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Research Article                                                                                                                                        Open Access

## Nonverbal Reasoning in Preschool Children: Investigating the Putative Risk of Secondhand Smoke Exposure and AttentionDeficit/Hyperactivity Disorder as a Mediator

Duneesha De Alwis, Mini Tandon*, Rebecca Tillman, Joan Luby

Department of Psychiatry, Washington University School of Medicine in St. Louis, Missouri, USA

*Corresponding author: tandonm@psychiatry.wustl.edu

## Abstract

Background: Exposure to secondhand smoke (SHS) has been found to be associated with cognitive deficits in children. However, relatively little  is  known  about the relationship between SHS exposure, cognitive deficits, and smoking-related psychopathology, specifically attention-deficit/hyperactivity disorder (ADHD) and externalizing disorders such as conduct disorder (CD) and oppositional defiant disorder (ODD) in preschool children.

Methods :  Children  (n  =  54)  between  the  ages  of  4  and  6  years  from  a  comprehensive,  longitudinal  study  of  preschool emotional development were included in this study. Each child's primary caregiver completed questionnaires and interviews related to childhood psychopathology. SHS exposure was estimated with the use of saliva cotinine values.

Results : After adjustment for sociodemographic factors (e.g., age, gender, an income-to-needs ratio) and for ADHD, CD, and ODD symptoms, exposure to SHS was found to be negatively associated with preschool children's nonverbal reasoning skills. Exposure to SHS continued to be negatively associated with nonverbal reasoning skills after adjustment for maternal education,  maternal  smoking  during  pregnancy,  and  maternal  reports  of  exposure  to  SHS  during  pregnancy  in  separate models.

Conclusions: Children  who  grow  up  in  an  environment  with  adults  who  smoke  are  vulnerable  to  several  social  and environmental risk factors. The findings of this study suggest that exposure to SHS during early childhood should also be considered as a potential variable in the risk trajectory and as a marker of other associated risks when formulating public health intervention and prevention strategies.

Keywords: Secondhand smoke, cognition, preschoolers, nicotine, nonverbal reasoning ability

## Introduction

There is substantial evidence to support the assertion that maternal smoking during pregnancy is associated with health risks such as preterm delivery,  low  birth  weight,  behavioral  problems, psychiatric disorders, and neurocognitive impairments in children (1,2). Despite this, the prevalence  of  smoking  during  pregnancy  in  the United  States  alone  has  been  estimated  to  occur among 13.5% of pregnant women (3). More recent research into tobacco use has focused on exposure to  secondhand  smoke  (SHS)  and  passive  smoking postnatally  and  its  negative  consequences  on  child development (4). SHS is smoke that is emitted from burning tobacco products (e.g., cigarettes, cigars) or smoke that has been exhaled or breathed out by a person  who  is  smoking.  There  is  no  known  safe level of SHS; therefore, any exposure to SHS is of concern (5). Approximately 60% of children between the ages of 4 and 11 years are exposed to SHS in the United States (6), with a high proportion of exposure happening in the home or in a car (7). The  Centers  for  Disease  Control  and  Prevention reports that 43.8 million adults in the United States are  currently  smokers,  with  men  reporting  higher rates of smoking (21.6%) as compared with women (16.5%) (3). The elevated levels of smoking among the  general  population  have  become  an  adverse environmental risk factor that can affect the children of non-smoking women.

Similar to the risks associated with maternal smoking during pregnancy, children who are exposed to SHS during the prenatal period also face adverse  health  risks,  such  as  neurodevelopmental problems  and  cognitive  impairment  (8,9).  Prenatal exposure  to  tobacco  smoke  has  been  found  to  be associated with psychiatric disorders such as attention-deficit/hyperactivity disorder (ADHD) (10,11),  conduct  disorder  (CD),  and  oppositional defiant  disorder  (ODD)  (12).  In  a  review  of  the literature, Linnet and  colleagues (13) posited a strong  association  between  exposure  to  tobacco smoke  during  pregnancy,  diagnostic  ADHD,  and ADHD symptoms in children. They also  reported psychological  stress  during  pregnancy  to  exert  a modest contribution to ADHD  symptoms in children.  It  has  been  reported  that  mothers  who smoke  during  pregnancy  often  continue  to  smoke after childbirth (14,15), which suggests that children with  ADHD  and  externalizing  behavior  problems are  at  risk  for  prenatal  environmental  exposure  as well as postnatal SHS. Despite the evidence showing  cognitive  and  academic  deficits  among young children with ADHD (16,17) and externalizing disorders (18,19), the effect of postnatal  SHS  exposure  on  child  developmentespecially  in  the  area  of  cognitive  developmenthas not been extensively explored.

Children  and  adolescents  between  the  ages  of  5 and 17 years with postnatal tobacco smoke exposure  have  been  found  to  exhibit  cognitive deficits  and  difficulties  with  academic  functioning (20-23).). Bauman and colleagues (20) found household exposure to SHS to be negatively associated  with  achievement  test  scores,  language, and spelling scores among children in eighth grade. Eskenazi and Trupin (24) reported lower neurobehavioral assessment scores (after controlling for covariates, including SHS exposure in utero) at the  age  of  5  years  among  children  whose  mothers smoked 20 or more cigarettes a day. Results from a longitudinal  study  by  Breslau  and  colleagues  (21) that  compared  children  of  smoking  mothers  with those  of  non-smoking  mothers  also  found  SHS exposure at the age of 5 years to be associated with reductions in intelligence tests scores at the ages of 6, 11 and 17 years. However, this association was no longer  statistically  significant  after  accounting  for maternal intelligence and education.

Few studies have explored the association between  cognitive  functioning  and  postnatal  SHS exposure among preschool-aged children, but this is a  developmental  period  that  may  be  of  unique importance due to rapid brain changes and possible sensitive  periods  in  brain  development.  A  recent study  of  preschool-aged  children  by  Jedrychowski and  colleagues  (8)  failed  to  find  an  association between  postnatal  environmental  tobacco  smoke exposure (as measured by the number of cigarettes smoked at home daily in the presence of the child) and  the  Mental  Development  Index  of  the  Bayley Scales of Infant Development at the ages of 12, 24 or 36 months. Similar negative findings were reported by Julvez and colleagues (25), who found that  the  mother's  postnatal  smoking  level  was  not negatively associated with any of the subtests of the McCarthy Scales of Children's Abilities in a cohort of 4-year-old Spanish children. A study by Baghurst and colleagues (26) found significantly lower scores on  the  Mental  Development  Index  of  the  Bayley Scales of Infant Development at the age of 2 years and  on  the  subtests  on  the  McCarthy  Scales  of Children's Abilities at the age of 4 years in a sample of  Australian  children  with  postnatal  exposure  to the mother's smoking. However, these scores were attenuated after accounting for socioeconomic status, quality of the home  environment, and mother's intelligence.

The  measure  of  postnatal  smoking  exposure  in the preceding studies relied primarily on interviews with the mother and did not include any biomarker confirmation,  which  is  the  most  valid  measure  of exposure (27,28). In addition, the cognitive measures  used  in  the  studies  of  preschool-aged children  are  limited  to  more  general  measures  of cognitive performance, such as the Bayley Scales of Infant  Development  and  the  McCarthy  Scales  of Children's Abilities. To date, no published study has explored the effect of SHS  exposure on the development of specific higher-order cognitive abilities,  such  as  reasoning  and  problem  solving. Children's reasoning and problem-solving skills have  been  found  to  be  strongly  correlated  with general intelligence (29) and working memory (30,31), which are known to be good predictors of academic  ability  (32-35).  Language  skills  develop rapidly during the preschool years. Thus, nonverbal reasoning  abilities are considered  to  be  reliable indices of cognitive development in young children, because  limited  language  skills  make  performance on  the  verbal  scales  of  standard  intelligence  tests somewhat challenging.

More importantly, the roles of smoking-associated neurobehavioral outcomes (e.g., ADHD, CD, ODD)  have not been well investigated when studying  the  relationship  between  postnatal  SHS exposure  and  cognitive  abilities  in  preschool-aged children.  Preschool-aged  children  are  a  vulnerable population at a higher risk for  SHS  exposure because they may spend a large amount of time in the home or in the physical proximity of a caregiver who  might  be  smoking  in  their  presence  (36).  In addition,  the  preschool  period  is  a  time  of  rapid brain development, with potential sensitive periods during  which  the  brain  may  be  more  affected  by environmental exposures (37). Related to this idea is that the identification of cognitive difficulties during the preschool years is important for early intervention  strategies  that  may  help  to  mitigate negative consequences. Thus, a better understanding of SHS  exposure and its effects on cognition-particularly  in  younger  preschool-aged children-is of great importance.

The current analysis attempts to address some of the existing limitations and to extend the literature by  using  a  community-based  sample  (oversampled for psychopathology) of preschool children between the ages of 4 and 6 years to explore the effects of SHS  exposure  (using  saliva  cotinine values)  on nonverbal reasoning abilities. The main goals of this study are as follows:

- 1) To examine if exposure to SHS is associated with deficits in nonverbal reasoning among preschool-aged children (after accounting for selected potential covariates that influence cognitive development); and
- 2) To examine whether the association between SHS  and  nonverbal  reasoning  among  preschoolaged  children  is  mediated  by  another  smokingassociated neurobehavioral outcome, specifically ADHD, CD, or ODD.

## Methods

## Sample

Children  between  the  ages  of  3.0  and  5.11  years were recruited from community daycare, preschool, and  primary  care  sites  in  the  St.  Louis  area  for  a longitudinal study focused on early childhood depression: The Preschool Depression Study. These sites  were  selected  at  random  with  the  use  of  a geographically stratified method (38). Children were oversampled  for  depressive  disorders  on  the  basis of parent responses to a validated screening checklist: The Preschool Feelings Checklist (39). A group of children with disruptive symptoms and a smaller group of healthy children were also recruited as psychiatric and healthy controls, respectively.  Preschoolers  with  chronic  medical  or neurological problems, mental retardation, or autistic  spectrum disorders were excluded.  The study  was  approved  by  the  Institutional  Review Board at Washington University School of Medicine in  St.  Louis.  Caregivers  provided  written  informed consent, and children provided verbal assent when this was appropriate for their age. This longitudinal study  is  ongoing,  and  the  data  reported  in  the current article are from the second annual wave of data  collection.  As  a  part  of  an  add-on  study  to examine the effects of SHS exposure on behavioral outcomes (K12 DA 000357; Principal investigator: Tandon), cotinine was assayed from 70 saliva samples that were originally obtained in the laboratory during the  second and third annual waves of the study for another purpose. The current analysis included only the children with valid measures of salivary cotinine at the ages of 4 and 6 years,  which  resulted  in  54  participants.  Cotinine measures  were  not  available  when  the  quantity  of saliva was  insufficient to complete  testing.  For detailed  information  about  this  cotinine  methodology, see the description later in this article as well as the research by Tandon and colleagues (40).

## Exposure to Secondhand Smoke

Exposure to SHS was estimated by levels of salivary cotinine,  which  is  a  reliable  biomarker  of  nicotine exposure  (27).  Saliva  samples  were  collected  and frozen during the second and third annual assessment  waves  (November  2004  through  June 2006 and October 2005 through April 2007, respectively), when the average ages of the children were 4 and 6 years, respectively. Additional information  related  to  maternal  smoking  during pregnancy  and  after  pregnancy  as  well  as  maternal recall of the child's exposure to SHS at the ages of 4 and 6 years was obtained via telephone interview at a later stage, when the average age of the child was 12.1 years (standard deviation, 8 months). All questions were adapted from the Timeline Followback interview method (41).

Maternal recall of smoking during pregnancy was established with the use of the following questions:

-  How much/often did you smoke in your first trimester?
-  What was the least you smoked during your first trimester?
-  What was the most you smoked during your first trimester?

The same questions were repeated for the second and third trimesters. Similar questions were used to establish  maternal  recall  of  SHS  exposure  during pregnancy  and  after  birth.  Maternal  recall  of  SHS during pregnancy was determined by the following questions:

-  Thinking  back  during  your  pregnancy,  how many  hours  per  day  were  you  in  the  same room with someone smoking cigarettes during your first trimester typically?
-  How  many  hours  per  day  were  you  in  the same room with someone smoking cigarettes during your first trimester at the least?
-  How  many  hours  per  day  were  you  in  the same room with someone smoking cigarettes during your first trimester at the most?

The same questions were repeated for the second and third trimesters.

Maternal  recall  of  SHS  exposure  after  birth  was determined by the following questions:

-  Can you recall,  if  any,  how  many  hours  per day your child was exposed to cigarette smoke at age 4 typically?
-  Can you recall,  if  any,  how  many  hours  per day your child was exposed to cigarette smoke at age 4 at the least?
-  Can you recall,  if  any,  how  many  hours  per day your child was exposed to cigarette smoke at age 4 at the most?

The  same  questions were repeated to assess exposure at the age of 6 years. Additional questions related to smoking were also considered, such as the following:

-  Do you currently smoke?
-  Have  you  smoked  since  your  child  was  6 months old (even just 1 cigarette)?
-  How many hours per day is your child in the same  room  with  someone  who  is  smoking cigarettes typically/at the least/at the most?

Fifty  children  had  a  measurable  level  of  cotinine at the age of 4 years, 45 children had a measurable level  at  the  age  of  6  years,  and  39  children  had measurable levels at the ages of both 4 and 6 years. For  children  who  had  cotinine  measures  at  both time  points,  the  highest  level  of  cotinine  was  used for  the  analysis.  Because  the  nonverbal  reasoning measure used in the current analysis was administered  during  the  second  wave,  when  the children were between 4 and 6 years old, responses to  the  preceding  smoking-related  questions  from the retrospective maternal interview were supplemented  to  determine  SHS  exposure  before that period.

## Cognitive Measures: Nonverbal Reasoning Ability

Children  were  individually  tested  with  a  range  of cognitive tests. For the current study, we focused on two  of  these  tests:  the  pattern  construction  and picture similarities subtests from  the nonverbal reasoning  component  of  the  Differential  Ability Scale  (42).  The  nonverbal  reasoning  tests  of  the Differential Ability Scale have been found to display good  convergent  validity  with other established measures of nonverbal reasoning, such as the performance intelligence component of the Wechsler Scales of Intelligence for Children (42-44).

## The Pattern Construction Subtest

The  pattern  construction  subtest  is  a  part  of  the preschool level assessments in the cognitive battery of  the  Differential  Ability  Scale,  and  it  has  been established as a reliable measure of nonverbal reasoning  ability  among  preschool-aged  children (42). The subtest required the children to construct a  design.  For  each  trial,  the  child  was  shown  a pattern  made with flat  squares  or solid  cubes  with black  and  yellow  patterns  on  each  side,  and  he  or she  was  then  asked  to  reconstruct  the  pattern. Standardized scoring and recording procedures were used.

## The Picture Similarities Subtest

This picture similarities subtest is also a part of the preschool level assessments of the cognitive battery of  the  Differential  Ability  Scale,  and  it  has  been found  to  be  a  reliable  measure  of  preschool-aged children's nonverbal reasoning ability (42). For each trial  of  this test, the children were shown a row of four  pictures  and  then  given  a  card  with  a  fifth picture.  Each  child  was  required  to  place  the  fifth card under one of the first four pictures that shared a common  element  or concept (42). Standard administration procedures were followed.

## Attention-Deficit/Hyperactivity Disorder, Conduct Disorder, and Oppositional Defiant Disorder

The Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) ADHD, CD, and ODD diagnoses  were  based  on  parent  reports  on the Preschool Age Psychiatric Assessment (45). This assessment has been established as an ageappropriate, comprehensive, diagnostic interview for the assessment of preschoolers with good testretest reliability (κ = 0.74) (46). The final diagnoses were  derived  with  the  use  of  computerized  DSMIV-based  algorithms,  which  are  standard  for  this measure. In the subsample included in these analyses, only 7.4% of participants (N = 4) qualified for  a  DSM-IV diagnosis of ADHD, whereas 5.6% (N = 3) and 13.0% (N = 7) qualified for DSM-IV diagnoses of CD and ODD, respectively. Thus, for the  current  analysis,  a  dimensional  severity  score was  created  by  summing  the  number  of  ADHD, CD, and ODD symptoms. This estimate of ADHD and  externalizing  severity  had  possible  values  that ranged from 0 to 36.

## Covariates

Sociodemographic covariates included sex, age, and an income-to-needs ratio . The income-to-needs ratio was defined as the total family income divided by the federal poverty level based on family size for the year most proximal to data collection. This ratio was  calculated  with  the  use  of  baseline  data  from the Preschool Depression Study. Caregivers' responses related to family income and total number of individuals living in the household were included in the calculation (47). Maternal education level, maternal prenatal smoking, and prenatal exposure  to  SHS  were  also  used  as  covariates  in supplementary analyses.

## Statistical Analysis

The  relationship  between  exposure  to  SHS  and nonverbal reasoning abilities was explored with the use of linear regression and mediation analyses. All statistical analyses were performed using SAS software (version 9.3). The age and sex of the child as well as the income-to-needs ratio were considered as potential covariates in all analyses. A hierarchical  regression  model  was  conducted  with sociodemographic variables (i.e., age, sex, and income-to-needs  ratio)  entered  in  step  1;  ADHD, CD,  and  ODD  symptoms  entered  in  step  2;  and cotinine measures entered in step 3 to estimate the unique and shared contribution of each variable for predicting nonverbal reasoning abilities. A mediation model  was  tested using the Process procedure  for  SAS  by  calculating  bias-corrected 95% confidence intervals using bootstrapping with 10,000  resamples  (48,49).  This  model  was  used  to determine  if  the  relationship  between  exposure  to SHS  and  nonverbal  reasoning  was  mediated  by ADHD  and  the  severity  of  externalizing  disorder symptoms.

The two measures of nonverbal reasoning ability (i.e., the pattern construction and picture similarities subtests of the Differential Ability Scale) showed a significant  correlation  ( r =  0.545, p <  .01),  so  an average  of  the  z-scores  of  the  two  subtest  values was  calculated  for  further  analysis.  The  skew  and kurtosis  values  for  all  of  the  variables  considered met  the  criteria  for  normality  (50),  except  for  the skewness index  for  the  measure  of  SHS  exposure. Because the skewness index for the cotinine levels was  more  than  1.00,  a  logarithmic  transformation was used to normalize the distribution. Winsorising was used to account for an outlier with a cotinine measure that was more than three standard deviations above the mean.

## Results

## Sample Characteristics

Sociodemographic information about the population  sample  is  presented  in  Table  1.  All children  were  between  the  ages  of  4.72  and  6.11 years (mean age, 5.51 years; standard deviation, 0.32 years). Nineteen percent of the children were from low-income families with annual family incomes of less than $20,000. Only  7.4%  of  the mothers reported an education level of a high-school diploma or lower; 35.2% reported completing some college, whereas a majority (57.4%) reported completing  4-year  degrees  or  receiving  graduate education or above. Ten mothers reported that they were  current  smokers  at  the  time  of  the  retrospective interview.

TABLE 1. Demographic Characteristics of 54 Children Between the Ages of 4 and 6 Years With Cotinine Measures*

|                                                        | %    | N    |
|--------------------------------------------------------|------|------|
| Characteristic                                         |      |      |
| At or below poverty level (≤$20,000/year)              | 19.6 | 10   |
| Male gender                                            | 57.4 | 31   |
| Mother reported prenatal secondhand smoke exposure     | 28.8 | 15   |
| Mother reported prenatal smoking                       | 14.8 | 8    |
| Maternal education                                     |      |      |
| High school diploma                                    | 7.4  | 4    |
| Some college                                           | 35.2 | 19   |
| 4-year college degree                                  | 27.8 | 15   |
| Graduate education                                     | 29.6 | 16   |
| Child DSM IV, ADHD                                     | 7.4  | 4    |
| Child DSM IV, CD                                       | 5.6  | 3    |
| Child DSM IV, ODD                                      | 13.0 | 7    |
|                                                        | Mean | SD   |
| Age                                                    | 5.51 | 0.32 |
| Child ADHD and externalizing symptoms (range, 0 to 21) | 5.17 | 4.87 |
| Income-to-need ratio (range, 0 to 4.74)                | 2.32 | 1.36 |

*There were 54 children with cotinine levels at age 4, age 6, or both age 4 and age 6. There were 50 children with cotinine levels at age 4 and 45 children with cotinine levels at age 6.

Note. ADHD=attention-deficit/hyperactivity disorder; CD=conduct disorder; ODD=oppositional defiant disorder

## Correlational Analyses

Descriptive statistics and the intercorrelations among the child's age and sex, the maternal education level, the income-to-needs ratio, the ADHD  and  externalizing  disorder  symptoms,  the cotinine  levels,  and  the  nonverbal  reasoning  skills are presented  in  Table  2. The  child's age, the maternal  education  level,  and  the  income-to-needs ratio were positively correlated with children's nonverbal reasoning abilities ( r = 0.363, p < .01; r = 0.355, p < .01; r = 0.429, p < .01, respectively). The log  cotinine  value  was  negatively  correlated  with maternal education ( r = -0.537, p < .001), nonverbal reasoning ability ( r = -0.471, p < .001), and incometo-needs ratio ( r =  -0.571, p <  .001).  The  log cotinine value continued to be negatively correlated with  nonverbal  reasoning  after  accounting  for  the child's  age  and sex,  the  income-to-needs ratio,  and the ADHD and externalizing disorder symptoms ( r =  -0.285, p =  .045).  The  relationship  between  the log cotinine value and nonverbal reasoning is shown in Figure 1.

Nonverbal Reasoning Z-Score Nonverbal Reasoning 2-Score by Log Cotinine Level (N = 54)

TABLE 2. Descriptive Statistics and the Intercorrelations Among Nonverbal Reasoning, Cotinine Measures, and Sociodemographic Variables

## Correlations

| Measures                      | 1       | 2       | 3        | 4        | 5     | 6        |
|-------------------------------|---------|---------|----------|----------|-------|----------|
| 1. Age                        | -       |         |          |          |       |          |
| 2. Gender                     | -0.030  | -       |          |          |       |          |
| 3. Maternal education         | -0.051  | -0.337* | -        |          |       |          |
| 4. Income-to-need-ratio       | 0.181   | -0.347* | 0.708*** | -        |       |          |
| 5. ADHD, CD, and ODD symptoms | -0.001  | -0.084  | -0.073   | -0.111   | -     |          |
| 6. Nonverbal reasoning        | 0.363** | 0.003   | 0.355**  | 0.429**  | 0.092 | -        |
| 7. Contine (log)              | -0.304* | 0.119   | 0.537*** | 0.571*** | 0.239 | -0.471** |

## Descriptive Statistics

|                                          |   Mean |   SD |
|------------------------------------------|--------|------|
| Pattern construction subtest (raw score) |  21.24 | 6.72 |
| Picture similarities subtest (raw score) |  15.31 | 3.53 |
| Cotinine level                           |   1.35 | 2.38 |

Note. ADHD=attention-deficit/hyperactivity disorder; CD=conduct disorder; ODD=oppositional defiant disorder

FIGURE 1. The negative relationship between log cotinine and nonverbal reasoning

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## Regression Analysis

Results  from  the  hierarchical  regression  model  are presented  in  Table  3.  Overall,  sociodemographic variables  (i.e.,  age,  sex,  and  income-to-needs  ratio) accounted  for  nearly  30%  of  the  variance  in  the children's  nonverbal  reasoning.  Adding  ADHD, CD, and ODD symptoms to the model in step 2

did  not  significantly  improve  the  model  (change F 1,49  =  1.74, p =  .194).  The  log  cotinine  values continued  to  account  for  a  small  but  significant (6%) proportion of the variance when added to the model in step 3, after accounting for sociodemographic variables and ADHD and externalizing  disorder  symptoms  (change F 1,48  = 4.24, p= .045).

t = 1.56

p = 125

t= -1.68, p = 100

t=-2.06, p =.045

TABLE 3. Hierarchical Regression Model Testing the Effects of Secondhand Smoke Exposure on Nonverbal Reasoning in 54 Children Between the Ages of 4 and 6 Years

|                           |   R 2 |   Estimate |   SE | T Value   | p Value   |   Effect Size* |
|---------------------------|-------|------------|------|-----------|-----------|----------------|
| Model 1                   | 0.291 |            |      | F = 6.83  | <.001     |                |
| Age                       |       |       0.66 | 0.28 | 2.39      | .021      |           0.10 |
| Female gender             |       |       0.24 | 0.19 | 1.27      | .210      |           0.03 |
| Income-to-needs ratio     |       |       0.23 | 0.07 | 3.35      | .002      |           0.18 |
| Model 2                   | 0.315 |            |      | F = 5.64  | <.001     |                |
| Age                       |       |       0.65 | 0.27 | 2.38      | .022      |           0.10 |
| Female gender             |       |       0.27 | 0.19 | 1.44      | .156      |           0.04 |
| Income-to-needs ratio     |       |       0.25 | 0.07 | 3.53      | <.001     |           0.20 |
| ADHD, CD and ODD symptoms |       |       0.02 | 0.02 | 1.32      | .194      |           0.03 |
| Model 3                   | 0.371 |            |      | F = 5.65  | <.001     |                |
| Age                       |       |       0.51 | 0.28 | 1.84      | .072      |           0.17 |
| Female gender             |       |       0.24 | 0.18 | 1.34      | .186      |           0.00 |
| Income-to-needs ratio     |       |       0.16 | 0.08 | 1.99      | .053      |           0.20 |
| ADHD, CD and ODD symptoms |       |       0.03 | 0.02 | 1.78      | .082      |           0.04 |
| Log cotinine              |       |      -0.34 | 0.17 | -2.06     | .045      |           0.08 |

*Measured by partial eta-squared

Note. ADHD=attention-deficit/hyperactivity disorder; CD=conduct disorder;

ODD=oppositional defiant disorder

FIGURE 2. Model illustrating the relationship between log cotinine levels and nonverbal reasoning in 54 children between the ages of 4 and 6 years. The dotted arrow indicates a significant direct effect of log cotinine on nonverbal reasoning. The solid arrows indicate total effects, which were not significant. Attention-deficit/hyperactivity disorder, conduct disorder, and oppositional defiant disorder symptoms did not mediate the relationship between log cotinine values and nonverbal reasoning.

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The  negative relationship between log cotinine levels and nonverbal reasoning skills persisted after accounting  for  prenatal  smoking  ( t =  -2.06, p = 0.045), and prenatal exposure to SHS ( t = -2.19, p = .034) as additional sociodemographic  covariates. Nonverbal reasoning and log cotinine values continued  to  be  marginally  negatively  associated after  accounting  for  maternal  education  (in  lieu  of income-to-needs  ratios)  ( t =  -1.95, p =  .058).  This negative  relationship  was  not  present  when  both maternal education and income-to-needs ratios were included  in  the  model.  Maternal  education  and income-to-needs ratios showed a strong correlation ( r =  0.708, p <.001),  so  both  variables  were  not included in the model.

## Mediation Models

Figure 2 shows a mediation design that investigates whether  the  effect  of  SHS  exposure  on  nonverbal reasoning  is  indirectly  influenced  by  ADHD  and

Nonverbal Reasoning externalizing  disorder  symptoms.  In  keeping  with the  prior  analyses,  children's  age  and  sex  and  the income-to-needs  ratio  were  included  as  covariates. The  direct  effect  of  SHS  on  nonverbal  reasoning was  -0.34  (standard  error,  0.17; p =  .045;  95% confidence interval, -0.67 to -0.01), which indicates that  exposure  to  SHS  is  negatively  associated  with children's  nonverbal  reasoning.  The  indirect  effect of  SHS  exposure  on  nonverbal  reasoning  through ADHD  and  externalizing  disorder  symptoms  was 0.06 (bootstrap standard error, 0.07; 95% bootstrap confidence interval, -0.03 to 0.26), which demonstrates that ADHD and externalizing disorder symptoms did not mediate the relationship between SHS  and  nonverbal reasoning in this sample.

## Discussion

The  current  study  assessed  the  strength  of  the relationship  between  exposure  to  SHS,  associated psychopathology (i.e.,  ADHD,  CD,  and  ODD symptoms),  and  nonverbal  reasoning  skills  among preschool  children  between  the  ages  of  4  and  6 years. The results indicated that exposure to SHS is negatively associated with nonverbal reasoning skills in preschool-aged children even after accounting for selected sociodemographic variables and associated psychopathology.  If  children  have  adults  in  their lives who  smoke  in  their presence, this is an indication that these children are exposed to several social and environmental risk factors that can have adverse effects on their development. The results of the current study highlight the importance of considering exposure to SHS during early childhood as a potential variable in the risk trajectory and as a marker  of  other  associated  risks  that  should  be taken  into  account  when  formulating  intervention and prevention strategies.

These findings are to some extent consistent with previous work by Baghurst and colleagues (26), who also reported significantly lower cognitive performance  in  a  group  of  children  with  SHS exposure  between  the  ages  of  2  and  4  years  in  a longitudinal study. However, those researchers reported that this adverse relationship was no longer significant after accounting for socioeconomic  status, the quality of  the  home environment,  and  the  mother's  intelligence.  This highlights the possibility that this association can be accounted  for  by  varied  social  and  environmental factors that influence the child-rearing environment but  that  were  not  considered  in  the  current  study. Similarly, Julvez and colleagues (25) and Jedrychowski and colleagues (8) did not find postnatal  SHS  exposure  to  be  associated  with  an elevated  risk  for  delays  in  cognitive  development. None of these three studies used biomarker information  to  assess  exposure  to  SHS,  measured reasoning skills, or incorporated the effects of smoking-related childhood psychopathology on cognitive development.

To our knowledge, the current study is the first to use  biomarker  information-in  this  case,  salivary cotinine levels-when studying the relationship between  SHS  and  cognitive  abilities  in  preschoolaged children. The use of the biomarker overcomes some  of  the  limitations  in  the  extant  literature related to the use of retrospective maternal reports. The results of the current study are consistent with the studies by Cho and colleagues (22) and Yolton and colleagues (23), both of which used biomarker information  (serum  or  urine  cotinine)  to  assess exposure  to  SHS  in  older  children.  Yolton  and colleagues (23) reported high serum cotinine levels to be associated with reduced math scores, reading scores, working memory, and spatial reasoning in a large sample of children between the ages of 6 and 16 years from the third wave of the National Health and Nutrition Examination Survey. However, these negative relationships-except for the reading scores-were attenuated after accounting for prenatal  smoking  exposure.  In  the  current  study, further  analyses  indicated  that  cotinine  measures and nonverbal reasoning were negatively associated even after accounting for maternal prenatal smoking and SHS  exposure  during the prenatal period. However,  a limitation of our design was that maternal prenatal smoking and SHS exposure during pregnancy were based on retrospective maternal self-reports.

Similarly, Cho and colleagues (22) reported higher urine cotinine levels to be associated with a range of cognitive deficits, including lower word-reading scores and overall slower performance in a sample of  Korean  children  between  the  ages  of  8  and  11 years. Aside from the studies that included biomarker information, several studies that involved the  use  of  maternal  reports  have  shown  elevated SHS  exposure  to  be  negatively associated with academic  abilities  (20,51)  and  intelligence  quotient scores (21) in older children. These findings suggest that, although prenatal smoking exposure has adverse effects on children's cognitive development at younger ages, the postnatal environment and SHS exposure  also  seem  to  exert  a  significant  negative influence  on  children's  cognitive  development  as they get older (4). Therefore, studies that investigate postnatal SHS exposure can add important information  to  understanding  risk  trajectories  (as compared with those studies that focus exclusively on prenatal exposure).

The  current  study  is  also  the  first  that  we  are aware  of  that  includes  a  comprehensive  and  stateof-the-art assessment of early childhood psychopathology and that therefore can meaningfully account for the influence of comorbid psychopathology  on  cognitive  skills  in  the  context of SHS exposure in a group of preschool children. Children with ADHD are known to show deficits in a range of cognitive tasks, including inhibitory control,  working  memory,  long-term  memory,  and reasoning abilities (52-54). ADHD is also known to be a risk factor for cigarette smoking (55). Prenatal tobacco  smoking  has  been  found  to  be  associated with both  inattentive and  hyperactive/impulsive symptoms in offspring (10,11), and greater exposure to  SHS has also been found to be associated with more  severe  ADHD  symptoms  among  preschool children (40). Similarly, studies have shown prenatal smoking and exposure to SHS to be associated with CD-related and ODD-related symptoms in children (56,57). In addition, children with externalizing disorders have been found to have lower intelligence levels, academic difficulties, and deficits in  the  areas  of  executive  functioning  (19,58,59). Therefore,  accounting  for  childhood  ADHD  and externalizing disorder symptoms as possible mediators in the relationship between SHS exposure is  of  importance  given  that  children  with  these characteristics may be in a position to bear the risks of prenatal and postnatal exposure to tobacco smoke.

Results  in  the  current  study  suggest  that  poor performance in nonverbal reasoning was not mediated by the presence of ADHD and externalizing disorder symptoms. Nonverbal reasoning  is  strongly  correlated  with  intelligence levels (29) and working memory (30,31), which are known to be predictive of academic abilities such as mathematical reasoning and reading comprehension (33,34). This indicates the importance of considering the long-term effects of SHS exposure on  preschoolers'  cognitive  deficits  when  planning early interventions. It also suggests that public health  programs  should  consider  the  deleterious effects of SHS exposure during early childhood on cognitive outcomes.

Some  limitations  of  this  study  are  noteworthy. First  of  all,  the  current  study  has  a  relatively  small sample size. Considering the unique population that is  being studied (i.e., preschool-aged children), it is of note that we were able to detect an effect even in a  small  sample  of  54  children.  One  of  the  major limitations  is  that  we  are  unable  to  disentangle  the effects  of  prenatal  and  postnatal  exposure  to  SHS. Postnatal  exposure  was  measured  with  the  use  of saliva  cotinine  values,  and  prenatal  exposure  was based  on  the  maternal  interview.  Eight  mothers (15%) reported that they smoked during pregnancy, and  28%  reported  that  they  were  exposed  to  SHS during  the  first,  second,  or  third  trimester.  Thus, some of the children in the sample were at risk for both prenatal and postnatal smoking  exposure, which  makes  it  difficult  to  disentangle  the  role  of each  on  cognitive  performance.  It  should  also  be noted  that  maternal  reports  may  be  limited  by retrospective recall bias, because the smoking interview was conducted during a later stage of the study.

It has also been reported that women who smoke during  pregnancy  or  during  the  child-bearing  and child-rearing years are more likely to live in poverty, to  be  less  educated,  and to  abuse  other  substances (e.g.,  alcohol);  they  also  often  suffer  from  anxiety and depression (2). Children who live in poverty are at a higher risk of being exposed to other environmental hazards (e.g., lead, certain chemicals) (8). All of these factors, in addition to  SHS exposure,  can  have  adverse  effects  on  a  child's cognitive development. It is also possible that other unmeasured  maternal  characteristics  (e.g.,  maternal intelligence,  parenting  style)  could  influence  these results.  Although  the  income-to-needs  ratio  was considered a covariate because it can be considered a  proxy  for  socioeconomic  status,  we  were  unable to  identify  and  control  for  many  key  social  and environmental factors that may have influenced the findings  of  this  study.  We  also  used  symptom counts  rather  than  formal  diagnostic  criteria to define ADHD, ODD, or CD because of the limited number  of  children  who  met  the  criteria  for  a formal DSM-IV diagnosis. This might have reflected  a  milder  risk  and  possibly  attenuated  the strength of the relationship between  ADHD, externalizing disorders, SHS exposure, and cognitive  performance.  Finally,  because  this  is  a cross-sectional  study,  we  cannot  make  any  causal inferences about  the  nature of the relationship between SHS exposure and cognitive performance.

Despite these  limitations,  to  our  knowledge,  this is the first study to use a biomarker (saliva cotinine) to measure SHS exposure. It is also the first study to consider an age-appropriate comprehensive assessment of related psychopathology as a potential  mediating  variable  that  could  influence cognitive performance among preschool-aged children. Overall, the findings obtained by this study may be helpful to clinical practice and public policy by creating an awareness of the long-term negative effects of passive smoking, and they may be useful when  formulating  early  intervention  strategies  to help high-risk child populations. Accounting for the comorbidity of ADHD and externalizing disorders in mothers and children also might be of importance when tailoring prevention, intervention, and treatment protocols. Future studies to investigate these risk relationships in larger samples are  indicated.  Large  epidemiological  samples  that control  for  maternal  smoking  during  pregnancy would  further  help  to  disentangle  some  of  the confounding  effects  of  the  present  study.  Ideally, larger prospective studies that include only participants exposed to postnatal (not prenatal) SHS would help to further inform the separate effects of these exposures on various neurocognitive domains. In addition, parental characteristics-especially maternal intelligence-should be considered as one of the key variables that influences this relationship. Controlling for  the  many  key  covariates  associated with SHS exposure will continue to be a challenge in related investigations. Longitudinal studies will be of particular importance to determine if the cognitive  deficits  identified  persist  with  age  and intervention.  However,  it  should  be  noted  that results like those of the present study are important because they can help to define the potential likely and unlikely causal variables and direct the focus of longitudinal studies,  which  are  inherently  much more effortful and expensive.

## Clinical Significance

Findings indicate that exposure to SHS is one of the numerous social and environmental risk factors that influences early childhood cognitive outcomes. These findings highlight the importance of considering the long-term effects of SHS exposure along with other potential risk factors when planning early interventions.

## Acknowledgments

This research was supported by the National Institutes of Health: NIDA/AACAP K12 DA000357  to  Mini  Tandon,  D.O.;  NIDA  T32 DA007313  to  Duneesha  De  Alwis,  Ph.D.;  and NIMH R01 MH 64769-02 to Joan Luby, M.D.

All authors declare no conflicts of interest.

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