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. Author manuscript; available in PMC: 2022 Aug 1.
Published in final edited form as: Addict Behav. 2021 Mar 26;119:106923. doi: 10.1016/j.addbeh.2021.106923

Associations of Childhood Executive Control with Adolescent Cigarette and E-Cigarette Use: Tests of Moderation by Poverty Level

W Alex Mason 1, Irina Patwardhan 2, Charles B Fleming 3, Amy L Stevens 4, Tiffany D James 5, Jennifer Mize Nelson 6, Kimberly Andrews Espy 7, Timothy D Nelson 8
PMCID: PMC8117112  NIHMSID: NIHMS1692688  PMID: 33826966

Abstract

Background:

Adolescent cigarette smoking has continued to decline, whereas electronic cigarette use has increased dramatically among youth. Nicotine use in any form, even at low levels, during adolescence can have adverse consequences, particularly for low-income individuals. To elucidate potential early intervention targets, this study examined childhood executive control (EC), a set of cognitive processes for directing attention and behavior, in relation to adolescent cigarette and e-cigarette onset, testing for differential prediction by poverty level.

Method:

Participants were 313 children (51% female, 64% European American) recruited in a small city in the Midwestern United States beginning in 2006 and then followed into adolescence between ages 14 and 16 years. EC was measured in the laboratory with performance-based tasks when children were age 5 years, 3 months. Self-reports of cigarette onset and e-cigarette onset were obtained in adolescence (Mage = 15.65 years). Overall, 24% of the sample was at or below the poverty line.

Results:

Cigarette onset was higher in the poverty group (17%) than in the non-poverty (8%) group, but e-cigarette onset did not differ by poverty level (36% poverty versus 38% non-poverty). Multiple group structural equation modeling revealed a statistically significant group difference such that EC ability was a significant negative predictor of e-cigarette onset for poverty but not for non-poverty youth. A similar group difference was evident as a trend for cigarette onset.

Conclusions:

Because EC has been shown to be modifiable, early interventions to improve EC for children living in poverty might help prevent adolescent e-cigarette onset.

Keywords: executive control, adolescence, cigarette smoking, e-cigarette use, poverty

1. Introduction

The lifetime prevalence of cigarette smoking among 12th graders in the United States dropped from 43.6% in 2009 to 22.3% in 2019 (Johnston et al., 2020), but smoking remains a significant public health concern. In recent years, adolescent use of electronic cigarettes (e-cigarettes) has increased dramatically. Past 30 day e-cigarette use escalated from 12% in 2017 to 28% in 2019 among high school students in the United States (Wang et al., 2019). E-cigarette devices can be used to deliver flavors and other substances, such as cannabis, but are now the most commonly used products for nicotine intake among adolescents (Centers for Disease Control and Prevention, 2019; Jamal et al., 2017). Adolescent nicotine use can disrupt ongoing brain maturation, thereby increasing risks for cognitive and behavioral difficulties (Office of the Surgeon General, 2016; Tobore, 2019). Initiation of nicotine use in any form, even at initially low levels, during adolescence increases risk for the progression to regular use and nicotine dependence (Azagba, Baskerville, & Minaker, 2015). Thus, there is a critical need to identify the childhood precursors of adolescent cigarette and e-cigarette onset to inform the development of interventions designed to prevent these health-compromising outcomes.

Childhood executive control (EC) may be one important precursor of adolescent cigarette and e-cigarette use. EC, also termed executive function (Diamond, 2013), refers to a set of related “top-down” cognitive processes for directing attention and behavior (Diamond, 2013). These processes include aspects related to working memory (the ability to hold and process new and stored information), inhibitory control (the ability to regulate automatic response tendencies and direct attention toward relevant stimuli), and flexible shifting (the ability to shift thought processes and display cognitive flexibility) (Garon, Bryson, & Smith, 2008). There is evidence that EC first becomes organized into a unitary construct, which does not differentiate among its three aspects, measurable via performance-based laboratory tasks around ages 3–5 years (Espy et al., 2016; Garon et al., 2008). EC also has been shown to be modifiable (Diamond, 2013; Diamond & Lee, 2011), making it a promising target for intervention.

Research generally has supported the expectation that stronger EC abilities are associated with less adolescent tobacco use, including cigarette smoking. For instance, Pentz et al. (2015) found that a composite measure of executive cognitive function ability, assessed via self-report in a sample of fourth-graders, was associated negatively with lifetime tobacco use assessed six months later. More commonly, investigators have examined only certain aspects of EC in relation to adolescent tobacco use. Fields et al. (2009) found, for example, that adolescent smokers performed worse than non-smokers on a laboratory-based behavioral assessment of impulsive decision-making in a cross-sectional study. In a longitudinal study, Romer et al. (2011) showed that better working memory ability at baseline, when participants were 10–12 years old, predicted lower scores on a measure of risk behavior that included indicators of cigarette smoking two years later. Other studies, however, have reported non-significant associations of EC with cigarette smoking (Wilens et al., 2011).

To date, studies examining EC in relation to e-cigarette use are few in number. Pentz et al. (2015) investigated associations between self-reported executive function and lifetime prevalence of e-cigarette use in a sample of 7th grade students using baseline data from a larger prevention trial. They reported that students with executive functioning problems were nearly five times more likely to have used e-cigarettes than their counterparts without such problems. Riggs and Pentz (2016) examined the specific EC aspect of inhibitory control, measured via self-report, in relation to the onset of cigarette, e-cigarette, and hookah use in a sample of 7th grade students. Results indicated that inhibitory control ability had significant negative associations with each outcome, including e-cigarette use, in both unadjusted analyses and adjusted analyses (controlling for age, gender, race, and socioeconomic status or SES) of the cross-sectional data.

Disparities in cigarette smoking by poverty level are well established, indicating that smokers are disproportionately economically disadvantaged (Casetta et al., 2017; US Department of Health and Human Services, 2014). Moreover, poverty appears to exacerbate the adverse health consequences of smoking, for instance, increasing the risk of progression from adolescent experimentation to regular smoking and nicotine dependence (Hiscock, Bauld, Amos, Fidler, & Munafò, 2012). Studies of poverty-related disparities in e-cigarette use are just beginning. Emergent findings are mixed (Moore et al., 2015; Simon et al., 2018), but there is some evidence that e-cigarette use is higher among low SES than high SES adolescents (Simon et al., 2017). In a survey of 19,000 adults, the lower cost of e-cigarettes compared to combustible tobacco cigarettes was indicated as a one of the reasons for initiation (Farsalinos, Romagna, Tsiapras, Kyrzopoulos, & Voudris, 2014). SES may moderate the associations of risk and protective factors with adolescent cigarette and e-cigarette use (e.g., Wellman et al., 2018); however, few studies have tested this possibility in regard to EC. In a rare exception, Riggs and Pentz (2016) found a significant interaction of inhibitory control with SES, whereby the cross-sectional relationships between inhibitory control and onset of cigarette, e-cigarette, and hookah use were only present for low SES students.

This study extends our ongoing work on the consequences of EC in a longitudinal sample of preschool-age children (and their parents) followed into adolescence. It was designed to address the methodological limitations of prior research and to fill gaps in knowledge about the links between EC and adolescent cigarette and e-cigarette use. Existing studies are informative, but have often relied on self-report measures of specific EC aspects (Pentz et al., 2015; Riggs & Pentz, 2016), which are subject to socially desirable responding and are not feasible to implement with young children. Most prior studies in this area of research also have been cross-sectional or short-term longitudinal. Thus, the degree to which childhood EC – measured via performance-based laboratory tasks during preschool, a critical period of EC development – predicts cigarette and e-cigarette onset differentially for low-income versus middle-income children is unknown. We addressed this knowledge gap, hypothesizing that stronger preschool EC ability (reflected by a unitary latent factor) would be associated with lower rates of adolescent cigarette and e-cigarette onset, but only for children in families with income levels at or below the poverty threshold.

2. Method

2.1. Participants and Procedures

Participants were 313 youth who took part in a longitudinal cohort sequential study on the development of executive control in preschool and its associated outcomes in adolescence. Originally, data were collected between 2006 and 2012, throughout the youths’ preschool and elementary school years (Clark et al., 2016) and is now continuing into adolescence. Exclusionary criteria for enrollment in the study included English not being the primary language spoken in the home, being diagnosed with speech or language delays, having a diagnosed developmental or behavioral disorder prior to enrollment, and families who were planning to move from the area. Toward the end of the recruitment phase, an attempt was made to oversample lower-income families to increase their representation in the study.

Of the 313 youth who completed EC data collection at age 5 years, 3 months, 234 contributed one or more annual assessments in adolescence (Mage = 15.65 years, SD = 1.13) between June 2017 and up to March 2020. The analysis sample was 51.1 % female, and was 63.6% European American, 13.4% Hispanic, 3.8% African American, 0.3% Asian, and 18.8% multiracial (similar to the population of the area). The median household income for the sample was $42,000 at the 5 years, 3 months assessment, and 39.8% of the mothers had a college or advanced degree; 37.1% were households headed by one parent.

The participants were enrolled in four cohorts at ages 3 years, 3 years 9 months, 4 years 6 months, and 5 years 3 months, and assessed every 9 months through age 5 years 3 months, where they completed a battery of lab-based tasks designed to measure EC along with additional questionnaires. Youth were first eligible to participate in the adolescent phase at age 14 years. Data is collected each year around the youth’s birthday. Due to the range of ages within the sample, some youth were already over 14 years old at the start of the adolescent phase, and so began data collection at that age. The current study used data from an adolescent phone interview that included questions about substance use.

Of the 313 children who completed the age 5 years, 3 months EC assessment, those who failed to provide data in adolescence were primarily from families who declined further participation or could not be located. Compared to families who completed any adolescent assessments, families lost to follow-up had lower mean income-to-needs ratio than those retained in the adolescent phase (d = .28, p = .033). All procedures were approved by the University of Nebraska-Lincoln Institutional Review Board.

2.2. Measures

Preschool Executive Control (EC).

Nine lab-based tasks were used to assess the three aspects of EC: working memory, inhibitory control, and flexible shifting. Tasks measuring working memory included Nine Boxes (adapted from (Diamond, Prevor, Callender, & Druin, 1997), Delayed Alternation (Espy, Kaufmann, & Glisky, 1999; Goldman, Rosvold, Vest, & Galkin, 1971), and Nebraska Barnyard (adapted from Noisy Book (Hughes, Dunn, & White, 1998)); Inhibitory control tasks included Big-Little Stroop (adapted from (Kochanska, Murray, & Harlan, 2000)), Go/No-Go (adapted from (Simpson & Riggs, 2006)), Shape School-Inhibit Condition (Espy, 1997; Espy, Bull, Martin, & Stroup, 2006), and Snack Delay (adapted from (Kochanska, Murray, Jacques, Koenig, & Vandegeest, 1996; Korkman, Kirk, & Kemp, 1998)). Flexible shifting was measured with Shape School-Switching Condition (Espy, 1997; Espy et al., 2006) and Trails-Switching Condition (modified from (Espy & Cwik, 2004) tasks. More information about each task can be found elsewhere (Espy et al., 2016). Consistent with our prior research (Nelson et al., 2017), a unitary latent EC factor was created using the nine tasks as indicators. A higher score on the latent EC factor represents better EC abilities.

Adolescent cigarette and e-cigarette use onset.

Adolescents responded to the following questions: “Have you ever used cigarettes?” and “Have you ever vaped or used electronic cigarettes, also called e-cigs?” If adolescents said yes at any wave of their adolescent surveys, their responses were coded 1 = Yes, 0 = No.

Covariates.

Child’s sex and family history of alcoholism were collected from the parental interview in preschool. Child sex was coded 1 = Male, 0 = Female. Family history of alcoholism was included as a measure of substance misuse in the family. Parents indicated if anybody in the child’s family (biological parents or biological grandparents) had been diagnosed with or treated for an alcohol problem (1 = Yes, 0 = No). Participants’ age at time of their last adolescent interview also was included as a covariate.

Poverty.

Poverty was measured by whether families fell below the federal poverty line at the age of 5 years, 3 months assessment point. Total annual household income, number of people in the household, and year of entry to the study were used to calculate family’s income-to-needs ratio by dividing the total household income (adjusted for the family size) by the federal poverty threshold for that year. Poverty status was based on whether the family’s income to-needs ratio was less than 1. By this measure, 24% of families were at or below the poverty line.

2.3. Data Analyses

The data were analyzed using Mplus version 8.3 (Muthén & Muthén, 2017). First, cross-group invariance of the latent EC factor was evaluated via multiple group confirmatory factor analysis (CFA) without inclusion of the covariates or outcomes, testing for configural and metric invariance of the measurement model across the two poverty groups (Dimitrov, 2010; Pendergast, von der Embse, Kilgus, & Eklund, 2017). The test of configural invariance examined whether the overall factor structure of latent EC held equally well, whereas the test of metric invariance examined whether the factor loadings of the latent EC were equivalent, across the two groups. Note that obtaining evidence for metric invariance is the prerequisite for moving on to tests of group differences in the structural paths.

Second, to examine group differences by poverty in the associations between latent EC and substance use, multiple group structural equation modeling (SEM) analyses were conducted, independently, for cigarette use and e-cigarette use with the inclusion of covariates. Figure 1 provides a graphical illustration of the models. We began by estimating a model in which each path estimate was allowed to differ between the groups. Next, we estimated a model in which each path estimate, one at a time, was forced to take on the same value across groups, and the model fit between these two models was compared via a chi-square difference test using the DIFFTEST function in Mplus. A final model for each outcome was estimated in which only paths that generated a statistically significant chi-square difference test (reflecting a group difference) were allowed to vary and all other paths were constrained across groups.

Figure 1:

Figure 1:

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Graphical Illustration of Multiple Group Structural Equation Models of Preschool Executive Control in Relation to Adolescent Cigarette Use and E-Cigarette Use by Poverty Level

Note. Multiple group SEM involves testing the same model across groups for each outcome (e.g., poverty group versus non-poverty group for cigarette use).

Model fit was evaluated using the chi-square statistic, the Comparative Fit Index (CFI), the Tucker Lewis Index (TLI), and the Root Mean Square Error of Approximation (RMSEA). A model was considered to fit the data adequately if the RMSEA ≤ .06 and the CFI/TLI ≥ .90 (Marsh, Hau, & Wen, 2004). Because of the dichotomous nature of the outcomes, analyses used the Weighted Least Squares Mean and Variance adjusted (WLSMV) estimator. In Mplus, pairwise deletion is used with categorical outcomes estimated with the WLSMV estimator. There were small amounts of missing data on poverty and a few tasks contributing to childhood EC, ranging from 1% to 2% (see Table 1). Otherwise, early childhood data were complete; therefore, CFA and SEM analyses were based on the full sample (N=313).

Table 1.

Descriptive statistics of the study variables

Poverty Group (N=75)
 Non-Poverty Group (N=236)
 Difference test
Study Variables N M/% SD N M/% SD t /χ2 df* p-value
Preschool EC tasks
Nine Boxes 75 5.56 1.87 236 5.71 1.84 0.62 123 0.540
Delayed Alternation 75 8.01 5.96 236 7.88 5.88 −0.17 123 0.867
Nebraska Barnyard 75 8.10 2.50 235 8.85 2.38 2.28 120 0.024
Big Little Stroop 74 1.55 0.49 235 1.40 0.56 2.23 139 0.027
Go/No-Go 75 2.55 0.74 236 2.73 0.50 1.93 97 0.056
Shape School - Inhibition 75 0.95 0.17 235 0.97 0.09 0.85 87 0.398
Shape School - Switching 75 0.86 0.17 235 0.87 0.15 0.48 116 0.631
Modified Snack Delay 75 29.09 10.32 233 27.11 9.54 −1.47 117 0.144
Trails 72 0.89 0.11 235 0.90 0.11 0.24 117 0.808
Adolescent Substance Use
E-cigarettes (yes) 53 19 (36%) 181 68 (38%) 0.05 1 0.820
Cigarettes (yes) 53 9 (17%) 181 14 (8%) 3.95 1 0.047
Adolescent Age 53 14.98 1.05 181 15.84 1.08 5.22 87 <.001
Baseline covariates
sex (male) 75 29 (39%) 236 124 (53%) 4.38 1 0.036
Family Alcoholism (yes) 75 27 (36%) 236 65 (28%) 1.95 1 0.162
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Note. EC = Executive control. Poverty information was missing for two families.

*

degrees of freedom for the t-test of independent means where equal variances are not assumed.

3. Results

3.1. Descriptive Statistics

Descriptive statistics are reported in Table 1. The prevalence of cigarette smoking was significantly higher in the poverty group (17%) compared to the non-poverty group (8%), χ2 (1, N=234) = 3.95, p = .046). By contrast, the prevalence of adolescent e-cigarette use did not differ between the poverty group (36%) and non-poverty group (38%), χ2 (1, N=234) = 0.05, p = .820).

3.2. Multiple Group CFAs

Multiple group CFAs established both configural and metric invariance, as reflected in a statistically non-significant difference in model fit, χ2 Diff (df=8, N=313) = 9.76, p = .282, between the configural invariance model (χ2= 59.19, df = 52, p = .229; CFI= 0.97, TLI = .95, RMSEA =.03) versus the metric invariance model (χ2= 69.06, df = 60, p = .198; CFI= 0.96, TLI = .95, RMSEA =.03). This indicates that the factor structure and factor loadings of latent EC did not differ significantly between the poverty and non-poverty groups. All factor loadings for the latent EC factor in the metric invariance CFA were statistically significant (p < .05), ranging in standardized values from .40 to .78.

3.3. Multiple group SEMs

Cigarette use.

Results for the final cigarette use model (χ2 = 164.77, df = 138, p = 0.06; CFI = .90; TLI = .90; RMSEA = .035) are shown in Table 2. The association of childhood EC with cigarette use did not significantly differ by poverty group; however, a trend was evident in the difference test between the fully unconstrained model and the model with the EC path constrained across groups (χ2 = 3.55, df = 1, p = 0.06).

Table 2.

Results for the final multiple group cigarette use model (adjusted for adolescent sex, age, and family history of alcoholism)

Poverty Group (N=75) Non-Poverty Group (N = 236)
b 95% CI p-value β b 95% CI p-value β
Cig on EC −0.13 −0.61, 0.35 0.592 −0.08 −0.13 −0.61, 0.35 0.592 −0.07
Cig on sex −0.44 −1.38, 0.50 0.361 −0.21 0.32 −0.29, 0.92 0.305 0.15
Cig on age 0.56 0.83, 0.72 <.001 0.29 −0.10 −0.32, 0.13 0.394 −0.10
Cig on FamAlc 0.31 −0.18, 0.80 0.213 0.15 0.31 −0.18, 0.80 0.213 0.14
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Note. CIG = Cigarette; EC = Executive control; FamAlc = Family history of alcoholism.

E-cigarette use.

Results for the final e-cigarette use model (χ2 = 165.62, df = 139, p = 0.06; CFI = .91; TLI = .90; RMSEA = .035) are shown in Table 3. There was a statistically significant between-group difference in the path from latent EC to adolescent e-cigarette use (χ2 = 4.28, df = 1, p = 0.039), such that higher childhood EC was significantly associated with less e-cigarette use in the poverty group (b = -1.00, β = -0.56, p = .001), but not in the non-poverty group (b = -.11, β = -0.06, p = .611).

Table 3.

Results for the final multiple group e-cigarette use model (adjusted for adolescent sex, age, and family history of alcoholism)

Poverty Group (N=75) Non-Poverty Group (N = 236)
b 95% CI p-value β b 95% CI p-value β
Ecig on EC −1.00 −1.60, −0.39 0.001 −0.56 −0.11 −0.53, 0.31 0.611 −0.06
Ecig on sex −0.28 −0.64, 0.07 0.114 −0.13 −0.28 −0.64, 0.07 0.114 −0.14
Ecig on age 0.25 0.10, 0.39 0.001 0.25 0.25 0.10, 0.39 0.001 0.25
Ecig on FamAlc 0.47 0.10, 0.84 0.013 0.21 0.47 0.10, 0.84 0.013 0.20
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Note. CIG = Cigarette; EC = Executive control; FamAlc = Family history of alcoholism.

4. Discussion

This study examined childhood executive control (EC), a set of cognitive processes for directing attention and behavior (Diamond, 2013), as a potential precursor to the onset of adolescent cigarette and e-cigarette use, testing possible income disparities in these long-term predictive relationships. The prevalence of e-cigarette use in poverty (36%) and non-poverty (38%) youth was comparable, whereas cigarette smoking was significantly more common in the former (17%) compared to the latter (8%). Results from multiple group structural equation modeling (SEM) analyses showed that latent EC during the preschool years significantly predicted e-cigarette use in adolescence only for those living in poverty. Low-income children’s EC ability reduced (and their EC deficiency increased) the likelihood of e-cigarette onset in adolescence; no such relationship was observed for their higher-income counterparts. The test of a group difference by poverty level for cigarette smoking reflected a similar trend, but was statistically non-significant.

Our results are consistent with well-documented disparities in rates of adolescent cigarette smoking by income level (Casetta et al., 2017; US Department of Health and Human Services, 2014). Emergent findings regarding such disparities in adolescent e-cigarette use are mixed (Moore et al., 2015; Simon et al., 2018), and we found no differences in the onset of vaping across poverty groups. Given the rapid rise in e-cigarette availability and its current widespread appeal among youth (Wang et al., 2019), it appears as though high levels of e-cigarette use cut across income levels. Further monitoring of e-cigarette use trends by income level is warranted, addressing not only onset but also type and frequency of use.

Findings suggest that EC abilities may protect youth against vaping, for example, by helping them inhibit impulses to accept peer offers to use e-cigarettes, recall strategies for redirecting interactions to prosocial activities, and engage their cognitive flexibility to implement those strategies. This is consistent with but extends the work of Riggs and Pentz (2016), who found the same basic association in a cross-sectional study of self-reported inhibitory control in a sample of 7th grade students grouped according to their eligibility for the free or reduce priced lunch program. EC may be particularly important for low-income youth raised in impoverished environments that carry additional risks for e-cigarette use, such as parental stress and neighborhood disorganization. Under these conditions, low-income children may benefit more from the protective effects of EC abilities than their higher income counterparts.

This study has some noteworthy limitations. The sample was drawn from one city in the Midwestern United States and participants were predominantly White. Results might not generalize to diverse adolescents in other regions or country contexts. Moreover, there was some evidence of selective attrition in that low-income families were less likely than their higher-income counterparts to participate in the adolescent phase of this ongoing study, although our analysis strategy permitted use of the entire sample of 313 youth. Cigarette use and e-cigarette use each were measured with a single self-report item; however, research generally has supported the validity of adolescent self-reports of substance use under conditions, such as those we have used, to ensure the confidentiality of responses (Patrick et al., 1994). The e-cigarette item did not permit a determination of what adolescents were vaping, either nicotine or other substances (e.g., cannabis) or just flavoring. Still, as noted, e-cigarette devices have become the most commonly used products for nicotine intake among adolescents (Centers for Disease Control and Prevention, 2019; Jamal et al., 2017). Finally, our study did not include a measure of family history of nicotine dependence; therefore, we used family history of alcoholism as a proxy, since co-use of alcohol and tobacco is common (Falk, Yi, & Hiller-Sturmhöfel, 2006).

5. Conclusions

This study of links between preschool EC and adolescent cigarette and e-cigarette onset has several strengths, including the long-term longitudinal design and performance-based assessment of EC captured as a latent variable corrected for measurement error. Importantly, EC has been shown to be modifiable (Diamond, 2013; Diamond & Lee, 2011). That EC in preschool is a particularly important predictor of e-cigarette use for low-income children (with a similar trend for cigarette smoking) suggests the value of tailored home enrichment and skills-training programs for families living in poverty early in child development, when EC is particularly malleable. Such programs may help vulnerable children develop and maintain their working memory, inhibitory control, and flexible shifting abilities that could bolster their resilience in the face of structural risks to protect against the onset of e-cigarette use.

Highlights.

  • Cigarette smoking onset was higher in poverty (17%) than non-poverty (8%) youth.

  • e-cigarette use onset was comparable in poverty (36%) and non-poverty (38%) youth.

  • Preschool executive control predicted e-cigarette onset only for youth in poverty.

  • A similar group trend was observed for cigarette smoking onset.

Acknowledgments

Funding was provided by the National Institute of Mental Health (MH065668), the National Institute of General Medical Sciences (P20GM130461), and the National Institute On Drug Abuse (DA041738) of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the funding agencies.

Footnotes

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Contributor Information

W. Alex Mason, University of Tennessee Health Science Center, Department of Preventive Medicine, 66 N. Pauline Street, Suite 637, Memphis, TN 38163.

Irina Patwardhan, Boys Town, Child and Family Translational Research Center, 13971 Flanagan Blvd, #101, Boys Town, NE 68010.

Charles B. Fleming, University of Washington, Center for the Study of Health and Risk Behaviors, 1100 NE 45th St., #300, Seattle, WA 98195

Amy L. Stevens, Boys Town, Child and Family Translational Research Center, 13971 Flanagan Blvd, #101, Boys Town, NE 68010

Tiffany D. James, University of Nebraska-Lincoln, Office of Research and Economic Development, 301 Canfield Administration, Lincoln, NE 68588

Jennifer Mize Nelson, University of Nebraska-Lincoln, Office of Research and Economic Development, 301 Canfield Administration and Department of Psychology, 238 Burnett Hall, Lincoln, NE 68588.

Kimberly Andrews Espy, University of Texas at San Antonio, Office of the Provost and Vice President for Academic Affairs, One UTSA Circle, Main Building, Suite 4.120, San Antonio, TX 78249.

Timothy D. Nelson, University of Nebraska-Lincoln, Department of Psychology, 238 Burnett Hall, Lincoln, NE 68588

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