Life changes and mental health among Chinese children and adolescents during the pandemic: a combination of cross-sectional, longitudinal and clustering studies

doi:10.21203/rs.3.rs-4209312/v1

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Research Article

Life changes and mental health among Chinese children and adolescents during the pandemic: a combination of cross-sectional, longitudinal and clustering studies

https://doi.org/10.21203/rs.3.rs-4209312/v1

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published 26 Jul, 2024

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The measures to prevent COVID-19 pandemic had caused significant life changes, which could be distressing for mental health among children and adolescents. We aimed to evaluate the short- and long-term effects of life changes on children’s mental health in a large Chinese cohort. Survey-based life changes during COVID-19 lockdown were measured among 7,829 Chinese students at Grade 1–9, including social contacts, lifestyles and family financial status. Clustering analysis was applied to identify potential patterns of these changes. Depressive and anxiety symptoms were measured using the Center for Epidemiologic Studies Depression Scale and Screen for Child Anxiety Related Emotional Disorders. Logistic regression models were used to investigate the associations between these changes, their patterns and the presence of depression/anxiety symptoms using both cross-sectional and longitudinal designs. We found that the prevalence of depression and anxiety symptoms decreased during pandemic (34.6–32.6%). However, during and shortly after lockdown, students who reported negative impacts on their study, social and outside activities and diet, and decreased electronic time and sugar-sweetened consumption, as well as family income decline and unemployment had increased risks of depressive/anxiety symptoms, and students with changed sleep time had increased depressive symptoms. These associations attenuated or disappeared one year later. Similar patterns were observed in clustering analysis, while only the group with severe impact on family financial status showed a sustained increase in depression symptoms. In summary, restrictive measures that changed children and adolescents’ daily life during COVID-19 lockdown showed negative effects on their mental health, with some commonalities and distinctions patterns in the manifestation of depression and anxiety symptoms.

Childhood and adolescence

COVID-19

depression

anxiety

longitudinal studies

Since the outbreak in December 2019, COVID-19 has spread rapidly worldwide and was declared a pandemic in March 2020[1]. Consequent implementation of restrictive measures was conducted by many countries to minimize the spread of the disease, including workplace and school closures, home confinements and social distancing rules. The pandemic and the implemented measures to prevent the spread of COVID-19 (e.g., quarantines or lockdowns) can both act as potential psychological stressors. Individuals, including children and adolescents, who were experiencing social distancing, isolation or lockdown may suffer from anxiety, depression, suicidal ideation, posttraumatic stress syndrome and eating disorders[2–4].

The reasons include but are not limited to the following aspects. First, peer interaction plays a pivotal role in building children and adolescents’ self-awareness, self-identity, and high-level cognitive processes [5, 6], which are likely to be interrupted by social deprivation and home confinements during the COVID-19 lockdown. Second, physical activity and decrease in sedentary behavior may help protect against mental health problems in children and adolescents [7, 8] for both internalizing and externalizing symptoms[9]. However, during the COVID-19 lockdown, there has been an increase in sleep time and screen time, as well as a decrease in physical activities[10, 11], which has be indicated as risk factors for anxiety and depression in children and adolescents[12]. Finally, the stressors within the family, caused by unemployment, illness of family members or friends, financial insecurity, household dysfunction during pandemic, can be transmitted to children and adolescents through their family members [13, 14], and their well-being may be significantly affected by parental negativity or family difficulties [15]. However, the majority of studies on risk factors of depression and anxiety on children and adolescents during the COVID-19 pandemic [16] are cross-sectional. Longitudinal studies on how different aspects and levels of stressors affect the mental health of children and adolescents, with large samples, are lacked [17–20] and important for future intervention policies in similar public health emergency events.

The aim of this study was to investigate the associations between life changes during the COVID-19 lockdown, including social contacts, lifestyles and family financial status, and depressive/anxiety symptoms in a large Chinese child and adolescent cohort, by using a combination of cross-sectional, longitudinal and clustering analyses. First, we examined how the different aspects and levels of COVID-19-related life changes affected mental health problems in children and adolescents in short- and long-term, with the hypothesis that higher levels of impact would be associated with higher risks of mental health problems. Second, we explored whether there were distinctive patterns of these life changes and whether these patterns would influence mental health in different ways.

Study Design and Participants

This study utilized the survey data from the Chengdu Positive Child Development (CPCD) Research project [21–23], which aims to understand the state of positive development and psychosocial and behavioral problems in children. The CPCD project was established between December 2019 and January 2020. It randomly recruited students in Grade 1–9 from five schools in Chengdu, Sichuan Province, Western China, including three primary schools and two middle high schools located in both downtown area and suburbs. Self-report and parental-report survey data were collected under the supervision of head teachers and investigators in each class.

CPCD is an ongoing project, and our study utilized the survey data in three waves (Fig. 1A). Wave 1 (W1) survey was conducted at baseline before the pandemic (from December 2019 to January 2020). A total of 8,968 students were invited to complete the survey, and 8,825 provided valid responses [21]. Wave 2 (W2) survey was collected during the pandemic (from June to July 2020), right after the epidemic subsided and the schools were reopened in China. A total of 7,985 students were followed up, among which 156 were excluded due to their missing data on life changes during COVID-19, leaving 7,829 individuals for the present study. Wave 3 (W3) survey was conducted after the students had normally attended schools for one year (from June to July 2021), and 6,459 were followed up. The main reasons for the loss of follow-up were that the collecting dates of follow-up surveys were too close to the high school entrance examination (14–15 July 2020 and 16–17 July 2021) for middle school students at Grade 9 and that children nearing graduation were lost in the next year's follow-up.

This project received ethical approval from the Ethics Committee of Sichuan University (K2020025). The participating schools, children and adolescents, and their parents have provided written informed consent, and participants had the right to withdraw at any stage.

Measures

Participant’s characteristics

Personal characteristics of age, gender and body mass index (BMI) calculated by health screening, as well as sociodemographic characteristics including types of caregivers, parents’ educational attainment and occupation, monthly family income and numbers of siblings, were collected from children’s caregivers at W1. COVID-19 related characteristics were collected from the children at W2 (see Appendix 1 for the questionnaire translated in English version), which included self-rated assessments of the seriousness, susceptibility, severity and self-prevention capability of COVID-19 by using a 4-point Likert scale ranging from 0 (“not at all”) to 3 (“extremely”), one dichotomous question asking if they or their family members were infected, and one question asking how many measures they knew for protection from infection (scores ranging from 0 to 9).

Changes in social contacts, lifestyles and family financial status during COVID-19

To investigate the life changes during COVID-19 pandemic, additional questions were added to W2 and W3 survey (Appendix 1) which were grouped into three dimensions. The first dimension is social contacts asking to what extent the social, study and outside activities were affected, with four levels ranging from “no impact” to “severe impact” reported from students. The second dimension is lifestyle which included the following aspects: (1) the extent to which diet was affected, with four levels ranging from “no impact” to “severe impact” reported from students; (2) changes in self-reported daily time spent on sleeping, exercise and at electronic screens, and the self-reported times of weekly sugar-sweetened beverage consumption during the COVID-19 blockade compared to pre-epidemic. (3) the change in BMI between W1 (from health-screening) and W2 (calculated by parent-reported weight and height). Except for diet, other changes in the second dimension were categorized into "decreased", "stable" and "increased" by tertiles in the statistical analyses (see Supplementary Table 1). The third dimension is parent-reported family financial status including the extent to which income was affected (with four levels ranging from “no impact” to “severe impact”) and one dichotomous item recording whether anyone was unemployed.

Depressive symptoms

The Chinese version of 20-item Center for Epidemiologic Studies Depression Scale (CES-D) was used to examine the depressive symptoms over the past week in all three survey waves[24]. Each item was rated on a 4-point scale ranging from 0 (“Not at all”) to 3 (“A lot”). This scale includes four reversed items (i.e., items 4, 8, 12, and 16). Potential scores of the CES-D scale range from 0 to 60 with higher score representing greater depressive symptoms, and a score of ≥ 16 is the cut-off to indicate depression[24]. Previous research has confirmed that the Chinese version of CES-D exhibits great reliability and validity[24, 25]. This scale displayed good reliability in all three survey waves (W1 Cronbach's α = 0.87; W2 Cronbach's α = 0.89; W3 Cronbach's α = 0.87).

Anxiety symptoms

The 41-item Screen for Child Anxiety Related Emotional Disorders (SCARED) was used to evaluate the anxiety symptoms in the past three months in all three waves[26]. Each item uses a 4-point scale ranging from 0 (“never”) to 3 (“often”). The SCARED scale score ranges from 0 to 82 with higher score representing more severe level of anxiety symptoms, and a total score ≥ 23 indicates the presence of anxiety[26]. The Chinese version of SCARED has been proven good validity and reliability according to prior research[26, 27]. This scale displayed good reliability (Cronbach's α = 0.95) in all three survey waves.

Statistical analyses

Cross-sectional analyses

We first examined the anxiety and depressive symptoms at W2, i.e., right after the lockdown period, by applying multivariable regression models to estimate their associations with life changes in social contacts, lifestyles and family financial status reported at W2. Risks of anxiety and depressive presence, defined as binary variables by their item cut-off scores respectively, were reported as odd ratios (OR).We used three models: (1) Model 1 adjusted for age, gender and baseline BMI; (2) Model 2 adjusted for all covariates in Model 1 and additionally monthly family income, types of caregivers and the economic level of the school area at baseline; (3) Model 3 adjusted for all covariates in Model 2 and additionally the children’s knowledge about measures to prevent COVID-19, self-rating of the seriousness collected at W2, and children’s mental health status at W1 (i.e., any symptoms of either anxiety or depression).

Longitudinal analyses

We examined the associations of mental health status reassessed at W3 after one year of lockdown and the life changes reported at W2 (same as above), by using the same models as in the cross-sectional analyses, except for the additional adjustment of mental health status at W2 in Model3.

Clustering analyses

We applied unsupervised clustering to identify subgroups of students with different patterns of life changes that may not be detectable by regression models. Input variables for clustering were the measurements of life changes during COVID-19 at W2, including changes in social contacts, lifestyles and family financial status (11 variables in 3 dimensions). The Hopkins value was estimated as 0.95, indicating that the data structure was non-random and suitable for clustering[28]. The Gower distance was applied to measure the dissimilarities between 7,829 children for mixed-type variables consisting of continuous, binary, and ordinal variates. The Gower distance is a metric that ranges from 0 to 1 and measures the average of partial dissimilarities across individuals (the closer the value is to 1, the more dissimilar the individuals are for a given pair) [29]. Subsequently, the hierarchical clustering was applied to the Gower distance matrix, where subjects were separated based on their pairwise dissimilarity, from lowest to highest, namely agglomerative clustering. The Silhouette method was applied as an overall goodness-of-fit of the clustering structure, and the optimal number of clusters was determined by the highest average silhouette width [30]. The hierarchy of clusters were visualized by a dendrogram graph using Ward.D2 method [31, 32]. After clustering, multivariable logistic regression models were applied to examine the risks of depression and anxiety symptoms at W2 and W3, respectively, in each cluster compared to the reference cluster.

All analyses were performed in R (v4.2.2)[33]. Clustering analyses used R packages cluster, stats, and NbClust [31, 33, 34]. All statistical tests were two-sided, and 95% confidence intervals (CI) are reported.

The final study included 7,829 participants with mean age as 10.6 years (SD = 2.17) and 51.40% being male. The prevalence of depressive symptoms decreased from 34.60% at W1 to 33.63%% at W2, and 32.62% at W3. Similarly, the prevalence of anxiety symptoms declined from 29.12% at W1 to 25.04% at W2, and 23.70% at W3. Participants’ characteristics, stratified by the presence of depressive and anxiety symptoms, were shown in Table 1. Several differences were observed in the sociodemographic characteristics in participants with depressive/anxiety symptoms compared to those without, including being older, more likely to be females and more likely to be obese. Moreover, the school areas are more likely to be in lower economic level and parents’ education attainment are lower among students with depressive and anxiety symptoms. Additional descriptive characteristics for participants in the longitudinal analyses were shown in Supplementary Table 2.

Table 1

Baseline characteristics of study participants (N = 7,829)
Characteristics	Depressive symptoms^a		Anxiety symptoms^b
Characteristics	No (n = 5,196)	Yes (n = 2,633)	No (n = 5,869)	Yes (n = 1,960)
Personal characteristics
Age, years, (mean (SD))^c	10.48 (2.12)	10.89 (2.22)	10.49 (2.17)	11.00 (2.11)
Gender, N (%)^d
Female	2,476 (47.7)	1,329 (50.5)	2,685 (45.7)	1,120 (57.1)
Male	2,720 (52.3)	1,304 (49.5)	3,184 (54.3)	840 (42.9)
BMI level^e, N (%)
Normal	3,642 (73.8)	1,909 (75.8)	4,158 (74.5)	1,393 (74.3)
Emaciation	190 (3.9)	74 (2.9)	215 (3.9)	49 (2.6)
Overweight	687 (13.9)	302 (12.0)	738 (13.2)	251 (13.4)
Obesity	416 (8.4)	233 (9.3)	468 (8.4)	181 (9.7)
Sociodemographic characteristics
Number of siblings, (mean (SD))	0.95 (1.39)	1.04 (1.58)	0.97 (1.46)	1.00 (1.46)
Monthly family income, yuan, N (%)
< 4000	842 (19.9)	473 (22.0)	936 (19.6)	379 (23.6)
4,000-6000	1,000 (23.7)	485 (22.6)	1,117 (23.4)	368 (22.9)
6,000-8000	738 (17.5)	364 (17.0)	864 (18.1)	238 (14.8)
8,000-10000	641 (15.2)	318 (14.8)	723 (15.2)	236 (14.7)
> 10000	1,003 (23.7)	507 (23.6)	1,125 (23.6)	385 (24.0)
Types of caregivers, N (%)
Parents	4,697 (92.9)	2,332 (91.6)	5,290 (92.6)	1,739 (91.8)
Grandparents	273 (5.4)	171 (6.7)	328 (5.7)	116 (6.1)
Others	88 (1.7)	44 (1.7)	93 (1.6)	39 (2.1)
Economic level of the school area^f, N (%)
Low	2,086 (40.1)	954 (36.2)	2,312 (39.4)	728 (37.1)
Middle	1,760 (33.9)	1,064 (40.4)	1,996 (34.0)	828 (42.2)
High	1,350 (26.0)	615 (23.4)	1,561 (26.6)	404 (20.6)
Paternal educational attainment, N (%)
Elementary and below	349 (6.9)	216 (8.5)	392 (6.9)	173 (9.1)
Junior secondary	2,082 (41.2)	1,063 (41.7)	2,348 (41.1)	797 (42.1)
Senior secondary	1,268 (25.1)	654 (25.7)	1,450 (25.4)	472 (24.9)
Vocational or technical	500 (9.9)	252 (9.9)	571 (10.0)	181 (9.6)
University and above	859 (17.0)	363 (14.2)	952 (16.7)	270 (14.3)
Maternal educational attainment, N (%)
Elementary and below	525 (10.4)	335 (13.1)	597 (10.5)	263 (13.9)
Junior secondary	1,987 (39.3)	1,047 (41.1)	2,244 (39.3)	790 (41.7)
Senior secondary	1,200 (23.7)	584 (22.9)	1,375 (24.1)	409 (21.6)
Vocational or technical	533 (10.5)	261 (10.2)	595 (10.4)	199 (10.5)
University and above	812 (16.1)	321 (12.6)	901 (15.8)	232 (12.3)
Paternal occupation, N (%)
Retired, unemployed, laid off, husband	93 (1.8)	38 (1.5)	103 (1.8)	28 (1.5)
White collar	1,509 (29.8)	710 (27.9)	1,677 (29.4)	542 (28.6)
Blue collar	1,988 (39.3)	1,091 (42.9)	2,281 (40.0)	798 (42.2)
Farmer	487 (9.6)	242 (9.5)	543 (9.5)	186 (9.8)
Self-employed	846 (16.7)	409 (16.1)	964 (16.9)	291 (15.4)
Others	133 (2.6)	55 (2.2)	140 (2.5)	48 (2.5)
Maternal occupation, N (%)
Retired, unemployed, laid off, housewife	810 (16.0)	374 (14.7)	879 (15.4)	305 (16.1)
White collar	1,471 (29.1)	715 (28.1)	1,655 (29.0)	531 (28.1)
Blue collar	1,223 (24.2)	652 (25.6)	1,407 (24.6)	468 (24.7)
Farmer	576 (11.4)	300 (11.8)	657 (11.5)	219 (11.6)
Self-employed	835 (16.5)	415 (16.3)	946 (16.6)	304 (16.1)
Others	144 (2.8)	90 (3.5)	168 (2.9)	66 (3.5)
^aDepressive symptoms: Children with depressive symptoms identified by the CES-D questionnaire (CES-D score ≥ 16).
^bAnxiety symptoms: Children with anxiety symptoms identified by the SCARED questionnaire (SCARED score ≥ 23).
^cMean (SD): Mean (Standard Deviation).
^dN (%): Number (%).
^eBMI level (%): The BMI level is determined based on the screening for overweight and obesity among school age children and adults developed by the National Health Commission of the People's Republic of China.

Cross-sectional and longitudinal analyses

Table 2 shows the adjusted ORs based on Model 3 for associations between life changes and depressive/anxiety symptoms during COVID-19 using cross-sectional and longitudinal designs, respectively. Results of Model 1 and 2 with different covariate adjustments are available in Supplementary Table 3–4.

Table 2

Risks of depressive and anxiety symptoms among students with different life changes during COVID-19 (Model 3^a)
Life changes	Depressive symptoms^b
	Cross-sectional study	Longitudinal study	Cross-sectional study	Longitudinal study
	OR (95% CI)^d	OR (95% CI)	OR (95% CI)	OR (95% CI)
Social contacts
Study activities
No impact	Ref	Ref	Ref	Ref
Mild impact	1.32 (1.14–1.53)	0.95 (0.80–1.12)	1.60 (1.35–1.89)	1.00 (0.83–1.21)
Moderate impact	1.96 (1.67–2.31)	1.29 (1.08–1.56)	1.93 (1.61–2.31)	1.23 (1.00-1.51)
Severe impact	2.15 (1.78–2.61)	1.21 (0.97–1.50)	1.89 (1.53–2.34)	1.11 (0.87–1.41)
Social activities
No impact	Ref	Ref	Ref	Ref
Mild impact	1.36 (1.17–1.58)	1.13 (0.96–1.35)	1.44 (1.22–1.71)	1.11 (0.92–1.35)
Moderate impact	1.64 (1.40–1.93)	1.25 (1.04–1.50)	1.57 (1.31–1.87)	1.11 (0.91–1.36)
Severe impact	1.68 (1.41-2.00)	0.98 (0.80–1.19)	1.86 (1.54–2.25)	1.34 (1.09–1.66)
Outside activities
No impact	Ref	Ref	Ref	Ref
Mild impact	1.36 (1.17–1.59)	1.12 (0.94–1.33)	1.46 (1.23–1.73)	1.11 (0.92–1.35)
Moderate impact	1.65 (1.40–1.94)	1.25 (1.04–1.50)	1.89 (1.58–2.26)	1.27 (1.04–1.56)
Severe impact	2.01 (1.69–2.40)	1.21 (0.99–1.47)	1.94 (1.60–2.35)	1.40 (1.13–1.73)
Lifestyles
Diet
No impact	Ref	Ref	Ref	Ref
Mild impact	1.28 (1.12–1.47)	1.21 (1.04–1.40)	1.23 (1.07–1.43)	1.14 (0.96–1.34)
Moderate impact	1.64 (1.38–1.95)	1.35 (1.11–1.65)	1.46 (1.21–1.76)	1.21 (0.97–1.50)
Severe impact	1.69 (1.35–2.09)	1.25 (0.98–1.60)	1.58 (1.24–1.99)	1.10 (0.83–1.44)
Sleep time change^e, hours/day
Stable	Ref	Ref	Ref	Ref
Decrease	1.51 (1.26–1.82)	1.18 (0.96–1.45)	1.08 (0.89–1.32)	1.03 (0.82–1.29)
Increase	1.28 (1.08–1.53)	1.18 (0.97–1.44)	0.91 (0.76–1.10)	1.04 (0.84–1.30)
Exercise time change^f, hours/day
Stable	Ref	Ref	Ref	Ref
Decrease	0.99 (0.81–1.21)	0.97 (0.77–1.22)	1.14 (0.91–1.43)	1.26 (0.98–1.63)
Increase	0.83 (0.68-1.00)	1.04 (0.84–1.30)	0.98 (0.79–1.22)	1.12 (0.88–1.44)
Electronic time change^g, hours/day
Stable	Ref	Ref	Ref	Ref
Decrease	1.28 (1.08–1.52)	1.04 (0.86–1.26)	1.28 (1.07–1.55)	1.02 (0.83–1.26)
Increase	1.16 (0.99–1.36)	1.04 (0.87–1.24)	1.00 (0.84–1.19)	0.90 (0.74–1.09)
Sugar-sweetened beverage consumption change^h, times/week
Stable	Ref	Ref	Ref	Ref
Decrease	1.57 (1.30–1.90)	1.05 (0.84–1.31)	1.39 (1.14–1.70)	1.09 (0.86–1.38)
Increase	1.00 (0.87–1.14)	0.96 (0.82–1.12)	0.82 (0.71–0.95)	1.01 (0.86–1.20)
BMI changeⁱ
Stable	Ref	Ref	Ref	Ref
Decrease	1.11 (0.97–1.28)	1.06 (0.91–1.25)	0.90 (0.78–1.05)	0.94 (0.78–1.12)
Increase	0.98 (0.85–1.14)	0.88 (0.75–1.04)	0.87 (0.74–1.02)	0.93 (0.78–1.12)
Family financial status
Income decline
No impact	Ref	Ref	Ref	Ref
Mild impact	1.21 (1.03–1.43)	1.05 (0.87–1.27)	1.31 (1.09–1.58)	0.99 (0.80–1.22)
Moderate impact	1.31 (1.09–1.58)	1.19 (0.96–1.46)	1.47 (1.20–1.80)	1.15 (0.92–1.45)
Severe impact	1.57 (1.29–1.91)	1.28 (1.03–1.59)	1.70 (1.37–2.11)	1.35 (1.06–1.72)
Unemployment in family members
Nobody	Ref	Ref	Ref	Ref
≥ 1 family member	1.29 (1.09–1.52)	1.04 (0.86–1.25)	1.33 (1.11–1.60)	1.08 (0.88–1.33)
^aModel 3: Logistic regression model adjusted for age, gender, BMI level, monthly family income, types of caregivers, the economic level of the school area, the children’s knowledge about measures to prevent COVID-19, self-rating of the seriousness and children’s mental health status at W1 (the longitudinal study additionally adjusted children’s mental health status at W2).
^bDepressive symptoms: Children with depressive symptoms identified by the CES-D questionnaire (CES-D score ≥ 16).
^cAnxiety symptoms: Children with anxiety symptoms identified by the SCARED questionnaire (SCARED score ≥ 23).
^dOR, odds ratio; CI, confidence interval.
^eSleep time change: Change in daily sleep time compared to pre-epidemic.
^fExercise time change: Change in daily exercise time compared to pre-epidemic.
^gElectronic time change: Change in daily electronic product use time compared to pre-epidemic, excluding time drawn by online learning activities.
^hSugar-sweetened beverage consumption change: Change in the number of sugar-sweetened beverage consumption per week compared to pre-epidemic.
ⁱBMI change: Changes in BMI after lockdown ended compared to pre-pandemic.

In the cross-sectional analyses, participants with self-reported impact on study, social and outside activities and diet as well as parent-reported declined family income consistently had higher risks of depressive/anxiety symptoms (ORs 1.21 to 2.15), with a dose-response relationship (i.e., higher level of impact indicating higher risks of mental health problems), compared to those reported no impacts on life changes (p Value for Trend all < 0.001). Furthermore, higher risks of depressive/anxiety symptoms were associated with a decrease in electronic time and sugar-sweetened beverage consumption, as well as the unemployment in family members (ORs 1.28 to 1.57). Sleep time changes, either increase or decrease, was solely associated with depression symptoms. Finally, no associations were found for BMI changes and exercise time changes.

In the longitudinal analyses where exposures were collected at W2 and outcomes newly recorded after one year at W3, we did not find any new association between students’ life changes and depression/anxiety symptoms in addition to those found in the cross-sectional analyses. If any, the degrees of associations were all attenuated, and some were disappeared (e.g., sleep time, electronic time, sugar-sweetened consumption and family unemployment). No dose-response relationships were found if only considering the associations passed significance threshold. The associations of increased risks of depressive and anxiety symptoms were only significant for moderate or severe impact on study, social and outside activities as well as severe impact on family decline (ORs 1.23 to 1.40). Moreover, mild or moderate impact on diet was only associated with depression symptoms (ORs 1.21 to 1.35).

Clustering analyses

The curve of average silhouette width with increasing number of clusters is shown in Supplementary Fig. 1, indicating four as the optimal number of clusters. The dendrogram graph for cluster visualization was in Supplementary Fig. 2. The distribution of life changes in each cluster was presented in the form of radar chart in Fig. 2, with mean values presented for each input variable (by converting ordinal and binary variables into integers), and in Supplementary Table 5.

Cluster 1 (labeled as “Stable”, N = 2,682) was characterized by the least impacts of social contacts (in three dimensions), diet and family financial status (i.e., income and employment). Cluster 2 (labeled as “Severe impact on family financial status”, N = 844) was characterized by the highest level of income decline and family member unemployment. Cluster 3 (labeled as “Severe impact on social contacts”, N = 1,278) was characterized by the highest levels of impact on three dimensions of social contacts, diet and exercise time. Cluster 4 (labeled as “Moderate impact on social contacts”, n = 3,025) was characterized by moderate levels of impact on three dimensions of social contacts.

Participant’s characteristics showed differences in the four clusters (Supplementary Table 6). Cluster 2 was significantly different from other clusters including the lowest monthly household income, the highest economic level of the school area and the lowest level of educational attainment for both parents, who were more likely to have blue-collar jobs. Participants in Cluster 3 were younger (mean age 9.98) and more likely to be males (57.2%). They perceived that the seriousness and severity of the epidemic to be higher and they would have a higher risk of being infected. In contrast, participants in Cluster 1 had the lowest perception of the seriousness and severity of the epidemic and believed that they were less likely to be infected with COVID-19.

We further tested the associations between the risks of anxiety and depressive symptoms in relation to the identified clusters based on Model 3 with full adjustments of covariates (Table 3, the results of Model 1 and 2 are in Supplementary Table 7–8). Compared to those in the Cluster 1 marked as “stable”, we observed higher risks of depressive and anxiety symptoms among participants in the other three clusters in the cross-sectional analyses. In the longitudinal analyses, however, only Cluster 2 (“Severe impact on family financial status”) showed an increased risk of depressive symptoms (OR = 1.60 [95% CI 1.24–2.06]) compared to the Cluster 1.

Table 3

Risks of depressive and anxiety symptoms with the identified clusters (Model 3^a)
Identified clusters	Depressive symptoms^b		Anxiety symptoms^c
	Cross-sectional study	Longitudinal study	Cross-sectional study	Longitudinal study
	OR (95% CI)^d	OR (95% CI)	OR (95% CI)	OR (95% CI)
Cluster 1: Stable	Ref	Ref	Ref	Ref
Cluster 2: Severe impact on family financial	1.61 (1.31–1.98)	1.60 (1.24–2.06)	1.69 (1.35–2.12)	1.03 (0.74–1.42)
Cluster 3: Severe impact on social contacts	1.90 (1.59–2.26)	1.01 (0.79–1.39)	1.87 (1.54–2.27)	1.26 (0.95–1.66)
Cluster 4: Moderate impact on social contacts	1.61 (1.31–1.98)	1.16 (0.97–1.39)	1.61 (1.38–1.88)	1.07 (0.86–1.34)
^aModel 3: Logistic regression model adjusted for age, gender, BMI level, monthly family income, types of caregivers, the economic level of the school area, the children’s knowledge about measures to prevent COVID-19, self-rating of the seriousness and children’s mental health status at W1 (the longitudinal study additionally adjusted children’s mental health status at W2).
^bDepressive symptoms: Children with depressive symptoms identified by the CES-D questionnaire (CES-D score ≥ 16).
^cAnxiety symptoms: Children with anxiety symptoms identified by the SCARED questionnaire (SCARED score ≥ 23).
^dOR, odds ratio; CI, confidence interval.

The present study utilized a large Chinese cohort to investigate the association between life changes (i.e., social contacts, lifestyles and family financial status) and mental health (i.e., depressive and anxiety symptoms) in children and adolescents during COVID-19 pandemic. Although the prevalence of depression and anxiety symptoms after pandemic was not higher than before, we did observe that students who reported that their social contacts, lifestyle and family financial status were negatively impacted during lockdown had increased risks of depression and anxiety symptoms at the same time. Such associations were no longer significant when we reassessed the students mental health one year after the lockdown, except for certain level(s) of study, social and outside activities and family income decline for both depression and anxiety, as well as diet for depression. After clustering on these students' life changes, we found that the group with severe impact on family financial status showed increased risk of depressive symptoms over the long term compared to the group who were relatively stable. Our study provides epidemiological knowledge of consequences of the COVID-19 pandemic and lockdown for children and adolescents’ mental health status in China with implementation of restrictive measures, highlighting the importance of developing tailor-made strategic plans for children and adolescents with different aspects of impact to address their unique needs in similar public health emergencies.

We found that there was a decreased trend in depressive and anxiety symptoms from W1 to W2 and W3 among the surveyed students during pandemic, which was unexpected and inconsistent with many previous studies[2, 35–37]. However, prior research on people’s mental health during COVID-19 also provide conflicting evidence[38–40]. The decreased prevalence of mental health problems could be due to many factors not restricted to better family functioning with increased family time and relationship building, reduction in peer stressors and academic pressures from the in-person school environment. In fact, our findings align with a Austrian study that reported better relationship quality associated with lower levels of psychological symptoms and a US study that found reduced mental health problems among youth during COVID-19[41, 42]. Moreover, we cannot rule out that students with mental health problems were more likely to drop out from the follow-up, resulting in decreased prevalence in the cohort.

Notably, however, for students who reported that their social contacts, lifestyle and family financial status were negatively impacted during lockdown, we observed increased risks of mental health problems at the same time point. As cross-sectional analyses cannot exclude reverse causation, it could also be that the mental health problems resulted in these life changes and negative self-reports. As a contrast, in the longitudinal analyses where depression and anxiety symptoms were collected one year after the life changes recorded, the degrees of associations were all attenuated, the dose-response relationship disappeared, and significant associations were only present at certain (levels) of changes. Although longitudinal analyses cannot establish causality, they provide stronger evidence for an association between an outcome and putative causative factors compared to cross-sectional analyses. However, even we assured that the exposures (at W2) occurred before the outcomes (at W3), the life changes between W2 and W3 during which the students had normally attended schools for one year were not adjusted for in our longitudinal analyses. For example, disturbed sleep time were associated with depressive symptoms in cross-sectional analyses, which is in line with previous studies [43–46] but not present in our longitudinal analyses. One explanation could be that the students’ sleep time were back to normal after W2. As we cannot distinguish which occurred first for the life changes and mental symptoms at W3, we chose to not adjust for life changes at W3 considering the possibility of reverse causation. Notably, the associations observed in the longitudinal analyses were unlikely to be violated by such unmeasured factors but just to be underestimated.

Among these findings, we observed several different risk factors for depressive and anxiety symptoms. For example, sleep time change, impact on diet and family financial status were more specifically associated with depression symptoms. We also noticed some inconsistency with previous studies. First, for BMI changes was not associated with mental health. This was inconsistent with previous studies that found in particular lockdown situation, the physical, nutritional and psychosocial factors were likely to create an unprecedented obesity environment for children and accompanying mental health problems[47, 48]. Notably, the height and weight data reported in W2 were from parental reports, which may introduce recall bias in our study. Second, the association between decreased exercise time and higher risk of anxiety symptoms highlights the importance of physical activity in mental health[12, 49], but its increase did not show protective effect, which differed from other studies[50, 51]. Third, the decreased electronic time also showed an association with increased depression and anxiety symptoms, which was different from some studies. This may be due to the strict management of these children at home by their caregivers, which could also be potential stressors. Meanwhile, the lack of variety in screen use was proven to be associated with mental health symptoms [52, 53].

The clustering analyses disentangled the life change patterns for children and adolescents, which also tended to influence their mental health status in different ways that was not obvious from the longitudinal analyses. Lifestyle changes seemed to play a less important role in the separation of clusters. Clustering analyses have the advantage of subgrouping subjects based on multidimensional factors which depict the global features, distinguish the effects, and provide insights for optimized intervention policies. Our clustering results were in line with previous clustering studies that demonstrated the heterogeneity in the population affected by the COVID-19 lockdown [54–56], but with larger sample size, multi-scales and longitudinal information.

Strength and limitations

The strength of this study includes the utilization of longitudinal data source, including one survey before the COVID-19 outbreak, one survey right after the COVID-19 lockdown, and one survey one year after the lifting of strict home confinements and prevention measures, which provides informative evidence for a potential impact of life changes on children and adolescents’ mental health during the COVID-19 pandemic. Moreover, our data-driven clustering analysis of multiple factors provided insights by revealing patterns and relationships that are not obvious or intuitive from variable-oriented methods. However, several limitations exist. First, self-reported changes in lifestyles may be influenced by recall bias and social desirability bias. Second, the study lacks data on the use of digital technology by children and adolescents to socialize with friends and to access social support during challenging times at COVID-19 lockdown, which has been demonstrated to alleviate negative feelings after social exclusion[57]^,[58]. Third, the study did not consider the social support received from family members, friends and others throughout the lockdown, which is crucial for preventing anxiety and depression [59, 60]. Fourth, our studies cannot control for unmeasured confounding, for example, different capacities to respond to the life changes (“resilience”) among students. Finally, although the longitudinal design showed the strength of associations between these risk factors and depression/anxiety symptoms, we lack the detailed information on when the exposures and outcomes occur and were unable to estimate the incidence rates. Future studies using more objective and comprehensive measures on students’ life changes, including both potential risk and protective factors, as well as utilization of approaches or experimental designs that could strengthen a causal relationship, are warranted for our findings’ replication and generalizability.

Our study illustrates that, although mental health problems did not increase in general Chinese children and adolescents’ population, the insufficient social contacts, disruptive lifestyles and financial difficulties were still putative risk factors for developing depressive or anxiety symptoms during the pandemic. The clustering results further highlight the impact of severe family financial problems on depressive symptoms. Early tailor-made psychological interventions for children and adolescents with different impacts to address their mental health needs may be beneficial in similar public health emergencies.

This work was supported by The Hong Kong Polytechnic University (Grant numbers 19H0642) and the Science and Technology Department of Sichuan Province, China (grant number 2020JDKP0021). The funder had no role in the study design, analysis, and interpretation of data and in writing the manuscript. The authors have no relevant financial or non-financial interests to disclose.

Acknowledgements

The authors thank all participants of this research, volunteers for the data collection, and administrative colleagues who helped finish this research project.

Funding

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Ethical approval

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975.

Data availability

Can be requested from the corresponding author.

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Life changes and mental health among Chinese children and adolescents during the pandemic: a combination of cross-sectional, longitudinal and clustering studies

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Abstract

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INTRODUCTION

METHODS

Study Design and Participants

Measures

Participant’s characteristics

Changes in social contacts, lifestyles and family financial status during COVID-19

Depressive symptoms

Anxiety symptoms

Statistical analyses

Cross-sectional analyses

Longitudinal analyses

Clustering analyses

RESULTS

Cross-sectional and longitudinal analyses

Clustering analyses

DISCUSSION

Strength and limitations

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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