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Do dimensions of childhood adversity differ in their direct associations with youth psychopathology? A meta-analysis

Published online by Cambridge University Press:  08 April 2024

Amy Hyoeun Lee*
Affiliation:
Department of Psychology, Hofstra University, Hempstead, NY, USA
Yukihiro Kitagawa
Affiliation:
Department of Psychology, University of Oregon, Eugene, OR, USA
Rebecca Mirhashem
Affiliation:
Department of Psychology, Stony Brook University, Stony Brook, NY, USA
Micaela Rodriguez
Affiliation:
Department of Psychology, University of North Carolina, Chapel Hill, NC, USA
Romola Hilerio
Affiliation:
Department of Psychology, Hofstra University, Hempstead, NY, USA
Kristin Bernard
Affiliation:
Department of Psychology, Stony Brook University, Stony Brook, NY, USA
*
Corresponding author: Amy Hyoeun Lee; Email: [email protected]
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Abstract

Growing evidence supports the unique pathways by which threat and deprivation, two core dimensions of adversity, confer risk for youth psychopathology. However, the extent to which these dimensions differ in their direct associations with youth psychopathology remains unclear. The primary aim of this preregistered meta-analysis was to synthesize the associations between threat, deprivation, internalizing, externalizing, and trauma-specific psychopathology. Because threat is proposed to be directly linked with socioemotional development, we hypothesized that the magnitude of associations between threat and psychopathology would be larger than those with deprivation. We conducted a search for peer-reviewed articles in English using PubMed and PsycINFO databases through August 2022. Studies that assessed both threat and deprivation and used previously validated measures of youth psychopathology were included. One hundred and twenty-seven articles were included in the synthesis (N = 163,767). Results of our three-level meta-analyses indicated that adversity dimension significantly moderated the associations between adversity and psychopathology, such that the magnitude of effects for threat (r’s = .21–26) were consistently larger than those for deprivation (r’s = .16–.19). These differences were more pronounced when accounting for the threat-deprivation correlation. Additional significant moderators included emotional abuse and youth self-report of adversity. Findings are consistent with the Dimensional Model of Adversity and Psychopathology, with clinical, research, and policy implications.

Type
Regular Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press

Introduction

Childhood adversity, such as abuse and neglect, exposure to violence, and institutional rearing, are associated with heightened risk for mental health problems across internalizing (e.g., depression, anxiety, somatic symptoms) and externalizing (e.g., aggression, conduct problems, substance use) dimensions of psychopathology (Kessler et al., Reference Kessler, McLaughlin, Green, Gruber, Sampson, Zaslavsky, Aguilar-Gaxiola, Alhamzawi, Alonso, Angermeyer, Benjet, Bromet, Chatterji, de Girolamo, Demyttenaere, Fayyad, Florescu, Gal, Gureje, Haro, Hu, Karam, Kawakami, Lee, Lépine, Ormel, Posada-Villa, Sagar, Tsang, Ustün, Vassilev, Viana and Williams2010; McLaughlin et al., Reference McLaughlin, Green, Gruber, Sampson, Zaslavsky and Kessler2012). In particular, experiences reflecting disruptions in the early caregiving relationships and/or exposure to interpersonal violence are associated with a nonspecific latent vulnerability for later mental health problems (McCrory & Viding, Reference McCrory and Viding2015). The Dimensional Model of Adversity and Psychopathology (DMAP; McLaughlin et al., Reference McLaughlin, Sheridan and Lambert2014; McLaughlin & Sheridan, Reference McLaughlin and Sheridan2016) conceptualizes adversity along two broad dimensions of threat and deprivation. Threat involves experiences of harm or threatened harm to a child’s physical integrity, such as physical abuse or violence exposures in the home or community, whereas deprivation comprises the absence of environmental input and complexity, such as neglect or food insecurity (McLaughlin et al., Reference McLaughlin, Sheridan and Lambert2014; McLaughlin & Sheridan, Reference McLaughlin and Sheridan2016). The DMAP framework posits that threat and deprivation confer risk for psychopathology via distinct developmental pathways, with threat-related experiences impacting primarily emotion processing (e.g., threat-safety discrimination; McLaughlin et al., Reference McLaughlin, Sheridan, Gold, Duys, Lambert, Peverill, Heleniak, Shechner, Wojcieszak and Pine2016) and deprivation-related experiences impacting neurocognitive abilities (e.g., executive function; Johnson et al., Reference Johnson, Policelli, Li, Dharamsi, Hu, Sheridan, McLaughlin and Wade2021).

Evidence in support of DMAP is growing, with recent meta-analyses demonstrating differential associations between threat and deprivation on developmental mechanisms implicated in psychopathology. For instance, Colich et al. (Reference Colich, Rosen, Williams and McLaughlin2020) examined the moderating effects of adversity dimension on the association between early life adversity and biological ageing across 54 studies and found that only threat, and not deprivation, was significantly associated with both pubertal timing and cellular ageing. Likewise, Johnson et al. (Reference Johnson, Policelli, Li, Dharamsi, Hu, Sheridan, McLaughlin and Wade2021) synthesized findings from 91 studies examining the association between early life adversity and youth executive functioning and found that deprivation was more strongly associated with lower inhibitory control and working memory when compared with threat. Thus, both theoretical and empirical evidence support potential differential effects of threat and deprivation on youth outcomes, but the moderating role of adversity dimension on youth psychopathology has not yet been examined in prior meta-analyses.

The DMAP framework does not hypothesize differential relations between adversity dimensions and specific psychopathology outcomes. Nonetheless, specific mental health problems have been theoretically and empirically linked with specific types of adversity reflecting the dimensions of threat and deprivation (see Wade et al., Reference Wade, Wright and Finegold2022 for a review). For instance, McLaughlin et al. (Reference McLaughlin, Sheridan and Lambert2014) have posited that threat is conceptually consistent with traumatic events as defined by the Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association, 2013). In turn, traumatic events precede the onset of posttraumatic stress disorder (PTSD) symptoms, and thus PTSD may be more strongly related to the threat dimension of adversity (e.g., Guyon-Harris et al., Reference Guyon-Harris, Humphreys and Zeanah2021). Conversely, symptoms of ADHD, captured in the externalizing dimension of psychopathology, have been strongly linked with experiences of deprivation, as documented by longitudinal studies of children with histories of institutional rearing, which reflects profound social and cognitive deprivation (Bos et al., Reference Bos, Zeanah, Fox, Drury, McLaughlin and Nelson2011). When considering psychopathology broadly, a recent meta-analysis by Baldwin et al. (Reference Baldwin, Wang, Karwatowska, Schoeler, Tsaligopoulou, Munafò and Pingault2023) synthesized findings across 34 quasi-experimental studies and demonstrated a larger effect size between emotional abuse and psychopathology relative to emotional or physical neglect. Taken together, evidence suggests that threat and deprivation may differ in how strongly they are associated with dimensions of internalizing, externalizing, and trauma-specific psychopathology, and these differential associations may be driven in part by links between adversity dimensions and specific mental health outcomes. Clarifying these associations could have important implications for prevention and intervention efforts with children exposed to adversity, given the differences in evidence-based intervention approaches for broad dimensions of psychopathology as well as specific disorders (e.g., internalizing vs. externalizing disorders vs. PTSD) and the need to prioritize treatment of comorbid problems.

Evidence of differential associations between threat, deprivation, and psychopathology

Prior studies testing DMAP have also suggested differential associations between threat, deprivation, and psychopathology outcomes. For instance, Miller et al. (Reference Miller, Sheridan, Hanson, McLaughlin, Bates, Lansford, Pettit and Dodge2018) found that threat in the first 6 years of life was uniquely associated with both internalizing and externalizing symptoms at age 17 in a sample of 585 children followed longitudinally. Consistent with the DMAP framework, the authors found an indirect effect of deprivation on externalizing symptomatology via verbal abilities, but no evidence of a direct effect from deprivation to internalizing or externalizing symptoms when simultaneously accounting for the effects of threat. Threat, on the other hand, was directly associated with both internalizing and externalizing problems. Miller et al. (Reference Miller, Machlin, McLaughlin and Sheridan2021) found a similar pattern of direct associations in a birth cohort of U.S. children, such that associations were found between threat and both internalizing and externalizing psychopathology at ages 5, 9, and 15, but not between deprivation and psychopathology at these ages. Similarly, Schäfer et al. (Reference Schäfer, McLaughlin, Manfro, Pan, Rohde, Miguel, Simioni, Hoffmann and Salum2021) evaluated DMAP in a large sample of Brazilian youth ages 6 to 17 (N = 2511) and found concurrent associations between threat and both internalizing and externalizing psychopathology. No such associations were found between deprivation and internalizing or externalizing psychopathology, nor between the two adversity dimensions and psychopathology assessed 3 years later. Interestingly, both threat and deprivation were associated with a general psychopathology factor at follow-up, though the effect size for deprivation appeared smaller than for threat (β = .072 and .177, respectively). Taken together, studies directly testing DMAP have found differences in the direct associations between threat and deprivation and internalizing and externalizing symptoms across childhood and adolescence.

Variability in adversity measurement and modeling approaches

Variability in current measurement approaches to childhood adversity may affect findings on the associations between adversity and youth psychopathology. For instance, consistent with the cumulative risk model (Evans et al., Reference Evans, Li and Whipple2013), many researchers continue to rely on the use of dichotomous variables to capture the presence or absence of specific types of adversity, which are often summed to obtain a composite score reflecting the degree of adversity exposures. Other investigators have emphasized the importance of accounting for additional features of adversities, such as frequency and severity, and modeling their relations with outcomes to better capture the variability in outcomes among adversity-exposed youth (Jackson et al., Reference Jackson, McGuire, Tunno and Makanui2019; McLaughlin et al., Reference McLaughlin, Sheridan, Gold, Duys, Lambert, Peverill, Heleniak, Shechner, Wojcieszak and Pine2016). Adversity can also be assessed via youth self-report, caregiver-report, or administrative records (e.g., Child Protective Services, U.S. Census), and these sources of information are often used independently or in various combinations. Indeed, within a single dataset, investigators may exercise researcher degrees of freedom and arrive at multiple iterations of adversity scores to be used in their analyses. Recent work has shown that these measurement practices and analytic decisions, including those pertaining to the operationalization of environmental experiences, can influence results and impact replicability (e.g., Demidenko et al., Reference Demidenko, Kelly, Hardi, Ip, Lee, Becker, Hong, Thijssen, Luciana and Keating2022). Thus, there is a need to directly examine the effects of measurement-related variables on the links between adversity and youth psychopathology.

Researchers examining the links between adversity and youth psychopathology have begun to directly compare measurement approaches and their effects on associations with youth mental health outcomes. Stein et al. (Reference Stein, Sheridan, Copeland, Machlin, Carpenter and Egger2022) compared cumulative risk and dimensional approaches and their associations with early psychopathology in a sample of preschool children. Using a cumulative risk score, they found relatively uniform associations between cumulative adversity and multiple early forms of psychopathology. However, using the DMAP approach, they found particularly strong associations between threat, but not deprivation, and behavioral problems, demonstrating that the ability to detect such differential effects depended in part on the conceptualization and modeling of adversity. In a large sample of 9- to 10-year-old children, Jeong et al. (Reference Jeong, Moore, Durham, Reimann, Dupont, Cardenas-Iniguez, Berman, Lahey and Kaczkurkin2023) examined both a general factor capturing diverse array of adversities and empirically derived specific factors of environmental adversities, and their associations with general and specific dimensions of psychopathology. They found that the general adversity factor was associated with externalizing problems (ADHD, conduct problems) but not internalizing or general psychopathology, whereas specific adversity factors were differentially associated with both general psychopathology and internalizing and externalizing problems. Schlensog-Schuster et al. (Reference Schlensog-Schuster, Keil, Von Klitzing, Gniewosz, Schulz, Schlesier-Michel, Mayer, Stadelmann, Döhnert, Klein, Sierau, Manly, Sheridan and White2022) examined child maltreatment and its associations with internalizing and externalizing disorder symptoms in a sample of youth ages 3 to 16. Separating physical forms of maltreatment into abuse (i.e., threat) and neglect (i.e., deprivation) factors, they found that abuse was associated with both internalizing and externalizing disorders while neglect was only associated with externalizing disorders. Interestingly, when emotional abuse and neglect were added to the model as a third factor capturing emotional maltreatment, only the association between abuse and externalizing disorders remained significant. Additionally, the use of latent variables reduces measurement error and should result in more accurate estimates with outcomes when compared to observed variables, but these differences have not been directly examined in prior meta-analyses.

In sum, these and other studies suggest that differences in the measurement and modeling of childhood adversity may contribute to the heterogeneity of findings relating adversity to psychopathology across childhood and adolescence. To ensure that such measurement and modeling differences are not driving discrepant findings in the existing literature, there is a need to systematically examine whether specific measurement (e.g., use of continuous versus dichotomous variables) and modeling parameters (e.g., use of latent factors versus observed variables) moderate the strength of associations between adversity and psychopathology.

The current meta-analysis

In the current preregistered meta-analysis, we sought to synthesize the associations between childhood adversity across the dimensions of threat and deprivation and internalizing, externalizing, and PTSD symptoms during childhood and adolescence. We included PTSD as an outcome in addition to symptoms in the internalizing and externalizing spectra, given that PTSD captures unique symptoms that have been understudied in the youth psychopathology literature (Forbes et al., Reference Forbes, Watts, Twose, Barrett, Hudson, Lyneham, McLellan, Newton, Sicouri, Chapman, McKinnon, Rapee, Slade, Teesson, Markon and Sunderland2023). No differential hypotheses regarding psychopathology type were made; instead, we aimed to examine the effects of adversity on each psychopathology outcome and evaluate whether the magnitude of these associations is moderated by adversity dimensions of threat versus deprivation. Given findings from prior work evaluating the DMAP framework (e.g., Schlensog-Schuster et al., Reference Schlensog-Schuster, Keil, Von Klitzing, Gniewosz, Schulz, Schlesier-Michel, Mayer, Stadelmann, Döhnert, Klein, Sierau, Manly, Sheridan and White2022), we preregistered the hypothesis that adversity dimension would significantly moderate the associations between adversity and each psychopathology outcome, such that threat would have larger direct effects with psychopathology relative to deprivation. We additionally sought to estimate the effect sizes of associations between threat, deprivation, and psychopathology while accounting for the overlap between the adversity dimensions (i.e., by using partial correlations). We hypothesized that the magnitude of effects when accounting for the overlap between threat and deprivation would be smaller than the magnitude of effects when not accounting for their overlap. This hypothesis was also preregistered, though the directionality was not specified in the preregistration. Finally, given the scope of our meta-analysis, we anticipated a wide range of adversity measurement practices to be captured in our sample of studies and expected heterogeneity of effects within and between studies based on these variables. As such, our final aim was to investigate whether the associations between adversity and psychopathology were moderated by measurement-related variables (e.g., dichotomous vs. continuous measures) and additional moderators (e.g., study and sample characteristics, inclusion of specific threat and deprivation indicators). Analyses of these additional moderators were considered exploratory in nature due to the lack of established findings on their effects. Consistent with the DMAP framework, we hypothesized that the use of measurement practices that account for additional variance within adversity (e.g., frequency, severity, multiple types of adversity) would strengthen the observed associations between adversity and psychopathology when compared to measurement that did not (e.g., dichotomous, single adversity type). This final hypothesis was not preregistered.

Method

Search strategy

The current study was preregistered on PROSPERO (CRD42021271879), consistent with the PRISMA reporting guidelines. Searches were conducted electronically on PsycINFO and PubMed databases for articles published in peer-reviewed journals through August 2022. Search terms were generated based on terms used in previous meta-analyses examining the differential effects of threat and deprivation (Colich et al., Reference Colich, Rosen, Williams and McLaughlin2020; Johnson et al., Reference Johnson, Policelli, Li, Dharamsi, Hu, Sheridan, McLaughlin and Wade2021) and youth psychopathology (Compas et al., Reference Compas, Jaser, Bettis, Watson, Gruhn, Dunbar, Williams and Thigpen2017). Search terms included words specific to experiences of threat and deprivation consistent with our operationalization of these dimensions (see below), internalizing, externalizing, PTSD symptoms, and childhood (see Table S1 for a full list of search terms). We additionally searched within studies citing the two commonly referenced publications in which the DMAP framework was originally proposed (McLaughlin et al., Reference McLaughlin, Sheridan and Lambert2014; McLaughlin & Sheridan, Reference McLaughlin and Sheridan2016).

Operationalization of primary constructs

Threat and deprivation

Given the large number of studies that have examined childhood adversities in relation to youth psychopathology prior to the introduction of the DMAP framework, we used the following operationalization of threat and deprivation in the current meta-analysis. Based on prior meta-analyses employing the DMAP framework (Colich et al., Reference Colich, Rosen, Williams and McLaughlin2020; Johnson et al., Reference Johnson, Policelli, Li, Dharamsi, Hu, Sheridan, McLaughlin and Wade2021), we defined threat as one or more of the following exposures to violence: (1) physical abuse, (2) sexual abuse, (3) emotional abuse, (4) witnessing domestic violence, and (5) exposure to violence outside the home (e.g., school, community, war). Thus, all studies had one or more of these indicators. If additional threat indicators were also included (e.g., medical trauma, natural disasters), these were coded as “Other” threat indicators. Deprivation was defined as the following: (1) physical, emotional, or another form of neglect, (2) food insecurity, (3) low cognitive stimulation or material provision, (4) institutionalization, and (5) poverty defined against a national benchmark. We initially included caregiver psychopathology and substance use as indicators of deprivation and not threat based on prior work by Henry et al. (Reference Henry, Gracey, Shaffer, Ebert, Kuhn, Watson, Gruhn, Vreeland, Siciliano, Dickey, Lawson, Broll, Cole and Compas2021) supporting this categorization but ultimately collapsed these into “Other” deprivation indicators due to low frequencies. Of note, DMAP posits that poverty is a risk factor for adversity across dimensions of threat and deprivation and a proxy of deprivation specifically (McLaughlin et al., Reference McLaughlin, Sheridan and Lambert2014); because we anticipated that poverty might be the only available indicator of deprivation in many studies that included threat (thus meeting full inclusion criteria), we included poverty as an acceptable indicator of deprivation but adhered to a more stringent definition (i.e., as defined against a national benchmark rather than relative standing within the study sample). We also planned to test as moderators the inclusion of specific indicators of threat and deprivation, including poverty.

Youth psychopathology

Youth psychopathology outcomes were defined as internalizing and externalizing spectra as well as their specific factors (i.e., depression, anxiety, and somatization for internalizing; aggression, conduct, delinquency, ADHD, and substance use for externalizing). Similarly, studies that included total PTSD symptom scores or those reflecting specific symptom clusters (if total scores were unavailable) were both included. For studies with psychopathology data at multiple time points, we included the time point that reflected the greatest length of time between adversity and psychopathology.

Inclusion/Exclusion criteria

Given the large number of studies examining links between adversity and psychopathology, we limited the current meta-analysis to peer-reviewed journal articles available in English. To be included in the meta-analysis, studies had to have at least one indicator of threat and one indicator of deprivation as defined above within a single sample. For a small subset of studies explicitly testing the DMAP framework by using composite scores or latent variables of threat and deprivation, we allowed additional study-specific indicators of threat and deprivation (e.g., lack of parental warmth) and coded these as ‘other’ for analyses. To ensure the quality of outcome measurement, only previously validated measures of youth internalizing, externalizing, and PTSD symptoms were included. Finally, we included only studies whose mean sample age was less than 18 at psychopathology assessment.

Screening and data extraction

Screening was conducted using Covidence online software, and data extraction was conducted using an online spreadsheet. After removing duplicates, two independent reviewers conducted screening of titles and abstracts based on the inclusion criteria. Articles with abstracts meeting inclusion/exclusion criteria as defined above were retrieved and screened again by two independent reviewers. We made attempts to contact authors for articles that appeared to meet our inclusion criteria but did not provide sufficient data (k = 220), with a 9% (k = 20) response rate. Two reviewers extracted data independently for the full set of included articles (k = 127). Specifically, the first author screened and extracted all studies, while the three-second authors screened and extracted approximately one-third of the studies each. Study team members met regularly to resolve conflicts during article screening and to achieve group consensus during data extraction.

Effect size coding

Effect sizes were coded as correlation coefficients (r). For a small subset of studies that only reported standardized regression coefficients (k = 14), beta coefficients were converted to r using the formula provided by Peterson and Brown (Reference Peterson and Brown2005). Original data type (correlation coefficient versus beta coefficient) was coded and examined as a moderator of each of the pooled estimates between adversity and psychopathology.

Moderators

Additional variables pertaining to adversity measurement, psychopathology assessment, study and sample characteristics, and threat and deprivation indicators were coded to be examined as moderators. Adversity measurement variables included: continuous (versus dichotomous) measurement, multiple adversity indicators (versus single type), latent (versus observed) adversity variable, ages of sample included at adversity measurement (each coded dichotomously; infancy = ages 0–2, early childhood = ages ≥ 2–8, middle childhood = ages ≥ 8–12, early adolescence = ages ≥ 12–15, late adolescence = ages ≥ 15–18), lifetime assessment (versus a specific time frame), and reporting source (each coded dichotomously; youth self-report, caregiver-report, records review). Sample age and reporting source categories were not mutually exclusive (e.g., studies could be coded as having both self- and caregiver-report or as including both early and middle childhood).

Psychopathology assessment variables included: psychopathology type (internalizing spectrum, depression, anxiety, somatization, externalizing spectrum, aggression, delinquency, ADHD, substance use, PTSD total, reexperiencing, avoidance, hyperarousal, cognition, and mood), developmental stages of children at assessment (early childhood, middle childhood, early adolescence, late adolescence), and effect size type (correlation coefficient versus standardized beta coefficient). Study and sample characteristics included: publication year, location of study (U.S., Canada, Australia, and U.K. versus other), mean sample age at psychopathology assessment, percentage of participants of color, and percentage of participants described as living in poverty.

Finally, each threat and deprivation indicator described above was coded dichotomously for each effect size (1 = yes, 0 = no), reflecting whether the indicator was included in the measurement of the adversity dimension (i.e., threat or deprivation) for that effect size. Several indicators were categorized into a catchall “Other” category for each adversity dimension based on low frequencies in our data. Specifically, natural disasters and medical trauma were coded as Other threat indicators, and institutionalization and caregiver psychopathology (including substance use) were coded as Other deprivation indicators. This “Other” category also encompassed study-specific indicators of threat or deprivation included in composite or latent variables of threat or deprivation.

Analytic plan

Analyses were conducted using the metafor package in R version 4.2.1 (R Foundation for Statistical Computing). We used a multilevel meta-analytic approach (Assink & Wibbelink, Reference Assink and Wibbelink2016), which accounts for dependence in effect sizes (i.e., multiple effects sizes coded from the same study). We modeled variance in effect sizes across three levels: between participants within each study (i.e., level 1, sampling variance), between effect sizes within the same study (i.e., level 2, within-study variance), and between studies (i.e., level 3, between-study variance). Thus, a random-effects three-level model was estimated using the Restricted Maximum Likelihood method with the Knapp and Hartung (Reference Knapp and Hartung2003) adjustment for each meta-analytic effect of interest. We then assessed the overall heterogeneity of results using the I 2 statistic (Higgins et al., Reference Higgins, Thompson, Deeks and Altman2003), followed by assessing heterogeneity of within-study and between-study variances using one-sided log-likelihood-ratio tests. All correlations were converted to Fisher’s z scores for analyses and converted back to correlation coefficients to aid interpretation.

The overall effect between adversity and each psychopathology outcome (i.e., internalizing, externalizing, PTSD symptoms) was first estimated, followed by the effect between threat and deprivation. For all studies that provided bivariate correlations between threat and deprivation, partial correlations (i.e., correlations between each adversity dimension and each psychopathology outcome accounting for the overlap between the dimensions) were calculated using the formula:

$${r_{xy.z}} = \;\;{{{r_{xy}} - {r_{xz}}*{r_{yz}}} \over {\sqrt {1 - r_{xz}^2} *\sqrt {1 - r_{yz}^2} }}$$

where x = threat (or deprivation), y = psychopathology, and z = deprivation (or threat). The effects between threat, deprivation, and each psychopathology outcome using partial correlations were estimated separately using the same steps outlined above.

Moderators

Hypotheses regarding moderators were not included in the preregistration. To ensure adequate representation at each level of the dichotomous moderators, we examined coded variables as a moderator only if a minimum of five studies provided effect sizes in each cell. Continuous moderators without a meaningful zero point were centered prior to analyses for ease of interpretation. Given the large number of moderators, a Bonferroni correction was applied to the alpha level (i.e., .05 divided by the number of individual moderators), yielding a family-wise error rate for each meta-analytic effect. A three-level model was fitted for each individual moderator, followed by a final three-level model that included all significant moderators based on the family-wise error rate. This method minimizes Type II error when testing categorical moderators and addresses potential multicollinearity among moderators (Assink & Wibbelink, Reference Assink and Wibbelink2016; Weisz et al., Reference Weisz, Kuppens, Eckshtain, Ugueto, Hawley and Jensen-Doss2013). For each significant dichotomous moderator in the final three-level model, follow-up analyses were conducted to estimate the effect size of interest at each level. Because adversity measurement variables were coded separately for threat and deprivation, only study and sample characteristics and adversity indicators were examined as moderators of the pooled estimate of the threat-deprivation correlation.

Publication bias

We used multiple methods to assess potential publication bias. First, we visually inspected funnel plots for asymmetry. We then conducted Egger’s test that examines whether sampling variance moderates the meta-analytic effect, a method recommended specifically for multilevel meta-analytic models (Rodgers & Pustejovsky, Reference Rodgers and Pustejovsky2021).

Results

Search results

Our initial search in the two databases yielded a total of 6323 records, from which 1940 duplicate records were removed. Of the remaining 4883 records, we excluded 4165 during title and abstract screening. Of the remaining 718 articles, we excluded 591 articles during full-text screening, resulting in a total of 127 articles included in the current meta-analysis (see Figure 1). All articles were published in peer-reviewed journals between 1984 and 2021. Among the 127 articles identified, 8 articles used data from LONGSCAN, 5 from Fragile Families and Well-Being Study, 3 from National Survey of Child and Adolescent Well-being (NSCAW) Study, and 2 from the ABCD Study. Additionally, 9 articles were comprised of 2 unique subsamples each, which were considered to be independent cohorts in our analyses though counted as the same article in the article count. In all, 122 unique cohorts totaling N = 163,767 individual participants contributed 2028 effect sizes in the current meta-analysis. See Table 1 for characteristics of studies included in meta-analysis.

Figure 1. PRISMA flow diagram.

Internalizing psychopathology

Overall effects (k = 98, ESs = 598, N = 127,071)

The overall effect between adversity and internalizing psychopathology was positive and significant, r = .21, 95% CI [.18, .23], t = 17.20, p < .001. There was significant heterogeneity within, σ2 = 0.019, χ2(2) = 5231.39, p < .001, and between, σ2 = 0.014, χ2(2) = 179.19, p < .001, studies, as indicated by one-sided log-likelihood ratio tests. From the total variance, 4.53% was attributed to within-study sampling variance (i.e., Level 1), 43.22% to within-study variance (i.e., Level 2), and 52.25% to between-study variance (i.e., Level 3). The overall proportion of variability in effect sizes attributed to differences between and within studies and not due to sampling error was 95.47%.

Moderation by adversity dimension

Adversity dimension (i.e., threat versus deprivation) was a significant moderator of the meta-analytic associations between adversity and internalizing psychopathology, F(1, 596) = 4.757, p = .030, such that the effect size for threat was significantly larger than the effect size for deprivation. The correlations between threat and internalizing psychopathology, r = .21, 95% CI [.19, .24], t = 17.02, p < .001, and deprivation and internalizing psychopathology, r = .19, 95% CI [.17, .22], t = 14.55, p < .001, were both positive and significant.

Other moderators

The final multilevel model with moderators included 11 variables based on a family-wise error rate of .001 (see Table S2 for results of individual moderator analyses). The overall model was significant, F(11, 584) = 15.892, p < .001. Full results of the final model are presented in Table S3. Estimated effect sizes between adversity and internalizing psychopathology were larger for effects that included emotional abuse in the measurement of threat (vs. effects that did not include emotional abuse), included emotional neglect in the measurement of deprivation (vs. those that did not), used multiple indicators of adversity (vs. a single indicator), used youth self-report of adversity (vs. no youth self-report), and where depression was the type of internalizing psychopathology (vs. anxiety, somatic symptoms, or internalizing symptoms broadly). The estimated effect size between adversity and internalizing psychopathology was smaller for effects that included miscellaneous deprivation indicators (e.g., low parental education, single-parent household) in their measurement of deprivation than for effects that did not include miscellaneous indicators. Inclusion of poverty as a deprivation indicator, timing of adversity in late adolescence, inclusion of records review in the measurement of adversity, inclusion of self-reported psychopathology, and time since adversity did not significantly moderate the association between adversity and psychopathology in the final model. Results of follow-up analyses yielding effect sizes at each level of significant categorical moderators are summarized in Table 2.

Table 1. Characteristics of Studies Included in Meta-Analysis (k = 127)

Table 2. Results of follow-up analyses examining significant moderators of the association between adversity and internalizing psychopathology

Note. Some studies provided effect sizes at both levels of categorical moderators.

Externalizing psychopathology

Overall effects (k = 69, ESs = 386, N = 82,603)

The overall effect between adversity and externalizing psychopathology was also positive and significant, r = .20, 95% CI [.16, .23], t = 11.64, p < .001. There was significant heterogeneity within, σ2 = 0.020, χ2(2) = 4626.62, p < .001, and between, σ2 = 0.019, χ2(2) = 153.66, p < .001, studies, as indicated by one-sided log-likelihood ratio tests. From the total variance, 4.13% was attributed to within-study sampling variance (i.e., Level 1), 32.13% to within-study variance (i.e., Level 2), and 36.74% to between-study variance (i.e., Level 3). The overall proportion of variability in effect sizes attributed to differences between and within studies and not due to sampling error was 95.87%.

Moderation by adversity dimension

Adversity dimension (i.e., threat versus deprivation) was again a significant moderator of the meta-analytic associations between adversity and externalizing symptoms, F(1, 384) = 37.072, p < .001, such that the effect size for threat was significantly larger than the effect size for deprivation. The pooled correlations between threat and externalizing, r = .22, 95% CI [.19, .25], t = 12.74, p < .001, and deprivation and externalizing, r = .16, 95% CI [.12, .19], t = 8.70, p < .001, problems were both positive and significant.

Other moderators

Based on a family-wise error rate of .001, the final multilevel model with moderators for externalizing problems included 3 variables: multiple indicators of adversity (versus single), inclusion of youth self-report of adversity, and inclusion of emotional abuse as a threat indicator (see Table S4 for results of individual moderator analyses). The overall model (see Table S5) was significant, F(3, 382) = 18.151, p < .001. Estimated effect sizes between adversity and externalizing psychopathology were larger for effects that included emotional abuse in the measurement of threat (vs. effects that did not include emotional abuse), used multiple indicators of adversity (vs. a single indicator), and included youth self-report of adversity (vs. no youth self-report). Results of follow-up analyses yielding estimated effect sizes at each level of significant categorical moderators are summarized in Table 4.

PTSD symptoms

Overall effects (k = 25, ESs = 121, N = 31,757)

The overall effect between adversity and PTSD symptoms was again positive and significant, r = .23, 95% CI [.17, .29], t = 7.26, p < .001. There was significant heterogeneity within, σ2 = 0.020, χ2(2) = 602.03, p < .001, and between, σ2 = 0.025, χ2(2) = 32.88, p < .001, studies, as indicated by one-sided log-likelihood ratio tests. Of the total variance, 2.72% was attributed to within-study sampling variance (i.e., Level 1), 29.76% to within-study variance (i.e., Level 2), and 65.62% to between-study variance (i.e., Level 3). The overall proportion of variability in effect sizes attributed to differences between and within studies and not due to sampling error was 97.28%.

Moderation by adversity dimension

Adversity dimension was a significant moderator of the meta-analytic associations between adversity and PTSD, F(1, 119) = 25.45, p < .001 symptoms, such that the effect size for threat was significantly larger than the effect size for deprivation. Consistent with the results for internalizing and externalizing psychopathology, the correlations between threat and PTSD symptoms, r = .26, 95% CI [.20, .32], t = 7.92, p < .001, and deprivation and PTSD symptoms, r = .17, 95% CI [.10, .23], t = 4.72, p < .001, were both positive and significant.

Other moderators

Based on a family-wise error rate of .002, the final multilevel model with moderators for PTSD symptoms only included youth self-report of adversity (see Table S6 for results of individual moderator analyses). The overall model was significant, F(1, 119) = 10.459, p = .002, Estimated effect sizes between adversity and PTSD symptoms were larger for effects that used youth self-report of adversity versus for those that did not use youth self-report. Results of follow-up analyses yielding estimates of effect sizes at each level of significant categorical moderators are summarized in Table 3.

Table 3. Results of follow-up analyses examining significant moderators of the association between adversity and externalizing psychopathology

Note. Some studies provided effect sizes at both levels of categorical moderators.

Table 4. Results of follow-up analyses examining significant moderators of the association between adversity and PTSD symptoms

Note. Some studies provided effect sizes at both levels of categorical moderators.

Threat and deprivation

Overall effects (k = 105, ESs = 401, N = 138,436)

The overall correlation between threat and deprivation was positive and significant, r = .29, 95% CI [.25, .33], t = 14.13, p < .001. There was significant heterogeneity within, σ2 = 0.046, χ2(2) = 6397.55, p < .001, and between, σ2 = 0.046, χ2(2) = 151.33, p < .001, studies, as indicated by one-sided log-likelihood ratio tests. Of the total variance, 1.63% was attributed to within-study sampling variance (i.e., Level 1), 35.16% to within-study variance (i.e., Level 2), and 63.20% to between-study variance (i.e., Level 3). The overall proportion of variability in effect sizes attributed to differences between and within studies and not due to sampling error was 98.37%.

Moderator analyses

The final multilevel model with moderators for the effect between threat and deprivation included sexual abuse, emotional abuse, community violence, and poverty based on a family-wise error rate of .003 (see Table S8 for results of individual moderator analyses). The overall model was significant, F(4, 396) = 29.58, p < .001 (see Table S9). Estimated effect sizes of the association between threat and deprivation were larger for effects that included emotional abuse as a threat indicator versus effects that did not include emotional abuse. The estimated effect size was smaller for effects that included sexual abuse and community violence as threat indicators (vs. effects that did not include these indicators) and for effects that included poverty as a deprivation indicator (vs. effects that did not include poverty). See Table 5 for the results of follow-up analyses with estimated effect sizes at each level of the significant moderators.

Table 5. Results of follow-up analyses examining significant moderators of the association between threat and deprivation

Publication bias

Egger’s test did not indicate concern for publication bias for any of the meta-analytic effects between adversity and psychopathology, Q moderation = 2.547, p = .110 for internalizing, Q moderation = 1.365, p = .243 for externalizing, Q moderation = 0.002, p = .966 for PTSD symptoms, nor for threat-deprivation correlation, Q moderation = 0.552, p = .457. See Figure S1 for the funnel plots.

Meta-analyses with partial correlations

The results of meta-analyses using partial correlations, along with the corresponding effects using bivariate correlations, are summarized in Figure 2.

Figure 2. Summary forest plot of multilevel meta-analytic effects between adversity dimensions and youth psychopathology using bivariate and partial correlations. Note. k = number of studies, ES = number of effect sizes. Partial correlations account for the correlation between threat and deprivation.

Outcome: Internalizing psychopathology

Threat ( k = 77, ESs = 276). The overall correlation between threat and internalizing psychopathology using partial correlations (i.e., accounting for the overlap between threat and deprivation) was positive and significant, r = .19, 95% CI [.16, .21], t = 13.46, p < .001. There was significant heterogeneity within, σ2 = 0.015, χ2(2) = 1871.61, p < .001, and between, σ2 = 0.015, χ2(2) = 51.45, p < .001, studies, as indicated by one-sided log-likelihood ratio tests. Of the total variance, 5.05% was attributed to within-study sampling variance (i.e., Level 1), 44.60% to within-study variance (i.e., Level 2), and 50.36% to between-study variance (i.e., Level 3). The overall proportion of variability in effect sizes attributed to differences between and within studies and not due to sampling error was 94.95%.

Deprivation ( k = 77, ESs = 175). The overall correlation between deprivation and internalizing psychopathology using partial correlations was positive and significant, r = .12, 95% CI [.09, .15], t = 7.85, p < .001. There was significant heterogeneity within, σ2 = 0.017, χ2(2) = 1062.23, p < .001, and between, σ2 = 0.015, χ2(2) = 32.71, p < .001, studies, as indicated by one-sided log-likelihood ratio tests. Of the total variance, 5.00% was attributed to within-study sampling variance (i.e., Level 1), 39.12% to within-study variance (i.e., Level 2), and 55.89% to between-study variance (i.e., Level 3). The overall proportion of variability in effect sizes attributed to differences between and within studies and not due to sampling error was 95.00%.

Outcome: Externalizing psychopathology

Threat ( k = 56, ESs = 183). The overall correlation between threat and externalizing psychopathology using partial correlations was positive and significant, r = .21, 95% CI [.15, .26], t = 7.49, p < .001. There was significant heterogeneity within, σ2 = 0.020, χ2(2) = 451.38, p < .001, and between, σ2 = 0.043, χ2(2) = 70.14, p < .001, studies, as indicated by one-sided log-likelihood ratio tests. Of the total variance, 2.66% was attributed to within-study sampling variance (i.e., Level 1), 11.00% to within-study variance (i.e., Level 2), and 86.34% to between-study variance (i.e., Level 3). The overall proportion of variability in effect sizes attributed to differences between and within studies and not due to sampling error was 97.34%.

Deprivation ( k = 56, ESs = 115). The overall correlation between deprivation and externalizing psychopathology using partial correlations was positive and significant, r = .08, 95% CI [.03, .12], t = 3.26, p = .001. There was significant heterogeneity within, σ2 = 0.019, χ2(2) = 253.37, p < .001, and between, σ2 = 0.029, χ2(2) = 47.80, p < .001, studies, as indicated by one-sided log-likelihood ratio tests. Of the total variance, 3.04% was attributed to within-study sampling variance (i.e., Level 1), 7.93% to within-study variance (i.e., Level 2), and 89.03% to between-study variance (i.e., Level 3). The overall proportion of variability in effect sizes attributed to differences between and within studies and not due to sampling error was 96.96%.

Outcome: PTSD symptoms

Threat ( k = 24, ESs = 80). The overall correlation between threat and PTSD symptoms using partial correlations was positive and significant, r = .23, 95% CI [.18, .28], t = 13.46, p < .001. There was significant heterogeneity within, σ2 = 0.013, χ2(2) = 212.00, p < .001, and between, σ2 = 0.015, χ2(2) = 11.97, p < .001, studies, as indicated by one-sided log-likelihood ratio tests. Of the total variance, 8.14% was attributed to within-study sampling variance (i.e., Level 1), 38.92% to within-study variance (i.e., Level 2), and 52.94% to between-study variance (i.e., Level 3). The overall proportion of variability in effect sizes attributed to differences between and within studies and not due to sampling error was 91.86%.

Deprivation ( k = 24, ESs = 39). The overall correlation between threat and PTSD symptoms using partial correlations was positive and significant, r = .08, 95% CI [.03, .12], t = 3.17, p = .003. There was significant heterogeneity within, σ2 = 0.011, χ2(2) = 48.36, p < .001, and between, σ2 = 0.010, χ2(2) = 15.67, p < .001, studies, as indicated by one-sided log-likelihood ratio tests. Of the total variance, 6.55% was attributed to within-study sampling variance (i.e., Level 1), 14.07% to within-study variance (i.e., Level 2), and 64.70% to between-study variance (i.e., Level 3). The overall proportion of variability in effect sizes attributed to differences between and within studies and not due to sampling error was 78.77%.

Discussion

The primary aims of our meta-analysis were to (1) synthesize the direct associations between childhood adversity and youth psychopathology, (2) test whether adversity dimension moderates this association, and (3) examine the magnitude of overall effects when accounting for the overlap between threat and deprivation (i.e., using partial correlations). We additionally examined effect size- and study-level moderators. When considering childhood adversity across both dimensions of threat and deprivation, we found significant overall effects between exposure to adversity and internalizing, externalizing, and PTSD symptoms during childhood and adolescence (r = .21, .20, and .23, respectively). Adversity dimension moderated each of these effects, such that the effects between threat and psychopathology (r = .21, .22, and .26 for internalizing, externalizing, and PTSD symptoms, respectively) were consistently larger than the corresponding effects between deprivation and each psychopathology outcome (r = .19, .16, and .17, respectively). These differences appear to be consistent with findings from studies comparing the direct effects of threat and deprivation and psychopathology during childhood and adolescence (Miller et al., Reference Miller, Sheridan, Hanson, McLaughlin, Bates, Lansford, Pettit and Dodge2018, Reference Miller, Machlin, McLaughlin and Sheridan2021). Simultaneously, partly inconsistent with these studies, our results indicated that both dimensions were significantly associated with youth psychopathology despite the significant moderation effect of adversity dimension. These findings can be understood within the DMAP framework as support for differential effects of threat and deprivation on developmental outcomes, and specifically, the purported direct effects of threat on socioemotional outcomes. Specifically, repeated exposure to threatening experiences may lead to neurodevelopmental alterations reflecting overresponsiveness to threat, directly affecting emotional reactivity and regulation. Such alterations reflect adaptive responses to dangerous environmental conditions but may become maladaptive in safer contexts, ultimately leading to increased risk for psychopathology.

Threat and deprivation often co-occur, making it important to assess both dimensions of adversity when examining their downstream effects. When synthesizing effects using partial correlations, which allowed us to account for the overlap between threat and deprivation, the meta-analytic effects between threat and psychopathology (r = .18, .20, and .23 for internalizing, externalizing, and PTSD symptoms, respectively), and deprivation and psychopathology (r = .12, .08, and .07), were each attenuated though remained positive and significant. Interestingly, though not directly tested, the degree of attenuation appeared larger for deprivation than for threat. For internalizing psychopathology specifically, the 95% confidence intervals for the pooled estimate with deprivation using bivariate correlations and the pooled effect using partial correlations did not overlap, suggesting that these estimates may significantly differ from one another. The same pattern of attenuation was present for externalizing and PTSD symptoms; although these estimates had overlapping confidence intervals with the corresponding effects using bivariate correlations, they were attenuated by half or more of the original estimated effect. In contrast, the attenuation observed was consistently smaller in magnitude for threat, with confidence intervals overlapping between pooled estimates for bivariate and partial correlations across the three outcomes.

These findings from meta-analyses conducted with partial correlations highlight the strong possibility that researchers could overestimate the association between experiences reflecting deprivation and youth psychopathology when they do not account for co-occurring experiences of threat. Thus, researchers should exercise caution when interpreting such effects and attempt to assess experiences consistent with both dimensions of adversity whenever possible. This recommendation is particularly important when the goal is to parse the potential differential effects of adversity dimensions on developmental outcomes, including emotional and behavioral outcomes. The tendency for effect overestimation appears to be less problematic for threat and youth psychopathology, perhaps because of the purported direct impact of threat on emotion processing and therefore on mental health. The links between early life threat-related adversity and socioemotional development also serve to explain the finding that the effects between threat and each youth psychopathology outcome were larger than those between deprivation and psychopathology.

Moderator analyses

Across all pooled estimates, we found significant heterogeneity within and between studies that were not explained by random sampling error. Tests of study- and sample-level moderators yielded important insights. Among the threat indicators, only emotional abuse emerged as a significant moderator between threat and both internalizing and externalizing problems, such that the studies that included emotional abuse in the measurement of threat had larger estimated effects between adversity and psychopathology than studies that did not include emotional abuse. Among deprivation indicators, inclusion of emotional neglect in the measurement of deprivation was similarly a significant moderator of the association between deprivation and internalizing symptoms only, with inclusion of emotional neglect again associated with larger effects compared to when it was not included. The inclusion of emotional abuse or emotional neglect were not significant moderators of the association between threat and deprivation, respectively, with PTSD symptoms. However, we note that given the relatively small number of studies examining PTSD symptoms and the smaller effect observed between deprivation and PTSD symptoms, it is possible that we were underpowered to detect a similar moderating roles of emotional abuse or emotional neglect for PTSD symptoms. Nonetheless, the results demonstrated the importance of including these two specific types of adversity when measuring experiences along the dimensions of threat and deprivation. Emotional maltreatment may be present across multiple types of adversity (e.g., other types of maltreatment), and thus be more robustly associated with youth mental health outcomes. It is also possible that the emotional (versus physical) nature of these adversities more directly impacts youth socioemotional development, predisposing them to emotional pathology. This finding is also consistent with recent studies highlighting the relative importance of emotional maltreatment in predicting youth mental health outcomes (e.g., Schlensog-Schuster et al., Reference Schlensog-Schuster, Keil, Von Klitzing, Gniewosz, Schulz, Schlesier-Michel, Mayer, Stadelmann, Döhnert, Klein, Sierau, Manly, Sheridan and White2022). Additionally, the use of miscellaneous indicators of deprivation (e.g., low parental education, single-parent household, forced displacement) resulted in the attenuation of the estimated effect between adversity and internalizing, but not externalizing or PTSD, symptoms. These results underscore the importance of including indicators that map directly onto material, cognitive, or emotional deprivation when assessing this dimension of adversity, rather than proxies of deprivation or adversities that may be linked with a general risk for adversity broadly or be more consistent with unpredictability, a recently proposed third dimension of adversity (Usacheva et al., Reference Usacheva, Choe, Liu, Timmer and Belsky2022).

Regarding adversity measurement, the use of youth self-report was consistently associated with larger estimated effects between adversity and internalizing, externalizing, and PTSD symptoms, whereas the use of multiple indicators (versus single) of adversity was associated with larger estimated effects for internalizing, but not externalizing or PTSD symptoms. These results are similar to meta-analytic findings on youth psychopathology assessment, which have found larger effects for youth self-report when compared to parent- or teacher-report (Huang, Reference Huang2017). Importantly, our results do not support excluding other sources of information given that we coded youth-, caregiver-, and records review as separate variables (i.e., the inclusion of self-report did not preclude the inclusion of other informants). Rather, they suggest that youth should be considered key informants of their own adverse experiences and their perspectives directly assessed in studies when measuring adversity dimensions. Indeed, recent studies have drawn attention to the importance of individuals’ own perceptions of stressful or adverse experiences, with implications for downstream effects on mental health. The results additionally underscore the importance of including multiple types of experiences within each dimension and further characterizing the variability within these experiences (e.g., severity, frequency), which is consistent with DMAP (see Berman et al., Reference Berman, McLaughlin, Tottenham, Godfrey, Seeman, Loucks, Suomi, Danese and Sheridan2022).

Regarding psychopathology assessment variables, the effect between adversity and internalizing psychopathology was larger when depression was the outcome assessed, compared to when the outcome was internalizing spectrum, anxiety, or somatic symptoms. This suggests the possibility of a specific vulnerability to depression that is associated with childhood adversity, which appears consistent with the evidence of shared putative biomarkers between depression and adversity (Ho & King, Reference Ho and King2021). No variables pertaining to psychopathology assessment were significant moderators of the association between adversity and externalizing or PTSD symptoms.

Moderator analyses for the pooled correlation between threat and deprivation, which was positive and significant, r = .29, indicated that the inclusion of emotional abuse as a threat indicator again strengthened the association between threat and deprivation compared to when emotional abuse was not included. In contrast, the inclusion of sexual abuse and community violence as indicators of threat was each associated with smaller estimated effects than when they were not included. Finally, the inclusion of poverty as a deprivation indicator was linked with smaller estimated effect between threat and deprivation, which may be due to poverty being associated with both dimensions of adversity.

Study strengths and limitations

The current meta-analysis was well-powered, allowing the inclusion of a large number of effect sizes across many studies, particularly for internalizing and externalizing psychopathology. Using a multilevel meta-analytic approach allowed us to examine multiple effect sizes within the same study, and analyses of publication bias demonstrated minimal concern for the impact of such bias on our findings. Additionally, the studies included in our meta-analysis reflected a diverse number of contexts (30 countries), with nearly half (45%) of the included studies conducted outside of North America. Thus, the results obtained are expected to be generalizable to a broad range of developmental contexts.

We also note several limitations of our study. First, the meta-analytic effects estimated here do not represent causal links between adversity and youth psychopathology due to the cross-sectional design of many included studies. More studies employing prospective and quasi-experimental designs are needed to accurately estimate the potential causal effects between threat, deprivation, and psychopathology (e.g., Baldwin et al., Reference Baldwin, Wang, Karwatowska, Schoeler, Tsaligopoulou, Munafò and Pingault2023). Second, it is possible that the exclusion of eligible studies with insufficient data biased our results, given that null associations may have been more likely in these studies. Third, perhaps because we took a conservative approach to evaluating the significance of individual moderators by employing a family-wise error rate, significant heterogeneity remained for all pooled estimates even after accounting for effects of moderators (see Results S1S4). Thus, variability across studies not captured in the variables examined as moderators here should be considered when interpreting the relevance of the meta-analytic findings and explored further in future studies. Finally, the number of studies examining links between adversity and youth PTSD symptoms was the lowest among our three psychopathology outcomes. This is worth noting because PTSD symptoms capture unique trauma-specific symptomatology that frequently present and co-occur with other internalizing and externalizing problems in youth with chronic histories of adversity (Grasso et al., Reference Grasso, Dierkhising, Branson, Ford and Lee2016). Prior research has demonstrated specific links between experiences of threat and later PTSD symptoms (Milojevich et al., Reference Milojevich, Norwalk and Sheridan2019). Future studies conducted with youth who have experienced adversity should assess PTSD symptoms, along with internalizing and externalizing problems to better understand the links between adversity dimensions and trauma-specific symptoms.

Implications for research, practice, and policy

Our findings are consistent with DMAP and extend prior empirical studies providing support for threat and deprivation as two key dimensions of childhood adversity. Findings suggest differing magnitudes of associations between threat and deprivation with youth psychopathology across internalizing, externalizing, and PTSD symptoms, such that threat appears more strongly associated with psychopathology outcomes, highlighting important considerations for future research. First, we note the crucial importance of assessing co-occurring threat and deprivation to ensure accurate estimation of the links between each dimension and youth psychopathology. Second, researchers must attend to the role of emotional forms of adversity, such as emotional abuse and neglect, as a transdiagnostic risk factor for youth psychopathology. This is consistent with Baldwin et al.’s (Reference Baldwin, Wang, Karwatowska, Schoeler, Tsaligopoulou, Munafò and Pingault2023) recent meta-analysis of quasi-experimental studies, which showed that emotional abuse resulted in stronger estimates of effects between childhood maltreatment and mental health outcomes relative to other forms of maltreatment, suggesting a potential causal effect. Given that threat is proposed to directly impact neurobiological pathways relevant to emotional processing, emotional abuse may be a particularly influential form of threat implicated in the development of mental health symptoms among youth, whereas emotional neglect may be a form of deprivation linked more strongly with internalizing symptoms when compared to other forms of deprivation (e.g., cognitive, material). Third, adversities based on racial ethnic minority status and other marginalized identities (e.g., racial trauma, discrimination, lack of access to healthcare) should be directly measured and studied in relation to youth mental health outcomes in future studies. Fourth, clearly defining the boundary conditions of deprivation appears to be an important direction for future research, given that less well-defined and/or widely accepted indicators of deprivation (e.g., study-specific indicators) resulted in the attenuation of the effect between deprivation and some forms of psychopathology in our meta-analysis. Future research should strive to clarify whether these forms of adversity are more consistent with other dimensions of adversity such as unpredictability and/or serve as a risk factor for adversities across all dimensions. Finally, when assessing adversity, researchers should employ measures that rely on continuous rather than dichotomous (i.e., absence or presence of an experience) characterization of adverse experiences, use multiple indicators to assess adversity dimensions and incorporate youth self-report whenever feasible.

For clinical practice, our findings suggest that in addition to the developmental pathways by which childhood adversity indirectly affects youth mental health trajectories, adversity across dimensions of threat and deprivation may be directly and broadly linked with youth mental health symptoms. Given the high prevalence of childhood adversity, a comprehensive and validated assessment of these experiences in treatment settings could facilitate accurate conceptualization of the mental health difficulties of individual youth, with implications for delivering effective treatment and improving trajectories of mental health functioning. Histories of threatening experiences, when accompanied by trauma-specific symptoms, are particularly likely to warrant and benefit from emotional processing via evidence-based therapies such as Trauma-Focused Cognitive Behavioral Therapy (Cohen et al., Reference Cohen, Mannarino and Deblinger2017). Despite the documented effectiveness of such therapies, disparities in both rates of adversity exposures and access to mental health treatments persist for marginalized populations such as youth of color in the U.S. and families living in poverty globally. To this end, policies that fund and support wider dissemination of well-established trauma-specific therapies and adaptation of such treatments for specific communities are needed to improve the mental health problems of youth who chronically experience such adversities. Support of such policies, in turn, has the potential to address longstanding inequities in mental health.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/S0954579424000737.

Acknowledgments

The authors thank Laura Perrone for providing consultation and resources in conducting a multilevel meta-analysis.

Funding statement

This research received no specific grant from any funding agency, commercial, or not-for-profit sectors.

Competing interests

The authors declare none.

Footnotes

Yukihiro Kitagawa, Rebecca Mirhashem, and Micaela Rodriguez contributed equally to this work.

References

References

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). American Psychiatric Press.Google Scholar
Assink, M., & Wibbelink, C. J. M. (2016). Fitting three-level meta-analytic models in R: A step-by-step tutorial. The Quantitative Methods for Psychology, 12(3), 154174. https://doi.org/10.20982/tqmp.12.3.p154 CrossRefGoogle Scholar
Baldwin, J. R., Wang, B., Karwatowska, L., Schoeler, T., Tsaligopoulou, A., Munafò, M. R., & Pingault, J.-B. (2023). Childhood maltreatment and mental health problems: a systematic review and meta-analysis of quasi-experimental studies. American Journal of Psychiatry, 180(2), 117126. https://doi.org/10.1176/appi.ajp.20220174 CrossRefGoogle ScholarPubMed
Berman, I. S., McLaughlin, K. A., Tottenham, N., Godfrey, K., Seeman, T., Loucks, E., Suomi, S., Danese, A. & Sheridan, M. A. (2022). Measuring early life adversity: A dimensional approach. Development and Psychopathology, 34(2), 499511.CrossRefGoogle ScholarPubMed
Bos, K., Zeanah, C. H., Fox, N. A., Drury, S. S., McLaughlin, K. A., & Nelson, C. A. (2011). Psychiatric outcomes in young children with a history of institutionalization. Harvard Review of Psychiatry, 19(1), 1524. https://doi.org/10.3109/10673229.2011.549773 CrossRefGoogle ScholarPubMed
Cohen, J. A., Mannarino, A. P., & Deblinger, E. (2017). Treating trauma and traumatic grief in children and adolescents. Guilford Publications.Google Scholar
Colich, N. L., Rosen, M. L., Williams, E. S., & McLaughlin, K. A. (2020). Biological aging in childhood and adolescence following experiences of threat and deprivation: A systematic review and meta-analysis. Psychological Bulletin, 146(9), 721.CrossRefGoogle ScholarPubMed
Compas, B. E., Jaser, S. S., Bettis, A. H., Watson, K. H., Gruhn, M. A., Dunbar, J. P., Williams, E., & Thigpen, J. C. (2017). Coping, emotion regulation, and psychopathology in childhood and adolescence: A meta-analysis and narrative review. Psychological Bulletin, 143(9), 939991. https://doi.org/10.1037/bul0000110 CrossRefGoogle ScholarPubMed
Demidenko, M. I., Kelly, D. P., Hardi, F. A., Ip, K. I., Lee, S., Becker, H., Hong, S., Thijssen, S., Luciana, M., Keating, D. P. (2022). Mediating effect of pubertal stages on the family environment and neurodevelopment: An open-data replication and multiverse analysis of an ABCD Study®. Neuroimage: Reports, 2(4), 100133. https://doi.org/10.1016/j.ynirp.2022.100133 CrossRefGoogle ScholarPubMed
Evans, G. W., Li, D., & Whipple, S. S. (2013). Cumulative risk and child development. Psychological Bulletin, 139(6), 13421396. https://doi.org/10.1037/a0031808 CrossRefGoogle ScholarPubMed
Forbes, M. K., Watts, A. L., Twose, M., Barrett, A., Hudson, J., Lyneham, H., McLellan, L., Newton, N., Sicouri, G., Chapman, C., McKinnon, A., Rapee, R., Slade, T., Teesson, M., Markon, K. E., & Sunderland, M. (2023). A hierarchical model of the symptom-level structure of psychopathology in youth. https://doi.org/10.31234/osf.io/7kcfz CrossRefGoogle Scholar
Grasso, D. J., Dierkhising, C. B., Branson, C. E., Ford, J. D., & Lee, R. (2016). Developmental patterns of adverse childhood experiences and current symptoms and impairment in youth referred for trauma-specific services. Journal of Abnormal Child Psychology, 44(5), 871886. https://doi.org/10.1007/s10802-015-0086-8 CrossRefGoogle ScholarPubMed
Guyon-Harris, K. L., Humphreys, K. L., & Zeanah, C. H. (2021). Adverse caregiving in early life: The trauma and deprivation distinction in young children. Infant Mental Health Journal, 42(1), 8795. https://doi.org/10.1002/imhj.21892 CrossRefGoogle ScholarPubMed
Higgins, J. P. T., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003). Measuring inconsistency in meta-analyses. BMJ: British Medical Journal, 327(7414), 557560.CrossRefGoogle ScholarPubMed
Ho, T. C., & King, L. S. (2021). Mechanisms of neuroplasticity linking early adversity to depression: Developmental considerations. Translational Psychiatry, 11(1), 517. https://doi.org/10.1038/s41398-021-01639-6 CrossRefGoogle ScholarPubMed
Huang, C. (2017). Cross-informant agreement on the child behavior checklist for youths: a meta-analysis. Psychological Reports, 120(6), 10961116. https://doi.org/10.1177/0033294117717733 CrossRefGoogle ScholarPubMed
Jackson, Y., McGuire, A., Tunno, A. M., & Makanui, P. K. (2019). A reasonably large review of operationalization in child maltreatment research: Assessment approaches and sources of information in youth samples. Child Abuse & Neglect, 87, 517. https://doi.org/10.1016/j.chiabu.2018.09.016 CrossRefGoogle ScholarPubMed
Jeong, H. J., Moore, T. M., Durham, E. L., Reimann, G. E., Dupont, R. M., Cardenas-Iniguez, C., Berman, M. G., Lahey, B. B., & Kaczkurkin, A. N. (2023). General and specific factors of environmental stress and their associations with brain structure and dimensions of psychopathology. Biological Psychiatry Global Open Science, 3(3), 480489. https://doi.org/10.1016/j.bpsgos.2022.04.004 CrossRefGoogle ScholarPubMed
Johnson, D., Policelli, J., Li, M., Dharamsi, A., Hu, Q., Sheridan, M. A., McLaughlin, K. A., & Wade, M. (2021). Associations of early-life threat and deprivation with executive functioning in childhood and adolescence: a systematic review and meta-analysis. JAMA Pediatrics, 175(11), e212511. https://doi.org/10.1001/jamapediatrics.2021.2511 CrossRefGoogle ScholarPubMed
Jose, J. P., & Cherayi, S. J. (2020). Effect of parental alcohol abuse severity and child abuse and neglect on child behavioural disorders in Kerala. Child Abuse & Neglect, 107. https://doi.org/10.1016/j.chiabu.2020.104608 CrossRefGoogle Scholar
Kasparek, S. W., Jenness, J. L., & McLaughlin, K. A. (2020). Reward processing modulates the association between trauma exposure and externalizing psychopathology. Clinical Psychological Science, 8(6), 9891006. https://doi.org/10.1177/2167702620933570 CrossRefGoogle ScholarPubMed
Kessler, R. C., McLaughlin, K. A., Green, J. G., Gruber, M. J., Sampson, N. A., Zaslavsky, A. M., Aguilar-Gaxiola, S., Alhamzawi, A. O., Alonso, J., Angermeyer, M., Benjet, C., Bromet, E., Chatterji, S., de Girolamo, G., Demyttenaere, K., Fayyad, J., Florescu, S., Gal, G., Gureje, O., Haro, J. M., Hu, C. Y., Karam, E. G., Kawakami, N., Lee, S., Lépine, J.-P., Ormel, J., Posada-Villa, J., Sagar, R., Tsang, A., Ustün, T. B., Vassilev, S., Viana, M. C., Williams, D. R. (2010). Childhood adversities and adult psychopathology in the WHO World Mental Health Surveys. British Journal of Psychiatry, 197(5), 378385. https://doi.org/10.1192/bjp.bp.110.080499 CrossRefGoogle ScholarPubMed
Knapp, G., & Hartung, J. (2003). Improved tests for a random effects meta- regression with a single covariate. Statistics in Medicine, 22(17), 26932710. https://doi.org/10.1002/sim.1482 CrossRefGoogle ScholarPubMed
McCrory, E. J., & Viding, E. (2015). The theory of latent vulnerability: Reconceptualizing the link between childhood maltreatment and psychiatric disorder. Development and Psychopathology, 27(2), 493505. https://doi.org/10.1017/S0954579415000115 CrossRefGoogle ScholarPubMed
McLaughlin, K. A., Green, J. G., Gruber, M. J., Sampson, N. A., Zaslavsky, A. M., & Kessler, R. C. (2012). Childhood adversities and first onset of psychiatric disorders in a national sample of US adolescents. JAMA Psychiatry, 69(11), 11511160.Google Scholar
McLaughlin, K. A., & Sheridan, M. A. (2016). Beyond cumulative risk: A dimensional approach to childhood adversity. Current Directions in Psychological Science, 25(4), 239245. https://doi.org/10.1177/0963721416655883 CrossRefGoogle Scholar
McLaughlin, K. A., Sheridan, M. A., Gold, A. L., Duys, A., Lambert, H. K., Peverill, M., Heleniak, C., Shechner, T., Wojcieszak, Z., & Pine, D. S. (2016). Maltreatment exposure, brain structure, and fear conditioning in children and adolescents. Neuropsychopharmacology, 41(8), 19561964. https://doi.org/10.1038/npp.2015.365 CrossRefGoogle ScholarPubMed
McLaughlin, K. A., Sheridan, M. A., & Lambert, H. K. (2014). Childhood adversity and neural development: Deprivation and threat as distinct dimensions of early experience. Neuroscience & Biobehavioral Reviews, 47, 578591. https://doi.org/10.1016/j.neubiorev.2014.10.012 CrossRefGoogle ScholarPubMed
Miller, A. B., Machlin, L., McLaughlin, K. A., & Sheridan, M. A. (2021). Deprivation and psychopathology in the Fragile Families Study: A 15-year longitudinal investigation. Journal of Child Psychology and Psychiatry, 62(4), 382391. https://doi.org/10.1111/jcpp.13260 CrossRefGoogle ScholarPubMed
Miller, A. B., Sheridan, M. A., Hanson, J. L., McLaughlin, K. A., Bates, J. E., Lansford, J. E., Pettit, G. S., & Dodge, K. A. (2018). Dimensions of deprivation and threat, psychopathology, and potential mediators: A multi-year longitudinal analysis. Journal of Abnormal Psychology, 127(2), 160170. https://doi.org/10.1037/abn0000331 CrossRefGoogle ScholarPubMed
Milojevich, H. M., Norwalk, K. E., & Sheridan, M. A. (2019). Deprivation and threat, emotion dysregulation, and psychopathology: Concurrent and longitudinal associations. Development and Psychopathology, 31(3), 847857.https://doi.org/10.1017/S0954579419000294 CrossRefGoogle ScholarPubMed
Peterson, R. A., & Brown, S. P. (2005). On the use of beta coefficients in meta-analysis. Journal of Applied Psychology, 90(1), 175181. https://doi.org/10.1037/0021-9010.90.1.175 CrossRefGoogle ScholarPubMed
Rodgers, M. A., & Pustejovsky, J. E. (2021). Evaluating meta-analytic methods to detect selective reporting in the presence of dependent effect sizes. Psychological Methods, 26(2), 141160. https://doi.org/10.1037/met0000300 CrossRefGoogle Scholar
Schäfer, J. L., McLaughlin, K. A., Manfro, G. G., Pan, P., Rohde, L. A., Miguel, E. C., Simioni, A., Hoffmann, M. S., & Salum, G. A. (2023). Threat and deprivation are associated with distinct aspects of cognition, emotional processing, and psychopathology in children and adolescents. Developmental Science, 26(1), e13267. https://doi.org/10.1111/desc.13267 CrossRefGoogle ScholarPubMed
Schlensog-Schuster, F., Keil, J., Von Klitzing, K., Gniewosz, G., Schulz, C. C., Schlesier-Michel, A., Mayer, S., Stadelmann, S., Döhnert, M., Klein, A. M., Sierau, S., Manly, J. T., Sheridan, M. A., & White, L. O. (2022). From maltreatment to psychiatric disorders in childhood and adolescence: The relevance of emotional maltreatment. Child Maltreatment, 29(1): 142154. https://doi.org/10.1177/10775595221134248 CrossRefGoogle ScholarPubMed
Stein, C. R., Sheridan, M. A., Copeland, W. E., Machlin, L. S., Carpenter, K. L. H., & Egger, H. L. (2022). Association of adversity with psychopathology in early childhood: Dimensional and cumulative approaches. Depression and Anxiety, 39(6), 524535. https://doi.org/10.1002/da.23269 CrossRefGoogle ScholarPubMed
Usacheva, M., Choe, D., Liu, S., Timmer, S., & Belsky, J. (2022). Testing the empirical integration of threat-deprivation and harshness-unpredictability dimensional models of adversity. Development and Psychopathology, 34(2), 513526. https://doi.org/10.1017/S0954579422000013 CrossRefGoogle ScholarPubMed
Wade, M., Wright, L., & Finegold, K. E. (2022). The effects of early life adversity on children’s mental health and cognitive functioning. Translational Psychiatry, 12(1), 244.CrossRefGoogle ScholarPubMed
Weisz, J. R., Kuppens, S., Eckshtain, D., Ugueto, A. M., Hawley, K. M., & Jensen-Doss, A. (2013). Performance of evidence-based youth psychotherapies compared with usual clinical care: A multilevel meta-analysis. JAMA Psychiatry, 70(7), 750761.CrossRefGoogle Scholar

References (Studies included in meta-analysis)

Aloba, O., Opakunle, T., & Ogunrinu, O. (2020). Childhood trauma questionnaire-short form (CTQ-SF): Dimensionality, validity, reliability and gender invariance among Nigerian adolescents. Child Abuse & Neglect, 101, 104357. https://doi.org/10.1016/j.chiabu.2020.104357 CrossRefGoogle ScholarPubMed
Alto, ME, Warmingham, JM, Handley, ED, Manly, JT, Cicchetti, D, & Toth, SL. (2022). The Association between patterns of trauma exposure, family dysfunction, and psychopathology among adolescent females with depressive symptoms from low-income contexts. Child Maltreat, 28(1), 130140. https://doi.org/10.1177/10775595211050303 CrossRefGoogle ScholarPubMed
Arata, C. M., Langhinrichsen-Rohling, J., Bowers, D., & O’Brien, N. (2007). Differential correlates of multi-type maltreatment among urban youth. Child Abuse & Neglect, 31(4), 393415. https://doi.org/10.1016/j.chiabu.2006.09.006 CrossRefGoogle ScholarPubMed
Auslander, W., Tlapek, S. M., Threlfall, J., Edmond, T., & Dunn, J. (2018). Mental health pathways linking childhood maltreatment to interpersonal revictimization during adolescence for girls in the child welfare system. Journal of Interpersonal Violence, 33(7), 11691191. https://doi.org/10.1177/0886260515614561 CrossRefGoogle ScholarPubMed
Bachmann, C. J., Beecham, J., O’Connor, T. G., Briskman, J., & Scott, S. (2022). A good investment: Longer-term cost savings of sensitive parenting in childhood. Journal of Child Psychology and Psychiatry, 63(1), 7887. https://doi.org/10.1111/jcpp.13461 CrossRefGoogle ScholarPubMed
Bagley, C., & Mallick, K. (2000). Prediction of sexual, emotional, and physical maltreatment and mental health outcomes in a longitudinal cohort of 290 adolescent women. Child Maltreatment, 5(3), 218226. https://doi.org/10.1177/1077559500005003002 CrossRefGoogle Scholar
Bennett, D. S., Sullivan, M. W., & Lewis, M. (2005). Young children’s adjustment as a function of maltreatment, shame, and anger. Child Maltreatment, 10(4), 311323. https://doi.org/10.1177/1077559505278619 CrossRefGoogle ScholarPubMed
Bi, S., & Keller, P. S. (2021). Parental empathy, aggressive parenting, and child adjustment in a noncustodial high-risk sample. Journal of Interpersonal Violence, 36(19–20), NP10371NP10392. https://doi.org/10.1177/0886260519870165 CrossRefGoogle Scholar
Blair, K. S., Aloi, J., Bashford-Largo, J., Zhang, R., Elowsky, J., Lukoff, J., Vogel, S., Carollo, E., Schwartz, A., Pope, K., Bajaj, S., Tottenham, N., Dobbertin, M., & Blair, R. J. (2022). Different forms of childhood maltreatment have different impacts on the neural systems involved in the representation of reinforcement value. Developmental Cognitive Neuroscience, 53, 101051. https://doi.org/10.1016/j.dcn.2021.101051 CrossRefGoogle ScholarPubMed
Bolger, K. E., & Patterson, C. J. (2001). Pathways from child maltreatment to internalizing problems: Perceptions of control as mediators and moderators. Development and Psychopathology, 13(4), 913940.CrossRefGoogle ScholarPubMed
Brown, S. E. (1984). Social class, child maltreatment, and delinquent behavior. Criminology: An Interdisciplinary Journal, 22(2), 259278. https://doi.org/10.1111/j.1745-9125.1984.tb00300.x CrossRefGoogle Scholar
Buckle, S. K., Lancaster, S., Powell, M. B., & Higgins, D. J. (2005). The relationship between child sexual abuse and academic achievement in a sample of adolescent psychiatric inpatients. Child Abuse & Neglect, 29(9), 10311047. https://doi.org/10.1016/j.chiabu.2004.12.013 CrossRefGoogle Scholar
Busso, D. S., McLaughlin, K. A., & Sheridan, M. A. (2017). Dimensions of adversity, physiological reactivity, and externalizing psychopathology in adolescence: Deprivation and threat. Psychosomatic Medicine, 79(2), 162171.CrossRefGoogle ScholarPubMed
Castro, M, Alcántara-López, M, Martínez, A, Fernández, V, Sánchez-Meca, J, & López-Soler, C. (2017). Mother’s IPV, child maltreatment type and the presence of PTSD in children and adolescents. International Journal of Environmental Research and Public Health, 14(9), 1077. https://doi.org/10.3390/ijerph14091077 CrossRefGoogle ScholarPubMed
Chahal, R., Miller, J. G., Yuan, J. P., Buthmann, J. L., & Gotlib, I. H. (2022). An exploration of dimensions of early adversity and the development of functional brain network connectivity during adolescence: Implications for trajectories of internalizing symptoms. Developmental Psychopathology, 34(2), 557571. https://doi.org/10.1017/S0954579421001814 CrossRefGoogle ScholarPubMed
Choe, C. & Yu, S. (2022). The effect of child abuse and neglect on trajectories of depressive symptoms and aggression in Korean adolescents: Exploring gender differences. International Journal of Environmental Research and Public Health, 19(10), 6160. https://doi.org/10.3390/ijerph19106160 CrossRefGoogle ScholarPubMed
Cohen, J. R., & Thakur, H. (2021). Developmental consequences of emotional abuse and neglect in vulnerable adolescents: A multi-informant, multi-wave study. Child Abuse & Neglect, 111, 104811. https://doi.org/10.1016/j.chiabu.2020.104811 CrossRefGoogle ScholarPubMed
Collings, S. J., Valjee, S. R., & Penning, S. L. (2013). Development and preliminary validation of a screen for interpersonal childhood trauma experiences among school-going youth in Durban, South Africa. Journal of Child and Adolescent Mental Health, 25(1), 2334. https://doi.org/10.2989/17280583.2012.722552 CrossRefGoogle ScholarPubMed
Company-Córdoba, R., Gómez-Baya, D., López-Gaviño, F., & Ibáñez-Alfonso, J. A. (2020). Mental health, quality of life and violence exposure in low-socioeconomic status children and adolescents of Guatemala. International Journal of Environmental Research and Public Health, 17(20). https://doi.org/10.3390/ijerph17207620 CrossRefGoogle ScholarPubMed
Cooley, J. L. & Taussig, H. N. (2021). Anger and attention problems as mechanisms linking maltreatment subtypes and witnessed violence to social functioning among children in out-of-home care. Child Maltreat, 27(4), 647657. https://doi.org/10.1177/10775595211038926 CrossRefGoogle ScholarPubMed
Crea, T. M., Easton, S. D., Florio, J., & Barth, R. P. (2018). Externalizing behaviors among adopted children: A longitudinal comparison of preadoptive childhood sexual abuse and other forms of maltreatment. Child Abuse & Neglect, 82, 192200. https://doi.org/10.1016/j.chiabu.2018.06.008 CrossRefGoogle ScholarPubMed
Cromer, K. D., & Villodas, M. T. (2017). The role of posttraumatic stress as a pathway to psychopathology among youth at high-risk for victimization. Psychology of Violence, 7(1), 1221. https://doi.org/10.1037/vio0000034 CrossRefGoogle Scholar
Crowley, T. J., Mikulich, S. K., Ehlers, K. M., Hall, S. K., & Whitmore, E. A. (2003). Discriminative validity and clinical utility of an abuse-neglect interview for adolescents with conduct and substance use problems. The American Journal of Psychiatry, 160(8), 14611469. https://doi.org/10.1176/appi.ajp.160.8.1461 CrossRefGoogle ScholarPubMed
de Oliveira, I. R., Matos-Ragazzo, A. C., Zhang, Y., Vasconcelos, N. M., Velasquez, M. L., Reis, D., Ribeiro, M. G., da Rocha, M. M., Rosario, M. C., Stallard, P., & Cecil, C. A. M. (2018). Disentangling the mental health impact of childhood abuse and neglect: A replication and extension study in a Brazilian sample of high-risk youth. Child Abuse and Neglect, 80, 312323. https://doi.org/10.1016/j.chiabu.2018.03.021 CrossRefGoogle Scholar
Derin, S., Selman, S. B., Alyanak, B., & Soylu, N. (2022). The role of adverse childhood experiences and attachment styles in social anxiety disorder in adolescents. Clinical Child Psychology and Psychiatry, 27(3), 644657. https://doi.org/10.1177/13591045221078085 CrossRefGoogle ScholarPubMed
Dhakal, S., Niraula, S., Sharma, N. P., Sthapit, S., Bennett, E., Vaswani, A., Pandey, R., Kumari, V., & Lau, J. Y. F. (2019). History of abuse and neglect and their associations with mental health in rescued child labourers in Nepal. Australian and New Zealand Journal of Psychiatry, 53(12), 11991207. https://doi.org/10.1177/0004867419853882 CrossRefGoogle ScholarPubMed
Duprey, E. B., Oshri, A., & Liu, S. (2020). Developmental pathways from child maltreatment to adolescent suicide-related behaviors: The internalizing and externalizing comorbidity hypothesis. Developmental Psychopathology, 32(3), 945959. https://doi.org/10.1017/S0954579419000919 CrossRefGoogle ScholarPubMed
Egeland, B., Yates, T., Appleyard, K., & van Dulmen, M. (2002). The long-term consequences of maltreatment in the early years: A developmental pathway model to antisocial behavior. Children’s Services: Social Policy, Research, & Practice, 5(4), 249260. https://doi.org/10.1207/S15326918CS0504_2 CrossRefGoogle Scholar
Ellis, W. E., & Wolfe, D. A. (2009). Understanding the association between maltreatment history and adolescent risk behavior by examining popularity motivations and peer group control. Journal of Youth and Adolescence, 38(9), 12531263. https://doi.org/10.1007/s10964-008-9318-3 CrossRefGoogle ScholarPubMed
Esparza-Del Villar, O. A., Chavez-Valdez, S. M., Montañez-Alvarado, P., Gutiérrez-Vega, M., & Gutiérrez-Rosado, T. (2021). Relationship between different types of violence and mental health in high school students from Northern Mexico. Journal of Interpersonal Violence, 37(17-18), NP15774NP15799. https://doi.org/10.1177/08862605211021964 CrossRefGoogle ScholarPubMed
Farrow, P., Simmons, J. G., Pozzi, E., Díaz-Arteche, C., Richmond, S., Bray, K., Schwartz, O., & Whittle, S. (2020). Associations between early life stress and anterior pituitary gland volume development during late childhood. Psychoneuroendocrinology, 122, 104868. https://doi.org/10.1016/j.psyneuen.2020.104868 CrossRefGoogle ScholarPubMed
Gardner, F., Waller, R., Maughan, B., Cluver, L., & Boyes, M. (2015). What are the risk factors for antisocial behavior among low-income youth in Cape Town? Social Development, 24(4), 798814. https://doi.org/10.1111/sode.12127 CrossRefGoogle Scholar
Goetschius, L. G., McLoyd, V. C., Hein, T. C., Mitchell, C., Hyde, L. W., & Monk, C. S. (2021). School connectedness as a protective factor against childhood exposure to violence and social deprivation: A longitudinal study of adaptive and maladaptive outcomes. Development and Psychopathology, 35(3), 12191234. https://doi.org/10.1017/S0954579421001140 CrossRefGoogle ScholarPubMed
Goodyear, R. K., Newcomb, M. D., & Locke, T. F. (2002). Pregnant Latina teenagers: Psychosocial and developmental determinants of how they select and perceive the men who father their children. Journal of Counseling Psychology, 49(2), 187201. https://doi.org/10.1037/0022-0167.49.2.187 CrossRefGoogle Scholar
Guo, J., Fu, M., Liu, D., Zhang, B., Wang, X., & van IJzendoorn, M. H. (2020). Is the psychological impact of exposure to COVID-19 stronger in adolescents with pre-pandemic maltreatment experiences? A survey of rural Chinese adolescents. Child Abuse & Neglect, 110(Part 2), 104667. https://doi.org/10.1016/j.chiabu.2020.104667 CrossRefGoogle ScholarPubMed
Haddad, C., Chidiac, J., Sacre, H., Salameh, P., Hallit, R., Obeid, S., Soufia, M., & Hallit, S. (2022). Prevalence and associated factors of social anxiety among Lebanese adolescents. Primary Care Companion for CNS Disorders, 24(3), 21m03061. https://doi.org/10.4088/PCC.21m03061 CrossRefGoogle ScholarPubMed
Hallit, J., Salameh, P., Haddad, C., Sacre, H., Soufia, M., Akel, M., Obeid, S., Hallit, R., & Hallit, S. (2020). Validation of the AUDIT scale and factors associated with alcohol use disorder in adolescents: Results of a National Lebanese Study. BMC Pediatrics, 20(1), 205. https://doi.org/10.1186/s12887-020-02116-7 CrossRefGoogle ScholarPubMed
Hamilton, J. L., Shapero, B. G., Stange, J. P., Hamlat, E. J., Abramson, L. Y., & Alloy, L. B. (2013). Emotional maltreatment, peer victimization, and depressive versus anxiety symptoms during adolescence: Hopelessness as a mediator. Journal of Clinical Child and Adolescent Psychology, 42(3), 332347. https://doi.org/10.1080/15374416.2013.777916 CrossRefGoogle ScholarPubMed
Hecker, T., Boettcher, V. S., Landolt, M. A., & Hermenau, K. (2019). Child neglect and its relation to emotional and behavioral problems: A cross-sectional study of primary school-aged children in Tanzania. Development and Psychopathology, 31(1), 325339. https://doi.org/10.1017/S0954579417001882 CrossRefGoogle ScholarPubMed
Hein, T. C., Goetschius, L. G., McLoyd, V. C., Brooks-Gunn, J., McLanahan, S. S., Mitchell, C., Lopez-Duran, N. L., Hyde, L. W., & Monk, C. S. (2020). Childhood violence exposure and social deprivation are linked to adolescent threat and reward neural function. Social Cognitive and Affective Neuroscience, 15(11), 12521259. https://doi.org/10.1093/scan/nsaa144 CrossRefGoogle ScholarPubMed
Heleniak, C., Bolden, C. R., McCabe, C. J., Lambert, H. K., Rosen, M. L., King, K. M., Monahan, K. C., & McLaughlin, K. A. (2021). Distress tolerance as a mechanism linking violence exposure to problematic alcohol use in adolescence. Research on Child and Adolescent Psychopathology, 49(9), 12111225. https://doi.org/10.1007/s10802-021-00805-0 CrossRefGoogle ScholarPubMed
Heleniak, C. & McLaughlin, K. A. (2020). Social-cognitive mechanisms in the cycle of violence: Cognitive and affective theory of mind, and externalizing psychopathology in children and adolescents. Developmental psychopathology, 32(2), 735750. https://doi.org/10.1017/S0954579419000725 CrossRefGoogle ScholarPubMed
Henry, L. M., Gracey, K., Shaffer, A., Ebert, J., Kuhn, T., Watson, K. H., Gruhn, M., Vreeland, A., Siciliano, R., Dickey, L., Lawson, V., Broll, C., Cole, D. A., & Compas, B. E. (2021). Comparison of three models of adverse childhood experiences: Associations with child and adolescent internalizing and externalizing symptoms. Journal of Abnormal Psychology, 130(1), 925. https://doi.org/10.1037/abn0000644 CrossRefGoogle ScholarPubMed
Hermenau, K., Eggert, I., Landolt, M. A., & Hecker, T. (2015). Neglect and perceived stigmatization impact psychological distress of orphans in Tanzania. European Journal of Psychotraumatology, 6, 28617. https://doi.org/10.3402/ejpt.v6.28617 CrossRefGoogle ScholarPubMed
Hodgdon, H. B., Suvak, M., Zinoviev, D. Y., Liebman, R. E., Briggs, E. C., & Spinazzola, J. (2019). Network analysis of exposure to trauma and childhood adversities in a clinical sample of youth. Psychological Assessment, 31(11), 12941306. https://doi.org/10.1037/pas0000748 CrossRefGoogle Scholar
Hoeboer, C., de Roos, C., van Son, G. E., Spinhoven, P., & Elzinga, B. (2021). The effect of parental emotional abuse on the severity and treatment of PTSD symptoms in children and adolescents. Child Abuse and Neglect, 111, 104775. https://doi.org/10.1016/j.chiabu.2020.104775 CrossRefGoogle ScholarPubMed
Hsieh, Y.-P., Shen, A. C.-T., Hwa, H.-L., Wei, H.-S., Feng, J.-Y., & Huang, S. C.-Y. (2020). Associations between child maltreatment, dysfunctional family environment, post-traumatic stress disorder and children’s bullying perpetration in a national representative sample in Taiwan. Journal of Family Violence, 36, 2736. https://doi.org/10.1007/s10896-020-00144-6 CrossRefGoogle Scholar
Hsieh, Y.-P., Shen, A. C.-T., Wei, H.-S., Feng, J.-Y., Huang, S. C.-Y., & Hwa, H.-L. (2016). Associations between child maltreatment, PTSD, and internet addiction among Taiwanese students. Computers in Human Behavior, 56, 209214. https://doi.org/10.1016/j.chb.2015.11.048 CrossRefGoogle Scholar
Huang, C.-C., Vikse, J. H., Lu, S., & Yi, S. (2015). Children’s exposure to intimate partner violence and early delinquency. Journal of Family Violence, 30(8), 953965. https://doi.org/10.1007/s10896-015-9727-5 CrossRefGoogle Scholar
Hunt, T. K. A., Slack, K. S., & Berger, L. M. (2017). Adverse childhood experiences and behavioral problems in middle childhood. Child Abuse & Neglect, 67, 391402. https://doi.org/10.1016/j.chiabu.2016.11.005 CrossRefGoogle ScholarPubMed
Jenness, J. L., Peverill, M., Miller, A. B., Heleniak, C., Robertson, M. M., Sambrook, K. A., Sheridan, M. A., & McLaughlin, K. A. (2020). Alterations in neural circuits underlying emotion regulation following child maltreatment: A mechanism underlying trauma-related psychopathology. Psychological Medicine, 51, 18801889. https://doi.org/10.1017/S0033291720000641 CrossRefGoogle ScholarPubMed
Jessar, A. J., Hamilton, J. L., Flynn, M., Abramson, L. Y., & Alloy, L. B. (2017). Emotional clarity as a mechanism linking emotional neglect and depressive symptoms during early adolescence. The Journal of Early Adolescence, 37(3), 414432. https://doi.org/10.1177/0272431615609157 CrossRefGoogle ScholarPubMed
Joo, Y. S., Kim, J., Lee, J., & Chung, I. J. (2021). Understanding the link between exposure to fine particulate matter and internalizing problem behaviors among children in South Korea: Indirect effects through maternal depression and child abuse. Health Place, 68, 102531. https://doi.org/10.1016/j.healthplace.2021.102531 CrossRefGoogle ScholarPubMed
Khodarahimi, S. (2014). The role of family violence on mental health and hopefulness in an Iranian adolescents sample. Journal of Family Violence, 29(3), 259268. https://doi.org/10.1007/s10896-014-9587-4 CrossRefGoogle Scholar
Kidman, R., Smith, D., Piccolo, L. R., & Kohler, H.-P. (2019). Psychometric evaluation of the Adverse Childhood Experience International Questionnaire (ACE-IQ) in Malawian adolescents. Child Abuse & Neglect, 92, 139145. https://doi.org/10.1016/j.chiabu.2019.03.015 CrossRefGoogle ScholarPubMed
Kobulsky, J. M., Yoon, S., Bright, C. L., Lee, G., & Nam, B. (2018). Gender-moderated pathways from childhood abuse and neglect to late-adolescent substance use. Journal of Traumatic Stress, 31(5), 654664. https://doi.org/10.1002/jts.22326 CrossRefGoogle ScholarPubMed
Kovačević, S. I., Šobot, V., Vejnović, A. M., Knežević, V., Milatović, J., & Šegan, D. (2022). Mental health problems in abused institutionalised Serbian adolescents and their use of social and mental health services. Journal of Evidence-Based Psychotherapies, 22(1), 2138. https://doi.org/10.24193/jebp.2022.1.2 CrossRefGoogle Scholar
Lee, A. H., Mirhashem, R., Bernard, K., & Dozier, M. (2023). Prospective associations between early childhood intimate partner violence exposure and middle childhood internalizing and externalizing psychopathology, Child Maltreatment, 28(2), 232242. https://doi.org/10.1177/10775595221100722 CrossRefGoogle ScholarPubMed
Lee, R. Y., Oxford, M. L., Sonney, J., Enquobahrie, D. A., & Cato, K. D. (2022). The mediating role of anxiety/depression symptoms between adverse childhood experiences (ACEs) and somatic symptoms in adolescents. Journal of Adolescence, 94(2), 133147. https://doi.org/10.1002/jad.12012 CrossRefGoogle ScholarPubMed
Lee, C., & Feng, J. (2021). From childhood victimization to internalizing and externalizing behavior problems through self-esteem in adolescence. Research in Nursing & Health, 44(6), 931944. https://doi.org/10.1002/nur.22188 CrossRefGoogle ScholarPubMed
Li, K., Zhan, X., Ren, L., Liu, N., Zhang, L., Li, L., Chen, T., Feng, Z., & Luo, X. (2022). The association of abuse and depression with suicidal ideation in Chinese adolescents: a network analysis. Frontiers in Psychiatry, 13, 853951. https://doi.org/10.3389/fpsyt.2022.853951 CrossRefGoogle ScholarPubMed
Li, S. T., Nussbaum, K. M., & Richards, M. H. (2007). Risk and protective factors for urban African American youth. American Journal of Community Psychology, 39(1–2), 2135. https://doi.org/10.1007/s10464-007-9088-1 CrossRefGoogle ScholarPubMed
Li, S., Zhao, F., & Yu, G. (2020). Childhood emotional abuse and depression among adolescents: roles of deviant peer affiliation and gender. Journal of Interpersonal Violence, 37(1-2), NP830NP850. https://doi.org/10.1177/0886260520918586 CrossRefGoogle ScholarPubMed
López-Soler, C., Alcántara-López, M., Castro, M., Sánchez-Meca, J., & Fernández, V. (2017). The association between maternal exposure to intimate partner violence and emotional and behavioral problems in Spanish children and adolescents. Journal of Family Violence, 32(2), 135144. https://doi.org/10.1007/s10896-016-9864-5 CrossRefGoogle Scholar
Lurie, L. A., Hangen, E. J., Rosen, M. L., Crosnoe, R., & McLaughlin, K. A. (2022). Reduced growth mindset as a mechanism linking childhood trauma with academic performance and internalizing psychopathology. Child Abuse & Neglect, 142, 105672.https://doi.org/10.1016/j.chiabu.2022.105672 CrossRefGoogle ScholarPubMed
Madigan, S., Wade, M., Plamondon, A., Vaillancourt, K., Jenkins, J. M., Shouldice, M., & Benoit, D. (2014). Course of depression and anxiety symptoms during the transition to parenthood for female adolescents with histories of victimization. Child Abuse & Neglect, 38(7), 11601170. https://doi.org/10.1016/j.chiabu.2014.04.002 CrossRefGoogle ScholarPubMed
Manly, J. T., Kim, J. E., Rogosch, F. A., & Cicchetti, D. (2001). Dimensions of child maltreatment and children’s adjustment: Contributions of developmental timing and subtype. Development and Psychopathology, 13(4), 759782.CrossRefGoogle ScholarPubMed
Manly, J. T., Oshri, A., Lynch, M., Herzog, M., & Wortel, S. (2013). Child neglect and the development of externalizing behavior problems: Associations with maternal drug dependence and neighborhood crime. Child Maltreatment, 18(1), 1729. https://doi.org/10.1177/1077559512464119 CrossRefGoogle ScholarPubMed
Maxwell, M. Y., Taylor, R. L., & Barch, D. M. (2022). Relationship between neighborhood poverty and externalizing symptoms in children: Mediation and moderation by environmental factors and brain structure. Child Psychiatry and Human Development, 54(6), 17101722. https://doi.org/10.1007/s10578-022-01369-w CrossRefGoogle ScholarPubMed
McGee, R. A., Wolfe, D. A., Yuen, S. A., Wilson, S. K., & Carnochan, J. (1995). The measurement of maltreatment: A comparison of approaches. Child Abuse & Neglect, 19(2), 233249. https://doi.org/10.1016/0145-2134(94)00119-F CrossRefGoogle ScholarPubMed
McNeilly, E. A., Peverill, M., Jung, J., & McLaughlin, K. A. (2021). Executive function as a mechanism linking socioeconomic status to internalizing and externalizing psychopathology in children and adolescents. Journal of Adolescence, 89, 149160. https://doi.org/10.1016/j.adolescence.2021.04.010 CrossRefGoogle ScholarPubMed
Menon, S. V., Cohen, J. R., Shorey, R. C., & Temple, J. R. (2018). The impact of intimate partner violence exposure in adolescence and emerging adulthood: A developmental psychopathology approach. Journal of Clinical Child and Adolescent Psychology, 47(Suppl 1), S497S508. https://doi.org/10.1080/15374416.2018.1437736 CrossRefGoogle ScholarPubMed
Miller, A. B., Machlin, L., McLaughlin, K. A., & Sheridan, M. A. (2021). Deprivation and psychopathology in the Fragile Families Study: A 15-year longitudinal investigation. Journal of Child Psychology and Psychiatry, 62(4), 382391. https://doi.org/10.1111/jcpp.13260 CrossRefGoogle ScholarPubMed
Miller, A. B., Sheridan, M. A., Hanson, J. L., McLaughlin, K. A., Bates, J. E., Lansford, J. E., Pettit, G. S., & Dodge, K. A. (2018). Dimensions of deprivation and threat, psychopathology, and potential mediators: A multi-year longitudinal analysis. Journal of Abnormal Psychology, 127(2), 160170. https://doi.org/10.1037/abn0000331 CrossRefGoogle ScholarPubMed
Miller-Graff, L., Yoon, S., Paulson, J. L., & Maguire-Jack, K. (2021). Pathways of internalizing and posttraumatic stress symptoms across childhood and adolescence. Research on Child and Adolescent Psychopathology, 49(1), 103116. https://doi.org/10.1007/s10802-020-00701-z CrossRefGoogle ScholarPubMed
Moussavi, Y., Wergeland, G. J., Bøe, T., Haugland, B. S. M., Larsen, M., & Lehmann, S. (2021). Internalizing symptoms among youth in foster care: Prevalence and associations with exposure to maltreatment. Child Psychiatry and Human Development, 53(2), 375388.https://doi.org/10.1007/s10578-020-01118-x CrossRefGoogle ScholarPubMed
Nguyen, H. T., Dunne, M. P., & Le, A. V. (2010). Multiple types of child maltreatment and adolescent mental health in Viet Nam. Bulletin of the World Health Organization, 88(1), 2230. https://doi.org/10.2471/BLT.08.060061 CrossRefGoogle ScholarPubMed
Nkuba, M., Hermenau, K., & Hecker, T. (2019). The association of maltreatment and socially deviant behavior—Findings from a national study with adolescent students and their parents. Mental Health and Prevention, 13, 159168. https://doi.org/10.1016/j.mhp.2019.01.003 CrossRefGoogle Scholar
Nöthling, J., Suliman, S., Martin, L., Simmons, C., & Seedat, S. (2019). Differences in abuse, neglect, and exposure to community violence in adolescents with and without PTSD and depression. Journal of Interpersonal Violence, 34(21–22), 43574383. https://doi.org/10.1177/0886260516674944 CrossRefGoogle ScholarPubMed
Oshri, A., Tubman, J. G., & Jaccard, J. (2011). Psychiatric symptom typology in a sample of youth receiving substance abuse treatment services: Associations with self-reported child maltreatment and sexual risk behaviors. AIDS and Behavior, 15(8), 18441856. https://doi.org/10.1007/s10461-011-9890-5 CrossRefGoogle Scholar
Papalia, N., Baidawi, S., Luebbers, S., Shepherd, S., & Ogloff, J. R. P. (2022). Patterns of maltreatment co-occurrence in incarcerated youth in Australia. Journal of Interpersonal Violence, 37(7–8), NP4341NP4371. https://doi.org/10.1177/0886260520958639 CrossRefGoogle ScholarPubMed
Park, A., & Kim, Y. (2018). The longitudinal influence of child maltreatment on child obesity in South Korea: The mediating effects of low self-esteem and depressive symptoms. Children and Youth Services Review, 87, 3440. https://doi.org/10.1016/j.childyouth.2018.02.012 CrossRefGoogle Scholar
Petrenko, C. L. M., Friend, A., Garrido, E. F., Taussig, H. N., & Culhane, S. E. (2012). Does subtype matter? Assessing the effects of maltreatment on functioning in preadolescent youth in out-of-home care. Child Abuse & Neglect, 36(9), 633644. https://doi.org/10.1016/j.chiabu.2012.07.001 CrossRefGoogle ScholarPubMed
Petrican, R., Miles, S., Rudd, L., Wasiewska, W., Graham, K. S., & Lawrence, A. D. (2021). Pubertal timing and functional neurodevelopmental alterations independently mediate the effect of family conflict on adolescent psychopathology. Developmental Cognitive Neuroscience, 52, 101032. https://doi.org/10.1016/j.dcn.2021.101032 CrossRefGoogle ScholarPubMed
Pirdehghan, A., Vakili, M., Rajabzadeh, Y., Puyandehpour, M., & Aghakoochak, A. (2016). Child abuse and mental disorders in Iranian adolescents. Iranian Journal of Pediatrics, 26(2), e3839. https://doi.org/10.5812/ijp.3839 Google ScholarPubMed
Raffaelli, M., Santana, J. P., de Morais, N. A., Nieto, C. J., & Koller, S. H. (2018). Adverse childhood experiences and adjustment: A longitudinal study of street-involved youth in Brazil. Child Abuse & Neglect, 85, 91100. https://doi.org/10.1016/j.chiabu.2018.07.032 CrossRefGoogle ScholarPubMed
Rakesh, D., Allen, N. B., & Whittle, S. (2021). Longitudinal changes in within-salience network functional connectivity mediate the relationship between childhood abuse and neglect, and mental health during adolescence. Psychological Medicine, 53(4), 15521564. https://doi.org/10.1017/S0033291721003135 CrossRefGoogle ScholarPubMed
Ribeiro, R. A. B., Rubin, B. B., Castelli, R. D., de Matos, M. B., Coelho, F. T., da Cunha Coelho, F. M., Pinheiro, K. A. T., da Silva, R. A., de Avila Quevedo, L., & Pinheiro, R. T. (2019). Childhood trauma and depressive symptoms in pregnant adolescents in Southern Brazil. International Journal of Public Health, 65(2), 197205. https://doi.org/10.1007/s00038-019-01311-3 CrossRefGoogle ScholarPubMed
Roque-Lopez, S., Llanez-Anaya, E., Álvarez-López, M. J., Everts, M., Fernández, D., Davidson, R. J., & Kaliman, P. (2021). Mental health benefits of a 1-week intensive multimodal group program for adolescents with multiple adverse childhood experiences. Child Abuse and Neglect, 122, 105349. https://doi.org/10.1016/j.chiabu.2021.105349 CrossRefGoogle ScholarPubMed
Saltz, S. B., Rozon, M., Pogge, D. L., & Harvey, P. D. (2020). Cyberbullying and its relationship to current symptoms and history of early life trauma: a study of adolescents in an acute inpatient psychiatric unit. Journal of Clinical Psychiatry, 81(1), 18m12170. https://doi.org/10.4088/JCP.18m12170 Google Scholar
Sekowski, M., Gambin, M., Cudo, A., Wozniak-Prus, M., Penner, F., Fonagy, P., & Sharp, C. (2020). The relations between childhood maltreatment, shame, guilt, depression and suicidal ideation in inpatient adolescents. Journal of Affective Disorders, 276, 667677. https://doi.org/10.1016/j.jad.2020.07.056 CrossRefGoogle ScholarPubMed
Sevenoaks, T., Fouche, J. P., Phillips, N., Heany, S., Myer, L., Zar, H. J., Stein, D. J., & Hoare, J. (2022). Childhood trauma and mental health in the Cape Town adolescent antiretroviral cohort. Journal of Child & Adolescent Trauma, 15(2), 353363. https://doi.org/10.1007/s40653-021-00362-0 CrossRefGoogle ScholarPubMed
Shaffer, A., Yates, T. M., & Egeland, B. R. (2009). The relation of emotional maltreatment to early adolescent competence: Developmental processes in a prospective study. Child Abuse & Neglect, 33(1), 3644. https://doi.org/10.1016/j.chiabu.2008.12.005 CrossRefGoogle ScholarPubMed
Shao, N., Gong, Y., Wang, X., Wei, J., Shi, J., Ding, H., Zhang, M., Kang, C., Wang, S., Chen, L., Yu, Y., & Han, J. (2021). Effects of polygenic risk score, childhood trauma and resilience on depressive symptoms in Chinese adolescents in a three-year cohort study. Journal of Affective Disorders, 282, 627636. https://doi.org/10.1016/j.jad.2020.12.114 CrossRefGoogle Scholar
Shen, A. C., Feng, J. Y., Wei, H. S., Hsieh, Y. P., Huang, S. C., & Hwa, H. L. (2019). Who gets protection? A national study of multiple victimization and child protection among Taiwanese children. Journal of Interpersonal Violence, 34(17), 37373761. https://doi.org/10.1177/0886260516670885 CrossRefGoogle ScholarPubMed
Silva, C. S., & Calheiros, M. M. (2020). Maltreatment experiences and psychopathology in children and adolescents: The intervening role of domain-specific self-representations moderated by age. Child Abuse & Neglect, 99, 104255. https://doi.org/10.1016/j.chiabu.2019.104255 CrossRefGoogle ScholarPubMed
Simmel, C. (2007). Risk and protective factors contributing to the longitudinal psychosocial well-being of adopted foster children. Journal of Emotional and Behavioral Disorders, 15(4), 237249. https://doi.org/10.1177/10634266070150040501 CrossRefGoogle Scholar
Snyder, S. M., & Merritt, D. H. (2014). Do childhood experiences of neglect affect delinquency among child welfare involved-youth? Children and Youth Services Review, 46, 6471. https://doi.org/10.1016/j.childyouth.2014.08.007 CrossRefGoogle Scholar
Stein, C. R., Sheridan, M. A., Copeland, W. E., Machlin, L. S., Carpenter, K. L. H., & Egger, H. L. (2022). Association of adversity with psychopathology in early childhood: Dimensional and cumulative approaches. Depression and Anxiety, 39(6), 524535. https://doi.org/10.1002/da.23269 CrossRefGoogle ScholarPubMed
Sullivan, T. P., Fehon, D. C., Andres-Hyman, R. C., Lipschitz, D. S., & Grilo, C. M. (2006). Differential relationships of childhood abuse and neglect subtypes to PTSD symptom clusters among adolescent inpatients. Journal of Traumatic Stress, 19(2), 229239. https://doi.org/10.1002/jts.20092 CrossRefGoogle ScholarPubMed
Sumner, J. A., Colich, N. L., Uddin, M., Armstrong, D., & McLaughlin, K. A. (2019). Early experiences of threat, but not deprivation, are associated with accelerated biological aging in children and adolescents. Biological Psychiatry, 85(3), 268278. https://doi.org/10.1016/j.biopsych.2018.09.008 CrossRefGoogle Scholar
Tang, W., Xu, D., & Xu, J. (2020). Impact of earthquake exposure, family adversity and peer problems on anxiety-related emotional disorders in adolescent survivors three years after the Ya’an earthquake. Journal of Affective Disorders, 273, 215222. https://doi.org/10.1016/j.jad.2020.04.044 CrossRefGoogle ScholarPubMed
Tang, W., Zhao, J., Lu, Y., Zha, Y., Liu, H., Sun, Y., Zhang, J., Yang, Y., & Xu, J. (2018). Suicidality, posttraumatic stress, and depressive reactions after earthquake and maltreatment: A cross-sectional survey of a random sample of 6132 Chinese children and adolescents. Journal of Affective Disorders, 232, 363369. https://doi.org/10.1016/j.jad.2018.02.081 CrossRefGoogle ScholarPubMed
Telman, M. D., Overbeek, M. M., de Schipper, J. C., Lamers-Winkelman, F., Finkenauer, C., & Schuengel, C. (2016). Family functioning and children’s post-traumatic stress symptoms in a referred sample exposed to interparental violence. Journal of Family Violence, 31, 127136. https://doi.org/10.1007/s10896-015-9769-8 CrossRefGoogle Scholar
Thepthien, B., & Htike, M. (2020). Associations between adverse childhood experiences and adverse health outcomes among adolescents in Bangkok, Thailand. Cogent Psychology, 7(1). https://doi.org/10.1080/23311908.2020.1832403 CrossRefGoogle Scholar
Tubman, J. G., Oshri, A., Duprey, E. B., & Sutton, T. E. (2021). Childhood maltreatment, psychiatric symptoms, and suicidal thoughts among adolescents receiving substance use treatment services. Journal of Adolescence, 89, 1827. https://doi.org/10.1016/j.adolescence.2021.03.002 CrossRefGoogle ScholarPubMed
Vahl, P., van Damme, L., Doreleijers, T., Vermeiren, R., & Colins, O. (2016). The unique relation of childhood emotional maltreatment with mental health problems among detained male and female adolescents. Child Abuse & Neglect, 62, 142150. https://doi.org/10.1016/j.chiabu.2016.10.008 CrossRefGoogle ScholarPubMed
van Berkel, S. R., Tucker, C. J., & Finkelhor, D. (2018). The combination of sibling victimization and parental child maltreatment on mental health problems and delinquency. Child Maltreatment, 23(3), 244253. https://doi.org/10.1177/1077559517751670 CrossRefGoogle ScholarPubMed
Vasic, J., Grujicic, R., Toskovic, O., & Pejovic Milovancevic, M. (2021). Mental health, alcohol and substance use of refugee youth. Frontiers in Psychiatry, 12, 713152. https://doi.org/10.3389/fpsyt.2021.713152 CrossRefGoogle ScholarPubMed
Vaughn-Coaxum, R. A., Dhawan, N., Sheridan, M. A., Hart, M. J., & Weisz, J. R. (2019). Dimensions of adversity in association with adolescents’ depression symptoms: Distinct moderating roles of cognitive and autonomic function. Development and Psychopathology, 32(3), 817830. https://doi.org/10.1017/S0954579419001172 CrossRefGoogle Scholar
Voth Schrag, R. J., Edmond, T., Tlapek, S. M., & Auslander, W. (2017). Exposure to economically abusive tactics among adolescent girls in the child welfare system. Child & Adolescent Social Work Journal, 34(2), 127136. https://doi.org/10.1007/s10560-016-0450-8 CrossRefGoogle Scholar
Wang, X., Ding, F., Cheng, C., He, J., Wang, X., & Yao, S. (2022). Psychometric properties and measurement invariance of the Childhood Trauma Questionnaire (Short Form) across genders, time points and presence of major depressive disorder among Chinese adolescents. Frontiers in Psychology, 13, 816051. https://doi.org/10.3389/fpsyg.2022.816051 CrossRefGoogle ScholarPubMed
Weissman, D. G., Rosen, M. L., Colich, N. L., Sambrook, K. A., Lengua, L. J., Sheridan, M. A., & McLaughlin, K. A. (2022). Exposure to violence as an environmental pathway linking low socioeconomic status with altered neural processing of threat and adolescent psychopathology. The Journal of Cognitive Neuroscience, 34(10), 18921905. https://doi.org/10.1162/jocn_a_01825 CrossRefGoogle ScholarPubMed
Wolf, S., & Suntheimer, N. M. (2019). A dimensional risk approach to assessing early adversity in a national sample. Journal of Applied Developmental Psychology, 62, 270281. https://doi.org/10.1016/j.appdev.2019.03.004 CrossRefGoogle Scholar
Yang, T., He, Y., Wu, S., Cui, X., Luo, X., & Liu, J. (2021). Association between schizoid tendencies and aggressive behaviors: Mediating and moderating influences in childhood trauma and life events among Chinese adolescents. Annals of General Psychiatry, 20(1), 51. https://doi.org/10.1186/s12991-021-00371-1 CrossRefGoogle ScholarPubMed
Yates, T. M., Dodds, M. F., Sroufe, L. A., & Egeland, B. (2003). Exposure to partner violence and child behavior problems: A prospective study controlling for child physical abuse and neglect, child cognitive ability, socioeconomic status, and life stress. Development and Psychopathology, 15(1), 199218. https://doi.org/10.1017/S0954579403000117 CrossRefGoogle ScholarPubMed
Yearwood, K., Vliegen, N., Chau, C., Corveleyn, J., & Luyten, P. (2021). Prevalence of exposure to complex trauma and community violence and their associations with internalizing and externalizing symptoms. Journal of Interpersonal Violence, 36(1–2), 843861. https://doi.org/10.1177/0886260517731788 CrossRefGoogle ScholarPubMed
Yonas, M. A., Lewis, T., Hussey, J. M., Thompson, R., Newton, R., English, D., & Dubowitz, H. (2010). Perceptions of neighborhood collective efficacy moderate the impact of maltreatment on aggression. Child Maltreatment, 15(1), 3747. https://doi.org/10.1177/1077559509349445 CrossRefGoogle ScholarPubMed
Yoon, D., Yoon, S., Pei, F., & Ploss, A. (2021). The roles of child maltreatment types and peer relationships on behavior problems in early adolescence. Child Abuse & Neglect, 112, 104921. https://doi.org/10.1016/j.chiabu.2020.104921 CrossRefGoogle ScholarPubMed
Yoon, S., Kobulsky, J. M., Yoon, D., & Kim, W. (2017). Developmental pathways from child maltreatment to adolescent substance use: The roles of posttraumatic stress symptoms and mother-child relationships. Children and Youth Services Review, 82, 271279. https://doi.org/10.1016/j.childyouth.2017.09.035 CrossRefGoogle ScholarPubMed
You, S., & Lim, S. A. (2015). Development pathways from abusive parenting to delinquency: The mediating role of depression and aggression. Child Abuse & Neglect, 46, 152162. https://doi.org/10.1016/j.chiabu.2015.05.009 CrossRefGoogle ScholarPubMed
Zeller, M. H., Noll, J. G., Sarwer, D. B., Reiter-Purtill, J., Rofey, D. L., Baughcum, A. E., Peugh, J., Courcoulas, A. P., Michalsky, M. P., Jenkins, T. M., & Becnel, J. N. (2015). Child maltreatment and the adolescent patient with severe obesity: Implications for clinical care. Journal of Pediatric Psychology, 40(7), 640648. https://doi.org/10.1093/jpepsy/jsv011 CrossRefGoogle ScholarPubMed
Zhang, Y., Cecil, C. C. A. M., Barker, E. D., Mori, S., & Lau, J. Y. F. (2019). Dimensionality of early adversity and associated behavioral and emotional symptoms: Data from a sample of Japanese institutionalized children and adolescents. Child Psychiatry and Human Development, 50(3), 425438. https://doi.org/10.1007/s10578-018-0850-4 CrossRefGoogle ScholarPubMed
Zhang, Y., Liao, H., Gu, J., & Wang, J. (2022). Anxiety and depression related to childhood maltreatment in teenagers: Comparing multiple individual risk model, cumulative risk model and latent profile analysis. Child Abuse & Neglect, 128, 105630. https://doi.org/10.1016/j.chiabu.2022.105630 CrossRefGoogle ScholarPubMed
Zhao, X., Chen, J., Chen, M.-C., Lv, X.-L., Jiang, Y.-H., & Sun, Y.-H. (2014). Left-behind children in rural China experience higher levels of anxiety and poorer living conditions. Acta Paediatrica, 103(6), 665670. https://doi.org/10.1111/apa.12602 CrossRefGoogle ScholarPubMed
Figure 0

Figure 1. PRISMA flow diagram.

Figure 1

Table 1. Characteristics of Studies Included in Meta-Analysis (k = 127)

Figure 2

Table 2. Results of follow-up analyses examining significant moderators of the association between adversity and internalizing psychopathology

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Table 3. Results of follow-up analyses examining significant moderators of the association between adversity and externalizing psychopathology

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Table 4. Results of follow-up analyses examining significant moderators of the association between adversity and PTSD symptoms

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Table 5. Results of follow-up analyses examining significant moderators of the association between threat and deprivation

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Figure 2. Summary forest plot of multilevel meta-analytic effects between adversity dimensions and youth psychopathology using bivariate and partial correlations. Note. k = number of studies, ES = number of effect sizes. Partial correlations account for the correlation between threat and deprivation.

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