Parental and social factors in relation to child psychopathology, behavior, and cognitive function

Parental and social factors have long-term impact on the neurodevelopment of offspring, but tend to highly covary with each other. Thus, it is difficult to parse out which parental and social factor contributes most to neurodevelopmental outcomes. This study aimed to assess clusters of parental and social factors associated with child psychopathology, behavioral problems, and cognition. This study employed the data of 11,875 children (9 to 11 years) from the Adolescent Brain Cognitive Development (ABCD) study. Principal component analysis (PCA) was performed on 39 environmental measures and 30 child behavior and cognitive measures separately to identify clusters of parental and social factors and clusters of child psychopathology, behaviour, and cognition. Regression analysis was used to examine independent effects of each cluster of parental and social factors on child psychopathology, behavioral problems, and cognition. Greater Parent Psychopathology cluster was associated with greater Child Psychopathology cluster. Moreover, greater Socioeconomic Status cluster was associated with greater child General Cognition and Executive Function but less Behavioral Inhibition clusters. Greater Proximal Social Environment and Interaction cluster were associated with less child Impulsive Behavior and Behavioral Inhibition, but greater Behavioral Activation cluster. The environmental clusters related to birth outcomes, maternal tobacco, and drug use were not significantly related to child psychopathology, behavior, and cognition. Our findings suggest that socioeconomic status, parental psychopathology, and social environment and interactions are the strongest risks for behavioral problems and cognitive performance in a general child population. Intervention programs should target modifiable factors within these domains.


Introduction
Parental, socioeconomic, and social factors, such as parent psychopathology, pregnancy complications, household income, parental education, and family environment, can have long-term impact on the neurodevelopment of offspring [1][2][3][4] . However, most of existing studies typically assess parental, socioeconomic, and social factors, and their influences on child psychopathology, behavior, and cognition, separately. These environmental factors not only play an important role in neurodevelopment, but also tend to covary highly with each other, which makes it difficult to parse out which parental and social factor contributes most to neurodevelopmental outcomes, or whether the risk is additive 5,6 .
The Adolescent Brain Cognitive Development (ABCD) study (version 2.0) acquired comprehensive information on prenatal and postnatal parental, socioeconomic, and social environment as well as child outcomes in 11,875 children aged at 9 to 11 years 7 . It provided a unique opportunity to assess each aspect of parental, socioeconomic, and psychosocial factors in relation with child psychopathology, behavioral problems, and cognition when considering the interplay of different aspects of parental and social factors. For this, we employed principal component analysis to identify clusters within a wide spectrum of parental, socioeconomic, and social environmental factors and clusters within a wide spectrum of child psychopathology, behavioral problems, and cognition. Such an approach provides a comprehensive map for understanding the contribution of individual aspects of parental, socioeconomic and social factors to child psychopathology, behavioral problems, and cognition, which potentially provides the guidance of future intervention on improving child neurodevelopment in a general population.

Participants
Participant data were obtained from the open baseline from the ongoing Adolescent Brain Cognitive Development (ABCD) study (release 2.0; https://abcdstudy.org/). Youth (n = 11,875) 9-11 years of age were recruited for this study and formed a similar proportion of males and females living in the United States. The sample selection criteria were targeted to reflect the sociodemographic proportion of the U.S. population as described in the ABCD study design 7 . All participants were administered assessments to obtain data on the respective youth's brain morphology, cognitive function, substance use, demographics, and environment 8 . Written informed consent was obtained from all parents, and all children provided assent to a research protocol approved by the institutional review board at each data collection site (https:// abcdstudy.org/study-sites/) 9 .
Of the 11,875 participants, we excluded 23 subjects with missing values of demographics, 3219 subjects with one or more missing values of the parental and social environmental measures, 914 subjects with one or more missing values of the questionnaires/tasks of the child psychopathology, behavior, and cognitive measures, and 283 subjects with missing values in either of these two data. Therefore, our study employed 11,875 participants and 8002 participants (67.4% of full sample) for statistical analysis, separately. Supplementary Table S1 in the Supplementary Material lists the subject id whose data were not included in this study.

Parental and social environmental measures
This study included 39 parental, socioeconomic, and social environmental measures, including10 measures of parent psychopathology, 6 maternal substance use measures, 5 developmental adversity measures, 7 social demographics, 5 proximal environmental measures, and 6 social interaction measures 8,10 .

Parent psychopathology
Parent psychopathology symptoms were assessed using the Adult Self Report (ASR) and Family History Assessment Module Screener (FHAM-S) questionnaires. The ASRprovides 8 empirically-based syndrome scales (anxious/depressed, withdrawn, somatic complaints, thought problems, attention problems, aggressive behaviour, rule-breaking behavior, and intrusive) 11 . FHAM-S reports the presence/absence of symptoms associated with alcohol and drug use, depression, and mania in all 1st and 2nd degree "blood relatives" of the youth 12 . The presence of alcohol and drug use problems of the child's relatives was defined as the family psychopathology risk of substance use disorders. Similarly, the accumulated presence of depression and mania was scored as the family psychopathology risk of mental disorders.

Maternal substance use
The parent-reported Developmental History Questionnaire was used to assess maternal consumption of tobacco, alcohol, and marijuana before and after the mothers knew that they were pregnancy [13][14][15] .

Developmental adversity
The developmental History Questionnaire 8 was used to assess prematurity, birth weight, pregnancy and birth complications and the Modified Ohio State University Traumatic Brain Injury Screen-Short Version 16 was employed to assess the parent-report overall brain injury/ concussion during the child's development.

Social demographics
The parent-report demographics battery from the PhenX toolkit measured social demographics of the parental highest education, household annual income, and marriage status 17 . Economic insecurity 18 , the grand total Uniform Crime Reports, Area Deprivation Index by the scaled weighted sum, and the estimated lead risk in census tract of primary residential address 19,20 were also employed to provide additional information about socioeconomic influences.

Proximal environment
The "Safety from Crime" items from the PhenX Toolkit was used to assess neighborhood safety and crime reports 21,22 . Additionally, children reported their school risk and protective factors via a 12-item Inventory for School Risk and Protective Factors of the PhenX toolkit 23 . Three measures was selected to assess a child's connectedness to his/her school, including school teacher and classroom environment, personal involvement in school, and alienation from academic goals.

Social interaction
The child-reported parental monitoring and acceptance, as well as the child-and parent-reported prosocial tendency and family conflicts were included to measure social interactions. Parent monitoring was accessed by a 5-item summary score of the Parental Monitoring Scale 24 . Parent acceptance was evaluated by the Acceptance Scale, a subscale of the Child Report of Behavior Inventory (CRPBI) 25 . Prosocial behavior (e.g., being nice, helping, caring) was assessed using the Prosocial Behavior Scale, a subscale from the "Strengths and Difficulties Questionnaire" (SDQ) 26 . Both parents and youth reported on the youth's prosocial behavior (e.g., being considerate of other people's feelings, often offering to help others). In order to assess the family conflicts, the ABCD protocol utilized a 9-item Family Conflict subscale of the Moos Family Environment Scale (FES) for the baseline protocol 27 .
For the parental and environmental measures related to psychopathology, maternal substance use, and developmental adversity, higher scores represents more severe psychiatric symptoms, worse substance use, and developmental adversity. For the measures of social demographics, proximal environment, and social interactions, higher scores represent better socioeconomic status, proximal environment, and social interactions. For the ease of interpretation, a few scores were inverted to align the direction in their same category as mentioned above. Figure 1 marks these inverted measures in parenthesis.

Child psychopathology, behavior, and cognition
This study employed 30 child psychopathology, behavior, and cognitive measures, including 10 child psychopathology measures, 9 behavior measures, and 11 cognitive measures 8,28 . To provide converging evidence about the youth's behavior, we also utilized the available data (n = 2440) with the teach-reported total behavior problems which were evaluated by the Brief Problem Monitor-Teacher Form 11 .

Child psychopathology
Child Psychopathology was assessed based on the parent report of Child Behavior Checklist (CBCL) 11 , the tenitem Mania Scale derived from the Parent General Behavior Inventory for Children and Adolescents 29 , and the Prodromal Questionnaire Brief Version 30 . This study included 8 empirically-based syndrome scales from CBCL (aggressive behavior, anxious/depressed, attention problems, rule-breaking behavior, somatic complaints, social problems, thought problems, and withdrawn/depressed scales), a risk score of bipolar variability in mood and behaviour, and a severity score of psychosis risk symptoms.

Child behavior
The 20-item Children-Short Form (UPPS-P) was used to assess five facets of impulsivity 31 , including negative and positive urgency, lack of planning, lack of perseverance, and sensation seeking. The 24-item Behavioral Inhibition/Activation Scales (BIS/BAS) were also utilized: BIS (e.g., worry, fearfulness), BAS drive (intensity of goal directed behavior), BAS reward responsiveness (excitement over reinforcing outcomes), and BAS fun seeking (enjoyment for its own sake, spontaneity) 32 .

Child cognition
The neurocognitive battery comprised of 11 tasks 28 and was administered using an iPad with one-on-one monitoring by a research assistant. Among the 11 cognitive tasks, there were 7 from the NIH Toolbox (http://www. nihtoolbox. org), including flanker (inhibitory control), dimensional change card sort (cognitive flexibility), list sorting working memory (working memory), picture sequence memory (episodic memory), pattern comparison processing speed (processing speed), picture vocabulary (vocabulary comprehension), and oral reading recognition tasks (reading decoding). ABCD also administered Matrix Reasoning Task from the Wechsler Intelligence Test for Children-V (fluid Reasoning) 33 , Little Man Task (LMT, visual-spatial processing), Rey Auditory Verbal Learning Test (RAVLT, auditory learning, memory, and recognition), and Cash Choice Task (a singleitem delayed gratification measure with dichotomous scoring). Notably, we employed the response accuracy of LMT, the delayed recall accuracy of RAVLT, and the total scaled score of Matrix Reasoning.
For measures related to child psychopathology and behavior, a higher score represented worse psychopathology and behavioral problems. For measures of child cognition, a higher score represented better cognitive ability.

Statistical analysis
Each score of 39 environmental measures and 30 child characteristics was first standardized with zero mean and unit variance using rank-based inverse Gaussian transformation 33,34 . Pearson's correlation coefficients were used to explore the associations of individual parental and social environment variables with individual child measures. Bonferroni correction was used to determine the significance of multiple correlations (the number of tests: 1170) at p < 0.001.
For multivariate analysis, principal component analyses (PCA) was first performed within all environmental measures and within the child characteristics, respectively 33 . Varimax rotation was applied to factor loadings of the PCs with eigenvalues greater than 1. The component scores were further computed based on the varimax rotated loadings beyond 0.35. This procedure ensured statistical independence of the PCs within the environmental measures and within the child characteristics.
Mixed effect models were used to examine associations of all environmental PCs with each child characteristic PCs. Age, sex and ethnicity were covariates. The information of twins, non-twin siblings, and 21 different research sites was entered as random effects. Bonferroni correction was used to determine the significance of statistical tests (n = 48) at p < 0.001.

Results
This study included 8022 out of 11,875 children (mean [SD] age, 9.9 [0.6] years; 47.8% girls; 57.0% white ethnicity) with the complete environmental and child characteristic data. Table 1 lists the 39 environmental measures and 30 child characteristics of subjects with the complete data (n = 8002) and all 11,875 subjects. The sample with the complete data (n = 8022) did not differ from the whole sample (n = 11,875) in most of measures. However, some environmental measures (i.e., household married percentage, parental education, lead risk, and parent report neighborhood safety) and cognitive measures (i.e., picture vocabulary, oral reading, list sorting, card sorting, picture sequencing, RAVLT delayed memory and matrix reasoning) were better in the sample with the complete data than the whole sample (see p-values in Table 1). The severity of child psychopathology (i.e., child aggressive behavior, child attention problems, and child rule-breaking behavior from CBCL, and the mania score). was slightly lower in the sample with the complete data than in the whole sample data (see p-values in Table 1). Figure 1 illustrates significant correlations between 39 parental and social environment measures and 30 child outcomes (Bonferroni corrected p < 0.001). This suggested strong correlations between parental and child psychopathology, between socioeconomic status and cognition, between social interactions and child psychopathology, and between proximal social environment and interactions and child impulsive behaviors. After controlling for age, gender, ethnicity, and the other environmental PC factors, greater Parent Psychopathology (standardized β = 0.56, [0.54, 0.58], p < 0.001) was associated with greater Child Psychopathology (Fig. 3a). Using available reports on the Teacher Report Form on child behaviors (n = 2440), the associations between Parent Psychopathology and teacher-reported child behavioral problems remained significant (standardized β = 0.06, [0.02, 0.11], p = 0.002). Moreover, greater  Group differences are tested using two-sample t-test with equal variance assumption for continuous variables and χ 2 tests for discrete variables. *Due to missing values, the sum of percentages may not equal to 100%.   Fig. 3a). The environmental PCs related to birth outcomes, maternal alcohol, tobacco, and drug use were not significantly related to child psychopathology, behavior, and cognition (Tables 2, 3). Our repeated analyses using the full study sample (n = 11,875) and mean imputation for missingness showed the similar findings as stated above (in Supplementary Figs. S1 and S2 of the Supplementary Material).

Discussion
This study showed the distinctive influences of the parental, socioeconomic, and social environmental factors on child psychopathology, behavioral problems, and cognition. As expected, strong relationships were found between Parent Psychopathology and Child Psychopathology, between Socioeconomic Status and child Cognition, and between Proximal Social Environment and Interaction and child Impulsive behaviors. What was unexpected, however, was our lack of identifying relationships between birth outcomes, maternal tobacco and drug use with child psychopathology, behavioral problems, and cognition.
Consistent with previous findings 35, 36 , we found strong association between the psychopathology in parents and their children. Child psychopathology was assessed by parents and thus there is a tendency that parents with greater psychopathology will also rate their child as having greater psychopathology. When we utilized teacher reported behavioral problems of the child and parent selfreport, the association remained significant, albeit less strong. Our findings provide further support for a potential genetic contribution for the transgenerational transmission of psychopathology from parents to behavioral characteristics of children.
This study also identified the associations of Socioeconomic Status with child General Cognition and Executive Function. This is congruent with previous findings, suggesting that lower Socioeconomic Status strongly predicts lower IQ and executive functions 3,37 . Most of previous studies employ household income and/ or parental education or both as the representation of Socioeconomic Status 20 . In contrast, we quantified Socioeconomic Status using a broad construct that incorporated Table 2 Associations of all environmental PCs with each child psychopathology and behavior problem PCs.  variation not only from household income and parental education, but also from a regional deprivation index. From this aspect, our study provided evidence supporting the idea of a reduction of poverty and increasing education at the level of both family and neighbourhood may help improve child cognitive development.
Unlike previous studies 38,39 , our findings did not support strong associations of birth outcomes, maternal tobacco, and drug use with child psychopathology, behavior, and cognition in this general child population. Nevertheless, when analyzing the association between maternal marijuana use and psychosis, we showed the similar result (p = 0.014 in Table 2) as that presented in 15 .
The lack of such associations among the PC scores is partly because our findings were obtained after controlling for Parental Psychopathology, Socioeconomic Status, and etc, suggesting that Parental Psychopathology and Socioeconomic Status had a greater effect on child neurodevelopmental outcomes. Most of existing studies generally focus only on a case-control or imbalanced designs and do not assess the comprehensive profile of parental, socioeconomic, and social factors and hence may not quantify true effects of maternal tobacco and drug use as well as birth outcomes on child neurodevelopment in a general population 40 .
One of the strengths of our study is that we employed a large population-based sample of children who are all participating in the ABCD baseline wave of data collection. Thus, we were able to incorporate a comprehensive assessment of parental, socioeconomic, and social environmental factors as well as child characteristics. Nevertheless, the reliance on cross-sectional data precludes any determination of causality. Moreover, the ABCD study sampled from the United States, which may limit the generalizability of our findings. Further research is necessary to explore across other ethnicities and cultures to enhance the potential generalization of our findings.
Our findings suggest that parental psychopathology, socioeconomic status, and social environment and interactions are the strongest risks for behavioral problems and cognitive performance in a general child population. These children should be targeted for intervention programs, with the possibility for including both primary and secondary prevention. U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, and U24DA041147. A full list of supporters is available at https://abcdstudy.org/ federal-partners/. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/study-sites/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. The ABCD data repository grows and changes over time. The ABCD data used in this report came from https://doi.org/10.15154/1517592.

Conflict of interest
The authors declare that they have no conflict of interest.
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