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Journal of Contemporary Criminal Justice 2019, Vol. 35(1) 103 –119

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Article

How Biosocial Research Can Improve Interventions for Antisocial Behavior

Andrea L. Glenn1 and Katie E. McCauley1

Abstract Biological factors have been found to influence the development of antisocial behavior. These factors also affect how individuals respond to the environment, including how individuals respond to interventions designed to reduce antisocial behavior. Interventions for youth with antisocial behavior may have the greatest impact if they are targeted toward youth who need it the most (e.g., those who are mostly likely to persist in their behavior problems) as well as youth who may benefit the most from the program. This article discusses potential benefits of a biosocial approach to interventions, as well as the potential ethical concerns that arise.

Keywords brain, genetics, hormones, intervention, ethics

Youth who develop antisocial behavior at early ages are at greater risk for criminal behavior in adulthood than those with later occurring antisocial behavior (Jaffee, Strait, & Odgers, 2012). Several interventions have been developed to prevent the development of behavior problems in youth who are showing early signs of antisocial behavior. However, these programs are often complex and expensive and the average effects are often modest (McCart, Priester, Davies, & Azen, 2006). Modest effects may be due to the fact that the programs are not uniformly effective—they do not work equally well for all children (Lochman et al., 2015). This produces several challenges for interventionists. First, it is critical to determine which youth are most in need of interventions. For example, in the absence of intervention, some youth may naturally “age out” of behavior problems whereas others may develop even more serious behav- ioral problems. These two groups may appear similar in behavioral symptoms at a

1The University of Alabama, Tuscaloosa, AL, USA

Corresponding Author: Andrea L. Glenn, Department of Psychology, Center for the Prevention of Youth Behavior Problems, The University of Alabama, Box 870348, Tuscaloosa, AL 35487 USA. Email: [email protected]

810608 CCJXXX10.1177/1043986218810608Journal of Contemporary Criminal JusticeGlenn and McCauley research-article2018

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single time point, but may develop very different trajectories (Moffitt, 1993). If we could determine which youth are on a trajectory for persistent behavior problems and target them with more intensive intervention programs, we would greatly improve our ability to reap the most benefit from limited intervention resources.

Another challenge is to determine which type of intervention might work best for a particular child. There is significant heterogeneity in youth with externalizing prob- lems, and research suggests that different factors may lead to behavioral problems in different individuals (Connell & Frye, 2006; Frick, 2012). Whereas some children may have deficits related to emotional responding, others may have difficulty with self-regulation and attention. Thus, to improve the effectiveness of interventions, it seems essential to understand the factors that affect intervention responses, and to use this information either in the selection of youth for intervention programs, or to tailor programs to specific youth.

Here, we suggest that biological factors provide information about variation between individuals, and thus a biosocial approach, combining biological and psycho- social information, may be useful in (a) determining which youth most need interven- tions, and (b) determining which intervention might work best for a particular child. This consideration of biological information alongside existing measures of psychoso- cial variables may improve our ability to reap the most benefit from our intervention efforts.

A study by Albert et al. (2015) provides a real-world example of the importance of considering biological factors in interventions. Albert et al. examined variants of a specific gene to see whether it affected responsiveness to the Fast Track intervention. Fast Track was a 10-year multilevel intervention program delivering services to high- risk children. Albert et al. (2015) found that for carriers of a particular variant of this gene, the intervention made a very large difference—18% of children receiving the intervention demonstrated externalizing psychopathology at adult follow-up com- pared with 75% of control group children with the same gene variant. However, for noncarriers of the gene variant, the intervention had no effect; there was no difference in the level of externalizing problems between those receiving the intervention and those not. An economic evaluation of Fast Track intervention estimated the total cost of the 10-year program to be US$58,000 per child (Foster & Jones, 2006). Thus, deliv- ering this kind of intervention to the children who are most likely to benefit would result in better outcomes overall. This study also suggests that in addition to benefiting more from the intervention, children with this gene variant would fare worse in the absence of intervention (i.e., 75% of children in the control group with this gene vari- ant showed behavior problems versus 57% of children in the control group without the variant). Finally, improving our understanding of why noncarrier youth did not respond, and focusing efforts on developing interventions that would benefit those youth would be a more effective use of resources than administering interventions to youth for whom they are unlikely to be effective.

Although using such biological information to “individualize” intervention pro- grams may improve intervention efficacy, there are ethical issues that arise concerning risks such as stigma, discrimination, privacy, and equity of service provisions. In this

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article, we will discuss these issues and propose potential solutions to minimize the possible negative effects of a biosocial approach.

Biological Factors Associated With Heterogeneity in Youth With Antisocial Behavior

Although research has identified average differences in biological factors between youth with antisocial behavior compared with controls, there is considerable evidence of heterogeneity in the biological factors that characterize these youth. There is not a single pathway of biological or environmental factors that leads to antisocial behavior; rather, different combinations of biological and environmental factors lead to antiso- cial behavior. This means that interventions designed for youth with antisocial behav- ior will be targeting children with different underlying biological factors, with different genes, different levels of hormones and neurotransmitters, and different levels of brain functioning. These factors, in addition to environmental factors, are likely to influence how the child will respond to the intervention.

Genes are one source of variability. It is now widely accepted that youth outcomes are the product of both the environment in which the child develops and genetic fac- tors that influence the individual characteristics of the child (e.g., the child’s tempera- ment, intelligence quotient [IQ]) and how the child responds to his or her environment. Behavioral genetics studies suggest that heritability of antisocial behavior is 40% to 50% (Salvatore & Dick, 2018). It is likely that different genes confer risk for antisocial behavior in different individuals, making it difficult to identify single genes with large effects for antisocial behavior as a whole. Studies assessing the whole genome have identified a number of promising genomic regions, but these remain to be replicated (Salvatore & Dick, 2018). A small number of candidate genes have been associated with conduct disorder (e.g., Ficks & Waldman, 2014). Although the influence of single genes on complex behavior such as antisocial behavior is likely to be very small, examining gene variants may be one way to acquire information about how an indi- vidual may respond to a particular intervention. Genetic information is relatively easy and inexpensive to collect, and it could potentially serve as a proxy for other types of information, such as information about brain functioning, which is more difficult to assess as it requires expensive techniques. Gene variants can represent variability in the biological pathway that begins at the molecular level (e.g., neurotransmitters) and then influences the functioning of brain regions (Hariri, 2009). For example, numer- ous studies have found associations between specific gene variants and levels of brain functioning and structure (Klasen et al., 2018; Kolla, Patel, Meyer, & Chakravarty, 2017; Waller et al., 2016). Therefore, in situations in which it is not feasible to conduct brain imaging, genetic information—and ideally information about combinations of genes—may be useful in determining which intervention may be best for a particular child, or which children are most in need of interventions.

Information about the stress response system may also be useful and relatively easy to collect. There is considerable heterogeneity in responding within youth with antiso- cial behavior. The stress response system includes the hypothalamic–pituitary–adrenal

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(HPA) axis and the autonomic nervous system. The HPA axis releases the hormone cortisol in response to stress, which serves to mobilize the body’s resources. Interestingly, some studies report antisocial behavior is associated with low cortisol levels (e.g., Loney, Butler, Lima, Counts, & Eckel, 2006) while others report elevated cortisol levels (van Bokhoven et al., 2005). Thus, it seems that different subtypes of antisocial youth may demonstrate different patterns of stress responsivity. It is likely that this is associated with individual differences in responding to the environment, including responding to interventions.

The autonomic nervous system consists of the sympathetic nervous system, which drives the fast-acting “fight-or-flight” response, and the parasympathetic nervous sys- tem, which acts in a regulatory capacity to return the body to rest. The functioning of these systems is commonly measured through physiological recordings of heart rate and skin conductance. Antisocial behavior in youth is often been associated with low resting heart rate (Gao, Huang, & Li, 2017) and reduced skin conductance levels and reactivity (Lorber, 2004). However, studies have also found evidence of increased skin conductance reactivity, primarily in relation to reactive aggression (Hubbard et al., 2002). Importantly, physiological reactivity may interact with environmental factors to increase or decrease the risk for antisocial behavior. Erath, El-Sheikh, Hinnant, and Cummings (2011) found that the association between harsh parenting and externaliz- ing behavior was stronger among children with low skin conductance reactivity com- pared to children with higher skin conductance reactivity. By measuring this biological variable, we may be able to better understand which children who may be exposed to negative environmental factors are most at risk for antisocial behavior.

Mixed findings also exist in the literature on antisocial behavior and parasympa- thetic nervous system activity, often measured via respiratory sinus arrhythmia (RSA), or variability in heart rate in synchrony with respiration. Beauchaine, Hong, and Marsh (2008) found that boys scoring higher in aggression had lower baseline levels of RSA. However, other studies have found that externalizing problems were positively associated with RSA (Dietrich et al., 2007; Gao et al., 2017). Other studies have not found aggression in youth to be associated with RSA (Aults, Cooper, Pauletti, Jones, & Perry, 2015; Calkins, Graziano, & Keane, 2007; Hinnant & El-Sheikh, 2009). Higher RSA is thought to reflect physiological flexibility and the ability to adapt to environmental stressors and regulate emotion (Fabes & Eisenberg, 1997); thus, it is generally considered adaptive (Beauchaine, 2001; Porges, Doussard- Roosevelt, & Maiti, 1994). However, excessive RSA reactivity has been linked with social maladjustment and anxiety in some studies (e.g., Gazelle & Druhen, 2009), and it has been argued that heightened parasympathetic relative to sympathetic activ- ity may indicate a passive coping response to stress, which may contribute to under- arousal and antisocial behavior (Gao et al., 2017). Thus, it seems that there is variability in parasympathetic nervous system functioning in youth with antisocial behavior. Because this system affects responding to environmental stressors and emotion regulation, it is likely that the parasympathetic activity could also influence how a particular child responds to an intervention.

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Finally, both structural and functional neuroimaging studies have found evidence of differences in brain structure in functioning in youth with antisocial behavior. Recently, Noordermeer, Luman, and Oosterlaan (2016) conducted a meta-analysis of 29 structural and functional neuroimaging studies in youth with oppositional defiant disorder and/or conduct disorder and found reduced structure and/or functioning in the amygdala, insula, striatum, and anterior cingulate. However, individual studies exam- ining subgroups of youth have found that the functioning of these regions can vary. For example, Viding et al. (2012) found that amygdala reactivity was higher in boys with conduct problems who did not have callous-unemotional traits, but low in boys with conduct problems with callous-unemotional traits. In adolescents, Dotterer, Hyde, Swartz, Hariri, and Williamson (2017) found that antisocial behavior was asso- ciated with increased amygdala reactivity, but that callous-unemotional traits were not associated with amygdala activity. Thus, it seems that there is variability within sam- ples of antisocial youth, suggesting that individual differences in biological factors are likely to underlie differential responsiveness to the environment.

It is important to note that not all youth with antisocial behavior will exhibit all of the biological abnormalities or differences described above. Whereas some youth may have deficits related to systems important for punishment processing that underlie their antisocial behavior, others may have deficits primarily in reward-related systems. Furthermore, not all youth with specific biological features have or will develop antiso- cial behavior. Fanti (2016) recently reviewed literature on the role of different physio- logical systems in understanding heterogeneity in conduct disorder. He found that conduct disorder is manifested in diverse ways physiologically, and that different sub- groups of youth with conduct disorder can score in opposite extremes on physiological measures. This heterogeneity cannot easily be accounted for using existing methods for subtyping youth based on behavioral or symptom-level assessments. Fanti (2016) sug- gests that future work should use physiological measures as grouping or clustering variables rather than creating groups based on co-occurring psychopathologies or traits.

Different biological risk factors likely underlie heterogeneity in how youth respond to the environment, including how they respond to interventions. A number of studies have demonstrated some of the ways in which biological factors influence responding to the environment, particularly in ways that may influence how a child would respond to an intervention.

Biological Factors Affect Responsiveness to the Environment

There is a growing body of research that takes a biosocial approach to understanding antisocial behavior in youth. Biological and environmental factors have a reciprocal relationship. On one hand, biological factors can influence how an individual responds to the environment. For example, individuals with a greater biological predisposition to anxiety are likely to respond to anxiety-inducing situations differently than those without such predisposition. On the other hand, environmental factors can change our

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biology. For example, prolonged exposure to stress can lead to altered functioning of the stress response system and even altered gene expression (Weaver, Meaney, & Szyf, 2006). In many cases, biosocial studies demonstrate that the presence of both a bio- logical risk factor and a social risk factor increases risk for antisocial behavior (Beaver, Schwartz, & Gajos, 2015; Byrd & Manuck, 2014).

One implication of this is that existing biological factors may influence responding to different types of environments. For example, biological factors including gene variants (Janssens et al., 2015), skin conductance reactivity (Gregson, Tu, & Erath, 2014), and cortisol levels (Rudolph, Troop-Gordon, & Granger, 2010) have been found to influence whether the experience of peer rejection is associated with antiso- cial behavior. Although most studies to date have examined how negative environ- mental factors interact with biological factors to predispose for antisocial behavior, biological factors will also influence responding to positive, enriching environments. Several studies have begun exploring the idea that some youth are more sensitive to environmental influences regardless of whether the environment is negative or posi- tive (Belsky, Bakermans-Kranenburg, & van IJzendoorn, 2007). The differential sus- ceptibility model suggests that children who are most at risk for adverse developmental outcomes may also be the ones who would benefit the most from intervention pro- grams and additional services; in other words, some children are especially responsive to the environment both “for better and for worse.” Next, we review studies that have examined how biological factors may influence responding to positive environmental changes that occur in the context of interventions.

Biological Factors Affect Intervention Responsiveness

Intervention during childhood has the potential to prevent a cascade of negative out- comes in youth who may be showing early signs of aggressive behavior (Dodge, 2009). As mentioned, intervention programs are often complex, expensive, and the average effects are often modest (McCart et al., 2006), possibly because the programs are not uniformly effective. Emerging research suggests that the individual character- istics of the youth participating in the intervention may be important in determining “what works for whom” (Albert et al., 2015). Several studies now have examined how biological factors influence intervention responsiveness.

First, studies have found that genes associated with dopamine (Bakermans- Kranenburg, van IJzendoorn, Pijlman, Mesman, & Juffer, 2008; Brody et al., 2014), serotonin (Brody, Beach, Philibert, Chen, & Murry, 2009), and glucocorticoids (Albert et al., 2015) moderate responses to interventions designed to reduce behavior problems and negative outcomes in youth. Recently, a genetic factor was found to moderate responsiveness to the Coping Power intervention (Glenn et al., 2018). Coping Power is one of the relatively few rigorously tested school-based programs currently available to address behavior problems among children at risk for antisocial behavior (Lochman & Wells, 2002, 2003, 2004). When implemented in the tradi- tional format, it involves group sessions that take place at the students’ schools, sepa- rate group sessions for parents, and supports to teachers. Because of concerns about

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potential iatrogenic effects of aggregating high-risk youth, a version of the program was developed in which children met individually with the interventionist rather than in group settings.

Glenn et al. (2018) found that a variant of the oxytocin receptor gene interacted with intervention delivery format in predicting responsiveness to the intervention. For youth with one variant of the gene, reductions in externalizing behavior were observed regard- less of intervention format. However, youth with the alternate variant of the gene who received the group-based intervention showed little improvement over the course of the intervention, and a worsening of symptoms during the follow-up year. In contrast, those with this variant who received the individual format of the intervention demonstrated reductions in externalizing problems. This suggests that genetic factors may be infor- mative when making decisions about which form of an intervention may work best for a particular child. In this case, the oxytocin receptor gene may influence susceptibility to social reinforcement and deviant peer influences—factors that may affect respon- siveness to group-based interventions more than individual interventions.

Because of the small effects of single genes, a more sophisticated approach may be to assess combinations of genes. Chhangur et al. (2017) recently examined the effect of five dopaminergic genes on responsiveness to the Incredible Years parent program. Boys carrying more of these gene variants showed the greatest reduction in parent- reported externalizing behavior in response to the intervention.

Physiological variables have also been associated with intervention responsive- ness. Stadler et al. (2008) found that children with behavior problems with lower rest- ing heart rate benefited less from an intensive day-care treatment and parent training. Importantly, they found that heart rate was a significant predictor of therapy success whereas other risk factors (initial levels of aggression, delinquency, attention prob- lems, internalizing behavior, cognitive functioning, and age) did not affect therapy success. This suggests that biological factors may help us to better predict which youth are likely to benefit from a particular intervention.

Similarly, Beauchaine et al. (2015) found that nonspecific fluctuations in skin con- ductance, which index sympathetic nervous system activity, predicted treatment responses to the Incredible Years intervention in preschool children with attention deficit hyperactivity disorder (ADHD). The Incredible Years intervention involves both parent and child training (20 weekly 2-hr sessions). Youth with fewer nonspecific fluctuations showed poorer treatment response on four of seven externalizing out- comes. Furthermore, the intervention was associated with longitudinal increases in electrodermal activity from pre- to post-treatment. This study emphasizes the impor- tance of understanding the mechanisms by which treatments have an effect. If we can identify which biological factors may be changed by intervention, we may be able to select youth who demonstrate particular deficits in these factors to receive this type of intervention.

Glenn et al. (in press) found that RSA and skin conductance level both moderated responsiveness to the Coping Power intervention. Youth with lower skin conductance levels (i.e., sympathetic nervous system functioning) at preintervention demonstrated greater reductions in teacher-rated proactive aggression from preintervention to a

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1-year follow-up than those with higher skin conductance levels; this effect was found regardless of intervention format (individual or group delivery). RSA interacted with intervention format to influence responsiveness in individuals with high initial levels of aggression; youth with low RSA (i.e., possibly lower emotion regulation) benefited from the individual but not group format of the intervention, whereas youth with high RSA benefited equally well from both formats.

It should be noted that it may not always be the case that children with the “risk” version of a particular biological factor will always be the children who do not respond well to interventions. For example, Bagner et al. (2012) found that young children born prematurely who had lower baseline RSA (e.g., possibly reflecting lower emo- tion regulation) showed greater improvements in disruptive behavior after receiving Parent–Child Interaction Therapy, a behavioral parent-training intervention.

Studies have also examined how hormones may be associated with intervention responsiveness. In youth with disruptive behavior disorders, van de Wiel, van Goozen, Matthys, Snoek, and van Engeland (2004) found that those with high cortisol respon- sivity at pretreatment showed more improvement in response to a structured interven- tion aimed to decrease aggressive behavior than those with a blunted cortisol stress response. Shenk et al. (2012) found that youth with disruptive behavior disorders with higher pretreatment testosterone, but not other hormones, were 4 times more likely to be nonresponders to a multifaceted psychological treatment.

Together, these studies indicate proof of concept that a biosocial approach may be useful in predicting which youth may benefit the most from intervention programs, as well as in identifying youth who are unlikely to respond and thus may require a differ- ent form (e.g., change in format, change in focus, or change in intensity) of interven- tion. More research is needed to replicate existing findings to develop measures that would be considered reliable predictors. Additional research is also needed to examine the effects of multiple biological factors simultaneously to gain a better understanding of which risk factors or combinations of factors show the strongest effects on interven- tion responding.

Finally, this work may also provide information that can be used to develop inter- ventions for youth that are more tailored and targeted to the specific needs of the individual. For example, interventions known to improve specific biological risk fac- tors could potentially delivered to youth who demonstrate those risk factors. Brotman et al. (2007) found that a 22-week family-based intervention in preschool children at risk for antisocial behavior was capable of increasing cortisol responses to a social challenge. Similarly, in a sample of 3- to 6-year-old foster children, Fisher, Stoolmiller, Gunnar, and Burraston (2007) found that a 12-month family-based therapeutic inter- vention was able to restore altered diurnal cortisol patterns to a level that became comparable with the patterns demonstrated by nonmaltreated children. Finally, Dorn, Kolko, Shenk, Susman, and Bukstein (2011) found that a psychosocial intervention for children with disruptive behavior disorders increased cortisol levels over time. Of note, these studies involve psychosocial forms of intervention; it is not necessarily the case that biological deficits must be targeted with biologically based forms of treatment.

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Opportunities to maximize the fit between children and programs have great poten- tial for improving intervention effects. Determining which individual characteristics matter holds the promise of enabling interventionists to tailor interventions to indi- viduals and improving efficiency and impact.

Using Biological Information to Determine Which Youth May Be Most in Need of Intervention

In addition to determining which interventions work for specific children, in the face of limited resources, it is equally important to determine which children may be most in need of interventions. Without any intervention, some youth may be at greater risk for escalating behavior problems, whereas others may “age out.” Studies have sug- gested that among children who demonstrate high levels of antisocial behavior, less than 50% will continue to exhibit these problems into adolescence (Barker & Maughan, 2009; Moffitt, 1993). Thus, not all youth may require intensive interventions. With limited resources available, it is critical to determine which youth are likely to have persistent behavior problems.

Biological factors may help to determine which youth are at greater risk for later behavior problems. Studies have found that genetic factors influence the trajectory of behavior problems (Fontaine, Rijsdijk, McCrory, & Viding, 2010; Latendresse et al., 2011). Dick et al. (2009) found that youth who showed persistent elevated trajectories of externalizing behavior from age 12 to 22 years were more likely to carry a specific variant of the GABRA2 gene. Schoorl, van Rijn, de Wied, van Goozen, and Swaab (2017) found that cortisol reactivity to stress may also be predictive of the trajectory of aggressive behavior problems. In a sample of 8- to 12-year-old boys with disruptive behavior disorders, cortisol stress reactivity and better cortisol recovery predicted a decline in aggression 6 and 12 months later. Although other factors such as parenting, conflict in the home, and child temperament have been found to predict trajectories of antisocial behavior (Barker & Maughan, 2009), the additional consideration of bio- logical factors may improve our predictive ability.

Brody et al. (2014) demonstrate how improved prediction of antisocial trajectories could benefit interventions. This study implemented a 7-week, family-based youth risk behavior prevention program for rural African Americans. For youth in the control condition who did not receive the intervention, male youth with the risk allele of a dopamine-related gene showed greater increases in substance use across 22 months than those without the risk allele. Youth with the risk allele who were in the interven- tion condition demonstrated significantly lower rates of substance use than those in the control condition. This study demonstrates two things: (a) in the absence of interven- tion (control condition only), some individuals are at much greater risk for substance use problems than others—these youth would presumably benefit most from interven- tion; and (b) the intervention is successful in reducing maladaptive behavior in these youth. In contrast, the intervention does little to change the behavior of the youth without the risk allele who did not show increased substance use in the absence of intervention. Thus, when developing intervention programs for youth with early

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antisocial behavior, it is important to determine which youth may be at greatest risk for a trajectory of persistent behavior problems, and who may require more intensive forms of intervention. Furthermore, if it is true that some youth may benefit greatly from interventions, it would be important to be able to identify these individuals.

In sum, to achieve the best overall outcomes, it seems essential to understand the factors that affect intervention responses, and to use this information either in the selection of youth for intervention programs, or to tailor programs to specific youth based on this information. Programs that reduce behavior problems and improve social, emotional, and academic functioning would have the greatest impact if they are targeted toward youth with the most biological risk. However, there are a number of concerns that arise regarding the use of biological information that should be thor- oughly considered.

Ethical Issues

The ideas discussed above have considerable implications for policy. However, it is important to keep in mind that to date, the available evidence is not strong enough to warrant the use of biological information in making decisions about interventions. Most of the studies discussed here have not been replicated, and it has yet to be deter- mined how effect sizes for different types of factors (biological, psychosocial, demo- graphic) compare in terms of predicting responsiveness or predicting trajectories of antisocial behavior. Furthermore, with the exception of single genetic polymorphisms, which likely have small effects, biological factors represent continuous dimensions. Applying such information in a practical setting would require thresholds for cut-off scores to be developed.

However, assuming that we can incorporate biological information into a method for predicting which youth are more likely to respond to interventions and which youth are not likely to respond, several issues may arise. Distinguishing youth based on bio- logical information brings up ethical issues concerning stigma, discrimination, and equity of service provisions (Ellis et al., 2011). To better understand the issues that may arise, we will present a hypothetical scenario using the Coping Power interven- tion as an example. As described previously, this preventive intervention was designed for fourth grade youth who have been identified primarily by teachers as demonstrat- ing aggressive behavior. Typically, the highest scorers in a given school will be selected for participation in the intervention. In other words, youth selected for the program have similarly high rates of externalizing problems prior to the intervention. One can imagine that a battery of tests could be conducted on these youth. In addition to gather- ing further information about symptoms, family factors, and demographics, tests could be conducted to collect biological information. This might include a cheek swab to test for specific genetic polymorphisms, measurement of heart rate and skin conductance levels, the collection of saliva samples for assessing hormone levels at rest and/or in response to a stressor, and neurocognitive tests measuring processes such as inhibitory control, working memory, and other executive functions. This information could be weighted according to results from research examining the relative influence of these

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variables in predicting Coping Power outcomes and used to categorize youth into dif- ferent groups. If we want to obtain the most benefit from limited resources, we could imagine dividing youth into three groups:

Group 1: Youth who are likely to benefit from Coping Power and who would likely to persist with antisocial behavior in the absence of intervention. These youth would receive the standard Coping Power intervention. Group 2: Youth who are less likely to persist with antisocial behavior regardless of whether they receive an intervention or not (e.g., similar to the adolescent-limited taxon described by Moffitt, 1993). These youth could receive a modified version of Coping Power that is less intensive. For example, briefer versions of Coping Power have been found to produce significant reductions in teacher ratings of children’s externalizing behaviors at long-term follow-ups (Lochman et al., 2014). Recently, a hybrid version of Coping Power that includes both face-to-face (12 child sessions, seven parent sessions) and Internet components has been developed which can also be administered more efficiently (Lochman et al., 2017). Group 3: Youth who are less likely to benefit from Coping Power and who would be likely to persist in antisocial behavior problems in the absence of intervention. These youth should be the target of modified or alternative interventions that may ultimately prove to be more effective for these youth. This might involve increasing the duration or intensity (e.g., frequency of sessions) of the intervention, adding components, changing the format (e.g., individual- vs. group-based format of Coping Power, described above), or providing more focus on a particular area. For example, if research suggests that these youth often have blunted cortisol reactivity, then selecting interventions or adding components of interventions that are known to increase cortisol reactivity may be promising. In other words, the more we are able to understand about how interventions work, and the more we know about the individuals for whom interventions do not work, the better we can match youth to interventions in a sophisticated way.

There are a few issues that arise from implementing such a procedure. Some may argue that providing different levels of services to different individuals based on bio- logical factors violates our sense of fairness and equality. However, if we are able to reliably identify individuals who likely will not benefit from a particular intervention, then it would seem questionable to provide publicly funded services that are unlikely to be effective simply so that everyone is “treated” in the same way (Belsky & van Ijzendoorn, 2015). Rather, by continually modifying and improving the above proce- dure, including furthering our research on finding programs that work for “nonre- sponders,” we will be able to achieve the best outcomes for all youth, and this would result in more equity overall.

There are also concerns about the collection and use of biological information, including the potential that it could be used to discriminate against particular youth. First, biological information collected from the child should be kept private and only used by interventionists; however, it is possible that collecting biological information

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from youth could result in this information being used in inappropriate ways. It could be argued that the types of information collected for this purpose may be less “reveal- ing” than information collected for some medical purposes (e.g., genes that confer high risk for a disease). As stated previously, each biological factor is likely to contrib- ute to only a small portion of variance in antisocial behavior, and thus it may be less likely to be used for malicious purposes. Data collected from youth would be multifac- eted, and the procedure for determining categories would be based on the combination of this data (biological and nonbiological). We, as a society, will need to decide whether the potential benefits of incorporating this information into existing methods of prevention and intervention are worth the risks to individual privacy.

Another concern is that by categorizing youth, there could be negative effects related to labeling. In particular, if parents or teachers perceive that a child has been catego- rized as a “nonresponder,” they may change how they interact with the child. Thus, it will be important to have procedures for explaining the goal of selecting a particular intervention for a specific child. When explaining the procedure to parents, it would be important to emphasize that biological factors, even genes, are not fixed and immuta- ble. Research suggests that these factors are modifiable through intervention.

Considering the perspective of parents/caregivers, the results of the initial testing might be explained as follows: For Group 1, parents would be told that we have an effective intervention that is known to work well for individuals who have similar characteristics to their child. For Group 2, parents would be told that youth who have similar characteristics to their child typically do not develop severe behavioral prob- lems, but we can offer an abbreviated intervention. Finally, for Group 3, parents would be told that we are providing an alternate form of intervention that may prove to work better for their child than the traditional intervention. Although outcomes in this case may be less certain, it would hopefully suggest to the parent that efforts are being made to reach the best possible outcome.

Finally, there are issues related to miscategorization of individuals. Interventionists will never be able to predict without error. However, the more research that is con- ducted in this area, the more we can improve our methods of predicting. It could be argued that even a rudimentary calculation based on a few factors could result in over- all intervention effects that exceed current effect sizes.

Conclusion

The average effects of interventions for youth with antisocial behavior are modest, which may result in policy makers being less inclined to fund large-scale intervention efforts (Belsky & van Ijzendoorn, 2015). However, these effects may partly be due to failure to consider heterogeneity in youth who demonstrate early signs of antisocial behavior. Interventions may be highly beneficial for some children, who may be more susceptible to the influence of the environment. Biological factors may help predict children’s likelihood of benefiting from intervention. Efforts are needed to further our understanding of how biological factors affect intervention responsiveness, as well as how interventions may alter biological factors. Further discussion of how we as a

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society want to deal with ethical issues that arise from using biological information to individualize interventions is also warranted.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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Author Biographies

Andrea L. Glenn is an Associate Professor in the Department of Psychology and the Center for the Prevention of Youth Behavior Problems at the University of Alabama. Her research interests include biopsychosocial approaches to understanding antisocial behavior and using biological information to individualize treatment and interventions for antisocial behavior (precision medi- cine). She is also interested in understanding the factors influencing psychopathic traits in youth and adults.

Katie E. McCauley is an undergraduate student at the University of Alabama who has worked in Dr. Glenn’s lab for the past 3 years.