nine points. A higher score indicates better methodological quality. Referring to the previous literature, studies that achieved five or more points on the NOS were considered high quality in this study (Aziz et al., 2006).

Statistical analysis

This study used Stata 12.0 software to process and analyze the extracted data and used the difference of continuous variables mean in the classical meta-analysis to select the standardized mean difference (SMD) of the score of each factor of the left-behind group and the non-left-behind group as the effect size. The pooled SMD and 95 percent confidence interval (CI) were calculated using a random-effects model for all analyses. Cochrane’s Q was used to test for heterogeneity, and the I² index was used to determine the degree of heterogeneity. The significance of the pooled SMD was determined by the z test, and a p value of less than 0.05 was considered significant. Egger’s linear regression test was used to carry out the bias analysis.

Results

Description of studies

Through the literature search in CNKI, Wanfang, Vip, PubMed, and Web of Science, 223 potential relevant studies were found. After reviewing the titles and abstracts, 12 studies were preliminarily included. After further evaluation for full text and data integrity, only six studies (Gao et al., 2007; He et al., 2012; Wang et al., 2011, 2015; Zhao et al., 2015; Zhou and Wang, 2011) met the inclusion criteria and were identified to conduct a meta-analysis, including five Chinese studies and one English study (six studies were removed due to the absence of the final report of CDI mean scores) (Chen et al., 2013; Liu et al., 2009; Shen et al., 2015; Wang et al., 2015a; Yang et al., 2010; Zhao et al., 2015). Figure 1 shows the literature screening process. All studies included were observational studies and adopted a cross-sectional design and utilized cluster sampling using schools as units for recruiting participants. The literature publication years were 2007, 2011 (n = 3), 2012, and 2015. The vast majority of studies included (n = 4) were conducted within a single province in China, while only one included study involved two Chinese provinces, and one study did not mention the survey sample region. The vast majority of study subjects of the included study were primary and middle school students (He et al., 2012; Wang et al., 2011, 2015; Zhao et al., 2015; Zhou and Wang, 2011), while only one included study examined the senior school students (Gao et al., 2007). The total sample size of the included studies was 5311 people, including 2639 left-behind children and 2672 control children, wherein the maximum sample size was 1884, and the minimum sample size was 462. Table 1 shows the summary characteristics of the included studies. The results of the assessment of quality using the NOS are also shown in Table 1. The six studies included described both the content and purpose of the study, but did not report the study design or possible bias and control methods in the research The average quality evaluation score of the included studies was 6.83, ranging from 5 to 8, indicating that these studies were able to provide fair-to-good evidence for a systematic review and meta-analysis.

Table 1.

General characteristics of included studies.

Study	Regions	Study type	Population	Mean scores: left-behind children	Mean scores: non-left-ehind children	Quality score
Wang et al. (2011b)	Hubei province	Observational study	Grade 4–6 primary school students	11.41 ± 7.20 (n = 590)	8.0 ± 5.80 (n = 285)	8
Zhao (2015)	Guizhou province	Observational study	Grade 6 primary school students	15.18 ± 6.21 (n = 366)	15.83 ± 6.50 (n = 182)	7
			Grade 1–2 junior school students
Wang et al. (2011a)	NA	Observational study	Grade 4–6 primary school students	41.04 ± 6.55 (n = 250)	41.79 ± 6.44 (n = 212)	5
			Grade 1–2 junior school students
Zhou and Wang (2011)	Anhui province	Observational study	Grade 3–6 primary school students	13.13 ± 6.46 (n = 486)	11.47 ± 6.17 (n = 1398)	6
Zhou and Wang (2011)			Grade 1–3 junior school students
He et al. (2012)	Hubei province	Observational study	Grade 4–6 primary school students	11.44 ± 7.21 (n = 590)	8.02 ± 5.83 (n = 285)	8
Gao et al. (2007)	Chongqing and Guizhou provinces	Observational study	Grade 5 primary school students	43.04 ± 7.79 (n = 357)	40.79 ± 6.50 (n = 310)	7
			Grade 2 junior school students
			Grade 1 senior school students

NA means not mentioned.

Meta-analysis results

The heterogeneity test revealed that there was significant heterogeneities between the six included studies (I² = 90.8%, p < 0.01), so we used the random-effects model to merge the effect values. Figure 2 shows a forest plot of the meta-analysis on depression in left-behind children in China. The meta-analysis results showed that the depression scores of left-behind children were higher than those of non-left-behind children, with a significant statistical difference (SMD: −0.233, 95% CI: −0.036 to −0.430, p < 0.05). Egger’s linear regression test was used to carry out the bias analysis for the six included studies. The results (see Figure 3) indicated that p = 0.346 > 0.05 in Egger’s test and clearly showed the regression line passed through the 0 point (95% CI contains 0); thus, there was no publication bias.

Discussion

Depression is a common disorder in Chia (Liang et al., 2014a; Liang and Lu, 2014; Liang et al., 2014b). So far, the depression status of left-behind children has not yet been fully investigated. This study is to date the first attempt to conduct a meta-analysis to examine the depression in left-behind children in China comprehensively and provide empirical investigations to investigate the mental health of Chinese left-behind children and provide policy recommendations on how to improve their mental health. Additionally, there were a total of 5311 participants in the six studies included in the meta-analysis, which helped us obtain a more accurate assessment.

The results show that on the whole, there is a significant difference between the depressive symptoms of left-behind children and non-left-behind children in rural China. This conclusion is consistent with the vast majority of studies on the depression status of left-behind children (Gao et al., 2007; He et al., 2012; Wang et al., 2011a; Zhou and Wang, 2011). The publication bias assessment shows that this study is stable and has high credibility. Based on the evidence-based data, the article supports the idea that left-behind children are in a more vulnerable position in terms of mental health, to a certain extent, helping clarify some controversies. This phenomenon may be due to many factors. The mobility of parents and the uneven development of urban and rural areas may be the two main factors. Parents working away from home, which means a long-term lack of parenting, nursing, care, and guidance for children. When parented by grandparents, they may also be subject to improper parenting, and their personal safety and basic daily care may not be guaranteed (Ye and Pan, 2011). Moreover, in the social environment in rural areas where education is lacking, left-behind children may be more susceptible to an unhealthy atmosphere (Wen and Lin, 2012). Therefore, the mental health, education status, health behaviors, family cohesion, quality of life, and personality traits of left-behind children should attract the attention of the entire society. More targeted psychological support resources, social assistance, and other arrangements should be provided in communities and schools, and we should identify at-risk children as early as possible to make appropriate interventions.

The meta-analysis also has some limitations. First, the included studies of this article did not come from a national study sample, which may lead to insufficient sample representativeness. Second, this article only included studies using the CDI without considering studies based on other scales, so this may have produced selection bias that will influence the generalizability of the conclusions in some way. Finally, the quantity of the included studies was relatively small. Therefore, more studies will be needed in order to achieve an in-depth analysis of depression among left-behind children in China.

Footnotes

Acknowledgements

The authors would like to thank all the participants for take part in this study. The authors are also grateful to the trained research assistants from Nanjing University for their contribution to this study.

Authors’ contributions

Y.L. and L.W. wrote the manuscript and performed the statistical analysis. L.W. participated in the design of the study. Y.L., L.W. and G.R. involved in the revising process, making proofreading and modification of the manuscript. Y.L. revised the manuscript and was responsible for the design of the study. All authors read and approved the final manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This paper is supported by the National Natural Science Fund for Excellent Young Scholar of 2016 (71622013): Social Security and Public Policy, the Key Project of National Social Science Fund (16AZZ014), and the General Program of National Natural Science Foundation of China (71473117 and 71173099).

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