Association of childhood adversities with psychosocial difficulties among Chinese children and adolescents

Abstract

Background

Adverse childhood experiences (ACEs) have been well recognized as risk factors for various adverse outcomes. However, the impacts of ACEs on psychological wellbeing among Chinese children and adolescents are unknown.

Methods

In total, 27 414 participants (6592 Grade 4–6 and 20 822 Grade 7–12 students) were included and information on ACEs and various psychosocial outcomes was collected. We identified subgroups with distinct psychosocial statuses using cluster analysis and logistic regression was applied to measure the associations of ACEs [individual, cumulative numbers by categories or co-occurring patterns identified by using multiple correspondence analysis (MCA)] with item- and cluster-specific psychosocial difficulties.

Results

Three and four cluster-based psychosocial statuses were identified for Grade 4–6 and Grade 7–12 students, respectively, indicating that psychosocial difficulties among younger students were mainly presented as changes in relationships/behaviours, whereas older students were more likely featured by deviations in multiple domains including psychiatric symptoms and suicidality. Strongest associations were found for threat-related ACEs (e.g. bullying experiences) with item- or cluster-based psychosocial difficulties (e.g. for cluster-based difficulties, the highest odds ratios = 1.72–2.08 for verbal bullying in Grade 4–6 students and 6.30–12.81 for cyberbullying in Grade 7–12 students). Analyses on cumulative numbers of ACEs and MCA-based ACE patterns revealed similar risk patterns. Additionally, exposure patterns predominated by poor external environment showed significant associations with psychosocial difficulties among Grade 7–12 students but not Grade 4–6 students.

Conclusions

Chinese adolescents faced different psychosocial difficulties that varied by age, all of which were associated with ACEs, particularly threat-related ACEs. Such findings prompt the development of early interventions for those key ACEs to prevent psychosocial adversities among children and adolescents.

Key Messages.

• Previous research has suggested that adverse childhood experiences (ACEs) have been associated with a series of adverse outcomes.

• Notable risks of psychosocial difficulties were associated with most included ACEs and the cumulative number of ACEs, among which threat-related ones were associated with the most pronounced increase, followed by poor external environment and deprivation-related ACEs.

• Cyberbullying showed consistently the strongest associations with all identified undermined psychosocial statuses in Grade 7–12 students compared with other ACEs.

• If confirmed in future studies with prospective data, those findings prompt public attention, as well as the development of early interventions for these key childhood adversities, for reducing psychosocial adversities and their prolonged impacts among Chinese children and adolescents.

Introduction

It is estimated that ∼59–66% of individuals worldwide are exposed to at least one adverse childhood experience (ACE)¹ and the prevalence in the Chinese population ranges from 27% to 70%.^2,3 Further, ACEs have been well recognized as risk factors for a wide range of adverse outcomes, including the presence of impaired psychological health and wellbeing,^4–8 physical illnesses,^9–11 behavioural problems^6,12,13 and sociological challenges.^13–15 Nevertheless, most previous studies were conducted on adults with retrospectively collected information on ACEs and study interests were mainly for adulthood consequences.^16,17 Also, in contrast with relatively well-studied ACEs among Western populations, only a handful of investigations with limited sample sizes have explored the impact of ACEs among Chinese adults.^18–20 Particularly, given that some ACEs are uniquely prevalent in certain populations, such as child–parent separation among migrant workers (i.e. children of rural families are left behind with relatives while parents seek jobs in urban cities) that is common in China but not Western countries,^21–23 as well as the heterogeneity between ethnic groups, studies with customized ACEs lists and outcome measurements are highly warranted to elucidate the consequences of ACEs exposure.

In addition, aside from the well-known long-term consequences, the role of ACEs among children and adolescents—populations that are at a stage of fast growing and personality shaping, and thereby are vulnerable to ACEs-induced immediate effect or initial changes—was less studied. Furthermore, unlike these psychiatric and somatic diseases that can be clinically diagnosed and used as objective measurements of impaired health among adults, the influences on adolescents can be rather nascent and are more often reflected by various subclinical psychosocial difficulties, which are more highly linked to one another than individual clinical diagnosis.^5,24

In this cross-sectional study conducted among 29 765 Chinese students, leveraging survey instruments for assessing ACEs (both those generally measured in Western populations and region-specific ones) and the current status from multiple psychological and sociological aspects, we aimed to comprehensively characterize the psychosocial status in children and adolescents, and identify homogeneous subgroups using cluster analysis. We further hypothesized that those studied ACEs could be individually or jointly associated with the risk of having specific and cluster-based psychosocial difficulties.

Methods

Study design

Our study was based on the Santai Youth Mental Health Promotion study that was conducted in Santai County—a relatively small-scale and median-level economic county located in Sichuan Province, in Southwest China, with a historically stable population structure. In brief, using a stratified random sampling method [by location (town/countryside and West/East/South/North) and type (private/public school)], 39 918 students registered in 31 primary (Grade 4–6, mainly students aged 10–13 years) and middle (Grade 7–12, mainly students aged 12–19 years) schools were invited to participate in a cross-sectional survey in 2019, which covered ∼30% of all students of these grades in Santai County. The survey was done through a variety of electronic questionnaires implemented in a WeChat applet ‘Psyclub’ and we applied age-specific data collection strategies (see detailed descriptions in Supplementary File Part 1, available as Supplementary data at IJE online). Specifically, the involved students filled in those questionnaires by themselves on a school computer (Grade 4–6 students and Grade 7–12 students who did not have a cellphone) or on a cellphone (Grade 7–12 students who owned a cellphone). Given that the former group comprised mainly students of young ages, trained teachers were allowed to give them some guidance (e.g. explaining the meanings of sentences when students raised a question and checking the completeness of questionnaires before submission) during the survey. In total, 29 765 students participated in this survey, which corresponded to an overall response rate of 74.56%. All participants and their guardians provided informed consent electronically prior to the data collection.

In the present study, after the removal of 2351 (7.90%) participants with missing data on ACEs and psychosocial status, we included 27 414 participants in the analyses (6592 Grade 4–6 students and 20 822 Grade 7–12 students) (see Supplementary Figure S1, available as Supplementary data at IJE online).

Identification of ACEs

Data on ACEs were collected by questionnaires using either scales that have been validated in Chinese students of similar age (results of those validation studies can be found in Supplementary Table S1, available as Supplementary data at IJE online)^25–29 or specific questions, which primarily included experiences related to threat, deprivation and poor external environment, without a limit on the earliest time of event occurrence (Supplementary Table S2, available as Supplementary data at IJE online).

Specifically, threat-related ACEs were considered as events that were physically or mentally threatening to the respondents, including bullying experience that was quantified by using an adapted version of the Delaware Bullying Victimization Scale-student (DBVS-S)²⁷ and natural disasters (defined as whether the respondents had been buried and/or injured, or witnessed relatives or others being buried/injured/died, or had damaged home or dwellings in two major earthquakes of the area).^30,31 Deprivation-related ACEs reflected some of the long-lasting unmet needs, both material and psychological, that included sickness of at least one parent or the respondent per se, less-educated parents (i.e. having at least one parent with less than a high-school degree) and child–parent separation (i.e. if the respondents had ever lived without one or both parents for >6 months consecutively). Lastly, ACEs relevant to the external growing-up environment (i.e. family dysfunction and poor school environment) were evaluated using the general functioning subscale of the Family Assessment Device and the Students’ Perceived School Climate Inventory (PSCI-M).²⁶

Item-specific psychosocial status

We assessed the psychosocial statuses of respondents, which were categorized into two major dimensions: psychological wellbeing and sociological status (Supplementary Table S3, available as Supplementary data at IJE online). Measures of psychological wellbeing referred to the variables relevant to suicidality (i.e. non-suicidal self-injury, suicidal ideation and behaviours), psychiatric symptoms (i.e. anxiety, depression, severe stress reactions and general emotional symptoms) and positive psychology (i.e. resilience and life satisfaction), mainly using well-validated scales that ask for relevant conditions that occurred within a pre-defined period (Supplementary Table S3, available as Supplementary data at IJE online). Also, we determined sociological status from the aspects of behaviours (i.e. internet overuse, hyperactivity, conduct problems and prosocial problems) (Supplementary Table S3, available as Supplementary data at IJE online) and relationships (i.e. subjective peer status and subjective social status) based on relevant items in corresponding self-evaluated scales (Supplementary Table S3, available as Supplementary data at IJE online) or designed images (Supplementary Figure S2 and S3, available as Supplementary data at IJE online).

Covariates

Data on age, gender and paternal and maternal occupation (i.e. farmer, blue-collar, white-collar, self-employed or others) were collected from the basic information questionnaire.

Statistical analysis

Association analyses between ACEs and item-specific psychosocial statuses

With the prior knowledge that there are huge differences regarding campus culture, academic pressure and modes of social contact for students in primary schools and those in middle and high schools,³² we performed separate analyses for Grade 4–6 (n = 6592) and Grade 7–12 (n = 20 822) participants.

Considering the lack of temporality of our cross-sectional data, we drew Directed Acyclic Graphs (DAGs) to clarify the possible role of involved covariates (e.g. paternal and maternal occupation) in the studied associations according to the hypothesized pathways. Consequently, the directional relationships illustrated by DAG supported the confounding effects of those covariates, except for a possible mediating role of paternal and maternal occupation on the pathway from less-educated parents to undermined cluster-based psychosocial status (Supplementary Figure S4, available as Supplementary data at IJE online). We therefore estimated the associations, represented as odds ratios (ORs) and 95% CIs, of the exposure to any, by category (threat-, deprivation- or poor external environment-related ACEs) or individual ACE (11 in total) with all psychosocial status-related items (15 for Grade 4–6 and 16 for Grade 7–12) using partially (age and gender) and fully (additionally added paternal and maternal occupation) adjusted multinomial logistic regressions. Since ACEs are not often exclusive events,³³ the collective effect of multiple ACEs exposures was then examined by calculating ORs between the cumulative number of ACEs in each category and the adverse outcomes.

Cluster analyses for identifying cluster-based psychosocial statuses

Besides the item-specific psychosocial status, considering the high correlation (Supplementary Figure S5, available as Supplementary data at IJE online) and possibly complex interplay between those studied items within an individual, we applied k-modes algorithm-based cluster analysis—a method that is well known for its high efficiency with large sample and categorical variables—to identify subgroups of participants with distinct psychosocial characteristics (i.e. data-driven cluster identification). In brief, using all categorical items related to psychosocial status (Supplementary Table S3, available as Supplementary data at IJE online), we calculated the dissimilarities between participants and assigned each observation to its closest cluster (i.e. participants were partitioned into clusters where observations in the same cluster were more similar to one another, in terms of psychosocial status, than those in different clusters). The optimal number of clusters in the data set was determined by using the elbow method.^34,35 We then visualized the differences in psychosocial measurements of the identified clusters through graphically presenting the mean standardized (z-score) score of each item in a radar chart and labelled each cluster with its featured status (Figure 2).

Figure 2.

Radar charts of cluster-based psychosocial statuses among Grade 4–6 (left) and Grade 7–12 (right) participants. Data points represent the mean standardized (z-score) score and a higher value indicates more severe problems with the corresponding psychosocial status

Association analyses between ACEs and cluster-based psychosocial statuses

Similarly to the association analyses for item-specific psychosocial statuses, we estimated the association between exposure to individual ACEs, as well as the cumulative number of ACEs, and cluster-based psychosocial status, using multivariate logistic regression, partially and fully adjusting for age, gender and paternal and maternal occupation. The cluster with the most favourable psychosocial status was considered as a reference.

Further efforts were made to detect the actual exposure patterns (i.e. considering the complexity regarding the co-occurrence of various ACEs within individuals) (Supplementary Figure S6, available as Supplementary data at IJE online) of our total study population using multiple correspondence analyses (MCA) (Supplementary Table S4, available as Supplementary data at IJE online). Last, the association analysis between MCA-identified exposure patterns (indexed by dimension scores produced by MCA, we considered individuals who scored above the top 90th percentile as the exposed) and the risk of having each cluster-based psychosocial difficulties was performed.

We repeated the above-mentioned analyses among males and females separately to detect the possible modification effects by gender. We used R (version 4.1.1) for association analysis (package nnet), MCA (package FactoMineR), creating radar charts (package ggplot2, fmsb) and DAG plotting (package daggity) and Python (version 3.8) for k-modes-based cluster analysis.³⁶ For all analyses, we also provided false discovery rate (FDR)-adjusted P-values considering the issue of multiple tests.

Results

The mean age of Grade 4–6 and Grade 7–12 participants was 10.3 (SD=1.0) and 14.6 (SD = 1.8) years, respectively. Whereas the distribution of paternal and maternal was largely similar, Grade 4–6 participants had slightly fewer female students (49.0% vs 50.6%) and a lower prevalence of any (56.6% vs 82.0%) or subtypes of ACEs (e.g. for threat-related 17.2% vs 30.3%, and for deprivation-related 34.4% vs 55.9%) compared with Grade 7–12 participants (Table 1).

Table 1.

Descriptive characteristics of socio-demographic information, adverse childhood experiences and items related to psychosocial status of the study population

Characteristic	Overall	Grade 4–6	Grade 7–12
Number of participants	27 774	6952	20 822
Socio-demographic information
Age, years (mean$\pm$ SD)	13.5 $\pm$2.45	10.3$\pm$ 1.00	14.6$\pm$1.77
Sex (female)	13 944 (50.2)	3405 (49.0)	10 539 (50.6)
Maternal occupation
Farmer	6484 (23.3)	1472 (21.2)	5012 (24.1)
Blue-collar	6772 (24.4)	1682 (24.2)	5090 (24.4)
White-collar	2921 (10.5)	867 (12.5)	2054 (9.9)
Self-employed	5211 (18.8)	1522 (21.9)	3689 (17.7)
Other	6386 (23.0)	1409 (20.3)	4977 (23.9)
Paternal occupation
Farmer	6035 (21.7)	1402 (20.2)	4633 (22.3)
Blue-collar	6417 (23.1)	1464 (21.1)	4953 (23.8)
White-collar	4834 (17.4)	1497 (21.5)	3337 (16.0)
Self-employed	5324 (19.2)	1576 (22.7)	3748 (18.0)
Other	5164 (18.6)	1013 (14.6)	4151 (19.9)
Adverse childhood experiences
Any ACEs	22 495 (76.5)	4241 (56.6)	18 254 (82.0)
Threat-related
Any threat-related ACEs	7501 (27.0)	1193 (17.2)	6308 (30.3)
Verbal bullying	2872 (10.3)	599 (8.6)	2273 (10.9)
Physical bullying	1508 (5.4)	374 (5.4)	1134 (5.4)
Social/relational bullying	2019 (7.3)	470 (6.8)	1549 (7.4)
Cyberbullying	748 (2.7)	92 (1.3)	656 (3.2)
Traumatic earthquake experience	5170 (17.4)	519 (6.9)	4651 (20.9)
1	5246 (18.9)	730 (10.5)	4516 (21.7)
2	973 (3.5)	214 (3.1)	759 (3.6)
$\hspace{1em}\ge$ 3	1282 (4.6)	249 (3.6)	1033 (5.0)
Deprivation-related
Any deprivation-related ACEs	14 032 (50.5)	2394 (34.4)	11 638 (55.9)
Less-educated parents	8563 (30.8)	1241 (17.9)	7322 (35.2)
Child–parent separation	5086 (18.3)	981 (14.1)	4105 (19.7)
Ill parents	4379 (15.8)	618 (8.9)	3761 (18.1)
Sickness	860 (3.1)	102 (1.5)	758 (3.6)
1	9842 (35.4)	1897 (27.3)	7945 (38.2)
2	3557 (12.8)	447 (6.4)	3110 (14.9)
$\ge$3	633 (2.3)	50 (0.7)	583 (2.8)
Poor external environment-related
Any poor external environment-related ACEs	15 011 (50.4)	2311 (30.8)	12 700 (57.0)
Family dysfunction	12 334 (44.4)	2009 (28.9)	10 325 (49.6)
Poor school environment	7197 (25.9)	652 (9.4)	6545 (31.4)
Items related to psychosocial status
Suicidality
Suicidal behaviours	1733 (6.2)	126 (1.8)	1607 (7.7)
Suicidal ideation	10 861 (39.1)	1142 (16.4)	9719 (46.7)
Non-suicidal self-harm	2737 (9.9)	213 (3.1)	2524 (12.1)
Psychiatric symptoms
Depression	4893 (17.6)	471 (6.8)	4422 (21.2)
Anxiety	5279 (19.0)	669 (9.6)	4610 (22.1)
Emotional symptomsa	2566 (9.2)	296 (4.3)	2270 (10.9)
Severe stress reaction	1838 (6.6)	–	1838 (8.8)
Positive psychology
Low resilience	2086 (7.5)	294 (4.2)	1792 (8.6)
Low life satisfaction	11 103 (40.0)	1571 (22.6)	9532 (45.8)
Behaviour-related factors
Internet overuse	3594 (12.9)	285 (4.1)	3309 (15.9)
Hyperactivitya	3621 (13.0)	497 (7.1)	3124 (15.0)
Conduct problemsa	3785 (13.6)	564 (8.1)	3221 (15.5)
Prosocial problemsa	3009 (10.8)	469 (6.7)	2540 (12.2)
Relationship-related factors
Peer relationship problemsa	1605 (5.8)	274 (3.9)	1331 (6.4)
Subjective peer statusb	3902 (14.0)	597 (8.6)	3305 (15.9)
Subjective social statusc	3009 (10.8)	543 (7.8)	2466 (11.8)

ACE, adverse childhood experience.

Figures are number (%) unless otherwise indicated.

^aEmotional symptoms, conduct problems, hyperactivity, peer relationship problems and prosocial problems show the number (percentage) of the abnormal individuals.

^bSubjective peer status shows the number (percentage) of individuals with the lowest perceived peer status (choice D, rejected).

^cSubjective social status shows the number (percentage) of individuals with a low subjective social status (score 0∼3).

Association between ACEs and item-specific psychosocial statuses

Figure 1 illustrates the associations of individual ACE with various psychosocial status-related items, in which the gradient of the blue colour indicates the magnitude of associations (the exact ORs with 95% CIs are displayed in Supplementary Table S5 and S6, available as Supplementary data at IJE online). In brief, the majority of studied ACEs were significantly associated with increased risk of having undermined item-specific psychosocial statuses [221/275 (80.4%) for Grade 4–6 and 251/286 (87.8%) for Grade 7–12 participants], with the most pronounced associations observed for threat-related ACEs [ranged from 1.25 (95% CI 1.01–1.54) to 24.14 (14.39–40.49) for Grade 4–6, and from 1.10 (1.04–1.18) to 38.86 (31.91–47.33) for Grade 7–12 participants], followed by poor external environment-related ACEs [ranged from 1.24 (1.11–1.39) to 9.57 (7.19–12.73) for Grade 4–6, and from 1.11 (1.05–1.18) to 7.73 (6.69–8.93) for Grade 7–12 participants] and deprivation-related ACEs [ranged from 1.18 (1.03–1.34) to 10.47 (6.72–16.33) for Grade 4–6, and from 1.08 (1.02–1.15) to 5.73 (4.88–6.72) for Grade 7–12 participants]. From the perspective of categories of psychosocial outcomes, psychological wellbeing, such as suicidality and psychiatric symptoms, was the one strongly affected by threat-related ACEs among both Grade 4–6 and Grade 7–12 participants, followed by sociological status such as peer relationships and conduct problems. Additionally, analyses on the cumulative number of ACEs in each category revealed a collective effect, i.e. a greater number of exposures was linked to generally larger ORs.

Figure 1.

Heat maps of associations (odds ratios) between exposure to individual adverse childhood experiences (any, single or cumulative number) and item-specific psychosocial statuses [Grade 4–6 (left) and one Grade 7–12 (right)]. *Represents P <0.05 after controlling for the multiple testing issue by using the false discovery rate (FDR) approach

Identification of cluster-based psychosocial statuses

Results of cluster analysis showed that Grade 4–6 participants were primarily partitioned into three subgroups (Figure 2)—named ‘normal psychosocial status’ (in purple colour, n = 2592, characterized by generally normal psychological wellbeing and sociological status), ‘low self-evaluation’ (in green colour, n = 3556, characterized by normal psychosocial status but relatively low self-perceived status among the peers) and ‘low resilience and externalizing problems’ (in orange colour, n = 804, characterized by a low level of resilience and life satisfaction along with high possibility of having externalizing problems), and the last two were considered as clusters indicating undermined psychosocial statuses.

Unlike the few deviations in the aspects of psychiatric symptoms and suicidality among Grade 4–6 participants, the clustered Grade 7–12 participants were featured by their substantial differences in these categories (Figure 2). Specifically, the identified four clusters were labelled as ‘normal psychosocial status’ (in purple colour, n = 12 527, characterized by generally normal psychological wellbeing and sociological status), ‘low life satisfaction and high self-evaluation’ (in green colour, n = 4010, characterized by low life satisfaction but relatively high perceived status among the peers), ‘low resilience and sociological problems’ (in orange colour, n = 1296, characterized by low level of resilience and sociological problems such as prosocial problems and low self-perceived peer status) and ‘internalizing and externalizing problems’ (in grey colour, n = 2989, characterized by widespread problems with their psychological wellbeing and sociological status).

Association between ACEs and cluster-based psychosocial statuses

In general, we observed largely identical estimates based on age- and gender- adjusted models and full models (Tables 2 and 3). Among Grade 4–6 participants, except for cyberbullying and traumatic earthquake experience, all studied ACEs were significantly associated with an increased risk of having ‘low resilience and externalizing problems’ status (Table 2). Specifically, the highest ORs were noted for exposure to poor external environment [family dysfunction: OR = 5.21 (95% CI 4.39–6.17); poor school environment: 2.86 (2.25–3.63)] and verbal [2.08 (1.58–2.72)] or social bullying [2.05 (1.51–2.80)]. With generally lower risk estimates, having the undermined status featured by ‘low self-evaluation’ was also linked to threat-related ACEs, particularly social/relational and verbal bullying, as well as ACEs reflecting poor external environment (Table 2).

Table 2.

Associations between exposure to adverse childhood experiences (ACEs; any or single exposure, cumulative number or multiple correspondence analysis-identified exposure patterns) and undermined cluster-based psychosocial status among Grade 4–6 students (n = 6952)

Adverse childhood experiences	Favourable psychosocial status	Undermined cluster-based psychosocial status
	Normal psychosocial status	Low self-evaluation			Low resilience and externalizing problems
	No. of cases (%) in exposed/unexposed group	No. of cases (%) in exposed/unexposed group	Age- and sex- adjusted OR (95% CI)a	Fully adjusted OR (95% CI)b	No. of cases (%) in exposed/unexposed group	Age- and sex- adjusted OR (95% CI)a	Fully adjusted OR (95% CI)b
Any or single exposure
Threat-related ACEs
Any	367 (30.8)/2225 (38.6)	658 (55.2)/2898 (50.3)	1.38 (1.20–1.58)*	1.38 (1.19–1.58)*	168 (14.1)/636 (11.0)	1.60 (1.30–1.95)*	1.60 (1.30–1.95)*
Verbal bullying	154 (25.7)/2438 (38.4)	350 (58.4)/3206 (50.5)	1.72 (1.42–2.10)*	1.72 (1.40–2.08)*	95 (15.9)/709 (11.2)	2.08 (1.58–2.72)*	2.08 (1.58–2.72)*
Physical bullying	109 (29.1)/2483 (37.7)	208 (55.6)/3348 (50.9)	1.38 (1.08–1.75)*	1.36 (1.07–1.73)	57 (15.2)/747 (11.4)	1.62 (1.16–2.27)*	1.62 (1.16–2.27)*
Social bullying	114 (24.3)/2478 (38.2)	285 (60.6)/3271 (50.5)	1.90 (1.51–2.36)*	1.88 (1.49–2.34)*	71 (15.1)/733 (11.3)	2.05 (1.51–2.80)*	2.05 (1.51–2.80)*
Cyberbullying	30 (32.6)/2562 (37.3)	48 (52.2)/3508 (51.1)	1.15 (0.73–1.82)	1.12 (0.70–1.77)	14 (15.2)/790 (11.5)	1.39 (0.73–2.64)	1.39 (0.73–2.64)
Earthquake experience	184 (37.2)/2408 (37.3)	250 (50.5)/3306 (51.2)	1.00 (0.82–1.22)	1.00 (0.82–1.22)	61 (12.3)/743 (11.5)	1.13 (0.83–1.52)	1.13 (0.83–1.52)
Deprivation-related ACEs
Any	858 (35.8)/1734 (38.0)	1202 (50.2)/2354 (51.6)	1.04 (0.93–1.15)	1.02 (0.91–1.15)	334 (14.0)/470 (10.3)	1.35 (1.14–1.60)*	1.35 (1.14–1.60)*
Less-educated parents	438 (35.3)/2154 (37.7)	629 (50.7)/2927 (51.3)	1.06 (0.93–1.22)	1.06 (0.92–1.22)	174 (14.0)/630 (11.0)	1.26 (1.02–1.54)	1.26 (1.02–1.54)
Child–parent separation	361 (36.8)/2232 (37.4)	476 (48.5)/3080 (51.6)	0.95 (0.83–1.11)	0.93 (0.80–1.08)	144 (14.7)/660 (11.1)	1.27 (1.02–1.57)*	1.27 (1.02–1.57)
Ill parents	229 (37.1)/2363 (37.3)	293 (47.4)/3263 (51.5)	0.92 (0.77–1.12)	0.92 (0.77–1.11)	96 (15.5)/708 (11.2)	1.36 (1.06–1.77)*	1.36 (1.06–1.77)
Sickness	29 (28.4)/2563 (37.4)	56 (54.9)/3500 (51.1)	1.43 (0.91–2.27)	1.42 (0.90–2.23)	17 (16.7)/787 (11.5)	1.86 (1.01–3.39)*	1.86 (1.01–3.39)*
Poor external environment
Any	1116 (49.3)/2431 (52.1)	641 (28.3)/1943 (41.6)	1.39 (1.25–1.57)*	1.39 (1.23–1.55)*	506 (22.4)/294 (6.3)	5.10 (4.31–6.05)*	5.10 (4.31–6.05)*
Family dysfunction	549 (27.3)/2037 (41.3)	986 (49.1)/2564 (52.0)	1.42 (1.26–1.60)*	1.42 (1.26–1.60)*	474 (23.6)/327 (6.6)	5.21 (4.39–6.17)*	5.21 (4.39–6.17)*
Poor school environment	183 (28.1)/2401 (38.2)	328 (50.3)/3219 (51.3)	1.35 (1.12–1.63)*	1.34 (1.11–1.62)*	141 (21.6)/659 (10.5)	2.86 (2.25–3.63)*	2.86 (2.25–3.63)*
Cumulative no. of exposures
Threat-related ACEs
1	243 (33.3)/2225 (38.6)	390 (53.4)/2898 (50.3)	1.20 (1.01–1.42)	1.19 (1.00–1.40)	97 (13.3)/636 (11.0)	1.32 (1.03–1.72)	1.32 (1.03–1.72)
2	63 (29.4)/2225 (38.6)	120 (56.1)/2898 (50.3)	1.40 (1.03–1.92)	1.39 (1.02–1.92)	31 (14.5)/636 (11.0)	1.62 (1.04–2.51)	1.62 (1.04–2.51)
$\hspace{1em}\ge$ 3	61 (24.5)/2225 (38.6)	148 (59.4)/2898 (50.3)	1.79 (1.32–2.41)*	1.77 (1.30–2.39)*	40 (16.1)/636 (11.0)	2.10 (1.39–3.16)*	2.10 (1.39–3.16)*
Deprivation-related ACEs
1	677 (35.7)/1734 (38.0)	973 (51.3)/2354 (51.6)	1.06 (0.94–1.19)	1.06 (0.94–1.19)	247 (13.0)/470 (10.3)	1.17 (0.99–1.40)	1.17 (0.99–1.40)
$\hspace{1em}\ge$ 2	181 (36.4)/1734 (38.0)	229 (46.1)/2354 (51.6)	0.91 (0.74–1.12)	0.91 (0.74–1.12)	87 (17.5)/470 (10.3)	1.51 (1.15–1.99)*	1.51 (1.15–1.99)*
MCA-identified exposure patterns (exposed—top 90th percentile)
Predominated by bullying	181 (26.2)/2403 (38.5)	392 (56.7)/3155 (50.6)	1.63 (1.36–1.97)*	1.62 (1.35–1.95)*	118 (17.1)/682 (10.9)	2.32 (1.80–2.97)*	2.23 (1.73–2.86)*
Predominated by less-educated parents	224 (32.9)/2360 (37.8)	342 (50.2)/3205 (51.3)	1.13 (0.94–1.34)	1.12 (0.94–1.34)	115 (16.9)/685 (11.0)	1.80 (1.42–2.29)*	1.70 (1.34–2.18)*
Predominated by sickness and earthquake experiences	239 (33.9)/2345 (37.7)	342 (48.5)/3205 (51.5)	1.06 (0.89–1.26)	1.05 (0.88–1.26)	124 (17.6)/676 (10.9)	1.90 (1.51–2.41)*	1.82 (1.45–2.32)*
Predominated by child–parent separation	381 (37.5)/2203 (37.2)	499 (49.2)/3048 (51.5)	0.95 (0.82–1.09)	0.94 (0.81–1.08)	135 (13.3)/665 (11.2)	1.19 (0.96–1.48)	1.13 (0.90–1.40)
Predominated by sickness and child–parent separation	220 (31.8)/2364 (37.9)	337 (48.7)/3210 (51.5)	1.14 (0.95–1.35)	1.12 (0.94–1.34)	135 (19.5)/665 (10.7)	2.25 (1.79–2.83)*	2.16 (1.70–2.72)*

MCA, multiple correspondence analysis; OR, odds ratio.

^aMultinomial logistic regression adjusted for sex and age was used to estimate ORs and 95% CIs.

^bMultinomial logistic regression adjusted for sex, age and paternal and maternal occupation was used to estimate ORs and 95% CIs.

^* P < 0.05 after controlling for multiple testing issue by using the false discovery rate approach.

Table 3.

Association between exposure to adverse childhood experiences (ACEs; any or single exposure, cumulative number or multiple correspondence analysis-identified exposure patterns) and undermined cluster-based psychosocial status among Grade 7–12 students (n = 20 822)

Adverse childhood experiences	Favourable psychosocial status	Undermined cluster-based psychosocial status
	Normal psychosocial status	Low life satisfaction and high self-valuation			Low resilience and sociological problems			Internalizing and externalizing problems
	No. of cases (%) in exposed/unexposed group	No. of cases (%) in exposed/unexposed group	Age- and sex- adjusted OR (95% CI)a	Fully adjusted OR (95% CI)b	No. of cases (%) in exposed/unexposed group	Age- and sex- adjusted OR (95% CI)a	Fully adjusted OR (95% CI)b	No. of cases (%) in exposed/unexposed group	Age- and sex- adjusted OR (95% CI)a	Fully adjusted OR (95% CI)b
Any or single exposure
Threat-related ACEs
Any	2868 (45.5)/9659 (66.6)	1408 (22.3)/2601 (17.9)	1.80 (1.67–1.95)*	1.80 (1.67–1.95)*	523 (8.3)/772 (5.3)	2.34 (2.08–2.64)*	2.34 (2.08–2.64)*	1509 (23.9)/1480 (10.2)	3.71 (3.42–4.06)*	3.71 (3.42–4.06)*
Verbal bullying	611 (26.9)/11 916 (64.2)	489 (21.5)/3520 (19.0)	2.77 (2.46–3.16)*	2.77 (2.46–3.16)*	264 (11.6)/1032 (5.6)	5.47 (4.66–6.42)*	5.47 (4.66–6.42)*	909 (40.0)/2080 (11.2)	9.97 (8.85–11.13)*	9.97 (8.85–11.13)*
Physical bullying	250 (22.0)/12 277 (62.4)	297 (26.2)/3712 (18.9)	3.94 (3.32–4.71)*	3.94 (3.32–4.71)*	150 (13.2)/1146 (5.8)	7.17 (5.75–8.85)*	7.17 (5.75–8.85)*	437 (38.5)/2552 (13.0)	10.49 (8.94–12.43)*	10.49 (8.94–12.43)*
Social bullying	365 (23.6)/12 162 (63.1)	357 (23.0)/3652 (18.9)	3.35 (2.89–3.90)*	3.35 (2.89–3.90)*	180 (11.6)/1116 (5.8)	5.93 (4.90–7.24)*	5.93 (4.90–7.17)*	647 (41.8)/2342 (12.2)	10.80 (9.39–12.43) *	10.80 (9.39–12.43)*
Cyberbullying	106 (16.2)/12 421 (61.6)	205 (31.2)/3804 (18.9)	6.30 (4.95–8.00)*	6.30 (4.95–8.00)*	91 (13.9)/1205 (6.0)	9.58 (7.17–12.68)*	9.58 (7.17–12.68)*	254 (38.7)/2735 (13.6)	12.81 (10.18–16.28)*	12.81 (10.18–16.28)*
Earthquake experience	2287 (51.1)/10 240 (62.7)	1071 (23.9)/2938 (18.0)	1.58 (1.46–1.73)*	1.58 (1.46–1.73)*	344 (7.7)/951 (5.8)	1.60 (1.40–1.82)*	1.60 (1.40–1.82)*	777 (17.3)/2212 (13.5)	1.62 (1.48–1.77)*	1.62 (1.48–1.77)*
Deprivation-related ACEs
Any	6364 (54.7)/6163 (67.1)	2395 (20.6)/1615 (17.6)	1.39 (1.28–1.49)*	1.39 (1.28–1.49)*	874 (7.5)/422 (4.6)	1.92 (1.68–2.16)*	1.92 (1.68–2.16)*	2005 (17.2)/984 (10.7)	1.92 (1.75–2.08)*	1.92 (1.75–2.08)*
Less-educated parents	4096 (55.9)/8431 (62.5)	1487 (20.3)/2523 (18.7)	1.17 (1.09–1.27)*	1.17 (1.09–1.27)*	544 (7.4)/752 (5.6)	1.40 (1.26–1.58)*	1.40 (1.26–1.58)*	1195 (16.3)/1794 (13.3)	1.31 (1.20–1.42)*	1.31 (1.20–1.42)*
Child–parent separation	228 (54.3)/10 299 (61.6)	834 (20.3)/3176 (19.0)	1.19 (1.08–1.30)*	1.19 (1.08–1.30)*	301 (7.3)/995 (6.0)	1.35 (1.17–1.55)*	1.35 (1.17–1.55)*	742 (18.1)/2247 (13.4)	1.49 (1.35–1.65)*	1.49 (1.35–1.65)*
Ill parents	1855 (49.3)/10 672 (62.6)	868 (23.1)/3142 (18.4)	1.54 (1.40–1.68)*	1.54 (1.40–1.68)*	318 (8.5)/978 (5.7)	1.80 (1.57–2.05)*	1.80 (1.57–2.05)*	720 (19.1)/2269 (13.3)	1.79 (1.63–1.97)*	1.79 (1.63–1.97)*
Sickness	207 (27.3)/12 320 (61.4)	145 (19.1)/3865 (19.3)	2.18 (1.75–2.72)*	2.18 (1.75–2.72)*	69 (9.1)/1227 (6.1)	3.19 (2.41–4.22)*	3.19 (2.41–4.22)*	337 (44.5)/2652 (13.2)	7.24 (6.05–8.67)*	7.24 (6.05–8.67)*
Poor external environment
Any	5910 (47.5)/6617 (79.0)	2594 (20.8)/395 (4.7)	2.51 (2.34–2.72)*	2.51 (2.34–2.72)*	1143 (9.0)/153 (1.8)	8.08 (6.75–9.58)*	8.08 (6.75–9.58)*	2803 (22.5)/1206 (14.4)	7.32 (6.55–8.17)*	7.24 (6.49–8.17)*
Family dysfunction	4739 (45.9)/7788 (74.2)	2372 (23.0)/1638 (15.6)	2.32 (2.16–2.51)*	2.32 (2.16–2.48)*	996 (9.6)/300 (2.9)	5.37 (4.66–6.11)*	5.31 (4.71–6.17)*	2218 (21.5)/771 (7.3)	4.81 (4.39–5.26)*	4.76 (4.35–5.21)*
School environment	2531 (38.7)/9995 (70.0)	1630 (24.9)/2379 (16.7)	2.61 (2.44–2.83)*	2.64 (2.44–2.86)*	695 (10.6)/600 (4.2)	4.39 (3.94–4.95)*	4.44 (3.90–4.95)*	1689 (25.8)/1300 (9.1)	4.95 (4.57–5.42)*	5.00 (4.57–5.42)*
Cumulative no. of exposures
Threat-related ACEs
1	2450 (54.3)/9659 (66.6)	980 (21.7)/2601 (17.9)	1.31 (1.21–1.43)*	1.30 (1.20–1.42)*	305 (6.8)/772 (5.3)	1.25 (1.09–1.43)*	1.26 (1.09–1.43)*	781 (17.3)/1480 (10.2)	1.48 (1.35–1.62)*	1.49 (1.35–1.63)*
2	227 (29.9)/9659 (66.6)	150 (19.8)/2601 (17.9)	2.18 (1.77–2.69)*	2.18 (1.77–2.69)*	79 (10.4)/772 (5.3)	3.82 (2.94–5.00)*	3.82 (2.94–5.00)*	303 (39.9)/1480 (10.2)	6.82 (5.70–8.17)*	6.82 (5.70–8.17)*
$\hspace{1em}\ge$ 3	191 (18.5)/9659 (66.6)	278 (26.9)/2601 (17.9)	4.81 (3.97–5.81)*	4.85 (4.01–5.87)*	139 (13.5)/772 (5.3)	8.67 (6.89–10.91)*	8.50 (6.75–10.70)*	425 (41.1)/1480 (10.2)	12.81 (10.70–15.33)*	13.07 (10.91–15.64)*
Deprivation-related ACEs
1	4580 (57.6)/6163 (67.1)	1588 (20.0)/1615 (17.6)	1.14 (1.06–1.23)*	1.14 (1.06–1.23)*	566 (7.1)/422 (4.6)	1.31 (1.17–1.48)*	1.31 (1.17–1.48)*	1211 (15.2)/984 (10.7)	1.17 (1.08–1.27)*	1.17 (1.08–1.27)*
2	1550 (49.8)/6163 (67.1)	683 (22.0)/1615 (17.6)	1.43 (1.30–1.58)*	1.43 (1.30–1.58)*	260 (8.4)/422 (4.6)	1.67 (1.43–1.93)*	1.67 (1.43–1.93)*	617 (19.8)/984 (10.7)	1.77 (1.58–1.95)*	1.77 (1.58–1.95)*
$\hspace{1em}\ge$ 3	234 (40.1)/6163 (67.1)	124 (21.3)/1615 (17.6)	1.63 (1.31–2.03)*	1.63 (1.31–2.03)*	48 (8.2)/422 (4.6)	1.82 (1.32–2.51)*	1.82 (1.32–2.51)*	177 (30.4)/984 (10.7)	3.06 (2.51–3.74)*	3.06 (2.51–3.74)*
MCA-identified exposure patterns (exposed—top 90th percentile)
Predominated by bullying	490 (23.8)/12 015 (64.2)	466 (22.6)/3536 (18.9)	3.29 (2.89–3.74)*	3.29 (2.86–3.78)*	247 (12.0)/1042 (5.6)	6.49 (5.47–7.69)*	6.42 (5.42–7.61)*	856 (41.6)/2125 (11.4)	11.70 (10.38–13.33)*	11.82 (10.49–13.46)*
Predominated by family dysfunction	793 (35.2)/11 712 (63.2)	584 (25.9)/3418 (18.5)	2.41 (2.16–2.72)*	2.44 (2.16–2.72)*	240 (10.7)/1049 (5.7)	3.22 (2.75–3.78)*	3.22 (2.75–3.78)*	636 (28.2)/2345 (12.7)	3.97 (3.56–4.48)*	4.01 (3.56–4.48)*
Predominated by poor school environment	885 (39.6)/11 620 (62.7)	529 (23.7)/3473 (18.7)	1.93 (1.73–2.18)*	1.93 (1.73–2.18)*	226 (10.1)/1063 (5.7)	2.66 (2.27–3.13)*	2.66 (2.27–3.13)*	596 (26.7)/2385 (12.9)	3.16 (2.80–3.53)*	3.16 (2.80–3.53)*
Predominated by child–parent separation	850 (43.5)/11 655 (61.9)	426 (21.8)/3576 (19.0)	1.60 (1.40–1.80)*	1.60 (1.42–1.80)*	187 (9.6)/1102 (5.9)	2.23 (1.86–2.64)*	2.25 (1.90–2.66)*	491 (25.1)/2490 (13.2)	2.64 (2.34–2.97)*	2.61 (2.32–2.94)*
Predominated by earthquake experiences	1142 (57.4)/11 363 (60.5)	422 (21.2)/3580 (19.1)	1.15 (1.02–1.30)*	1.16 (1.03–1.30)*	125 (6.3)/1164 (6.2)	1.07 (0.89–1.31)	1.07 (0.88–1.30)	300 (15.1)/2681 (14.3)	1.15 (1.01–1.32)*	1.15 (1.01–1.32)*

MCA, multiple correspondence analysis; OR, odds ratio.

^aMultinomial logistic regression adjusted for sex and age was used to estimate ORs and 95% CIs.

^bMultinomial logistic regression adjusted for sex, age and paternal and maternal occupation was used to estimate ORs and 95% CIs.

^* P < 0.05 after controlling for multiple testing issue by using the false discovery rate approach.

Likewise, the risk of having all cluster-based psychosocial difficulties increased among ACEs-exposed Grade 7–12 participants (Table 3[TQ1]). The highest risk was observed for having ‘internalizing and externalizing problems’ [ORs ranged from 1.31 (95% CI 1.20–1.42) for less-educated parents to 12.81 (10.18–16.28) for cyberbullying], followed by ‘low resilience and sociological problem’ [ORs ranged from 1.35 (1.17–1.55) for child–parent separation to 9.58 (7.17–12.68) for cyberbullying] and ‘low life satisfaction and high self-evaluation’ [ORs ranged from 1.17 (1.09–1.27) for less-educated parents to 6.30 (4.95–8.00) for cyberbullying]. Also, similarly to the findings among younger adolescents, threat-related ACEs showed a stronger magnitude of association with having undermined cluster-based psychosocial difficulties than poor external environment and deprivation-related ACEs (Table 3).

Analyses on the impact of the cumulative number of threat- and deprivation-related ACEs on cluster-based psychosocial status suggested a dose–response manner of these observed associations for both Grade 4–6 and Grade 7–12 participants (Tables 2 and 3).

Further, analysis of associations between MCA-identified exposure patterns and cluster-based psychosocial statuses indicated that, for both Grade 4–6 and Grade 7–12 participants, a bullying-predominated exposure pattern was associated with the highest ORs of having cluster-based psychosocial difficulties (Tables 2 and 3).

In subgroup analyses, we basically obtained comparable risk patterns between female and male students (Supplementary Figure S7 and Supplementary Tables S7 and S8, available as Supplementary data at IJE online). In addition, most of the observed associations remained significant after the application of the FDR approach for controlling the multiple testing issue (Figure 1, Tables 2 and 3, Supplementary Figures S5–S7 and Supplementary Tables S5–S8, available as Supplementary data at IJE online).

Discussion

To the best of our knowledge, this is the largest study to have explored the association of ACEs with psychosocial consequences among Chinese children and adolescents aged 10–19 years. Particularly, besides studying psychosocial status determined by a variety of relevant items that were directly collected from questionnaires, our efforts in identifying the cluster-based psychosocial status demonstrated notable deviations from the perspective of psychosocial status among children and adolescents at different developmental stages in China. Specifically, the primary-school students posed a low prevalence of psychological problems but presented their psychosocial difficulties mainly by changes in relationship- and behaviour-related factors, whilst psychosocial difficulties in the middle- and high-school students were featured by deviations in all measured domains. Furthermore, analyses on cluster-based psychosocial difficulties demonstrated stronger influences from studied ACEs among older (i.e. Grade 7–12) than younger (Grade 4–6) students, although with consistently the highest risk estimates observed for threat-related ACEs. The importance of ACEs related to poor external environment (i.e. family dysfunction and poor school environment) and child–parent separation seems more significant for Grade 7–12 students. These findings remain valid after considering the co-occurrence of multiple types of ACEs, highlighting both individual and joint effects of ACEs on the risk increase of psychosocial difficulties among Chinese adolescents.

Our data indicated that ∼76% of Chinese children and adolescents had experienced at least one ACE (56.6% and 82.0% of Grade 4–6 and Grade 7–12 participants, respectively), which was higher but basically at a comparable level compared with previous studies conducted in similar (e.g. 75%)²⁰ or different (59–66%)¹ young populations. It is notable that our study has a customized definition of ACEs in which region-specific childhood adversities, as well as poor external environment-related ACEs, were taken into account. These factors were considered important given the unique family structure of the study population (i.e. parents being predominantly farmers and blue-collar workers) and the undeniable academic pressure among Chinese students in general. Also, the prevalent experience of child–parent separation (18.3%) reflected a serious social problem of ‘left-behind children’, which, however, remained largely unexplored in existing studies.

Our attempt to identify the subgroups of participants with distinct psychosocial statuses, based on cluster analysis, was novel, where the visualized results of the affected categories indicated notable age-specific differences. The less psychological but more evident behaviour-related problems among younger participants were in line with the trajectory described in studies of personality development which declared that children aged <12 years were in the beginning stage of an upward swing for dispositional traits, who tend to reveal few clear signs of distress but express their emotion by externalized behaviours. Also, the prominent deviations in multiple psychological and sociological aspects that were observed among older adolescents gained support from the notion that psychosocial problems become complex with age and the formation of personal traits, manifested in multiple dimensions including psychiatric symptoms or disorders,³⁷ aggressive and risk-taking behaviours such as self-harm and suicide,³⁸ as well as problems with social functioning (i.e. peer relations).

Consistently with prior studies, our analyses revealed that, out of the three broad categories of studied ACEs, threat-related adversities were those showing the strongest association with the risk of having psychosocial difficulties. A similar conclusion was drawn in a recent study with longitudinal data on ACEs and depressive symptoms collected from 3931 participants who were followed from childhood to adolescence, in which the researcher observed a higher elevation of depression trajectories among individuals with threat-related ACEs than those with other types of ACEs.³⁹ Another study involving 247 children and adolescents aged 8–16 years proposed a possible underlying mechanism related to accelerated development by demonstrating that threat-related early-life adversities, but not deprivation-related ones, were associated with accelerated DNA methylation age and advanced pubertal stage, which could partially explain the observed links between threat-related adversities and greater depressive symptoms.⁴⁰ Other efforts on understanding the biological pathways primarily focused on childhood stress and its relevant dysregulations of the nervous (e.g. hypothalamic–pituitary–adrenocortical axis)⁴¹ and immune⁴² systems. In addition, it is notable that cyberbullying, as a type of thread-related adversity that is more prevalent among Grade 7–12 students (3.2%) than Grade 4–6 students (1.3%), showed consistently the strongest (ORs = 6.30–12.81) associations with all identified undermined psychosocial statuses in older students compared with other ACEs. Together with prior evidence indicating higher possibilities of having psychiatric symptoms (depression, anxiety and suicide ideation)⁴³ but lower life satisfaction⁴⁴ for children or adolescents suffering cyberbullying, such findings highlight the importance of guidance or surveillance on healthy social media and digital device use/contacts, particularly for students at middle-school age.

The major strengths of our study include the large sample size (nearly 30 000 participants) of a homogenous population as well as the enriched data about psychological and social factors, which provide optimal conditions for exploring the major notable deviations in terms of psychosocial status among the study population via a data-driven approach. The measure of various ACEs (including experiences that uniquely existed in the study area) at the stage of early life (10–19 years old) reduces the possibility of recall bias, which particularly matters for studying less severe ACEs, such as verbal bullying. In addition, the further consideration of concurrent ACEs, by the cumulated number or exposure patterns identified by using MCA, allows the estimates for the joint effect of multiple ACE exposures.

Several limitations should be noted. First, the identification of ACEs was based on self-report data, which are prone to social desirability bias (i.e. the tendency of respondents to give socially desirable responses instead of those reflecting their true feelings), leading to possibly underestimated associations. Also, the retrospective measures of ACEs, although demonstrated as being in moderate agreement with prospectively recorded ACEs, may be subject to recall bias, which is likely towards underestimated effects of ACEs on objectively measured outcomes but exaggerated effects on self-reported outcomes. Second, although we can infer the temporal order from some chronologically definite ACEs, such as early-life earthquake experiences and child–parent separation, to these psychosocial outcomes of interest, the cross-sectional study design provides no basis for any causal inference. For other ACEs with less time-specific identity, the concern about the lack of temporality is even bigger, as those measured psychosocial features might be prior to or co-occurred with ACEs and thereby impossibly be the consequences of ACEs. The same issue is also applicable to involved covariates (i.e. paternal and maternal occupation) because it could be colliders (i.e. consequences of both studied exposure and outcome) or mediators (i.e. on the pathway from exposure to outcome), instead of confounders. However, based on hypothesized pathways, the findings of DAGs provide rationale for using paternal and maternal occupation as confounders in most of the association analyses. Also, we retrieved largely identical results based on age- and gender- adjusted models and full models, which could partially alleviate the worry that the involvement of such variables has led to heavily attenuated risk estimates. Third, despite the application of well-validated questionnaires, age-specific data collection strategy and training for involved teachers, we cannot rule out the possibilities that the collected data from young students could be biased by some factors, such as misunderstanding, tiredness to long scales or misguidance from teachers or parents. Also, as younger participants by default have less chance of being exposed to cumulative ACEs, risk estimates from those two age groups (i.e. Grade 4–6 students and Grade 7–12 students) might not be directly comparable. Additionally, although the missing rate of data was low (i.e. 7.90% of participants were excluded due to missing data on ACEs or studied psychosocial items), it is possible that individuals with missing data were more affected and thereby the issue of missing not at random might exist, which could result in underestimated associations. Fourth, whereas the use of a stratified random sampling method and the satisfiable response rate that may guarantee acceptable repetitiveness of our study sample to the target population, we included participants primarily from the rural or suburban areas of one county, which might not be a nationally representative sample of Chinese students of those ages. Future research should ideally involve children and adolescents with different regions and socio-economic backgrounds to achieve better generalizability. Last, despite of enriched data on ACEs, some key ACEs, including verbal, physical and sexual abuse from caregivers, parental psychiatric disorders and parental separation, are still missing in our study. Future studies focusing on those key ACEs on psychosocial outcomes in Chinese children and adolescents are highly warranted.

Conclusion

In conclusion, our study made a comprehensive assessment of psychosocial status among Chinese children and adolescents at different developmental stages. Furthermore, the association analyses demonstrated strong associations between various ACEs, particularly threat and poor external environment-related ACEs, and these identified psychosocial difficulties. If confirmed in future prospective studies, these findings may facilitate refined efforts to prevent these key ACEs or develop early interventions for reducing the psychosocial adversities, as well as their prolonged impacts, among the Chinese population.

Ethics approval

The study was approved by the biomedical research ethics committee of West China Hospital (reference number: 2019-77).

Data availability

The data underlying this article will be shared on reasonable request to the corresponding author.

Supplementary data

Supplementary data are available at IJE online.

Author contributions

H.S., Y.S. and Y.L. were responsible for the study conceptualization and design. Y.L., Y.H. and W.Z. conducted data collection and project management. Y.S. performed data cleaning and analyses. Y.S., Y.Y., C.H., H.Y., J.Z. and H.S. interpreted the data. H.S., Y.L. and Y.S. drafted and revised the manuscript. All authors had full access to all the data in the study and can take responsibility for the integrity of the data and the accuracy of the data analysis. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

Funding

This work was supported by the 1·3.5 Project for Disciplines of Excellence, West China Hospital, Sichuan University (grant no. ZYYC21005 to H.S.) and by the National Development and Reform Commission 2018 Digital Economy Pilot Projects for Application of Healthcare Big Data Innovation Project [grant no. (2018) 5 to W.Z.].

Conflict of interest

None declared.