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Joint developmental trajectories of internalizing and externalizing problems from mid-childhood to late adolescence and childhood risk factors: Findings from a prospective pre-birth cohort

Published online by Cambridge University Press:  04 January 2024

Sarita Bista*
Affiliation:
Specialty of Child and Adolescent Health, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
Robert J. Tait
Affiliation:
National Drug Research Institute & enAble Institute, Faculty of Health Sciences, Curtin University, Perth, WA, Australia
Leon M. Straker
Affiliation:
School of Allied Health, Faculty of Health Science, Curtin University, Perth, WA, Australia
Ashleigh Lin
Affiliation:
The University of Western Australia, Perth, WA, Australia
Katharine Steinbeck
Affiliation:
Discipline of Child and Adolescent Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia The Children’s Hospital at Westmead, Westmead, NSW, Australia
Petra L. Graham
Affiliation:
School of Mathematical and Physical Sciences, Macquarie University, Macquarie Park, NSW, Australia
Melissa Kang
Affiliation:
General Practice Clinical School, Sydney Medical School, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
Sharyn Lymer
Affiliation:
Biostatistics Consultant, Sydney, NSW, Australia
Monique Robinson
Affiliation:
Telethon Institute for Child Health Research, Telethon Kids Institute, The University of Western Australia, Perth, WA, Australia
Jennifer L. Marino
Affiliation:
Specialty of Child and Adolescent Health, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia Department of Obstetrics and Gynaecology, Royal Women’s Hospital, The University of Melbourne, Parkville, VIC, Australia Centre for Adolescent Health, Murdoch Children’s Research Institute, Parkville, VIC, Australia
S. Rachel Skinner
Affiliation:
Specialty of Child and Adolescent Health, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia The Children’s Hospital at Westmead, Westmead, NSW, Australia
*
Corresponding author: Sarita Bista; Email: sarita.bista@sydney.edu.au
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Abstract

There is limited evidence on heterogenous co-developmental trajectories of internalizing (INT) and externalizing (EXT) problems from childhood to adolescence and predictors of these joint trajectories. We utilized longitudinal data from Raine Study participants (n = 2393) to identify these joint trajectories from 5 to 17 years using parallel-process latent class growth analysis and analyze childhood individual and family risk factors predicting these joint trajectories using multinomial logistic regression. Five trajectory classes were identified: Low-problems (Low-INT/Low-EXT, 29%), Moderate Externalizing (Moderate-EXT/Low-INT, 26.5%), Primary Internalizing (Moderate High-INT/Low-EXT, 17.5%), Co-occurring (High-INT/High-EXT, 17%), High Co-occurring (Very High-EXT/High-INT, 10%). Children classified in Co-occurring and High Co-occurring trajectories (27% of the sample) exhibited clinically meaningful co-occurring problem behaviors and experienced more adverse childhood risk-factors than other three trajectories. Compared with Low-problems: parental marital problems, low family income, and absent father predicted Co-occurring and High Co-occurring trajectories; maternal mental health problems commonly predicted Primary Internalizing, Co-occurring, and High Co-occurring trajectories; male sex and parental tobacco-smoking uniquely predicted High Co-occurring membership; other substance smoking uniquely predicted Co-occurring membership; speech difficulty uniquely predicted Primary Internalizing membership; child’s temper-tantrums predicted all four trajectories, with increased odds ratios for High Co-occurring (OR = 8.95) and Co-occurring (OR = 6.07). Finding two co-occurring trajectories emphasizes the importance of early childhood interventions addressing comorbidity.

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

Internalizing (i.e., social withdrawal, anxiety, depression, and somatic complaints) and externalizing (i.e., destructive behaviors, aggression, and attention difficulties) problems often develop in early childhood (Achenbach, Reference Achenbach2001). These problems can be persistent, potentially leading to an adverse developmental pathway through adolescence and young adulthood (Angold & Costello, Reference Angold and Costello1993; Costello et al., Reference Costello, Mustillo, Erkanli, Keeler and Angold2003). High internalizing and externalizing problem behaviors in early childhood are associated with several poor outcomes across childhood, adolescence, and adulthood, including poor academic results, alcohol use disorders, and mood and anxiety disorders (Brennan et al., Reference Brennan, Shaw, Dishion and Wilson2012; Korhonen et al., Reference Korhonen, Luoma, Salmelin, Siirtola and Puura2018; Loth et al., Reference Loth, Drabick, Leibenluft and Hulvershorn2014; Meque et al., Reference Meque, Dachew, Maravilla, Salom and Alati2019; Roza et al., Reference Roza, Hofstra, Van Der Ende and Verhulst2003; Wiggins et al., Reference Wiggins, Mitchell, Hyde and Monk2015). There is a growing body of literature on individual-level trajectories of childhood internalizing and externalizing problems. Most studies investigating individual trajectories have considered internalizing and externalizing problem behaviors as two different pathways to different outcomes and, therefore, focused on either internalizing or externalizing problems (e.g., Achenbach et al., Reference Achenbach, Ivanova, Rescorla, Turner and Althoff2016 for review; Bongers et al., Reference Bongers, Koot, Van Der Ende and Verhulst2004; Sterba et al., Reference Sterba, Prinstein and Cox2007). What is unclear is how changes in these two problem behaviors are related to each other and how their combined effect influences outcomes (Achenbach et al., Reference Achenbach, Ivanova, Rescorla, Turner and Althoff2016; Gilliom & Shaw, Reference Gilliom and Shaw2004).

Co-occurring development of internalizing and externalizing problems

Internalizing and externalizing problems are not mutually exclusive nor independent. Evidence suggests that the childhood psychopathologies categorized as internalizing and externalizing problems are continuous and often co-occur or overlap (Achenbach et al., Reference Achenbach, Ivanova, Rescorla, Turner and Althoff2016; Angold et al., Reference Angold, Costello and Erkanli1999; Caspi & Moffitt, Reference Caspi and Moffitt2018; Colder et al., Reference Colder, Scalco, Trucco, Read, Lengua, Wieczorek and Hawk2013; Gilliom & Shaw, Reference Gilliom and Shaw2004; Oland & Shaw, Reference Oland and Shaw2005; Willner et al., Reference Willner, Gatzke-Kopp and Bray2016). However, the pathways to co-occurrence are not well understood, although several hypotheses have been studied (Angold et al., Reference Angold, Costello and Erkanli1999; Oland & Shaw, Reference Oland and Shaw2005 for review). Some studies support a causal (directional) hypothesis that early externalizing problems prospectively predict risk for internalizing problems or vice versa (Flouri et al., Reference Flouri, Papachristou, Midouhas, Ploubidis, Lewis and Joshi2019; Gooren et al., Reference Gooren, van Lier, Stegge, Terwogt and Koot2011; Loeber & Keenan, Reference Loeber and Keenan1994; Moilanen et al., Reference Moilanen, Shaw and Maxwell2010; Van der Ende et al., Reference Van der Ende, Verhulst and Tiemeier2016). Other studies support a reciprocal hypothesis that internalizing and externalizing problems are reciprocally related to each other and changes in one are often associated with changes in the other (Gilliom & Shaw, Reference Gilliom and Shaw2004; Keiley et al., Reference Keiley, Bates, Dodge and Pettit2000). The general developmental psychopathology model suggests individual differences in the origin, course, and outcomes of normative and psychopathological developmental processes with shared vulnerabilities or risks (e.g., genetic, and environmental influence) causing internalizing and externalizing problems to co-occur (Caspi & Moffitt, Reference Caspi and Moffitt2018; Fanti & Henrich, Reference Fanti and Henrich2010; Oland & Shaw, Reference Oland and Shaw2005 for review; Rutter & Sroufe, Reference Rutter and Sroufe2000; Wang et al., Reference Wang, Eisenberg, Valiente and Spinrad2016, Reference Wang, Galán, Lemery-Chalfant, Wilson and Shaw2020). Irrespective of approach, co-occurring problems are associated with unique outcomes. For example, compared to children having internalizing or externalizing problems alone, children with co-occurring internalizing and externalizing problems often show more mental health and physical problems, increased behavioral and/or psychosocial maladjustment, and poorer academic performance during their childhood and adolescence (Newman et al., Reference Newman, Moffitt, Caspi and Silva1998; Oland & Shaw, Reference Oland and Shaw2005). However, the etiology of co-occurring internalizing and externalizing problems remains unclear. Moreover, many studies have distinguished internalizing and externalizing problems as distinct forms of problem behaviors, despite evidence showing co-occurrence of these problem behaviors (Achenbach et al., Reference Achenbach, Ivanova, Rescorla, Turner and Althoff2016). Methodologically strong empirical research is needed to understand the etiology of this co-occurrence from childhood to adolescence.

Previous studies of developmental psychopathology propose that co-occurring problems represent distinct, meaningful syndromes, and therefore, highlight the importance of investigating co-occurrence from a developmental perspective using longitudinal statistical methods to understand how children diverge from healthy development (low or moderate internalizing and externalizing) and follow trajectories of primary (high internalizing with low externalizing or vice versa) or co-occurring problem behavior (high internalizing with high externalizing) (e.g., Fanti & Henrich, Reference Fanti and Henrich2010; Gilliom & Shaw, Reference Gilliom and Shaw2004; Keiley et al., Reference Keiley, Lofthouse, Bates, Dodge and Pettit2003; Oland & Shaw, Reference Oland and Shaw2005; Shi et al., Reference Shi, Ettekal, Deutz and Woltering2020). Advances in person-centered methods allow researchers to investigate heterogeneity in co-occurring developmental trajectories over time (Fanti & Henrich, Reference Fanti and Henrich2010; Gilliom & Shaw, Reference Gilliom and Shaw2004; Hinnant & Mona, Reference Hinnant and Mona2013; Wiggins et al., Reference Wiggins, Mitchell, Hyde and Monk2015). Among these methods, longitudinal data-driven methods such as parallel-process latent growth mixture models (e.g., latent class growth analysis (LCGA), growth mixture model (GMM)) are useful for identifying heterogeneous subclasses with varying joint trajectories (Muthén & Muthén, Reference Muthén and Muthén2000). Few studies have used such methods to detect distinct joint trajectories of internalizing and externalizing problems from childhood to adolescence (e.g., Duprey et al., Reference Duprey, Oshri and Liu2020; Fanti & Henrich, Reference Fanti and Henrich2010; Picoito et al., Reference Picoito, Santos and Nunes2020; Shi et al., Reference Shi, Ettekal, Deutz and Woltering2020; Wiggins et al., Reference Wiggins, Mitchell, Hyde and Monk2015), with most providing evidence of different trajectory subtypes including primary internalizing, primary externalizing, co-occurring internalizing and externalizing, and with a relatively larger proportion of the sample having low internalizing and externalizing problems (normative or low-problems). However, less is known about the etiology of these trajectory subtypes.

Childhood individual and family environmental factors

Understanding how individual factors and family environment relate to changing problem behaviors and their co-occurrence across childhood and adolescence is key to understanding the etiology of developmental psychopathology (Angold et al., Reference Angold, Costello and Erkanli1999; Cicchetti & Rogosch, Reference Cicchetti and Rogosch1996, Reference Cicchetti and Rogosch2002; Leve et al., Reference Leve, Kim and Pears2005; Oland & Shaw, Reference Oland and Shaw2005). Developmental psychopathological research encourages consideration of the matrix of individual and social-contextual contributors or antecedents, and a multiplicity of processes and outcomes of individuals at a person-oriented level within existing longitudinal data (Cicchetti & Rogosch, Reference Cicchetti and Rogosch1996; Rutter & Sroufe, Reference Rutter and Sroufe2000). To understand these dynamic processes, studies linking childhood individual, and environmental, including family and social-contextual, risk-factors with variations in problem behaviors have identified numerous risks for internalizing and externalizing problems in childhood and adolescence, and suggest that children following different trajectories of internalizing and externalizing problems may also differ in these risk-factors (Gilliom & Shaw, Reference Gilliom and Shaw2004; Keiley et al., Reference Keiley, Lofthouse, Bates, Dodge and Pettit2003; Leve et al., Reference Leve, Kim and Pears2005; Oland & Shaw, Reference Oland and Shaw2005). In terms of age, earlier longitudinal studies assessing internalizing and externalizing problems from childhood to adolescence suggest that, in general, internalizing behavior trajectory is relatively stable during childhood and increases during adolescence (Bongers et al., Reference Bongers, Koot, Van der Ende and Verhulst2003). However, Achenbach et al. (Reference Achenbach, Howell, Quay, Conners and Bates1991) provided evidence that internalizing problems (somatic complaints, withdrawal, anxiety, and depression) increased with age among clinically referred children aged 4 to 16 years. Evidence also shows that internalizing trajectories vary by sex, and being female is a risk factor for internalizing problems (Achenbach et al., Reference Achenbach, Howell, Quay, Conners and Bates1991; Keiley et al., Reference Keiley, Lofthouse, Bates, Dodge and Pettit2003; Leve et al., Reference Leve, Kim and Pears2005), with girls showing higher level of internalizing problems with steeper increases from childhood to adolescence than boys (Angold et al., Reference Angold, Erkanli, Silberg, Eaves and Costello2002; Keiley et al., Reference Keiley, Lofthouse, Bates, Dodge and Pettit2003). However, a meta-analysis of 310 samples of children aged 8 to 16 years using Children’s Depression Inventory found that girls’ depression scores are slightly lower than boys’ during childhood, but increase during adolescence between ages 12 and 16 surpassing boys’ scores (Twenge & Nolen-Hoeksema, Reference Twenge and Nolen-Hoeksema2002). Additionally, studies on childhood internalizing problems show that environmental risk factors such as low SES, exposure to a negative familial context, and maternal depression are associated with internalizing problems (Duggal et al., Reference Duggal, Carlson, Sroufe and Egeland2001; Fanti & Henrich, Reference Fanti and Henrich2010; Keiley et al., Reference Keiley, Bates, Dodge and Pettit2000; Leve et al., Reference Leve, Kim and Pears2005), and child temperamental unadaptability and female gender are predictors of higher internalizing symptoms (Keiley et al., Reference Keiley, Lofthouse, Bates, Dodge and Pettit2003).

There are variations in findings regarding externalizing problems from childhood to adolescence. While some longitudinal studies find decreasing externalizing behaviors for both sexes over time from childhood to adolescence (Bongers et al., Reference Bongers, Koot, Van der Ende and Verhulst2003, Reference Bongers, Koot, Van Der Ende and Verhulst2004; Leve et al., Reference Leve, Kim and Pears2005; Shi & Ettekal, Reference Shi and Ettekal2020; Shi et al., Reference Shi, Ettekal, Deutz and Woltering2020), others show that externalizing behavior such as status violations increases over time, although aggression, opposition, and property violations decrease on average (Bongers et al., Reference Bongers, Koot, Van Der Ende and Verhulst2004). In an epidemiological study, Barker et al. (Reference Barker, Oliver and Maughan2010) found three trajectories of conduct problems across ages 4 to 13 years. Among the “early-onset persistent” group, problems persisted over the whole period, while behavior problems decreased and prosocial behaviors increased among the “childhood-limited” group, and adjustment problems (internalizing as well as externalizing) increased while prosocial behaviors decreased after about 10 years of age among the “adolescent-onset” group (Barker et al., Reference Barker, Oliver and Maughan2010). Several studies suggest that, in general, males develop higher externalizing difficulties than females (Achenbach et al., Reference Achenbach, Howell, Quay, Conners and Bates1991; Bongers et al., Reference Bongers, Koot, Van Der Ende and Verhulst2004; Keiley et al., Reference Keiley, Bates, Dodge and Pettit2000, Reference Keiley, Lofthouse, Bates, Dodge and Pettit2003). Some studies have found that early deficiencies in cognitive functioning, and difficult temperament, are related to chronic externalizing problems (Fanti & Henrich, Reference Fanti and Henrich2010; Miner & Clarke-Stewart, Reference Miner and Clarke-Stewart2008). Furthermore, family adversity, maternal depression, low socioeconomic status (SES), single-parent status, and child temperament have been found as the strongest predictors of externalizing problems (Ackerman et al., Reference Ackerman, D’Eramo, Umylny, Schultz and Izard2001; Brennan et al., Reference Brennan, Hall, Bor, Najman and Williams2003; Fanti & Henrich, Reference Fanti and Henrich2010; Keiley et al., Reference Keiley, Lofthouse, Bates, Dodge and Pettit2003; Leve et al., Reference Leve, Kim and Pears2005). For example, Leve et al. (Reference Leve, Kim and Pears2005) provided evidence of childhood temperament, family environment, and interaction of these variables as predictors of changes in externalizing behaviors of children across the ages of 5 to 17 years, showing sex differences, with maternal depression predicting increases in boys’ externalizing behavior when impulsivity was low, and harsh discipline predicting increases in girls’ externalizing behavior when impulsivity was high or when fear/shyness was low.

Additionally, risk factors that predict co-occurring problems may be distinct from those that predict single-problem behaviors (Chen & Simons-Morton, Reference Chen and Simons-Morton2009; Duprey et al., Reference Duprey, Oshri and Liu2020; Fanti & Henrich, Reference Fanti and Henrich2010; Keiley et al., Reference Keiley, Lofthouse, Bates, Dodge and Pettit2003; Shi et al., Reference Shi, Ettekal, Deutz and Woltering2020), although there is a concept of common and unique risk-factors. A common risk factor predicts multiple trajectory subtypes, so the same childhood factor can lead to developing different forms of problem behaviors (Cicchetti & Rogosch, Reference Cicchetti and Rogosch1996), whereas a unique factor predicts only one specific trajectory (Keiley et al., Reference Keiley, Lofthouse, Bates, Dodge and Pettit2003). The concept of a common risk factor aligns with the theory of multifinality, which specifies that a single vulnerability factor, any one component, or a single starting point, may result in diverse developmental outcomes (Cicchetti & Rogosch, Reference Cicchetti and Rogosch1996). This concept of multifinality pertains to the question to what extent the early childhood risk factors predict differential trajectories of pure vs. co-occurring behaviors (Fanti & Henrich, Reference Fanti and Henrich2010). For example, as evidence of multifinality, Fanti & Henrich (Reference Fanti and Henrich2010) found that medical risk, difficult temperament, or maternal depression were risk factors for the co-occurring trajectory as well as pure externalizing trajectory.

Furthermore, risk factors for problem behaviors act in a cumulative manner (Atzaba-Poria et al., Reference Atzaba-Poria, Pike and Deater-Deckard2004), and empirical studies have examined additive effects of multiple individual risk factors including child’s sex, ethnicity, temperament, and personality (Fanti & Henrich, Reference Fanti and Henrich2010; Leve et al., Reference Leve, Kim and Pears2005; Shi et al., Reference Shi, Ettekal, Deutz and Woltering2020; Wang et al., Reference Wang, Eisenberg, Valiente and Spinrad2016); and, cognitive and language problems (Bornstein et al., Reference Bornstein, Hahn and Suwalsky2013; Fanti & Henrich, Reference Fanti and Henrich2010; Flouri et al., Reference Flouri, Midouhas and Joshi2014; Petersen et al., Reference Petersen, Bates, D'Onofrio, Coyne, Lansford, Dodge, Pettit and Van Hulle2013; Shi et al., Reference Shi, Ettekal, Deutz and Woltering2020). There are also family environmental risk factors that appear to have additional effects, such as maternal depression, family adversity, maternal education, peer rejection, low support home environment (Fanti & Henrich, Reference Fanti and Henrich2010; Shi et al., Reference Shi, Ettekal, Deutz and Woltering2020), harsh parenting (Wiggins et al., Reference Wiggins, Mitchell, Hyde and Monk2015), maltreatment (Duprey et al., Reference Duprey, Oshri and Liu2020), maternal substance use, low income, and parental status (Picoito et al., Reference Picoito, Santos and Nunes2020). Evidence shows that child’s temperament and maternal depression are commonly associated with primary externalizing and co-occurring trajectories (e.g., Fanti & Henrich, Reference Fanti and Henrich2010). Further, a combination of more adverse childhood individual and family environmental factors has been associated with more severe co-occurring trajectories (e.g., Fanti & Henrich, Reference Fanti and Henrich2010; Shi et al., Reference Shi, Ettekal, Deutz and Woltering2020).

In understanding the etiology of psychopathology, extensive research has conceptualized early childhood temperament traits as a core feature in explaining childhood or adolescent psychopathology. Although some suggest that genetic factors also have an important role (Wang et al., Reference Wang, Galán, Lemery-Chalfant, Wilson and Shaw2020), evidence is limited. Studies focusing on temperament suggest that early childhood temperament traits can be broadly defined in three domains: reactivity (which encompasses irritability, and negative affect, i.e., behaviors responding to frustration, anger, fear, and sadness); sociability; and attention (Rothbart, Reference Rothbart2007). These traits influence concurrent and prospective internalizing and externalizing problems throughout childhood and adolescence (Fanti & Henrich, Reference Fanti and Henrich2010; Forbes et al., Reference Forbes, Rapee, Camberis and McMahon2017; Keiley et al., Reference Keiley, Lofthouse, Bates, Dodge and Pettit2003; Shi et al., Reference Shi, Ettekal, Deutz and Woltering2020; Wang et al., Reference Wang, Eisenberg, Valiente and Spinrad2016). Among these domains, high levels of childhood reactivity, i.e., negative affect or negative emotionality, which also encompass temper-tantrums, are found as risk factors for subsequent psychopathology (Davis et al., Reference Davis, Votruba-Drzal and Silk2015; Forbes et al., Reference Forbes, Rapee, Camberis and McMahon2017; Hoyniak et al., Reference Hoyniak, Donohue, Quiñones-Camacho, Vogel, Perino, Hennefield, Tillman, Barch and Luby2022; Leve et al., Reference Leve, Kim and Pears2005; Potegal & Davidson, Reference Potegal and Davidson2003; Roy et al., Reference Roy, Klein, Angelosante, Bar-Haim, Leibenluft, Hulvershorn, Dixon, Dodds and Spindel2013; Van den Akker et al., Reference Van den Akker, Hoffenaar and Overbeek2022; Wang et al., Reference Wang, Eisenberg, Valiente and Spinrad2016). Forbes and colleagues (2017) showed that high levels of preschool emotional reactivity predicted higher symptom trajectories of depression, anxiety, conduct disorder, and attention-deficit hyperactivity disorder from childhood to early adolescence (Forbes et al., Reference Forbes, Rapee, Camberis and McMahon2017). Temper-tantrums are behavioral displays largely due to frustration (Wakschlag et al., Reference Wakschlag, Estabrook, Petitclerc, Henry, Burns, Perlman, Voss, Pine, Leibenluft and Briggs-Gowan2015) and emotion dysregulation (Vogel et al., Reference Vogel, Jackson, Barch, Tillman and Luby2019; Wakschlag et al., Reference Wakschlag, Choi, Carter, Hullsiek, Burns, McCarthy, Leibenluft and Briggs-Gowan2012), and are characterized in parental narratives as: crying, screaming, shouting, falling to the floor, kicking, hitting, pulling/pushing, running away, freezing, stamping, whining, throwing something, and clinging to someone (Potegal & Davidson, Reference Potegal and Davidson2003; Van den Akker et al., Reference Van den Akker, Hoffenaar and Overbeek2022). These are common early childhood behaviors, but existing research, while limited, suggests that persistent and severe temper-tantrums are clinically significant (Carlson et al., Reference Carlson, Potegal, Margulies, Gutkovich and Basile2009) and demonstrate risk for later psychopathology (Hoyniak et al., Reference Hoyniak, Donohue, Quiñones-Camacho, Vogel, Perino, Hennefield, Tillman, Barch and Luby2022; Roy et al., Reference Roy, Klein, Angelosante, Bar-Haim, Leibenluft, Hulvershorn, Dixon, Dodds and Spindel2013; Van den Akker et al., Reference Van den Akker, Hoffenaar and Overbeek2022) and poorer outcomes in adulthood, e.g., poorer occupational and marital functioning (Caspi et al., Reference Caspi, Elder and Bem1987). Low childhood self-regulation (including temper-tantrums) is associated with negative adolescent academic, health, and mental wellbeing outcomes (Howard & Williams, Reference Howard and Williams2018). However, the common and unique associations of temper-tantrums in differentiating the development of primary and co-occurring internalizing and externalizing problems remain unclear. Similarly, research shows that poor early language skills are associated with both internalizing and externalizing problems (Chow & Wehby, Reference Chow and Wehby2018 for review and meta-analysis); however, evidence is limited on how childhood language ability predicts subsequent co-occurring internalizing and externalizing problems (e.g., Shi et al., Reference Shi, Ettekal, Deutz and Woltering2020).

Thus, there is limited evidence on how childhood individual and family environmental factors differentiate longitudinal psychopathology trajectory subtypes, particularly co-occurring trajectories, with multifinality an open question (Cicchetti & Rogosch, Reference Cicchetti and Rogosch1996). Additionally, the findings of extant studies are inconsistent, due to methodological differences and because most studies in this area are based on clinical, at-risk, or single-gender samples. In sum, there is very limited methodologically strong research using large longitudinal community samples including all sexes and a broad range of childhood risk-factors, allowing greater understanding of joint development of internalizing and externalizing problems from childhood to adolescence.

The present study adds to the literature by focusing on examining heterogeneity in the co-development of internalizing and externalizing problems from childhood through mid-adolescence using a person-centered approach (parallel-process LCGA) and determining a broad range of childhood risk factors including child’s individual factors (sex, temper-tantrum, and language ability), maternal factors (age, ethnicity, mental health, education) and family factors (marital problems, father living with family, family income, parental smoking, parental substance use) for heterogenous trajectories in a large longitudinal representative community sample. Although the present study takes a developmental psychopathology perspective, embedded in an ecological framework (Bronfenbrenner, Reference Bronfenbrenner1977), this is a data-driven study and therefore does not aim to test hypotheses arising from theoretical aspects of developmental psychopathology. However, we seek to provide evidence for the concepts of multifinality of developmental psychopathology, i.e., common factors associated with varied trajectories, as well as unique factors predicting a particular trajectory, by including individual, maternal, and family factors, based on the prior evidence of association of these factors with internalizing, externalizing and co-occurring problems, and based also on the availability of the data.

Our specific aims are to investigate empirically derived joint developmental trajectories of primary and co-occurring internalizing and externalizing problems from ages 5 to 17 years, and to determine how individual, maternal, and family risk-factors are associated with those trajectory subtypes.

Methods

Design

The analysis uses deidentified data from Generation 2 (Gen2) participants of The Raine Study, an intergenerational prospective cohort study. Gen2 were the children born (live births, n = 2868; female = 1414 (49%)) from women (Generation 1 (Gen1); n = 2900), recruited from a tertiary maternity hospital and nearby clinics in Perth, Western Australia from May 1989 to November 1991. Gen2 questionnaire data include follow-ups in the perinatal period and years 1, 2, 3, 5, 8, 10, 14, 17, 18, 20, 22, and 27 years: these follow-up years are approximately the mean age at each survey. In addition to data from Gen1 at giving birth, this study included follow-ups at 2, 3, 5, 8, 10, 14, and 17 years. Detailed information on the Gen2 cohort profile is published elsewhere (McKnight et al., Reference McKnight, Newnham, Stanley, Mountain, Landau, Beilin, Puddey, Pennell and Mackey2012; Straker et al., Reference Straker, Hall, Mountain, Howie, White, McArdle and Eastwood2015, Reference Straker, Mountain, Jacques, White, Smith, Landau, Stanley, Newnham, Pennell and Eastwood2017). Previous comparisons of the Raine Study cohort at six timepoints including four used in this study (birth, year 8, year 14, and year 17) and two in adulthood (years 20 and 22) with the contemporaneous Western Australian population found that the study cohort remained broadly representative (Straker et al., Reference Straker, Mountain, Jacques, White, Smith, Landau, Stanley, Newnham, Pennell and Eastwood2017). The Raine Study and each follow-up received institutional ethics approval. These now have overarching approval from the University of Western Australia (RA/4/20/5722). This specific project was approved by Curtin University (HRE2019-0774).

Participants

We analyzed data from the 2393 Gen2 participants (83.4% of the total cohort; 1236 (51.7%) male sex at birth) with child internalizing and externalizing scores for at least one follow-up at 5, 8, 10, 14, or 17 years. Participants missing these data at all follow-ups (n = 475) were excluded. We examined whether the included and excluded participants were different in terms of their characteristics using variables with no missing data (i.e., child’s (Gen2) sex at birth, mother’s age at child’s birth, mother’s ethnicity, and father’s ethnicity). This analysis showed that the included and excluded participants were significantly different in terms of these demographic variables. Excluded participants were more likely to be girls (54.1% vs 48.4% among included participants, p = 0.0219); to have younger mothers at child’s birth (mean age = 25.7 years vs. 28.5 years among included participants, p < .0001); non-White mothers (18.3% vs 11.3% among included participants, p < .0001); and non-White fathers (20.6% vs 11.0% among included participants, p < .0001). To reduce the bias caused by these differences, we controlled for these variables in the predictive models for the subsequent analysis.

Measures

Outcome variables

Internalizing and externalizing T-scores were the outcome variables for the trajectory analysis, assessed at years 5, 8, 10, 14, and 17 follow-ups using Child Behavior Checklist (CBCL/4–18 questionnaire with 118 items reported by parents at all ages (Achenbach, Reference Achenbach1991a), a widely used, reliable tool for psychological categorization (Warnick et al., Reference Warnick, Bracken and Kasl2008). A different version of the questionnaire (CBCL/2–3), designed for 2–3-year-olds, was used for the year 2 follow-up. In CBCL/2–3, only 59 of the items have counterparts in CBCL/4–18, while 40 items were specifically designed for the younger age group (Achenbach, Reference Achenbach1991b). Therefore, we excluded age 2 from our trajectory analysis. Further, to ensure continuity across follow-ups, parent report was used for all ages even when youth self-report data were available (years 14 and 17).

The internalizing problems composite scale includes symptoms of mood disturbance, including anxiety, depression, and social withdrawal. The externalizing problems composite scale includes symptoms of impulsivity, hyperactivity, conflict with others, and violation of social norms (Achenbach, Reference Achenbach2001). Each CBCL item has a 3-point scale: 0 (not true), 1 (somewhat or sometimes true), and 2 (very true or often true). Raw internalizing and externalizing composite scale scores were converted to T-scores using age- and gender-standardized normative data included in the manual (Achenbach, Reference Achenbach1991a, Reference Achenbach2001). T-scores are continuous age- and gender-standardized scores with a normal distribution (mean = 50, standard deviation = 10), allowing comparisons across groups or trajectories from school-age childhood to adolescence. Higher internalizing and externalizing composite scale T-scores indicate greater problems, with T-scores ≤59 considered as non-clinical/low-problems/normal, 60-63 as sub-clinical or borderline, and >63 as clinically significant.

Unlike T-scores for the CBCL individual syndrome (“narrowband”) scales (e.g., aggressive behavior), T-scores for the internalizing and externalizing composite (“broadband”) scales are not truncated at lower values. Therefore, using T-scores for the internalizing and externalizing composite scales for the statistical analyses yields the results similar to those using the raw scores, without losing any differentiation by truncation. Furthermore, for analyses that include both sexes and different age ranges, using T-scores for internalizing and externalizing problems takes into account the sex/age differences in scores and limits confounding (Achenbach, Reference Achenbach1991a, Reference Achenbach2001). See Supplementary Appendix 1 for more information on using T-scores.

Predictors

We used a broad range of childhood individual and family environment variables as predictors of joint trajectories. Childhood individual characteristics included: sex recorded at birth (male/female); language ability indicator measured by a single question, ‘Is child’s speech clear/Child difficult to understand’ (reported at year 3 follow-up and dichotomized as: No/clear <75% of time vs Clear $\geq$ 75% time/all time); and ‘Temper-tantrum’, one item from CBCL-2/3 (a version administered to children at ages 2/3, (Achenbach, Reference Achenbach1992)) reported at year 2 follow-up (dichotomized as: Not true/Somewhat or sometimes true vs Very or often true). Although temper-tantrum is one of the internalizing/externalizing problem items of the CBCL questionnaire, we used this variable measured at year 2 follow-up as a predictor and, therefore, does not overlap with the same item of internalizing/externalizing problems used for the trajectory analysis (i.e., age 5 and onwards).

Maternal variables included: mother’s age at child’s birth (referred as ‘mother’s age’ hereafter); mother’s ethnicity (dichotomized: White/non-White); mother’s highest level of education (reported at year 8 follow-up, categorized as: 1 no qualifications/did not complete high school; 2 high school/trade/technical education; and 3 university degree); and mother’s lifetime mental/emotional problems (reported at year 8 follow-up: yes/no).

Family variables included: father’s ethnicity (dichotomized: White/non-White); father’s highest level of education (reported at year 8 follow-up, categorized as: 1 no qualifications/did not complete high school; 2 high school/trade/technical education; and, 3 university degree); family income (reported at year 5 follow-up and dichotomized: ≤ $40,000 vs >$40,000 per year)(in 1995/96 the median income in Australia was $28,494); father living with family (reported at year 5 follow-up: yes/no); parental marital problems in previous year (reported at year 5 follow-up: yes/no); either parent smoking cigarettes daily (reported at year 3 follow-up and dichotomized: yes/no); and, family member smoking other substances (e.g., cigars/pipe/cannabis/cocaine) (reported at year 3 follow-up and dichotomized: yes/no).

For reasons of data availability, we used two variables measured at the year 8 follow-up: mother’s highest level of education and lifetime mental health problems, as risk factors in the predictive models. Our justification is that the date of highest qualification was likely to precede the follow-up and that lifetime mental health problems, by design, includes problems at earlier ages. We did not use father’s mental health problems and father’s highest qualifications in the predictive model due to relatively strong (medium-high to high) associations with some other variables and potential multicollinearity problems. Also, there may be an accuracy issue for father’s mental health problems because the questionnaire including this variable was answered by mothers as the primary caregivers in most cases, although there was a small proportion of the cases evaluated by fathers or other close caregivers. See Table S1 for the bivariate associations among characteristic variables, and Table S2 for the multicollinearity check in Supplementary Appendix 1.

Statistical analysis

We used SAS Studio 3.8 to conduct descriptive analysis. Trajectory analyses using latent class growth analysis (LCGA) were performed in Mplus Version 8.8 using a full information maximum likelihood approach with robust standard errors (Enders, Reference Enders2010), which handles missing data in internalizing (INT) and externalizing (EXT) problems. In assessing the development trajectories of children’s internalizing and externalizing problems at five follow-ups (years 5, 8, 10, 14, and 17), prior to specifying latent classes, we specified unconditional single-class linear, quadratic, and cubic latent growth models as the initial examination of the means and growth patterns of observed internalizing and externalizing variables over time. The model fit results and graphs indicated the quadratic model for this data. The cubic models for both internalizing and externalizing data had nonconvergence and nonidentification problems. Five time points are enough for quadratic growth models, but not to detect the cubic trend (Whittaker & Khojasteh, Reference Whittaker and Khojasteh2017) (See Supplementary Appendix 2 for more detail on choosing the quadratic model).

We, then, estimated unconditional LCGA models (Nagin & Nagin, Reference Nagin and Nagin2005) separately for each internalizing and externalizing process with varying numbers of classes (i.e., one to six classes) specifying linear and quadratic slopes to assess the shape of the trajectories and number of classes. This preliminary analysis suggested quadratic slopes for both internalizing and externalizing processes. Then, to identify the joint developmental trajectories, we performed unconditional quadratic parallel-process LCGA with varying numbers of classes (i.e., two to six classes) allowing means of intercept and slope growth factors to vary across classes.

To select an optimal parallel-process LCGA model, model fit was assessed using a combination of criteria: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), sample-adjusted BIC (saBIC), number of parameters, and Vuong-Lo-Mendell-Rubin Likelihood Ratio Test (VLMR LRT), and Bootstrapped Likelihood Ratio Test (BLRT) (for the contender models only). Lower absolute values of AIC, BIC, and saBIC indicate better model fit, and significant VLMR LRT and BLRT p-values (<0.05) suggest the current model with an additional class (k) significantly improves model fit compared to the previous model with one less class (k-1) (Nylund et al., Reference Nylund, Asparouhov and Muthén2007). Additionally, a combination of other selection criteria was also considered: entropy and class-specific average posterior probability of assignment (APPA) for classification quality, sample size (>5%), substantive importance, and interpretability of each trajectory class (van der Nest et al., Reference van der Nest, Lima Passos, Candel and van Breukelen2020). Entropy close to 0.8 or greater indicates adequate classification precision, and APPA values close to 1 (ideally>0.8) indicate a good fit and greater precision of the members’ assignment to the class (Muthén & Muthén, Reference Muthén and Muthén2000; Nagin & Nagin, Reference Nagin and Nagin2005; Nagin, Reference Nagin1999; Weller et al., Reference Weller, Bowen and Faubert2020; van der Nest et al., Reference van der Nest, Lima Passos, Candel and van Breukelen2020). See Appendix 2 for a brief review of the cutoff values of entropy and APPA.

Then, we applied a manual three-step BCH (short form of Bolck, Croon, and Hagenaars (Bolck et al., Reference Bolck, Croon and Hagenaars2004)) method (Asparouhov & Muthén, Reference Asparouhov and Muthén2021) for the LCGA models which were the candidates for the selection. This method saved BCH weights and predicter variables for the further analyses in the next step. The BCH weights account for the measurement error of latent class variables (See Supplementary Appendix 2 for more description of BCH method).

In the next step, multinomial logistic regression using BCH weights was performed to identify predictors of the trajectory classes. This multinomial logistic regression model was specified using all predictors (child, maternal, and family variables) simultaneously so that estimates for each predictor are adjusted for effects of other predictors, i.e., controlling for potential confounding effects. Prior to this, we examined bivariate associations among categorical variables using Chi-squared tests for Phi Coefficients (2x2 contingency tables) and Cramer’s V tests for larger contingency tables to check the potential multicollinearity problems. We also performed Variance Inflation Factor and Tolerance to examine the multicollinearity problems among the variables included in the predictive models (See Table S2 for the results of these tests in Supplementary Appendix 1). Missing data in the predictors were imputed before performing multinomial logistic regressions using multiple imputations method (using 100 datasets) which is a part of the BCH method in Mplus (Asparouhov & Muthén, Reference Asparouhov and Muthén2021). See Supplementary Appendix 2 for more on the multiple imputation methods applied here.

Results

Optimal class joint trajectory model

Based on the model fit indices and other selection criteria (Table 1), we selected a five-class parallel-process LCGA model which included: Low-problems (Low-INT/Low-EXT, 29%, n = 695), Moderate Externalizing (Moderate-EXT/Low-INT, 26.5%, n = 634), Primary Internalizing (Moderate High-INT/Low-EXT, 17.5%, n = 420), Co-occurring (High-INT/High-EXT, 17%, n = 406), High Co-occurring (Very High-EXT/High-INT, 10%, n = 238) trajectory classes (Figure 1). See Supplementary Appendix 3 for more on selection of the five-class model. We also performed Wald chi-squared tests and z-tests using MODEL TEST and MODEL CONSTRAINTS commands in Mplus to test whether the identified classes were significantly different in terms of mean intercept and mean slope growth factors (each test conducted separately for internalizing trajectories and externalizing trajectories from the parallel-process model). These tests showed that the identified trajectory classes were significantly different (p < .01) in intercepts as well as slopes of internalizing problem trajectories as well as externalizing problem trajectories. See Table S3 in Supplementary Appendix 3 for estimated means of intercept, slope, and quadratic slope growth factors of the five-class model.

Table 1. Model fit comparison of models with an increasing number of trajectory classes

Note. LRT, Likelihood Ratio Test.

Figure 1. Joint trajectories of internalizing (INT) and externalizing (EXT) T-scores from age 5 to 17 years for each class of five-class parallel-process LCGA model.

Descriptive characteristics of overall sample and trajectory classes

Table 2 presents descriptive statistics of outcome variables. Both mean internalizing and externalizing T-scores gradually declined during the adolescent period. At each timepoint, correlations between internalizing (INT) and externalizing (EXT) T-scores were moderately high, ranging from 0.57 to 0.62.

Table 2. Descriptive statistics and correlations for internalizing (INT) and externalizing (EXT) T-scores

Note. *All correlation values were statistically significant with p < .001. Age is the approximate mean age at the respective follow-up year. Std Dev = standard deviation.

Table 3 presents descriptive characteristics of the sample (n = 2393; 52% boys, 48% girls) and each trajectory class. Characteristics were measured at years 3 to 8 follow-ups. The sample was characterized by larger proportions of children who had mother older than 28 years (mean age = 28.5 years) at child’s birth (54%), White parents (89%), parents with qualification of high school/trade/technical education (mothers: 55% and fathers: 57%), lower family income (≤$40,000/year, 57%), mother with no mental health problems (78%), father living with family (74%), parents reporting no marital problems (84%), parents not smoking cigarettes (73%), and none in the family smoking other substances (e.g., cannabis) (88%).

Table 3. Descriptive characteristics of the sample (n = 2393) and five joint trajectory classes based on the most likely class membership*

* Note: Proportions of the classes estimated by the model and the proportions of the most likely class membership are slightly different in Mplus results.

a Y = follow-up year which approximately equates to mean age of child at that year.

b qual. = qualification.

c MH = mental health.

d For comparative purposes, $40,000 in 1995 adjusted for inflation would be $73,492 in 2021 (Source: Reserve Bank of Australia. (2022). Inflation Calculator. Accessed 14 Sept 2022; Available from: https://www.rba.gov.au/calculator/annualDecimal.html).

Descriptive characteristics of each trajectory class in Table 3 showed that the Low-problems class was characterized by larger proportions of children with older mothers, White fathers, both parents with university degrees, both parents without mental health conditions, fathers living with the family, higher family income (>$40,000/year), parents who were nonsmokers, and no one in the family smoking other substances. The Moderate Externalizing class had larger proportions of children who had White mothers, mothers with no mental health conditions, and parents reporting no marital problems. The Primary Internalizing class was characterized by larger proportions of children whose mothers were older, non-White, with mental health problems, both parents with university degree, father living in the home, and higher family income. Larger proportions of children in the two high-problem co-occurring classes, Co-occurring, and High Co-occurring, had mothers who were younger (15–20 years) at child’s birth, had no educational qualifications and had mental health problems, had fathers not living with family, had parents reporting marital problems, had parent(s) who smoked and family members smoking other substances, and lower family income. Furthermore, in the High Co-occurring class, larger proportions of participants were boys and had fathers with no qualification.

Multinomial logistic regression of trajectory classes: Association with childhood individual and family predictors

After selecting the five-class model, multinomial logistic regression was performed to determine the factors that predicted class membership in each joint trajectory class of internalizing and externalizing problems, controlling for the effects of other childhood predictors. Table 4 presents the odds ratios (ORs) and 95% confidence intervals (CIs) for predictors in each class, considering the Low-problems class as the reference group.

Table 4. Multinomial logistic regression: Predictors of joint trajectory classes of internalizing and externalizing problems from childhood to mid-adolescence, considering Low-Problems as a reference class (N = 2393)

Note: Significant (p < 0.05) estimates are bolded.

a Mean standardized. SD = standard deviation.

b Y = follow-up year which approximately equates to mean age of child at that year.

Compared with the Low-problems class (Table 4), those in the Moderate Externalizing class were more likely to have persistent temper-tantrums, younger mothers, and parent(s) who smoked cigarettes. Children in the Primary Internalizing class were more likely to have persistent temper-tantrums, speech difficulties (less/not clear), non-White mothers, and mothers with mental health problems. In the Co-occurring class, children were more likely to have persistent temper-tantrums, mothers with mental health problems, parents with marital problems, family members smoking other substances, lower family income, and father not living at home. In the High Co-occurring class, children were more likely to be male, and have persistent temper-tantrums, younger mothers, mothers with mental health problems, parents with marital problems, lower family income, and parent(s) who smoked cigarettes (Table 4).

Further multinomial logistic regression analyses were performed to compare trajectory classes with each other, other than the reference class. See Table S4 in Supplementary Appendix 4 for the results. These results showed that, compared with High Co-occurring group, children in the Co-occurring group were more likely to be girls. Compared with the Primary Internalizing group, the Co-occurring group had children with persistent temper-tantrums, White mothers, and low family income, and children in the High Co-occurring group were boys, who had persistent temper-tantrums, had younger mothers, White mothers, parents with marital problems, low family income, and mothers less likely to be university educated. Compared with the Moderate Externalizing group, children in the Co-occurring group were likely to have persistent temper-tantrums, non-White mothers, parents with marital problems and maternal mental health problems, and children in the High Co-occurring group were boys, had persistent temper-tantrums, had not clear/less clear speech, had parents with marital problems, had mothers with mental health problems, and had low family income.

Discussion

The present study contributes by investigating the development of co-occurring and distinct trajectories of internalizing and externalizing problems from childhood to mid-adolescence in a large general population cohort sample using parallel-process LCGA (i.e., a person-centered approach) and a broad range of childhood individual, maternal, and family factors predicting membership in these trajectories. We identified five joint trajectories of internalizing and externalizing problems, including two distinct and clinically significant high co-occurring trajectories, a moderately high internalizing trajectory, and two different trajectories exhibiting lower levels of both internalizing and externalizing problem behaviors. We identified a mix of unique and common risk factors predicting these trajectories and found multiple unique and common, but more adverse childhood individual, maternal, and family factors associated with the two high co-occurring trajectories compared to the lower problem trajectories.

Trajectory classes

We found some differences in the joint trajectory classes identified in the current study to those identified previously using parallel-process latent growth mixture modeling (e.g., Duprey et al., Reference Duprey, Oshri and Liu2020; Flouri et al., Reference Flouri, Papachristou, Midouhas, Joshi, Ploubidis and Lewis2018; Hinnant & Mona, Reference Hinnant and Mona2013; Oerlemans et al., Reference Oerlemans, Wardenaar, Raven, Hartman and Ormel2020; Picoito et al., Reference Picoito, Santos and Nunes2020; Shi et al., Reference Shi, Ettekal, Deutz and Woltering2020; Wiggins et al., Reference Wiggins, Mitchell, Hyde and Monk2015), although some findings resembled their findings. Discrepant to other studies, the identification of a High Co-occurring class, in addition to a Co-occurring class, was a novel finding of this study. These two high-severity co-occurring classes revealed the existence of heterogeneity in the severity of co-occurring problems. Notably, around a third of the sample (27%) fell into these two higher-severity classes (10% belonging to High Co-occurring and 17% to Co-occurring) exhibiting clinically significant co-occurring problems. Children in the High Co-occurring class showed clinically elevated externalizing scores (mean T-scores >63) with sub-clinical (i.e., at-risk) internalizing problems (mean T-scores between 60 and 63), and those in the Co-occurring class had both internalizing and externalizing problems at sub-clinical levels (mean T-scores between 60 and 63). In contrast, children in the other three non-co-occurring trajectory groups, with primary internalizing (Primary Internalizing) or low internalizing and externalizing problems (Low-problems and Moderate Externalizing classes), showed non-clinical behavioral problems (mean T-scores ≤59). Thus, these findings support the notion that co-occurrence of high-problem behaviors should be considered as a distinct syndrome or symptomology (Angold & Costello, Reference Angold and Costello1992; Lilienfeld, Reference Lilienfeld2003; O’Connor et al., Reference O’Connor, McGuire, Reiss, Hetherington and Plomin1998). Importantly, children who show high co-occurring problem behaviors are at significantly elevated risk for more severe psychopathology in the future than children with primary internalizing or externalizing problems (Oerlemans et al., Reference Oerlemans, Wardenaar, Raven, Hartman and Ormel2020; Papachristou & Flouri, Reference Papachristou and Flouri2020).

While we did not find a primary high internalizing trajectory class (high internalizing with low externalizing problems) as found in some other studies (e.g., Chen & Simons-Morton, Reference Chen and Simons-Morton2009; Fanti & Henrich, Reference Fanti and Henrich2010; Hinnant & Mona, Reference Hinnant and Mona2013), we identified a class with moderately elevated internalizing and low externalizing problems (i.e., Primary Internalizing class). Although non-clinical, children in this group showed moderately high internalizing problems with mean T-scores above average (i.e., >50 and <59) at most of the follow-ups. A possible explanation for not identifying high internalizing with low externalizing problems could be that, in general, children who have high internalizing problems (clinical or sub-clinical) also demonstrate high externalizing problems (Lee & Stone, Reference Lee and Stone2012), as we have found in our two co-occurring groups. We also speculate that one reason this class (Primary Internalizing) does not reach clinical thresholds for ‘pure internalizing’ may be that parents did not sufficiently observe high internalizing problems among children who had low externalizing problems. Similarly, we did not identify a pure externalizing trajectory (high externalizing with low internalizing problems), as found in some studies (Chen & Simons-Morton, Reference Chen and Simons-Morton2009; Fanti & Henrich, Reference Fanti and Henrich2010; Nivard et al., Reference Nivard, Lubke, Dolan, Evans, St Pourcain, Munafò and Middeldorp2017; Shi et al., Reference Shi, Ettekal, Deutz and Woltering2020). The reason for this is unclear. However, it could be due to using exclusively parent-reported data. As observed in the previous work in this cohort, by mid-adolescence, when youth report their own symptoms (self-report), a greater proportion of self-reports than parent-reports reach clinical thresholds (Robinson et al., Reference Robinson, Doherty, Cannon, Hickey, Rosenthal, Marino and Skinner2019). Nevertheless, Oerlemans et al. (Reference Oerlemans, Wardenaar, Raven, Hartman and Ormel2020) used self-report measures and found lower T-scores in the final follow-up (age 19) than in earlier waves for most of their trajectory classes, and Leve et al. (Reference Leve, Kim and Pears2005) reported decreasing externalizing scores over time for both boys and girls, although internalizing behaviors increased over time for girls.

Interestingly, we found that both internalizing and externalizing trajectories in the High Co-occurring class (where boys were more likely to be in this class) decreased slightly during adolescence. Some previous studies (e.g., Leve et al., Reference Leve, Kim and Pears2005; Shi & Ettekal, Reference Shi and Ettekal2020; Shi et al., Reference Shi, Ettekal, Deutz and Woltering2020) support our finding; however, the reasons are unclear. Some suggest that externalizing behaviors are more likely to be overt during early childhood but more covert during later years (Bongers et al., Reference Bongers, Koot, Van der Ende and Verhulst2003), and therefore, parents may not know the full range of externalizing problem behaviors in which adolescents engage (Leve et al., Reference Leve, Kim and Pears2005), as shown in a separate analysis of the Raine Study comparing parent report to youth self-report CBCL (Robinson et al., Reference Robinson, Doherty, Cannon, Hickey, Rosenthal, Marino and Skinner2019). Another possible explanation for not finding evidence of adolescent-onset behavioral problems in this study could be a limitation of the CBCL, which does not measure all adolescent-onset problem behaviors, such as robbery and physical assault. As suggested by the previous studies (Leve et al., Reference Leve, Kim and Pears2005; Robinson et al., Reference Robinson, Doherty, Cannon, Hickey, Rosenthal, Marino and Skinner2019), adolescents might be better reporters than parents. Furthermore, there is also evidence showing a modest positive association between changes in externalizing problems and changes in internalizing problems (i.e., decrease (or increase) in one domain follows a decrease (or increase) in the other) among children assessed in kindergarten through seventh grade (Keiley et al., Reference Keiley, Bates, Dodge and Pettit2000), and among female adolescents (Lee & Stone, Reference Lee and Stone2012; Measelle et al., Reference Measelle, Stice and Hogansen2006), indicating externalizing problems can precipitate internalizing problems (and vice versa) as well as reciprocal effects across the two domains (Lee & Stone, Reference Lee and Stone2012). This evidence is consistent with our findings of declining co-occurrence of internalizing and externalizing problems in the High Co-occurring class during adolescence.

Risk factors

The analysis of risk factors associated with identified trajectories revealed that persistent temper-tantrum, maternal mental health problems, younger mother, lower family income, parental marital problems, and father not living with family were relevant in explaining more severe trajectories (High Co-occurring, Co-occurring, or Primary internalizing), compared to Low-problems group. In examining how these risk-factors differentiated the co-occurring groups (Co-occurring and High Co-occurring) from other trajectory groups, we found that children in both co-occurring trajectories displayed a more severe profile of childhood risk factors. Child’s persistent temper-tantrum, maternal mental health problems, low family income, parental marital problems, and father not living with family were associated with both Co-occurring and High Co-occurring groups, compared with Low-problems group. Younger mother and parental cigarette smoking predicted membership in the High Co-occurring group. Family member’s substance smoking (other than cigarettes) was uniquely associated with the Co-occurring group, and male sex was uniquely associated with the High Co-occurring trajectory. These findings are consistent with previous studies (e.g., Fanti & Henrich, Reference Fanti and Henrich2010; Leve et al., Reference Leve, Kim and Pears2005; Oerlemans et al., Reference Oerlemans, Wardenaar, Raven, Hartman and Ormel2020; Shi et al., Reference Shi, Ettekal, Deutz and Woltering2020; Wiggins et al., Reference Wiggins, Mitchell, Hyde and Monk2015; Willner et al., Reference Willner, Gatzke-Kopp and Bray2016) supporting the notion that a combination of multiple biological (e.g., sex), individual (e.g., temperament, language ability), and contextual (e.g., family environment) variables is associated with the emergence and maintenance of co-occurrent problem behaviors through some additive or interactive mechanisms (Atzaba-Poria et al., Reference Atzaba-Poria, Pike and Deater-Deckard2004; Cicchetti & Toth, Reference Cicchetti and Toth1998).

Importantly, our results showed that two factors, maternal mental health problems and persistent temper-tantrum, were the common factors for all three more severe groups (High Co-occurring, Co-occurring, and Primary Internalizing). That is, children who had persistent temper-tantrum and whose mothers had mental health problems were at greater risk for developing either Primary Internalizing or Co-occurring or High Co-occurring problems. This result is the evidence of multifinality, i.e., children sharing similar characteristics may exhibit different patterns of later developmental outcomes. Due to the ongoing dynamic transaction of risk and protective processes experienced uniquely by individuals, varied outcomes will emerge (Cicchetti & Rogosch, Reference Cicchetti and Rogosch1996, Reference Cicchetti and Rogosch2002). Similarly, low family income, marital problems, and father not living at home with family appeared as the common factors for the two co-occurring trajectories. Elucidating a developmental psychopathology approach, a previous study (Cicchetti & Toth, Reference Cicchetti and Toth1998) shows a conceptual framework suggesting that children of depressed mothers are at increased risk of behavioral problems, but maternal depression impacts child problem behavior via several contextual risk factors (e.g., social support, marital quality, and family conflict) as mediators. Other studies suggest a framework that socioeconomic disadvantage contributes to negative parenting practices impacting children’s behavioral problems through higher levels of parental psychological distress and marital conflict (Conger et al., Reference Conger, Conger and Martin2010; Roubinov & Boyce, Reference Roubinov and Boyce2017). We chose a different approach to examine direct association of childhood maternal mental health and several other maternal and family factors as predictors with each trajectory, consistent with other empirical studies (Shi et al., Reference Shi, Ettekal, Deutz and Woltering2020). Our findings (including substantially higher odds of maternal mental health problems in both co-occurring groups compared with Low-problems group) exhibit an additive effect and suggest that children who have mothers with mental health problems during their early childhood along with an accumulated profile of multiple other adverse individual and family factors have higher risk of adverse concurrent problem behavior trajectories through childhood to adolescence, concordant with previous studies (e.g., Fanti & Henrich, Reference Fanti and Henrich2010; Wiggins et al., Reference Wiggins, Mitchell, Hyde and Monk2015).

Similarly, some studies have suggested that the association between child temperament characteristics and child internalizing and externalizing problem behaviors is moderated by family environmental factors (Leve et al., Reference Leve, Kim and Pears2005; Morris et al., Reference Morris, Silk, Steinberg, Sessa, Avenevoli and Essex2002). Although we did not formally test the moderating effects of family variables in the present study, our results accord with this finding. We found that persistent temper-tantrums were associated with all four trajectory classes, compared to Low-problems class, but the effect magnitudes were substantially higher for the two severe co-occurring classes than the other classes, i.e., children with persistent temper-tantrums were nine times more likely to be in High Co-occurring class and six times more likely to be in Co-occurring, as opposed to approximately three times more likely to be in the other two classes. This finding supports the previous research suggesting persistent temper-tantrums may be markers of subsequent psychiatric disorders (Belden et al., Reference Belden, Thomson and Luby2008). Also, in both co-occurring trajectories, the effect magnitudes for persistent temper-tantrums were greater whereas the effect magnitudes for maternal mental health problems and marital problems were also greater, along with other associated adverse risk factors suggesting an additive effect of accumulated adverse family factors, as shown in prior studies (Atzaba-Poria et al., Reference Atzaba-Poria, Pike and Deater-Deckard2004; Capaldi et al., Reference Capaldi, Pears, Kerr, Owen and Kim2012; Fanti & Henrich, Reference Fanti and Henrich2010; Keiley et al., Reference Keiley, Lofthouse, Bates, Dodge and Pettit2003; Leve et al., Reference Leve, Kim and Pears2005; Oland & Shaw, Reference Oland and Shaw2005; Shi et al., Reference Shi, Ettekal, Deutz and Woltering2020). This suggests that children with persistent temper-tantrums at 2 years of age who also experienced a severe profile of accumulated adverse family factors were at greater risk of exhibiting subsequent severe co-occurring internalizing and externalizing problems.

Although studies show that language deficit is associated with both internalizing and externalizing problems (Chow & Wehby, Reference Chow and Wehby2018), an interesting difference in our findings was that we found a unique association of speech difficulties with the Primary Internalizing group, which partially agrees with a study showing an association between language deficit and emotional disturbance among students (Nelson et al., Reference Nelson, Benner, J. and Cheney2005). These findings suggest that children with speech difficulties may have problems with communicating and interpreting social cues and are at risk of developing higher internalizing problems. Contrary to prior work (e.g., Leve et al., Reference Leve, Kim and Pears2005), we did not find a significant association between female sex and membership in the Primary Internalizing class or the co-occurring problem classes, compared to Low-problems class. However, we found that children with speech difficulties and temper-tantrums, non-White mothers, and maternal mental health problems were at greater risk of having higher internalizing with low externalizing problems. On the other hand, our results showed that children who experienced fewer or less severe individual, maternal, and family risk factors, i.e., with a more positive and enhanced family environment (protective factors), were more likely to have low levels of internalizing and externalizing problems (i.e., Low-problems class).

Strengths

The analysis used a relatively large representative community sample with extended follow-up (birth to about 17 years) plus individual, maternal, and family factors, thus enabling the inclusion of a broader range of childhood predictors. The parallel-process LCGA approach was methodologically strong and allowed the analysis of two domains simultaneously to investigate co-occurrent conditions. This enabled the identification of distinct trajectory classes with varying severity of joint development including those at risk of severe co-occurrence.

Limitations

Although extensively used and validated, the CBCL uses survey data based on diagnostic criteria, rather than providing a clinical diagnosis (Warnick et al., Reference Warnick, Bracken and Kasl2008), with results reflecting a spectrum of mental wellbeing. Further, to ensure continuity across follow-ups, parent report was used even though youth self-report data were available at year 14- and 17 follow-up. Additionally, we used the term “predictor” but note that parental educational level and mental health problems were assessed at the year 8 follow-up and so cannot “predict” items measured at the year 5 follow-up, even though highest qualification is likely to pre-date that time. However, parental mental health problems were ascertained as “lifetime mental health problems”, which are more likely to pre-date the analysis time course, and therefore mother’s mental health problems, which have been used in the predictive model, can be considered as a risk-factor.

Also, questionnaires were completed by the primary caregiver, mostly but not always mothers. Consequently, “mother’s mental health problems” may not be accurately reported for some cases. Further, parental mental health problems or stress may influence parental evaluation of children’s emotional and behavioral problems. Furthermore, some risk factors (mother’s mental health problems, temper-tantrum, and speech problems) used in the predictive analysis were measured by a single question, instead of a questionnaire measuring a construct related to each of these variables. While some suggest that a single item can sometimes be as effective and reliable as a multi-item questionnaire construct (Verster et al., Reference Verster, Sandalova, Garssen and Bruce2021), measurement reliability could be an issue.

Implications

The identification of more than one-quarter of the sample with co-occurring internalizing and externalizing problems emphasizes the importance of considering co-occurrence of problem behaviors as a distinct syndrome and delivering interventions that simultaneously support these developmental vulnerabilities. Our findings indicate the additive effects of early childhood risk factors, including maternal mental health problems and persistent temper-tantrum, may set the stage for the development of elevated internalizing problems and co-occurring internalizing and externalizing problems. Those exhibiting co-occurring and high co-occurring problems had typically incurred multiple adverse childhood factors including persistent temper-tantrum and maternal mental health problems, supporting the theory of multifinality as well as additive effects of multiple family factors including low income, marital problems and father not living with family. This in turn suggests the importance of broad-based community interventions in earlier life and in childhood that promote child competence and support adaptive family relationships to prevent children’s developmental failures and thereby related disorders (Cicchetti & Toth, Reference Cicchetti and Toth1998). This is consistent with the recent emphasis on holistic early-life interventions occurring within the first 1000 and 2000 days of life (Darling et al., Reference Darling, Bamidis, Burberry and Rudolf2020; New South Wales Ministry of Health, 2019). Further, the association of maternal mental health problems associated with elevated internalizing trajectory and both co-occurring trajectories emphasizes the importance of identifying at-risk mothers and fostering social support and other specific interventions to alleviate parental mental health problems, particularly among mothers, which is important to enhance parent-child adaptive communication and interaction, and to reduce larger family stresses such as marital discord (Cicchetti & Toth, Reference Cicchetti and Toth1998). The finding of greater association of persistent temper-tantrums with high co-occurring psychopathologies and elevated internalizing trajectories indicates that persistent temper-tantrums may be markers of future psychiatric disorders. This association also indicates that children of mothers with mental health problems are at risk of development of a wide range of emotional and behavioral problems, which could be due to that these mothers may find difficulties in getting involved with positive and effective parenting (Mesman et al., Reference Mesman, Stoel, Bakermans-Kranenburg, van Ijzendoorn, Juffer, Koot and Alink2009). Additionally, we found childhood-onset problem behaviors, but we did not find evidence of adolescent-onset problems, which could be due to using a community-based sample where there may not be a lot of adolescent-onset problem behaviors, as in the high-risk or clinical sample. Another explanation could be, as suggested by prior evidence, that childhood-onset problems and adolescent-onset problems (e.g., depressive disorders) may be differentially influenced by genetic and psychosocial factors (Cicchetti & Rogosch, Reference Cicchetti and Rogosch2002). However, psychosocial stressors including higher familial problems (such as higher rates of criminality, substance abuse in the family, or family discord) may have a more prominent role in childhood-onset problems than in the adolescent-onset problem behavior (Cicchetti & Rogosch, Reference Cicchetti and Rogosch2002). These findings underscore that interventions for child, mother, and family environment should, where possible, be undertaken early when problems are more remediable during early childhood.

Additionally, an association between children with speech difficulties and higher internalizing problems with low externalizing problems indicate that these children may have communication and social skill problems and are at risk of developing associated internalizing problems. Overall, these results suggest that if individual risk-factors such as temper-tantrums and speech difficulties are undiagnosed and untreated at early ages, children will be at greater risk of developing higher psychopathologies during their childhood and adolescence. Taken together, these findings highlight the importance of early identification of risk-factors as well as early mental health and preventive interventions including broad-based community interventions early in life before behavioral outcomes (e.g., antisocial behaviors) and emotional psychopathology become problematic, which will lead to more positive developmental outcomes for the whole family, a positive foundation for the child’s physical, social-emotional and cognitive development, and significant benefits to the community (Forbes et al., Reference Forbes, Rapee, Camberis and McMahon2017; Shonkoff & Fisher, Reference Shonkoff and Fisher2013). These findings may also help design interventions and treatment strategies by providing information on unique and common risk-factors influencing different subtypes of trajectories including moderate externalizing, primary internalizing, and co-occurring and high co-occurring emotional and behavioral problem trajectories.

Conclusion

Around a third of the sample exhibited co-occurring and high co-occurring trajectories of internalizing and externalizing problems and displayed a more severe profile of individual, maternal, and family risk-factors, indicating the importance of considering co-occurrence when a child presents with high internalizing and high externalizing problems. Our findings emphasize the severe consequences of maternal mental health problems and persistent temper-tantrum as well as family factors including marital problems, father not living with family, and low income, on the development of internalizing and externalizing problems and their co-occurrence during childhood through to adolescence. Earliest possible targeted interventions and broad-based social support are emphasized to reduce modifiable risk-factors, e.g., persistent temper-tantrums, speech difficulties, maternal mental health problems, family discord, parental smoking, or substance use, and associated subsequent behavioral and mental health problems during late childhood and adolescence.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/S0954579423001505.

Acknowledgements

We would like to acknowledge the Raine Study participants and their families for their ongoing participation in the study and the Raine Study team for study co-ordination and data collection.

Funding statement

We also thank the Australian National Health and Medical Research Council (NHMRC) for their long-term contribution to funding the study over the last 30 years. The core management of the Raine Study is funded by The University of Western Australia, Curtin University, Telethon Kids Institute, Women and Infants Research Foundation, Edith Cowan University, Murdoch University, The University of Notre Dame Australia, and the Raine Medical Research Foundation. The Raine Study Gen2 17-year follow-up was funded by NHMRC (Stanley et al, 353514, Straker et al, 323200): Gen2 20-year follow-up was funded by NHMRC (Skinner et al 634509); Gen2-27 follow-up was funded by NHMRC (Mori et al 1102106, Eastwood et al, 1109057) and Curtin University.

Robert J. Tait and Jennifer L. Marino were supported by NHMRC grant 1161445. Petra L. Graham and Jennifer L. Marino were supported by NHMRC grant 1134894.

Competing interests

The author(s) declare none.

Footnotes

JLM and SRS are joint senior authors.

References

Achenbach, T. M. (1991a). Integrative guide for the 1991 CBCL/4-18. YSR, and TRF Profiles.Google Scholar
Achenbach, T. M. (1991b). Manual for child behavior checklist 4 18, 1991 profile. Department of Psychiatry, University of Vermont. https://lccn.loc.gov/90072106 Google Scholar
Achenbach, T. M. (1992). Manual for child behavior checklist/2-3 and 1992 profile. University of Vermont.Google Scholar
Achenbach, T. M. (2001). Manual for ASEBA school-age forms & profiles. University of Vermont, Research Center for Children, Youth & Families.Google Scholar
Achenbach, T. M., Howell, C. T., Quay, H. C., Conners, C. K., & Bates, J. E. (1991). National survey of problems and competencies among four-to sixteen-year-olds: Parents' reports for normative and clinical samples. In Monographs of the society for research in child development (pp. i130). https://doi.org/10.2307/1166156 Google Scholar
Achenbach, T. M., Ivanova, M. Y., Rescorla, L. A., Turner, L. V., & Althoff, R. R. (2016). Internalizing/externalizing problems: Review and recommendations for clinical and research applications,Journal of the American Academy of Child and Adolescent Psychiatry, 55(8), 647656. https://doi.org/10.1016/j.jaac.2016.05.012 CrossRefGoogle ScholarPubMed
Ackerman, B. P., D’Eramo, K. S., Umylny, L., Schultz, D., & Izard, C. E. (2001). Family structure and the externalizing behavior of children from economically disadvantaged families. Journal of Family Psychology, 15(2), 288300. https://doi.org/10.1037/0893-3200.15.2.288 CrossRefGoogle ScholarPubMed
Angold, A., & Costello, E. J. (1992). Comorbidity in children and adolescents with depression. Child and Adolescent Psychiatric Clinics of North America, 1(1), 3151. https://doi.org/10.1016/S1056-4993(18)30610-2 CrossRefGoogle Scholar
Angold, A., & Costello, E. J. (1993). Depressive comorbidity in children and adolescents. American Journal of Psychiatry, 150(12), 17791791. https://doi.org/10.1176/ajp.150.12.1779 Google ScholarPubMed
Angold, A., Costello, E. J., & Erkanli, A. (1999). Comorbidity. Journal of Child Psychology and Psychiatry, 40(1), 5787. https://doi.org/10.1111/1469-7610.00424 CrossRefGoogle ScholarPubMed
Angold, A., Erkanli, A., Silberg, J., Eaves, L., & Costello, E. J. (2002). Depression scale scores in 8-17-year-olds: Effects of age and gender. Journal of Child Psychology and Psychiatry, 43(8), 10521063. https://doi.org/10.1111/1469-7610.00232 CrossRefGoogle ScholarPubMed
Asparouhov, T., & Muthén, B. (2021). Auxiliary variables in mixture modeling: Using the BCH method in Mplus to estimate a distal outcome model and an arbitrary secondary model. Mplus Web Notes, 21(2), 122. https://www.statmodel.com/examples/webnote.shtml Google Scholar
Atzaba-Poria, N., Pike, A., & Deater-Deckard, K. (2004). Do risk factors for problem behaviour act in a cumulative manner? An examination of ethnic minority and majority children through an ecological perspective. Journal of Child Psychology and Psychiatry, 45(4), 707718. https://doi.org/10.1111/j.1469-7610.2004.00265.x CrossRefGoogle Scholar
Barker, E. D., Oliver, B. R., & Maughan, B. (2010). Co-occurring problems of early onset persistent, childhood limited, and adolescent onset conduct problem youth. Journal of Child Psychology and Psychiatry, 51(11), 12171226. https://doi.org/10.1111/j.1469-7610.2010.02240.x CrossRefGoogle ScholarPubMed
Belden, A. C., Thomson, N. R., Luby, & J. L. (2008). Temper tantrums in healthy versus depressed and disruptive preschoolers: Defining tantrum behaviors associated with clinical problems. Journal of Pediatrics, 152(1), 117122. https://doi.org/10.1016/j.jpeds.2007.06.030 CrossRefGoogle ScholarPubMed
Bolck, A., Croon, M., & Hagenaars, J. (2004). Estimating latent structure models with categorical variables: One-step versus three-step estimators. Political Analysis, 12(1), 327. https://doi.org/10.1093/pan/mph001 CrossRefGoogle Scholar
Bongers, I. L., Koot, H. M., Van der Ende, J., & Verhulst, F. C. (2003). The normative development of child and adolescent problem behavior. Journal of Abnormal Psychology, 112(2), 179192. https://doi.org/10.1037/0021-843X.112.2.179 CrossRefGoogle ScholarPubMed
Bongers, I. L., Koot, H. M., Van Der Ende, J., & Verhulst, F. C. (2004). Developmental trajectories of externalizing behaviors in childhood and adolescence. Child Development, 75(5), 15231537. https://doi.org/10.1111/j.1467-8624.2004.00755.x CrossRefGoogle ScholarPubMed
Bornstein, M. H., Hahn, C.-S., & Suwalsky, J. T. D. (2013). Language and internalizing and externalizing behavioral adjustment: Developmental pathways from childhood to adolescence. Development and Psychopathology, 25(3), 857878. https://doi.org/10.1017/S0954579413000217 CrossRefGoogle ScholarPubMed
Brennan, L. M., Shaw, D. S., Dishion, T. J., & Wilson, M. (2012). Longitudinal predictors of school-age academic achievement: Unique contributions of toddler-age aggression, oppositionality, inattention, and hyperactivity. Journal of Abnormal Child Psychology, 40(8), 12891300. https://doi.org/10.1007/s10802-012-9639-2 CrossRefGoogle ScholarPubMed
Brennan, P. A., Hall, J., Bor, W., Najman, J. M., & Williams, G. (2003). Integrating biological and social processes in relation to early-onset persistent aggression in boys and girls. Developmental Psychology, 39(2), 309323. https://doi.org/10.1037/0012-1649.39.2.309 CrossRefGoogle ScholarPubMed
Bronfenbrenner, U. (1977). Toward an experimental ecology of human development. American Psychologist, 32(7), 513531. https://doi.org/10.1037/0003-066X.32.7.513 CrossRefGoogle Scholar
Capaldi, D. M., Pears, K. C., Kerr, D. C. R., Owen, L. D., & Kim, H. K. (2012). Growth in externalizing and internalizing problems in childhood: A prospective study of psychopathology across three generations. Child Development, 83(6), 19451959. https://doi.org/10.1111/j.1467-8624.2012.01821.x CrossRefGoogle ScholarPubMed
Carlson, G. A., Potegal, M., Margulies, D., Gutkovich, Z., & Basile, J. (2009). Rages—what are they and who has them? Journal of Child and Adolescent Psychopharmacology, 19(3), 281288. https://doi.org/10.1089/cap.2008.0108 CrossRefGoogle Scholar
Caspi, A., Elder, G. H., & Bem, D. J. (1987). Moving against the world: Life-course patterns of explosive children. Developmental Psychology, 23(2), 308313. https://doi.org/10.1037/0012-1649.23.2.308 CrossRefGoogle Scholar
Caspi, A., & Moffitt, T. E. (2018). All for one and one for all: Mental disorders in one dimension. American Journal of Psychiatry, 175(9), 831844. https://doi.org/10.1176/appi.ajp.2018.17121383 CrossRefGoogle ScholarPubMed
Chen, R., & Simons-Morton, B. (2009). Concurrent changes in conduct problems and depressive symptoms in early adolescents: A developmental person-centered approach. Development and Psychopathology, 21(1), 285307. https://doi.org/10.1017/S0954579409000169 CrossRefGoogle ScholarPubMed
Chow, J. C., & Wehby, J. H. (2018). Associations between language and problem behavior: A systematic review and correlational meta-analysis. Educational Psychology Review, 30(1), 6182. https://doi.org/10.1007/s10648-016-9385-z CrossRefGoogle Scholar
Cicchetti, D., & Rogosch, F. A. (1996). Equifinality and multifinality in developmental psychopathology. Development and Psychopathology, 8(4), 597600. https://doi.org/10.1017/S0954579400007318 CrossRefGoogle Scholar
Cicchetti, D., & Rogosch, F. A. (2002). A developmental psychopathology perspective on adolescence. Journal Of Consulting And Clinical Psychology, 70(1), 620. https://doi.org/10.1037/0022-006X.70.1.6 CrossRefGoogle ScholarPubMed
Cicchetti, D., & Toth, S. L. (1998). The development of depression in children and adolescents. American Psychologist, 53(2), 221241. https://doi.org/10.1037/0003-066X.53.2.221 CrossRefGoogle ScholarPubMed
Colder, C. R., Scalco, M., Trucco, E. M., Read, J. P., Lengua, L. J., Wieczorek, W. F., & Hawk, L. W. (2013). Prospective associations of internalizing and externalizing problems and their co-occurrence with early adolescent substance use. Journal of Abnormal Child Psychology, 41(4), 667677. https://doi.org/10.1007/s10802-012-9701-0 CrossRefGoogle ScholarPubMed
Conger, R. D., Conger, K. J., & Martin, M. J. (2010). Socioeconomic status, family processes, and individual development. Journal of Marriage and Family, 72(3), 685704. https://doi.org/10.1111/j.1741-3737.2010.00725.x CrossRefGoogle ScholarPubMed
Costello, E. J., Mustillo, S., Erkanli, A., Keeler, G., & Angold, A. (2003). Prevalence and development of psychiatric disorders in childhood and adolescence. Archives of General Psychiatry, 60(8), 837844. https://doi.org/10.1001/archpsyc.60.8.837 CrossRefGoogle ScholarPubMed
Darling, J. C., Bamidis, P. D., Burberry, J., & Rudolf, M. C. J. (2020). The First Thousand Days: Early, integrated and evidence-based approaches to improving child health: Coming to a population near you? Archives of Disease in Childhood, 105(9), 837841. https://doi.org/10.1136/archdischild-2019-316929 CrossRefGoogle ScholarPubMed
Davis, S., Votruba-Drzal, E., & Silk, J. S. (2015). Trajectories of internalizing symptoms from early childhood to adolescence: Associations with temperament and parenting. Social Development (Oxford, England), 24(3), 501520. https://doi.org/10.1111/sode.12105 Google Scholar
Duggal, S., Carlson, E. A., Sroufe, L. A., & Egeland, B. (2001). Depressive symptomatology in childhood and adolescence. Development and Psychopathology, 13(1), 143164. https://doi.org/10.1017/S0954579401001109 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. Development and Psychopathology, 32(3), 945959. https://doi.org/10.1017/S0954579419000919 CrossRefGoogle ScholarPubMed
Enders, C. K. (2010). Applied missing data analysis. Guilford Press. books.google.com.Google Scholar
Fanti, K. A., & Henrich, C. C. (2010). Trajectories of pure and co-occurring internalizing and externalizing problems from age 2 to age 12: Findings from the National Institute of Child Health and Human Development Study of Early Child Care. Developmental Psychology, 46(5), 11591175. https://doi.org/10.1037/a0020659 CrossRefGoogle ScholarPubMed
Flouri, E., Midouhas, E., & Joshi, H. (2014). Family poverty and trajectories of children’s emotional and behavioural problems: The moderating roles of self-regulation and verbal cognitive ability. Journal of Abnormal Child Psychology, 42(6), 10431056. https://doi.org/10.1007/s10802-013-9848-3 CrossRefGoogle ScholarPubMed
Flouri, E., Papachristou, E., Midouhas, E., Joshi, H., Ploubidis, G. B., & Lewis, G. (2018). Early adolescent outcomes of joint developmental trajectories of problem behavior and IQ in childhood. European Child & Adolescent Psychiatry, 27(12), 15951605. https://doi.org/10.1007/s00787-018-1155-7 CrossRefGoogle ScholarPubMed
Flouri, E., Papachristou, E., Midouhas, E., Ploubidis, G. B., Lewis, G., & Joshi, H. (2019). Developmental cascades of internalising symptoms, externalising problems and cognitive ability from early childhood to middle adolescence. European Psychiatry, 57, 6169. https://doi.org/10.1016/j.eurpsy.2018.12.005 CrossRefGoogle ScholarPubMed
Forbes, M. K., Rapee, R. M., Camberis, A.-L., & McMahon, C. A. (2017). Unique associations between childhood temperament characteristics and subsequent psychopathology symptom trajectories from childhood to early adolescence. Journal of Abnormal Child Psychology, 45(6), 12211233. https://doi.org/10.1007/s10802-016-0236-7 CrossRefGoogle ScholarPubMed
Gilliom, M., & Shaw, D. S. (2004). Codevelopment of externalizing and internalizing problems in early childhood. Development and Psychopathology, 16(2), 313333. https://doi.org/10.1017/S0954579404044530 CrossRefGoogle ScholarPubMed
Gooren, E. M. J. C., van Lier, P. A. C., Stegge, H., Terwogt, M. M., & Koot, H. M. (2011). The development of conduct problems and depressive symptoms in early elementary school children: The role of peer rejection. Journal of Clinical Child & Adolescent Psychology, 40(2), 245253. https://doi.org/10.1080/15374416.2011.546045 CrossRefGoogle ScholarPubMed
Hinnant, J. B., & Mona, E. L. S. (2013). Codevelopment of externalizing and internalizing symptoms in middle to late childhood: Sex, baseline respiratory sinus arrhythmia, and respiratory sinus arrhythmia reactivity as predictors. Development and Psychopathology, 25(2), 419436. https://doi.org/10.1017/S0954579412001150 CrossRefGoogle ScholarPubMed
Howard, S. J., & Williams, K. E. (2018). Early self-regulation, early self-regulatory change, and their longitudinal relations to adolescents’ academic, health, and mental well-being outcomes. Journal of Developmental & Behavioral Pediatrics, 39(6), 489496. https://doi.org/10.1097/DBP.0000000000000578 CrossRefGoogle ScholarPubMed
Hoyniak, C. P., Donohue, M. R., Quiñones-Camacho, L. E., Vogel, A. C., Perino, M. T., Hennefield, L., Tillman, R., Barch, D. M., & Luby, J. L. (2022). Developmental pathways from preschool temper tantrums to later psychopathology. Development and Psychopathology, 35(4), 16431655. https://doi.org/10.1017/S0954579422000359.CrossRefGoogle ScholarPubMed
Keiley, M. K., Bates, J. E., Dodge, K. A., & Pettit, G. S. (2000). A cross-domain growth analysis: Externalizing and internalizing behaviors during 8 years of childhood. Journal of Abnormal Child Psychology, 28(2), 161179. https://doi.org/10.1023/A:1005122814723 CrossRefGoogle ScholarPubMed
Keiley, M. K., Lofthouse, N., Bates, J. E., Dodge, K. A., & Pettit, G. S. (2003). Differential risks of covarying and pure components in mother and teacher reports of externalizing and internalizing behavior across ages 5 to 14. Journal of Abnormal Child Psychology, 31(3), 267283. https://doi.org/10.1023/A:1023277413027 CrossRefGoogle Scholar
Korhonen, M., Luoma, I., Salmelin, R., Siirtola, A., & Puura, K. (2018). The trajectories of internalizing and externalizing problems from early childhood to adolescence and young adult outcome. Journal of Child Adolescent Psychology, 2, 712. https://urn.fi/URN:NBN:fi:tuni-201910213963.Google Scholar
Lee, E. J., & Stone, S. I. (2012). Co-occurring internalizing and externalizing behavioral problems: The mediating effect of negative self-concept. Journal Of Youth And Adolescence, 41(6), 717731. https://doi.org/10.1007/s10964-011-9700-4 CrossRefGoogle ScholarPubMed
Leve, L. D., Kim, H. K., & Pears, K. C. (2005). Childhood temperament and family environment as predictors of internalizing and externalizing trajectories from ages 5 to 17. Journal of Abnormal Child Psychology, 33(5), 505520. https://doi.org/10.1007/s10802-005-6734-7 CrossRefGoogle ScholarPubMed
Lilienfeld, S. O. (2003). Comorbidity between and within childhood externalizing and internalizing disorders: Reflections and directions. Journal of Abnormal Child Psychology, 31(3), 285291. https://doi.org/10.1023/A:1023229529866 CrossRefGoogle ScholarPubMed
Loeber, R., & Keenan, K. (1994). Interaction between conduct disorder and its comorbid conditions: Effects of age and gender. Clinical Psychology Review, 14(6), 497523. https://doi.org/10.1016/0272-7358(94)90015-9 CrossRefGoogle Scholar
Loth, A. K., Drabick, D. A. G., Leibenluft, E., & Hulvershorn, L. A. (2014). Do childhood externalizing disorders predict adult depression? A meta-analysis. Journal of Abnormal Child Psychology, 42(7), 11031113. https://doi.org/10.1007/s10802-014-9867-8 CrossRefGoogle ScholarPubMed
McKnight, C. M., Newnham, J. P., Stanley, F. J., Mountain, J. A., Landau, L. I., Beilin, L. J., Puddey, I. B., Pennell, C. E., & Mackey, D. A. (2012). Birth of a cohort—the first 20 years of the Raine study. Medical Journal of Australia, 197(11), 608–10. https://doi.org/608.10.5694/mja12.10698 CrossRefGoogle ScholarPubMed
Measelle, J. R., Stice, E., & Hogansen, J. M. (2006). Developmental trajectories of co-occurring depressive, eating, antisocial, and substance abuse problems in female adolescents. Journal of Abnormal Psychology, 115(3), 524538. https://doi.org/10.1037/0021-843X.115.3.524 CrossRefGoogle ScholarPubMed
Meque, I., Dachew, B. A., Maravilla, J. C., Salom, C., & Alati, R. (2019 (2019/10/01)). Externalizing and internalizing symptoms in childhood and adolescence and the risk of alcohol use disorders in young adulthood: A meta-analysis of longitudinal studies. Australian & New Zealand Journal of Psychiatry, 53(10), 965975. https://doi.org/10.1177/0004867419844308 CrossRefGoogle ScholarPubMed
Mesman, J., Stoel, R., Bakermans-Kranenburg, M. J., van Ijzendoorn, M. H., Juffer, F., Koot, H. M., & Alink, L. R. A. (2009). Predicting growth curves of early childhood externalizing problems: Differential susceptibility of children with difficult temperament. Journal of Abnormal Child Psychology, 37(5), 625636. https://doi.org/10.1007/s10802-009-9298-0 CrossRefGoogle ScholarPubMed
Miner, J. L., & Clarke-Stewart, K. A. (2008). Trajectories of externalizing behavior from age 2 to age 9: Relations with gender, temperament, ethnicity, parenting, and rater. Developmental Psychology, 44(3), 771786. https://doi.org/10.1037/0012-1649.44.3.771 CrossRefGoogle ScholarPubMed
Moilanen, K. L., Shaw, D. S., & Maxwell, K. L. (2010). Developmental cascades: Externalizing, internalizing, and academic competence from middle childhood to early adolescence. Development and Psychopathology, 22(3), 635653. https://doi.org/10.1017/S0954579410000337 CrossRefGoogle ScholarPubMed
Morris, A. S., Silk, J. S., Steinberg, L., Sessa, F. M., Avenevoli, S., & Essex, M. J. (2002). Temperamental vulnerability and negative parenting as interacting predictors of child adjustment. Journal of Marriage and Family, 64(2), 461471. https://doi.org/10.1111/j.1741-3737.2002.00461.x CrossRefGoogle Scholar
Muthén, B., & Muthén, L. K. (2000). Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism-Clinical and Experimental Research, 24(6), 882891. https://doi.org/10.1111/j.1530-0277.2000.tb02070.x CrossRefGoogle ScholarPubMed
Nagin, D. S. (1999). Analyzing developmental trajectories: A semiparametric, group-based approach. Psychological Methods, 4(2), 139157. https://doi.org/10.1037/1082-989X.4.2.139 CrossRefGoogle Scholar
Nagin, D. S., & Nagin, D. (2005). Group-based modeling of development. Harvard University Press. https://www.hup.harvard.edu/catalog.php?isbn=9780674016866 10.4159/9780674041318CrossRefGoogle Scholar
Nelson, R., Benner, J., J., G., & Cheney, D. (2005). An investigation of the language skills of students with emotional disturbance served in public school settings. The Journal of Special Education, 39(2), 97105. https://doi.org/10.1177/002246690503900205 CrossRefGoogle Scholar
New South Wales Ministry of Health. The first 2000 days framework, (2019). https://www1.health.nsw.gov.au/pds/ActivePDSDocuments/PD2019_008.pdf.Google Scholar
Newman, D. L., Moffitt, T. E., Caspi, A., & Silva, P. A. (1998). Comorbid mental disorders: Implications for treatment and sample selection. Journal of Abnormal Psychology, 107(2), 305311. https://doi.org/10.1037/0021-843X.107.2.305 CrossRefGoogle ScholarPubMed
Nivard, M. G., Lubke, G. H., Dolan, C. V., Evans, D. M., St Pourcain, B., Munafò, M. R., & Middeldorp, C. M. (2017). Joint developmental trajectories of internalizing and externalizing disorders between childhood and adolescence. Development and Psychopathology, 29(3), 919928. https://doi.org/10.1017/S0954579416000572 CrossRefGoogle ScholarPubMed
Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 535569. https://doi.org/10.1080/10705510701575396 CrossRefGoogle Scholar
O’Connor, T. G., McGuire, S., Reiss, D., Hetherington, E. M., & Plomin, R. (1998). Co-occurrence of depressive symptoms and antisocial behavior in adolescence: A common genetic liability. Journal of Abnormal Psychology, 107(1), 2737. https://doi.org/10.1037//0021-843x.107.1.27 CrossRefGoogle ScholarPubMed
Oerlemans, A. M., Wardenaar, K. J., Raven, D., Hartman, C. A., & Ormel, J. (2020). The association of developmental trajectories of adolescent mental health with early-adult functioning. PloS One, 15(6), e0233648e0233648. https://doi.org/10.1371/journal.pone.0233648 CrossRefGoogle ScholarPubMed
Oland, A. A., & Shaw, D. S. (2005). Pure versus co-occurring externalizing and internalizing symptoms in children: The potential role of socio-developmental milestones. Clinical Child and Family Psychology Review, 8(4), 247270. https://doi.org/10.1007/s10567-005-8808-z CrossRefGoogle ScholarPubMed
Papachristou, E., & Flouri, E. (2020). Distinct developmental trajectories of internalising and externalising symptoms in childhood: Links with mental health and risky behaviours in early adolescence. Journal of Affective Disorders, 276, 10521060. https://doi.org/10.1016/j.jad.2020.07.130 CrossRefGoogle ScholarPubMed
Petersen, I. T., Bates, J. E., D'Onofrio, B. M., Coyne, C. A., Lansford, J. E., Dodge, K. A., Pettit, G. S., & Van Hulle, C. A. (2013). Language ability predicts the development of behavior problems in children. Journal of Abnormal Psychology, 122(2), 542557. https://doi.org/10.1037/a0031963 CrossRefGoogle ScholarPubMed
Picoito, J., Santos, C., & Nunes, C. (2020). Emotional and behavioural pathways to adolescent substance use and antisocial behaviour: Results from the UK Millennium Cohort Study. European Child & Adolescent Psychiatry, 30(11), 111. https://doi.org/10.1007/s00787-020-01661-x Google ScholarPubMed
Potegal, M., & Davidson, R. J. (2003). Temper tantrums in young children: 1. Behavioral composition. Journal of Developmental & Behavioral Pediatrics, 24(3), 140147. https://doi.org/10.1097/00004703-200306000-00002 CrossRefGoogle ScholarPubMed
Robinson, M., Doherty, D. A., Cannon, J., Hickey, M., Rosenthal, S. L., Marino, J. L., & Skinner, S. R. (2019). Comparing adolescent and parent reports of externalizing problems: A longitudinal population-based study. British Journal of Developmental Psychology, 37(2), 247268. https://doi.org/10.1111/bjdp.12270 CrossRefGoogle ScholarPubMed
Rothbart, M. K. (2007). Temperament, development, and personality. Current Directions in Psychological Science, 16(4), 207212. https://doi.org/10.1111/j.1467-8721.2007.00505.x CrossRefGoogle Scholar
Roubinov, D. S., & Boyce, W. T. (2017). Parenting and SES: Relative values or enduring principles? Current Opinion in Psychology, 15, 162167. https://doi.org/10.1016/j.copsyc.2017.03.001 CrossRefGoogle ScholarPubMed
Roy, A. K., Klein, R. G., Angelosante, A., Bar-Haim, Y., Leibenluft, E., Hulvershorn, L., Dixon, E., Dodds, A., & Spindel, C. (2013). Clinical features of young children referred for impairing temper outbursts. Journal of Child and Adolescent Psychopharmacology, 23(9), 588596. https://doi.org/10.1089/cap.2013.0005 CrossRefGoogle ScholarPubMed
Roza, S. J., Hofstra, M. B., Van Der Ende, J., & Verhulst, F. C. (2003). Stable prediction of mood and anxiety disorders based on behavioral and emotional problems in childhood: A 14-year follow-up during childhood, adolescence, and young adulthood. American Journal of Psychiatry, 160(12), 21162121. https://doi.org/10.1176/appi.ajp.160.12.2116 CrossRefGoogle ScholarPubMed
Rutter, M., & Sroufe, L. A. (2000). Developmental psychopathology: Concepts and challenges. Development and Psychopathology, 12(3), 265296. https://doi.org/10.1017/S0954579400003023 CrossRefGoogle ScholarPubMed
Shi, Q., & Ettekal, I. (2020). Co-occurring trajectories of internalizing and externalizing problems from grades 1 to 12: Longitudinal associations with teacher-child relationship quality and academic performance. Journal of Educational Psychology, 113(4), 808829. https://doi.org/10.1037/edu0000525 CrossRefGoogle Scholar
Shi, Q., Ettekal, I., Deutz, M. H. F., & Woltering, S. (2020). Trajectories of pure and co-occurring internalizing and externalizing problems from early childhood to adolescence: Associations with early childhood individual and contextual antecedents. Developmental Psychology, 56(10), 19061918. https://doi.org/10.1037/dev0001095.CrossRefGoogle ScholarPubMed
Shonkoff, J. P., & Fisher, P. A. (2013). Rethinking evidence-based practice and two-generation programs to create the future of early childhood policy. Development and Psychopathology, 25(4pt2), 16351653. https://doi.org/10.1017/S0954579413000813 CrossRefGoogle ScholarPubMed
Sterba, S. K., Prinstein, M. J., & Cox, M. J. (2007). Trajectories of internalizing problems across childhood: Heterogeneity, external validity, and gender differences. Development and Psychopathology, 19(2), 345366. https://doi.org/10.1017/S0954579407070174 CrossRefGoogle ScholarPubMed
Straker, L. M., Hall, G. L., Mountain, J., Howie, E. K., White, E., McArdle, N., & Eastwood, P. R. (2015). Rationale, design and methods for the 22 year follow-up of the Western Australian Pregnancy Cohort (Raine) Study. BMC Public Health, 15(1), 116. https://doi.org/10.1186/s12889-015-1944-6 CrossRefGoogle ScholarPubMed
Straker, L., Mountain, J., Jacques, A., White, S., Smith, A., Landau, L., Stanley, F., Newnham, J., Pennell, C., & Eastwood, P. (2017). Cohort profile: The Western Australian Pregnancy Cohort (Raine) Study-Generation 2. International Journal of Epidemiology, 46(5), 13841385j. https://doi.org/10.1093/ije/dyw308 Google ScholarPubMed
Twenge, J. M., & Nolen-Hoeksema, S. (2002). Age, gender, race, socioeconomic status, and birth cohort difference on the children’s depression inventory: A meta-analysis. Journal of Abnormal Psychology, 111(4), 578588. https://doi.org/10.1037//0021-843x.111.4.578 CrossRefGoogle ScholarPubMed
Van den Akker, A. L., Hoffenaar, P., & Overbeek, G. (2022). Temper tantrums in toddlers and preschoolers: Longitudinal associations with adjustment problems. Journal of Developmental and Behavioral Pediatrics, 43(7), 409417. https://doi.org/10.1097/dbp.0000000000001071 CrossRefGoogle ScholarPubMed
Van der Ende, J., Verhulst, F. C., & Tiemeier, H. (2016). The bidirectional pathways between internalizing and externalizing problems and academic performance from 6 to 18 years. Development and Psychopathology, 28(3), 855867. https://doi.org/10.1017/S0954579416000353 CrossRefGoogle ScholarPubMed
van der Nest, G., Lima Passos, V., Candel, M. J. J. M., & van Breukelen, G. J. P. (2020). An overview of mixture modelling for latent evolutions in longitudinal data: Modelling approaches, fit statistics and software. Advances in Life Course Research, 43, 100323. https://doi.org/10.1016/j.alcr.2019.100323 CrossRefGoogle ScholarPubMed
Verster, J. C., Sandalova, E., Garssen, J., & Bruce, G. (2021). The use of single-item ratings versus traditional multiple-item questionnaires to assess mood and health. European Journal of Investigation in Health, Psychology and Education, 11(1), 183198. https://doi.org/10.3390/ejihpe11010015 CrossRefGoogle ScholarPubMed
Vogel, A. C., Jackson, J. J., Barch, D. M., Tillman, R., & Luby, J. L. (2019). Excitability and irritability in preschoolers predicts later psychopathology: The importance of positive and negative emotion dysregulation. Development and Psychopathology, 31(3), 10671083. https://doi.org/10.1017/S0954579419000609 CrossRefGoogle ScholarPubMed
Wakschlag, L. S., Choi, S. W., Carter, A. S., Hullsiek, H., Burns, J., McCarthy, K., Leibenluft, E., & Briggs-Gowan, M. J. (2012). Defining the developmental parameters of temper loss in early childhood: Implications for developmental psychopathology. Journal of Child Psychology and Psychiatry, 53(11), 10991108. https://doi.org/10.1111/j.1469-7610.2012.02595.x CrossRefGoogle ScholarPubMed
Wakschlag, L. S., Estabrook, R., Petitclerc, A., Henry, D., Burns, J. L., Perlman, S. B., Voss, J. L., Pine, D. S., Leibenluft, E., & Briggs-Gowan, M. L. (2015). Clinical implications of a dimensional approach: The normal: Abnormal spectrum of early irritability. Journal of the American Academy of Child & Adolescent Psychiatry, 54(8), 626634. https://doi.org/10.1016/j.jaac.2015.05.016 CrossRefGoogle ScholarPubMed
Wang, F. L., Eisenberg, N., Valiente, C., & Spinrad, T. L. (2016). Role of temperament in early adolescent pure and co-occurring internalizing and externalizing problems using a bifactor model: Moderation by parenting and gender. Development and Psychopathology, 28(4pt2), 14871504. https://doi.org/10.1017/S0954579415001224 CrossRefGoogle ScholarPubMed
Wang, F. L., Galán, C. A., Lemery-Chalfant, K., Wilson, M. N., & Shaw, D. S. (2020). Evidence for two genetically distinct pathways to co-occurring internalizing and externalizing problems in adolescence characterized by negative affectivity or behavioral inhibition. Journal of Abnormal Psychology, 129(6), 633645. https://doi.org/10.1037/abn0000525 CrossRefGoogle ScholarPubMed
Warnick, E. M., Bracken, M. B., & Kasl, S. (2008). Screening efficiency of the Child Behavior Checklist and Strengths and Difficulties Questionnaire: A systematic review. Child and Adolescent Mental Health, 13(3), 140147. https://doi.org/10.1111/j.1475-3588.2007.00461.x CrossRefGoogle ScholarPubMed
Weller, B. E., Bowen, N. K., & Faubert, S. J. (2020). Latent class analysis: A guide to best practice. Journal of Black Psychology, 46(4), 287311. https://doi.org/10.1177/0095798420930 CrossRefGoogle Scholar
Whittaker, T. A., & Khojasteh, J. (2017). Detecting appropriate trajectories of growth in latent growth models: The performance of information-based criteria. The Journal of Experimental Education, 85(2), 215230. https://doi.org/10.1080/00220973.2015.1123669 CrossRefGoogle Scholar
Wiggins, J. L., Mitchell, C., Hyde, L. W., & Monk, C. S. (2015). Identifying early pathways of risk and resilience: The co-development of internalizing and externalizing symptoms and the role of harsh parenting. Development and Psychopathology, 27(4pt1), 12951312. https://doi.org/10.1017/S0954579414001412.CrossRefGoogle Scholar
Willner, C. J., Gatzke-Kopp, L. M., & Bray, B. C. (2016). The dynamics of internalizing and externalizing comorbidity across the early school years. Development and Psychopathology, 28(4pt1), 10331052. https://doi.org/10.1017/S0954579416000687 CrossRefGoogle ScholarPubMed
Figure 0

Table 1. Model fit comparison of models with an increasing number of trajectory classes

Figure 1

Figure 1. Joint trajectories of internalizing (INT) and externalizing (EXT) T-scores from age 5 to 17 years for each class of five-class parallel-process LCGA model.

Figure 2

Table 2. Descriptive statistics and correlations for internalizing (INT) and externalizing (EXT) T-scores

Figure 3

Table 3. Descriptive characteristics of the sample (n = 2393) and five joint trajectory classes based on the most likely class membership*

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Table 4. Multinomial logistic regression: Predictors of joint trajectory classes of internalizing and externalizing problems from childhood to mid-adolescence, considering Low-Problems as a reference class (N = 2393)

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