







































Children and Teenagers 
ISSN 2576-3709 (Print) ISSN 2576-3717 (Online) 

Vol. 3, No. 2, 2020 
www.scholink.org/ojs/index.php/ct 

50 
 

Original Paper 

Not Race or Age but Their Interaction Predicts Pre-Adolescents’ 

Inhibitory Control 
Shervin Assari1,2* & Golnoush Akhlaghipour MD3 

1 Department of Urban Public Health, Charles R Drew University of Medicine and Science, Los Angeles, 

CA, USA 
2 Department of Family Medicine, Charles R Drew University of Medicine and Science, Los Angeles, 

CA, USA 
3 Department of Neurology, UCLA, Los Angeles, CA, USA 
* Shervin Assari, E-mail: assari@umich.edu; Tel.: +(734)-232-0445; Fax: +734-615-8739 

 

Received: October 12, 2020     Accepted: October 21, 2020     Online Published: November 5, 2020 

doi:10.22158/ct.v3n2p50                             URL: http://dx.doi.org/10.22158/ct.v3n2p50 

 

Abstract 

Background: African American pre-adolescents are at a higher risk of risky behaviors such as 

aggression, drug use, alcohol use, and subsequent poor outcomes compared to Caucasian 

pre-adolescents. All these high-risk behaviors are connected to low levels of Inhibitory Control (IC). 

Aim: We used the Adolescent Brain Cognitive Development (ABCD) data to compare Caucasian and 

African American pre-adolescents for the effect of age on pre-adolescents IC, a driver of high-risk 

behaviors. Methods: This cross-sectional analysis included 4,626 pre-adolescents between ages 9 and 

10 from the ABCD study. Regression was used to analyze the data. The predictor variable was age 

measured in months. The main outcome was IC measured by a Stop-Signal Task (SST). Race was the 

effect modifier. Results: Overall, age was associated with IC. Race also showed a statistically significant 

interaction with age on pre-adolescents’ IC, indicating weaker effects of age on IC for African American 

than Caucasian pre-adolescents. Conclusion: Age-related changes in IC are more pronounced for 

Caucasian than African American pre-adolescents. To eliminate the racial gap in brain development 

between African American and Caucasian pre-adolescents, we should address structural and societal 

barriers that alter age-related development for racial minority pre-adolescents. Social and public 

policies, rather than health policies, are needed to address structural and societal barriers that hinder 

African American adolescents’ brain development. Interventions should add resources to the urban 

areas that many African American families live in so their children can have better age-related brain 



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development. Such changes would be essential given IC in pre-adolescents is a predictor of a wide range 

of behaviors. 

Keywords 

Race, ethnicity, age, age-related development, pre-adolescents, impulse, brain, inhibitory control 

 
1. Introduction 

Inhibitory Control (IC), the ability to control one’s own impulses in order to select a more appropriate 

behavior in line with long-term goals (Chikara, Lo, & Ko, 2020; Deater-Deckard, Li, Lee, King-Casas, & 

Kim-Spoon, 2019), is closely correlated with a wide range of risk behaviors and factors such as poor diet 

and unhealthy eating, obesity and high body mass index, poor academic performance, weak social 

relations, problem behaviors, aggression, substance use, and early sexual debut (Bartholdy et al., 2019; 

Bessette et al., 2020; Cabello, Gutierrez-Cobo, & Fernandez-Berrocal, 2017; Dieter et al., 2017; Ely et al., 

2020; Huijbregts, Warren, de Sonneville, & Swaab-Barneveld, 2008; Humphrey & Dumontheil, 2016; 

Porter et al., 2018; Troller-Renfree et al., 2019). Low IC is also a characteristic of Attention Deficit 

Hyperactivity Disorder (ADHD) (Neely et al., 2017). Pre-adolescents from high Socioeconomic Status 

(SES) non-Hispanic Caucasian families who show high IC levels would be less likely to engage in a wide 

range of risk behaviors, relative to high SES pre-adolescents when compared to their counterparts from 

low SES and African American families (Deater-Deckard et al., 2019; Froeliger et al., 2017; Hao, 2017; 

Hsieh & Chen, 2017; Nakamichi, 2017). Some research suggests that IC may be specifically crucial for 

boys’ risk-taking behaviors, such as aggressive behavior (Cabello et al., 2017). Low IC may be one of 

the many mechanisms explaining racial and economic disparities in high-risk behaviors (Cueli, Areces, 

Garcia, Alves, & Gonzalez-Castro, 2020; Deater-Deckard et al., 2019; Mora-Gonzalez et al., 2020; 

Porter et al., 2018; Zhang, Wang, Liu, Song, & Yang, 2017). 

Compared to Caucasian pre-adolescents, African American pre-adolescents are at an increased risk of 

aggression (Cotten et al., 1994) and early sexual debut (Cavazos-Rehg et al., 2009). As these undesired 

behavioral outcomes early in life are shown to be gateways for a wide range of future economic, 

emotional, and behavioral outcomes later in life (Burchinal et al., 2011; Cohen & Sherman, 2005; Gorey, 

2009; Hair, Hanson, Wolfe, & Pollak, 2015), there is a need to study why and how IC is lower in African 

American and Caucasian pre-adolescents. Such knowledge has the potential to help with closing racial 

inequalities later in life (Burchinal et al., 2011; Cohen & Sherman, 2005; Gorey, 2009; Hair et al., 2015). 

There is a close overlap between race and SES in the United States of America, meaning that African 

Americans have lower SES and experience a higher level of a wide range of adversities (Ahmad, Zulaily, 

Shahril, Syed Abdullah, & Ahmed, 2018; Merz, Tottenham, & Noble, 2018; Valencia, Tran, Lim, Choi, 

& Oh, 2019). As such, African American adolescents face high levels of food insecurity, housing 

insecurity, family instability, economic adversities, stress, trauma, and financial difficulties (DeSantis et 

al., 2007; Dismukes et al., 2018; Hanson et al., 2015; Miller & Taylor, 2012). However, an open question 



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is whether the effects of race and associated SES are direct (Alvarado, 2018; Barreto, de Figueiredo, & 

Giatti, 2013; Hemovich, Lac, & Crano, 2011; Schreier & Chen, 2013) or may operate by delaying 

healthy age-related changes in the brain. While Caucasians, through low access to SES and associated 

buffers and resources, live in a context in which age-related development may naturally occur (Alvarado, 

2018; Barreto et al., 2013; Hemovich et al., 2011; Schreier & Chen, 2013), the same may not be accurate 

for African Americans whose daily life means low access to resources, high stress, and trauma (Kaufman, 

Cooper, & McGee, 1997) that may interfere with healthy age-related brain development.  

Both mediation (Bell, Sacks, Thomas Tobin, & Thorpe, 2020; Fuentes, Hart-Johnson, & Green, 2007; 

Kaufman et al., 1997; Samuel, Roth, Schwartz, Thorpe, & Glass, 2018) and moderation (Assari, 2017d; 

Assari, 2018a) explanations have been tested for racial health inequalities across age groups, including 

but not limited to pre-adolescents and adolescents. The first clusters of hypotheses, more traditional ones, 

have attributed racial gaps in pre-adolescents outcomes to the existing SES or stress gaps between 

African American and Caucasian families (Bell et al., 2020; Fuentes et al., 2007; Kaufman et al., 1997; 

Samuel et al., 2018). In these hypotheses, low SES and high stress emerge across racial minorities, 

including African American pre-adolescents (Assari, 2016, 2017b; Assari, Khoshpouri, & Chalian, 

2019). If these hypotheses are supported, then a real solution to closing racial inequalities is eliminating 

the SES gap through economic policies that redistribute income (e.g., tax policies, minimum wage). As 

such, African American families’ economic empowerment becomes the core strategy for closing the 

racial inequalities in pre-adolescents and beyond (Williams, 1999; Williams, Costa, Odunlami, & 

Mohammed, 2008). 

The alternative explanation, however, argues that SES indicators (Assari, 2017d; Assari, 2018a), age, 

and other resources show weaker effects for African Americans than Caucasians, a pattern known as 

Minorities’ Diminished Returns (MDRs) Supported by extensive recent literature under the umbrella 

term MDRs, all economic and non-economic resources such as education (Assari, Farokhnia, & Mistry, 

2019) parental education (Assari, 2018d; Assari, 2018b; Assari, 2018e), income (S. Assari, C. H. 

Caldwell, & R. Mincy, 2018a; Assari, Thomas, Caldwell, & Mincy, 2018), marital status (Assari & 

Bazargan, 2019a), and coping (Assari, 2017a, 2017c; Assari & Lankarani, 2016b) all generate 

less-than-expected tangible developmental outcomes for African Americans than Caucasians. This is 

partly due to the qualitative difference between African American and Caucasian families’ lives, so the 

latter gets and the former does not get the opportunities to mobilize their resources to secure tangible 

outcomes (Assari, 2017d, 2018a, 2018e; Assari, Caldwell, & Mincy, 2018a; Assari, Caldwell, & 

Zimmerman, 2018; Assari & Hani, 2018). As a result of these MDRs, we observe worse than expected 

outcomes across all SES levels of African American families (Assari, 2017d; Assari, 2018a; Assari, 

Caldwell, & Mincy, 2018a; S. Assari, C. H. Caldwell, & R. B. Mincy, 2018b; Assari, Thomas, et al., 

2018). That is, low and high SES African American adolescents show the same (high) level of 

impulsivity (Assari, Caldwell, & Mincy, 2018a), ADHD (Assari & Caldwell, 2019a), depression (Assari 



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& Caldwell, 2018a), anxiety (Assari, Caldwell, & Zimmerman, 2018), aggression (Assari, Caldwell, & 

Bazargan, 2019), grade point average (GPA) (Assari S, 2019; Assari & Caldwell, 2019b; Assari, 

Caldwell, et al., 2019), and substance use (Assari, Caldwell, et al., 2019) while for Caucasian adolescents, 

high SES means low risk. If MDRs are true (Assari & Caldwell, 2018a; Assari, Caldwell, et al., 2019; 

Assari, Caldwell, & Mincy, 2018a; Assari, Caldwell, & Mincy, 2018b; Assari, Thomas, et al., 2018), 

then a real solution requires moving beyond SES and targeting structural inequalities that hinder one 

group and promote the other. 

1.1 Aims 

To fill the literature gap on social and developmental determinants of IC, which itself is a mechanism for 

a wide range of undesired behaviors (Bartholdy et al., 2019; Bessette et al., 2020; Cabello et al., 2017; 

Dieter et al., 2017; Ely et al., 2020; Huijbregts et al., 2008; Humphrey & Dumontheil, 2016; Porter et al., 

2018; Troller-Renfree et al., 2019), and to expand the MDRs literature, we studied the separate, additive, 

and interactive effects of race and age on pre-adolescents IC. To do so, we compared African American 

and Caucasian pre-adolescents for the effects of age on IC. As suggested by the MDRs, we expected 

age-related changes in IC, however, we expected these changes to be smaller for African American than 

Caucasian pre-adolescents. The results would have implications for pre-adolescents, and beyond, IC is a 

core predictor of high-risk behaviors (Bartholdy et al., 2019; Bessette et al., 2020; Cabello et al., 2017; 

Dieter et al., 2017; Ely et al., 2020; Humphrey & Dumontheil, 2016; Porter et al., 2018; Troller-Renfree 

et al., 2019) and may explain why family SES and race are linked to many high-risk behaviors 

pre-adolescents (Bruce et al., 2013; Holochwost, Volpe, Gueron-Sela, Propper, & Mills-Koonce, 2018; 

Skowron, Cipriano-Essel, Gatzke-Kopp, Teti, & Ammerman, 2014; Swingler, Isbell, Zeytinoglu, 

Calkins, & Leerkes, 2018; Zaidman-Zait & Shilo, 2018). 

 

2. Methods 

2.1 Design and Settings 

A secondary analysis was performed with a cross-sectional design. We used data from the Adolescent 

Brain Cognitive Development (ABCD) study (Alcohol Research: Current Reviews Editorial, 2018; 

Casey et al., 2018; Karcher, O’Brien, Kandala, & Barch, 2019; Lisdahl et al., 2018; Luciana et al., 

2018). ABCD, a landmark study of brain development from pre-adolescence to emerging adults, is a 

unique study in the United States. Although details of the ABCD methods, measures, design, sample, 

and sampling are described elsewhere (Alcohol Research: Current Reviews Editorial, 2018; Auchter et 

al., 2018), here we briefly review them. 

2.2 Participants and Sampling 

In the ABCD, we only included pre-adolescents who were between the ages of 9 and 10 years. The 

ABCD pre-adolescents were enrolled from multiple cities across the states. Overall, pre-adolescents 

were recruited to the ABCD study from a total of 21 sites. The primary strategy for sampling in the 



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ABCD study was recruiting from school systems (Garavan et al., 2018). In the current analysis, the 

sample was 4626 participants. Our analysis’s inclusion criteria were having valid data on race, ethnicity, 

age, family SES, and task-based IC. Additionally, participants should only be African American or 

Caucasian. 

2.3 Study Variables 

The study variables included race, ethnicity, age, sex, family SES (parental education), family marital 

status, and task-based IC. 

Inhibitory Control (IC). The ABCD study applied the Stop-signal Task (SST) to measure 

pre-adolescents’ IC levels. The SST used in the ABCD applied two runs of 180 trials. Pre-adolescent 

subjects were shown images of a black arrow that were either pointing to right or left. These pictures 

were displayed on the monitor while the participant was in the scanner. Participants were asked to click 

the appropriate button that corresponds with the arrow direction as soon as they can see the image. 

Participants were instructed that they should all use their dominant hand. From all 180 trials, 30 did not 

display either of the options, signaling the participant to inhibit their answers. These were randomly 

dispersed throughout the trial. IC in this study was defined as a successful inhibition of motor response. 

Impulsivity was defined as answering with a wrong answer or an unsuccessful inhibition. For this study, 

IC was captured as the total number of “Stop” trials answered incorrectly (tfmri_sst_all_beh_incrs_nt). 

IC was treated as a continuous measure. A higher score was indicative of a higher level of IC (Carver, 

Livesey, & Charles, 2001; Clark, King, & Turner, 2020; Dupuis et al., 2019; Hiraoka, Kinoshita, 

Kunimura, & Matsuoka, 2018).  

Race. Race, a self-identified variable, was a binary variable: 1 for African Americans and 0 for 

Caucasians (reference category).  

Age. Age (months), calculated as the difference between birth and the time of enrollment to the study, 

measured in months, was reported by parents. 

Sex. A dichotomous variable, sex was coded as below: males = 1, females = 0. 

Marital status. Parental marital status, a dichotomous variable, was self-reported by the parents and was 

coded as married = 1 vs. other = 0. 

Parental Educational Attainment. Participants were asked, “What is the highest grade or level of school 

you have completed or the highest degree you have received?” Responses ranged from 0 for never 

attended or kindergarten only to 21 for a doctoral degree. This variable, with a range between 1 and 21, 

was treated as an interval variable. 

2.4 Data Analysis 

The statistical package, SPSS, was applied for data analysis. Mean, Standard Deviation (SD), frequency, 

and relative frequency (%) were used to describe the study variables. We also performed an independent 

t-test and Chi-square test for bivariate comparison of the groups for the study variables. For multivariable 

modeling, four regression models were applied. Model 1, an overall model, was performed without the 



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interaction terms. Model 2, another overall model, also added an interaction term between race and age 

(months). Model 3 and Model 4 were tested in Caucasian and African American pre-adolescents. In our 

models, age was used as the predictor, sex and family SES as the covariates, IC as the outcome, and race 

as the effect modifier. Unstandardized coefficient (b), SE, 95% CI, and p-value were reported for our 

model. p equal or less 0.05 was significant. 

2.5 Ethics 

The ABCD study protocol received Institutional Review Board (IRB) approval from several institutions, 

including but not limited to the University of California, San Diego (UCSD). All participating 

pre-adolescents provided assent. All participating parents signed informed consent (Auchter et al., 2018). 

As we only performed a secondary analysis of fully de-identified data, our study did not require an IRB 

review (exempt from a full IRB review). 

 

3. Results 

3.1 Descriptives 

A total number of 4626 9-10 years old pre-adolescents were analyzed. Participants were mainly 

Caucasian (n = 3513; 75.5%), and only 24.1 (n=1113) were African Americans. Table 1 presents a 

summary of the descriptive statistics for the total sample and Caucasian and African American 

pre-adolescents.  

 

Table 1. Data overall and by Race (n = 4,626) 

 All  Caucasians  African Americans  

 n  % n  % n  % 

Race       

   Caucasian 3513 75.9 3513 100.0 - - 

   African American 1113 24.1 - - 1113 100.0 

Ethnicity       

   Non-Hispanic 3872 83.7 2855 81.3 1017 91.4 

   Hispanic 754 16.3 658 18.7 96 8.6 

Sex       

   Male 2273 49.1 1713 48.8 560 50.3 

   Female 2353 50.9 1800 51.2 553 49.7 

Marital status*       

   Other 1437 31.1 692 19.7 745 66.9 

   Married 3189 68.9 2821 80.3 368 33.1 

 Mean SD Mean SD Mean SD 

Age (Year) 118.44 7.41 118.36 7.44 118.69 7.32 

Parental Educational Attainment* 16.82 2.53 17.26 2.36 15.42 2.55 



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Financial Difficulties* 0.07 0.15 0.04 0.12 0.14 0.21 

Family Income* 7.26 2.43 7.90 1.95 5.26 2.70 

IC (Total number of “Stop” trials 

answered incorrect) 
27.01 7.20 27.16 6.77 26.54 8.41 

IC= Inhibitory Control, SD= Standard Deviation 

* p < 0.05 

 

3.2 Multivariate Analysis: All 

Table 2 shows a summary of the two regression models’ results in the overall (pooled) sample. Model 1 

(Main Effect Model) did not show a significant effect of age on IC. Model 2 (Interaction Model) showed 

an interaction between race and age on IC, suggesting that the effect of age on IC is weaker for African 

American compared to Caucasian pre-adolescents. 

 

Table 2. Overall Regression Models (n = 4,626) 

 
Model 1 

Main Effects 

Model 2 

Interaction Effects 

 B SE 95% CI p B SE 95% CI P 

Race (African American) -0.44 0.29 -1.02 0.13 .132 7.37 3.99 -0.45 15.19 .065 

Ethnicity (Hispanic) -0.36 0.30 -0.95 0.22 .225 -0.37 0.30 -0.95 0.22 .218 

Sex (Male) 1.40 0.21 0.99 1.82 < .001 1.40 0.21 0.99 1.82 < .001 

Married household 0.04 0.29 -0.53 0.60 .898 0.05 0.29 -0.52 0.62 .866 

Parental Educational Attainment 0.04 0.05 -0.06 0.15 .419 0.05 0.05 -0.06 0.15 .401 

Financial Difficulty -0.16 0.76 -1.66 1.34 .834 -0.14 0.76 -1.63 1.36 .857 

Family Income 0.03 0.07 -0.10 0.17 .633 0.03 0.07 -0.11 0.16 .684 

Age 0.02 0.01 -0.01 0.05 .196 0.03 0.02 0.00 0.07 .037 

Age x Race - - - - - -0.07 0.03 -0.13 0.00 .050 

Outcome: IC: Total number of “Stop” trials answered incorrect 

b= Unstandardized Regression Coefficient 

SE= Standard Error 

CI= Confidence Interval 

 

3.3 Multivariate Analysis: Each race 

Table 3 summarizes two regression models, one in Caucasians, and one in African Americans. We found 

the effect of age on IC for Caucasian but not African American pre-adolescents. 

 

 



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Table 3. Race-specific Regression Models (n = 4,626) 

   
Model 3 

Caucasians 
   

Model 4 

African Americans 
 

 B SE 95% CI p B SE 95% CI p 

Ethnicity (Hispanic) -0.48 0.32 -1.10 .128 -1.52 -1.00 0.91 .270 0.78 -1.10 

Sex (Male) 1.41 0.23 0.96 .000 6.20 1.35 0.50 .008 2.34 2.67 

Married household -0.07 0.32 -0.70 .822 -0.23 0.36 0.63 .575 1.60 0.56 

Parental Educational Attainment 0.05 0.06 -0.07 .412 0.82 0.05 0.12 .661 0.29 0.44 

Financial Difficulty -0.47 1.03 -2.48 .647 -0.46 0.03 1.25 .980 2.49 0.02 

Family Income -0.07 0.08 -0.23 .403 -0.84 0.15 0.13 .240 0.41 1.17 

Age  0.03 0.02 0.00 .026 2.23 -0.03 0.03 .370 0.04 -0.90 

Outcome: IC: Total number of “Stop” trials answered incorrect  

b= Unstandardized Regression Coefficient 

SE= Standard Error 

CI= Confidence Interval 

 

4. Discussion 

We found that IC correlates with age for Caucasian but not African American pre-adolescents. Due to the 

race by age interaction, age-related brain development in pre-adolescents may be delayed/hindered. This 

finding is an indicator of diminished age-related brain development of African American than Caucasian 

pre-adolescents.  

Diminishing returns of age on IC is in line with the MDRs of family SES on IC (Assari, 2020c). It is also 

in line with the diminished returns of SES on impulsivity (Assari, Caldwell, & Mincy, 2018a), attention 

deficit hyperactivity disorder (Assari & Caldwell, 2019a), depressed mood (Assari & Caldwell, 2018a), 

anxious mood (Assari, Caldwell, & Zimmerman, 2018), aggressive behaviors (Assari, Caldwell, et al., 

2019), academic achievement (Assari S, 2019; Assari & Caldwell, 2019b; Assari, Caldwell, et al., 2019), 

and tobacco use (Assari, Caldwell, et al., 2019). In other studies, MDRs were found for childhood trauma 

and stress (Assari, 2020a; Assari, 2020b). 

This is not the first study on MDRs, but it extends the literature by documenting MDRs of age-related 

brain development in African American when compared with Caucasian pre-adolescents. Many 

empirical studies have already documented MDRs for African Americans (Assari, 2018a, 2018c; S. 

Assari, 2019a; Assari, Farokhnia, et al., 2019). Past research shows that MDRs are not limited to 

pre-adolescents as they can be seen for all age groups such as adolescents (Assari, Caldwell, & Mincy, 

2018a; Assari, Caldwell, & Mincy, 2018b; Assari, Thomas, et al., 2018), adults (Assari, 2018a), and 

older adults (Assari & Lankarani, 2016a). Also, MDRs is not a pattern that can be exclusively seen for 

African Americans (Assari, Thomas, et al., 2018). In fact, same patterns are shown for Hispanic (Assari, 



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2018g; Shervin Assari, 2019; Assari, Farokhnia, et al., 2019; Shervin & Ritesh, 2019) Asian American 

(Assari, Boyce, Bazargan, & Caldwell, 2020), Native American (Assari & Bazargan, 2019a), Lesbian, 

Gay, Bisexual (LGB) (S. Assari, 2019a), poor Caucasian (Assari, Boyce, Bazargan, Caldwell, & 

Zimmerman, 2020), and even immigrant (Assari, 2020b) people. 

Several potential intuitive mechanisms may explain MDRs of age-related brain development in African 

American pre-adolescents. African American families and their pre-adolescents face many stressors and 

adversities, including financial stress, race-related stress, and environmental pollutants (Marshall et al., 

2020). Unfortunately, these structural aspects impact the lives of African Americans across SES levels 

(Assari, 2018a; Assari, 2018h). African Americans have a low chance of upward social mobility (Chetty, 

Hendren, Kline, & Saez, 2014) and pay very high costs when they succeed (Hudson, Sacks, Irani, & 

Asher, 2020). For African American families, stress and discrimination are always high, regardless of 

SES (Assari, 2018b; Assari, Gibbons, & Simons, 2018a; Assari, Gibbons, & Simons, 2018b; Assari, 

Lankarani, & Caldwell, 2018; Assari & Lankarani, 2018). For African American families, low SES 

means living in poor areas, and high SES means high exposure to Caucasian families, which means very 

high levels of exposure to discrimination (Assari, Gibbons, et al., 2018a; Assari, Gibbons, et al., 2018b). 

Stress across domains, including but not limited to race-related discrimination, interferes with normal 

brain development (Assari & Caldwell, 2018b; Assari, Lankarani, et al., 2018; Assari, Preiser, Lankarani, 

& Caldwell, 2018). 

An example of structural causes of inequalities that generates MDRs in the USA is residential 

segregation. As a result of residential segregation, African American families live in resource-scarce 

environments full of stress and poverty. Due to residential segregation, African American 

pre-adolescents attend poor schools across SES levels (Assari, Boyce, Bazargan, Caldwell, et al., 2020; 

Boyce, Bazargan, Caldwell, Zimmerman, & Assari, 2020; Boyce, 2020). As a result, African American 

adolescents do not access many educational resources that stimulate brain development (Assari, 2019b; 

Assari, 2019; Assari & Caldwell, 2019b). However, poor education and schooling are only among the 

many differences in the lives of Caucasian and African American families (Jefferson et al., 2011). 

African American parents report a high level of stress across all SES levels (Assari, 2020a; Assari & 

Bazargan, 2019b). High SES African American families experience more, not less, discrimination 

compared to low SES African American families (Assari, Gibbons, et al., 2018a; Assari, Gibbons, et al., 

2018b; Assari, Lankarani, et al., 2018; Hudson, Bullard, et al., 2012; Hudson, Puterman, 

Bibbins-Domingo, Matthews, & Adler, 2013), which is in part due to proximity to Whites (Assari, 2018b; 

Assari & Lankarani, 2018).  

It is important to note that MDRs reflect a particular class of disadvantage for racial minorities (Assari, 

2017d; Assari, 2018a). While some disadvantages are due to lack of access to SES resources, MDRs of 

SES and age mean that African Americans experience poor outcomes across the same resources and 

assets (e.g., SES or age). Thus, research, practice, and policy should not merely focus on differential 



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access to SES and different profiles of exposure to risk factors as causes of inequality. Policymakers and 

researchers should be aware that some observed inequalities are due to differential returns of age, SES, 

and other resources and assets. This type of disadvantage places African Americans at high risk across all 

levels of resources. It is also more difficult to undo MDRs-related inequalities than those that are due to 

poverty and low SES (Assari, 2018a; Assari, 2018h).  

MDRs theory is a sociological rather than a biological explanation of health inequality. Among 

multilevel causes of MDRs, including economic, psychological, and societal mechanisms (Assari, 2018a; 

Assari, 2018h), racism and discrimination have a leading role. Racism operates across multiple 

institutions and social structures (Assari, 2018a; Assari, 2018h). If MDRs are due to racism, then a real 

solution to health disparities should also address MDRs-related inequalities. Such an approach requires 

increasing racial justice in the US. Age can only generate the same brain development when all racial 

groups have the same opportunity for brain development (Hudson et al., 2020; Hudson, Bullard et al., 

2012; Hudson, Neighbors, Geronimus, & Jackson, 2012).  

African American families may stay in poor neighborhoods at all SES levels(Assari, Boyce, Caldwell, 

Bazargan, & Mincy, 2020). Caucasians, however, live in low-stress environments when they have high 

SES (Assari, 2018b; Assari, Preiser, & Kelly, 2018). Thus, even when they have similar SES, African 

American families’ living conditions drastically differ from those of their Caucasian counterparts (Assari, 

2018f; Assari & Bazargan, 2019b; Assari & Bazargan, 2019b; Assari S; Assari, 2019; Assari, Gibbons et 

al., 2018a; Assari, Gibbons et al., 2018b; Assari, Lankarani et al., 2018). Similarly, across all SES levels, 

African American adolescents spend time with high-risk peers(Boyce et al., 2020; Shanika Boyce, 2020). 

However, high SES Caucasian adolescents have low-risk peers and family members (Assari, Boyce, 

Bazargan, & Caldwell, 2020; Assari, Caldwell et al., 2019). The current study only documented MDRs 

of age-related brain development without digging into their societal and contextual causes. We argue that 

age shows a weaker effect on the brain development of children in less enriched environments. 

4.1 Implications 

The major implications of knowledge regarding MDRs-related inequalities are that it helps us rethink the 

structural causes of inequalities. Such knowledge is essential for finding the societal causes of racial 

disparities. It even helps us move our policies beyond equal access as a goal. In the presence of MDRs, 

equal access fails to generate equal outcomes. Due to MDRs of resources/assets such as SES and age, 

equality does not generate equity. To achieve equity, we need to equalize access to SES and the very 

societal conditions that surround African American children’s development. The daily experiences of 

African American families should be improved. Thus, age-related brain development would be more 

equal across racial groups. 

4.2 Limitations 

All studies, particularly secondary analysis of some existing data, are limited in their methodology. As 

our study used a cross-sectional approach, we cannot make causal inferences. While IC does not cause 



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age, age may impact IC. Still, longitudinal data are needed for establishing racial differences in the causal 

link between age and IC. More research is needed on MDRs of age as a source of brain development. 

MDRs are commonly shown for SES indicators such as parental education, family income, and marital 

status; however, less is unknown about MDRs of age-related brain development. Finally, we only 

described the MDRs of age-related brain development without exploring the mechanisms of the observed 

MDRs. Future work is needed on the role of SES, trauma, stress, context, and family in explaining the 

observed MDRs of age-related brain development in African American adolescents. 

 

5. Conclusion 

For 9-10-year old American children, age is a predictor of IC for Caucasian children. For African 

American pre-adolescents, however, IC remains poor at all ages. That means the brain’s age-related 

development that shapes IC differs for African American and Caucasian pre-adolescents. The results 

may help us understand why high-risk behaviors such as alcohol use, aggression, and early sexual debut 

are more common in African American than Caucasian children and adolescents. 

 

Author Contributions: SA: data analysis, conceptualization, draft, revision, and approval, GA: revision, 

conceptualization, revision, and approval. 

DEAP Acknowledgment: DEAP is a software provided by the Data Analysis and Informatics Center of 

ABCD located at the UC San Diego with generous support from the National Institutes of Health and the 

Centers for Disease Control and Prevention under award number U24DA041123. The DEAP project 

information and links to its source code are available under the resource identifier RRID: SCR_016158. 

ABCD Funding: The ABCD Study is supported by the National Institutes of Health and additional 

federal partners under award numbers U01DA041022, U01DA041028, U01DA041048, U01DA041089, 

U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, 

U01DA041174, U24DA041123, U24DA041147, U01DA041093, and U01DA041025. A full list of 

supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites 

and a complete listing of the study investigators can be found at 

https://abcdstudy.org/Consortium_Members.pdf. ABCD consortium investigators designed and 

implemented the study and/or provided data but did not necessarily participate in analysis or writing of 

this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of 

the NIH or ABCD consortium investigators. The ABCD data repository grows and changes over time. 

The current paper used the Curated Annual Release 2.0, also defined in NDA Study 634 

(doi:10.15154/1503209). 

Author Funding: SA is supported by the National Institutes of Health (NIH) grants CA201415 02, 

U54MD007598, DA035811-05, U54MD008149, D084526-03, and U54CA229974.  

Conflicts of Interest: None. 



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