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The longitudinal, bidirectional relationships between parent reports of child secondhand smoke exposure and child smoking trajectories

Ashley H. Clawson, Ph.D.,1 Elizabeth L. McQuaid, Ph.D., ABPP, 2

Shira Dunsiger, Ph.D.,3 Kiera Bartlett, Ph.D,4 &

Belinda Borrelli, Ph.D. 5

1 Oklahoma State University, Department of Psychology, 116 North Murray, Stillwater, OK 74078

2 Bradley/Hasbro Children’s Research Center, Alpert Medical School of Brown University and Rhode Island Hospital. 1 Hoppin Street Providence, RI 02903.

3 Centers for Behavioral and Preventive Medicine, Alpert Medical School of Brown University and The Miriam Hospital. Coro West, Suite 309, 164 Summit Ave, Providence, RI 02906.

4, Manchester Centre for Health Psychology School of Psychological Sciences, Manchester Academic Health Science Centre, The University of Manchester. Coupland 1 Building, Oxford Road, Manchester M13 9PL, UK.

5 Boston University, Henry M. Goldman School of Dental Medicine, Department of Health Policy & Health Services Research. 560 Harrison Avenue, 3rd floor, Boston, MA 02118.

Corresponding author: Ashley H. Clawson, Ph.D.: ahum@okstate.edu

The study was conducted at The Miriam Hospital, when Dr. Borrelli was employed there. This study was completed when Dr. Clawson was affiliated with the Centers for Behavioral and Preventive Medicine and the Bradley/Hasbro Children’s Research Center. Dr. Clawson is now employed at Oklahoma State University.

Funding: This work was supported by the National Institutes of Health (5 R01 HL062165-09 (B. Borrelli, PI) and 5 T32 HL076134-09 (R. Wing, PI)).

Conflict of Interest: The authors have no conflicts of interest.

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AbstractThis study examines the longitudinal relationships between child smoking and secondhand

smoke exposure (SHSe). Participants were 222 parent-child dyads. The parents smoked, had a

child with (48%) or without asthma, and were enrolled in a smoking/health intervention. Parent-

reported child SHSe was measured at baseline and 4, 6, and 12-month follow-ups; self-reported

child smoking was assessed at these points and at 2-months. A parallel process growth model

was used. Baseline child SHSe and smoking were correlated (r = 0.30). Changes in child SHSe

and child smoking moved in tandem as evidenced by a correlation between the linear slopes of

child smoking and SHSe (r = 0.32), and a correlation between the linear slope of child smoking

and the quadratic slope of child SHSe (r = -0.44). Results may inform interventions with the

potential to reduce child SHSe and smoking among children at increased risk due to their

exposure to parental smoking.

Keywords: parent; child; smoking; secondhand smoke exposure; longitudinal

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INTRODUCTION

Parental smoking, the most common source of child secondhand smoke exposure (SHSe)

(Ding et al. 2010), poses immediate risk for the parent and compounded risk for youth due to the

health effects of SHSe and the increased risk of smoking uptake (U.S. Department of Health and

Human Services 2006). About 41% of children aged 3-11 and 34% of children aged 12-19 are

exposed to SHS (Homa et al. 2015). Parental smoking plays an important role in youth smoking,

and has been linked to adolescent intentions to smoke, smoking initiation, early onset, rapid

escalation, and persistent smoking, with longer parental tobacco exposure related to increased

risk (Chassin et al. 2008; Fuemmeler et al. 2013; Kandel et al. 2015; Leonardi-Bee et al. 2011;

Mays et al. 2014; Peterson et al. 2006; Schuck, Otten, Kleinjan, et al. 2013; Vuolo and Staff

2013; Weden and Miles 2012). This risk appears to be modifiable; smoking initiation rates are

lower among children whose parents quit smoking (den Exter Blokland et al. 2004; Otten et al.

2007; Vuolo and Staff 2013).

Studies that examine the relationships between parent and child smoking hypothesize that

parental modeling of smoking is associated with offspring’s observational learning and increased

likelihood to initiate smoking, consistent with social ecological theories (Bandura 1986, 2004).

These studies utilize assessments of parents’ smoking history and/or current smoking status as

indicators of parental modeling of smoking, rather than assessing children’s proximity to

parental smoking. Though children are generally aware of parental smoking (Harakeh et al.

2006), the use of parent smoking status as a proxy for parental modeling fails to capture whether

children witness the smoking behavior, therefore engaging in observational learning. Measuring

children’s SHSe may be a satisfactory proxy of parental modeling of smoking because SHSe

would reflect exposure to parental smoking. Though less prevalent than studies that analyze

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parent current smoking status as a risk factor for child smoking, some studies have examined the

relationship between child SHSe and child smoking. A systematic review found that SHSe was

associated with child smoking initiation and current smoking (Okoli and Kodet 2015). More

research is needed to further understand the longitudinal associations between children’s SHSe

and smoking patterns.

A child’s behavior also has the potential to influence other family members’ behavior

(Bandura 1986, 2004); yet only one study has examined how child smoking predicts later

parental smoking (Schuck, Otten, Engels, et al. 2013). Shuck et al. conducted a longitudinal

study that examined the cross-lagged associations between self-reported child smoking and

parental smoking: more smoking among children predicted more subsequent smoking among

parents (Schuck, Otten, Engels, et al. 2013). The authors propose a family smoking contagion

effect, i.e., more smoking among one family member is associated with increased smoking

among other family members (Schuck, Otten, Engels, et al. 2013).

Most parents who smoke do not want their children to smoke (Bottorff et al. 2013; Tilson

et al. 2005); therefore, some parents who smoke may respond to concerns about their child

smoking by reducing the amount they smoke in the presence of their child (thereby reducing

modeling and SHSe). For example, in a study that examined the initial effectiveness of an

intervention to help parents who smoke restrict children’s access to parental tobacco, parents

reported reductions in their child’s SHSe (Robinson et al. 2015). Because this was not a

treatment target, it was hypothesized that the intervention may have increased parental concern

about youth smoking, which in turn led to changes in parental smoking behavior. A recent trial

found that providing parents who had called a Quitline and quit smoking for 24 hours with a

program focused on preventing offspring smoking improved parental abstinence rates (Jackson

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et al. 2016). Thus, parents may react to concerns about offspring smoking by changing their own

smoking behaviors in an effort to deter smoking among their children.

The current study uses data from a larger smoking cessation induction trial to examine

the longitudinal, bidirectional relationships between child self-reported smoking and parent

reported child SHSe. It was hypothesized that longitudinal changes in parent-reported child

SHSe and child self-reported smoking would be associated, i.e., these processes would change in

tandem. Across individuals, processes can change together over time in multiple patterns, e.g.,

one process increases while another decreases, both processes increase, etc. For the present

study, two change patterns were hypothesized: 1) that increases in parent-reported child SHSe

would be associated with increases in child self-reported smoking (i.e., a positive association

between slopes), and 2) that increases in child self-reported smoking would be associated with

decreases in parent-reported child SHSe (i.e., a negative association between slopes). The

former hypothesis is based on the literature on parental smoking and modeling (Chassin et al.

2008; Fuemmeler et al. 2013; Kandel et al. 2015; Leonardi-Bee et al. 2011; Mays et al. 2014;

Okoli and Kodet 2015; Peterson et al. 2006; Schuck, Otten, Kleinjan, et al. 2013; Vuolo and

Staff 2013; Weden and Miles 2012) and smoking contagion (Schuck, Otten, Engels, et al. 2013);

the latter hypothesis is based on data that parents who smoke may reduce their smoking around

their child for fear of the child increasing smoking (Jackson et al. 2016; Robinson et al. 2015).

Results from this study may enhance the understanding of the relationships between children’s

passive and active smoking and inform tobacco interventions that have the potential to reduce

both child SHSe and smoking.

METHODS

Participants

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This is a secondary analysis of a subset of data from a smoking cessation induction/health

education study for caregivers who smoke (BLINDED). The present study includes 222

caregiver-child dyads families with a child from 8-17 years old. Our sample includes children

with asthma (hereafter referred to as the asthma group; n = 107) and without asthma (hereafter

referred to as the healthy child group; n = 115).

Caregivers of children with asthma were recruited primarily from emergency departments

and urgent care; caregivers of healthy children were recruited from community events and

publicity. Caregivers were eligible for participation if they met these requirements: smoked ≥3

cigarettes per day for the last year and had smoked at least 100 cigarettes, were the primary

caregiver of a child between the age 3-17, ≥18 years old, were not pregnant or planning to

become pregnant, were reachable by telephone, were fluent in English, and were not enrolled in

cessation treatment, using medication or nicotine replacement therapy to quit smoking.

Caregivers of children with asthma were eligible for participation if their child had experienced

an asthma exacerbation in the last two months necessitating urgent care (urgent care visit or

hospitalization). In the asthma group, families with target children who had other significant

respiratory illnesses were excluded; in the healthy child group, families with a child with asthma

or significant respiratory illness were excluded. Participants did not have to want to quit

smoking to enroll but had to be willing to discuss smoking and have home-based health

education visits.

All participants completed two home visits that involved health education and

motivational interviewing for smoking. Participants in the asthma groups received health

education focused on asthma; the healthy group received education on general child health. After

the home visits, participants in the asthma group (n=341) were randomized to receive one of two

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types of six counseling calls over four months: guidelines-based asthma education (National

Asthma Education and Prevention Program 2007) plus child wellness (n=171) or the same

asthma education plus a motivational intervention for smoking (n=170). After the home visits,

parents in the healthy child group received six counseling calls focused only on child wellness

(n=219). Only families with children ≥ 8 years old reported on child smoking; thus the current

sample is comprised of 222 families with a child from 8-17 years old (asthma groups- n = 107;

healthy child group- n = 115). The present sample enabled us to examine the study aim among a

heterogeneous population, including youth at greater risk for tobacco-related consequences;

however, power limitations precluded our ability to compare outcomes based on asthma status.

Participants who wanted to quit within 30 days were provided with an 8-week supply of

Transdermal Nicotine Patch treatment at no cost. Additional details of the larger study can be

found in (BLINDED). The study was approved by our Institutional Review Board. Data were

collected in Rhode Island and Massachusetts from 2008-2013. Informed consent was obtained

from all individual participants included in the study. More detailed information about the study

design can be found in (BLINDED REF).

Measures

Demographic (age, race, gender, education, income) and caregiver smoking history

variables (cigarettes smoked per day, Fagerstrom Test for Nicotine Dependence (Heatherton et

al. 1991), home smoking ban (no smoking allowed in the home), presence of other smokers in

the home) were assessed via caregiver self-report at baseline.

Parent Reports of Child Secondhand Smoke Exposure (SHSe). Assessments for parent-

reported child SHSe were completed at baseline and 4, 6, and 12-months. Parent-reported child

SHSe was assessed with a structured caregiver interview that produced a composite score of the

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number of cigarettes a child was exposed to from all people (excluding exposure from a child’s

personal smoking) and all places during the past week. Higher scores indicated higher SHSe. The

composite score has demonstrated good reliability and validity (Matt et al. 2000). Objective

SHSe was measured at baseline with a passive nicotine air monitor (i.e., dosimeter) that the child

wore for one week. Dosimeters have good validity (Leaderer and Hammond 1991). They were

only used in the present study to establish the validity of parent-reported SHSe. Baseline

assessments of objective child SHSe and parent-reported SHSe were correlated, r = .28, p < .001.

Parent-reported child SHSe was used as a dependent variable because it excludes children’s

SHSe from personal active smoking; SHSe as measured by passive dosimetery could not

delineate parent vs. child smoking. Additionally, there were two more assessment points for

parent-reported child SHSe, allowing for an examination of non-linear change. Participants

received $20 per completed questionnaire and $10 for returning the dosimeters in good

condition.

Child Smoking Status. Children were queried about their smoking status by an

interviewer without the parent present at baseline and at 2, 4, 6, and 12-months. Smoking status

was determined via validated questions about smoking quantity and frequency (Udry 2003). A

smoking status composite score was compiled with higher scores indicating greater smoking

and/or smoking-related exposure: (1) never smoked, (2) tried or puffed a cigarette, (3) had

smoked cigarette, but had not smoked a 100 cigarettes and had not smoked during the past

month, (4) had smoked a cigarette and had smoked 100 cigarettes, but had not smoked during the

past month, (5) had smoked a cigarette during the past month, but had not smoked 100 cigarettes,

and (6) had smoked 100 cigarettes and smoked during the past month.

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Bioverified Parent Quit Status (7 day point prevalence abstinence (PPA)). At follow-up

visits, self-reported caregiver quit status, (i.e., self-report of no smoking in the past 7 days), was

verified using carbon monoxide testing (Bedfont, CO Ecolyzer). Self-reported quit was

confirmed if readings were < 9 ppm and readings > 9ppm were recoded as smokers (Benowitz et

al. 2002).

Data Analysis

Analyses were performed with Mplus. Outliers were identified via graphical techniques;

five cases with outlying values for SHSe were removed. SHSe was non-normally distributed

with large variance so a square-root transformation was used. Standardized results are presented.

We used latent growth curve modeling to examine how longitudinal changes in child smoking

and child SHSe were related. This type of analysis is a recommended approach for studying

youth smoking (Darling and Cumsille 2003). First, separate latent growth curve models were fit

to child SHSe and child smoking to establish the best fitting growth curve for each process.

Second, a parallel process growth model was used to examine the correlations between the latent

growth factors (intercepts and slopes) of parent-reported child SHSe and child self-reported

smoking. The final model included baseline child age and time-varying bioverified parent quit

status (7 day PPA). Study group was included in an earlier model but due to significant

correlation with child age, it was removed from the final model in order to avoid

multicollinearity. Model fit was estimated with the χ2, Comparative Fit Index (CFI),

Standardized Root Mean Square Residual (SRMR), Akaike Information Criterion (AIC), and

Bayesian Information Criterion (BIC) (Geiser 2013; Hu and Bentler 1999).

RESULTS

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Participant characteristics are provided in Table 1. Correlations and descriptive statistics

are provided in Table 2. First, separate latent growth curve models for parent-reported child

SHSe and child self-reported smoking were tested; the best fitting growth curve was quadratic

for child SHSe and linear for child smoking (Table 3). Next, a parallel process growth model

concurrently examined the trajectories of parent-reported child SHSe and child self-reported

smoking and the correlations between the latent growth factors of each process (Figure 1). Model

fit was satisfactory (Table 3) (Geiser 2013; Hu and Bentler 1999). The final model controlled for

baseline child age and time-varying parent bioverified quit status.

It should be noted that model fit and resulting patterns remained similar when study

group (variable reflecting if participant was in the healthy group, asthma group with no

additional smoking counseling, or asthma group with additional smoking counseling), was added

to the model; however, risk of collinearity was high, and thus it was removed from the final

model (Table 3). The pattern of results in the final model were also similar to a multiple group

model that was stratified by healthy vs. asthma status (and controlled for child age, time-varying

parent bioverified quit status, and study group (in the asthma portion of the model)), the

correlations between slope growth factors were not significant, likely due to reduced power.

Growth Patterns of Parent-reported Child SHSe and Child Self-reported Smoking

Parent-reported Child SHSe. Baseline levels of parent-reported SHSe were different

from zero (M = 1.04, p <.001) and there was significant variability (σ = 1.00, p <.001). There

were linear decreases in parent-reported SHSe over time (M = -0.58, p <.001) followed by

increases in parent-reported SHSe (M = 0.44, p =.001), the latter representing the quadratic

growth over time. There was significant variability in the linear (σ = 0.99, p <.001) and quadratic

growth terms (σ = 0.98, p <.001). Higher parent-reported SHSe at baseline was associated with

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fewer linear decreases in parent-reported SHSe (r = -0.79, p <.001) and more quadratic increases

(r = 0.71, p <.001). The relationship between the linear and quadratic parent-reported SHSe

slopes, commonly assessed as part of model interpretation, was significant (r = -0.98, p <.001),

suggesting that more linear SHSe decreases were associated with fewer quadratic increases.

Child Self-reported Smoking. Baseline child smoking levels were different from zero (M

= 1.36, p <.001) and had significant variability (σ = 0.88, p <.001). Child smoking increased

over time (M = 0.31, p < .001), with significant variability in the linear growth term (σ = 0.98, p

<.001). Higher baseline child smoking levels were modestly associated with more increases in

child smoking over time, although this trend did not reach significance (r = 0.46, p = 0.08).

Correlations between Growth Factors for Parent-reported Child SHSe and Child Self-

reported Smoking

Next, the correlations between the intercepts and slopes of parent-reported child SHSe

and child self-reported smoking were examined. Higher baseline parent-reported child SHSe was

associated with higher baseline child self-reported smoking (r = 0.30, p = 0.001). Baseline levels

of parent-reported child SHSe were not correlated with the growth of child smoking across time

(r = -0.004, p = 0.97). Baseline child smoking levels were not associated with linear or quadratic

growth in parent-reported SHSe (r = 0.02, p = 0.88; r = -0.15, p = 0.27, respectively).

To examine the bidirectional, longitudinal relationships between growth in parent-

reported child SHSe and child self-reported smoking over time, the correlations between each of

the processes’ slopes were examined. The linear slope of child smoking and the linear slope of

parent-reported child SHSe were correlated (r = 0.32, p = 0.04). This correlation indicates that

these processes change in tandem: More changes in parent-reported child SHSe were associated

with more changes in child self-reported smoking.

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The positive correlation between child SHSe and smoking linear slopes identified that

these processes change together; further examination of individual children’s slope values

allowed for an exploration of how child SHSe and smoking changed across time for each

participant. Each participant was mapped to a growth pattern (e.g., a linear smoking slope term

and linear SHSe slope term was linked to each participant). Data from the scatterplot of the two

linear slopes were examined to ascertain how each child changed in each process (Figure 2). We

subdivided the plot of the two linear slopes into 4 quadrants and calculated the number of

participants who fell within each quadrant; the mean slope values of child smoking and parent-

reported SHSe within each quadrant were also examined. Results suggest the following patterns:

1) parent-reported child SHSe increased and child smoking decreased (n = 6, 2.7%), 2) parent-

reported child SHSe increased and child smoking increased (n = 66, 29.7%), 3) parent-reported

child SHSe decreased and child smoking increased (n = 95, 42.8%), and 4) parent-reported child

SHSe decreased and child smoking decreased (n = 55, 24.8%).

The linear slope of child self-reported smoking and the quadratic slope of parent-reported

child SHSe were also associated (r = -0.44, p = 0.02), indicating that more increases in child

smoking were associated with fewer changes in the quadratic growth of parent-reported child

SHSe. Participants moved in tandem via the following patterns: 1) child smoking increased with

negative quadratic growth in parent-reported child SHSe (n = 77, 34.7%), 2) child smoking

increased with positive quadratic growth in parent-reported child SHSe (n = 84, 37.8%), 3) child

smoking decreased with positive quadratic growth in parent-reported child SHSe (n = 55,

24.8%), and 4) child smoking decreased with negative quadratic growth in parent-reported child

SHSe (n = 6, 2.7%).

DISCUSSION

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Rates of child SHSe and youth smoking are higher among families with a parent who

smokes than in the general population (Homa et al. 2015; Vuolo and Staff 2013). Parental

smoking is associated with increased adolescent intentions to smoke and smoking (Chassin et al.

2008; Fuemmeler et al. 2013; Kandel et al. 2015; Leonardi-Bee et al. 2011; Mays et al. 2014;

Peterson et al. 2006; Schuck, Otten, Kleinjan, et al. 2013; Vuolo and Staff 2013; Weden and

Miles 2012). It has been hypothesized that this is through the mechanism of observational

learning or possibly ‘smoking contagion,’ where behavior observed within the family is modeled

by other family members (Bandura 2004; Schuck, Otten, Engels, et al. 2013). Child smoking

behavior also has the potential to impact parental smoking behavior; parents may respond to

increases in child smoking by reducing the amount they smoke in the presence of their child (and

thereby reducing SHSe). Our study identified that the longitudinal patterns of parent-reported

child SHSe and child self-reported smoking were associated. Further, different patterns of how

parent-reported child SHSe and child self-reported smoking changed across time together were

found, including that 1) increases in child SHSe were associated with increases in child smoking

and 2) increases in child smoking were associated with decreases in child SHSe. This study

found that changes in child SHSe and smoking are related, highlighting the potential family-level

health benefits of interventions that focus on breaking the intergenerational transmission of

tobacco exposure and uptake among families with a parent who smokes.

This is the first study to examine how longitudinal changes in parent-reported child SHSe

and child self-reported smoking are related over time. Our study design allowed us to effectively

examine this aim because (1) changes in parent-reported child SHSe were considered likely

during the intervention and (2) our longitudinal data allowed for a an examination of trajectories,

which has been deemed as a more appropriate modeling approach for examining youth smoking

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as compared to static evaluations of smoking (Darling and Cumsille 2003). The current study

used parent-reported child SHSe; this is likely a more accurate measure of child proximity to

parental smoking than report of parental smoking status. This measurement approach also allows

SHSe from the child’s own smoking to be excluded. As hypothesized, higher levels of baseline

parent-reported child SHSe were associated with higher levels of child smoking, and parent-

reported child SHSe and smoking changed in tandem over time.

Two change patterns were hypothesized: 1) increases in parent-reported SHSe would be

associated with increases in child smoking and 2) increases in child smoking would be associated

with decreases in parent-reported child SHSe. Because the correlations between the linear and

quadratic slopes of parent-reported child SHSe and the linear slope for child smoking were

significant, indicating that the processes change in tandem, further examination of individual

change patterns was warranted; both of the hypothesized change patterns were confirmed. The

most prevalent change pattern was that as self-reported child smoking increased, parent-reported

child SHSe decreased. When comparing the linear slopes of child smoking and parent-reported

child SHSe, this pattern was identified in around 43% of youth; a similar pattern was seen when

examining the correlation between the linear child smoking slope and the quadratic parent-

reported SHSe slope (about 35% of children had increases in smoking and continued decreases

in SHSe). If parents are aware of their child’s smoking, this could indicate agreement with

previous research that suggested that when parents know their child is smoking, or become

aware of child risk for smoking, they reduce the amount of time they smoke in the presence of

their child (Robinson et al. 2015). Parents may hope that if they smoke around their child less, it

will be modeled less and the child will reduce their smoking. It is unclear from the present

research whether parents were aware of their child’s smoking. Previous research has indicated

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that parental and child reports of child smoking have fair to substantial agreement

(Kappa .55-.67) (Harakeh et al. 2006); however, child perceptions of adults knowledge of their

smoking may be lower (Ditre et al. 2008). To confirm our hypothesis, future research could

verify parental reasons for reducing their child’s SHSe.

An alternative hypothesis for our finding may be that as children started smoking more

they started to spend more time away from home. This hypothesis seems plausible, although our

analysis controlled for child age and this behavior is likely less feasible for younger children.

One implication of these data may be that interventions could incorporate feedback to parents

who smoke about their children’s increased risk of smoking to motivate parents to reduce child

SHSe and prevent child smoking initiation/escalation. To date, only one intervention has aimed

to change both child smoking and SHSe among children with parents who smoke. The

intervention, a self-directed program for the parent-child dyad that focused on increasing

antismoking socialization and home smoking bans among families with a parent who smokes,

reduced offspring smoking initiation, but did not increase home smoking bans (Jackson and

Dickinson 2003, 2006). A later evaluation of this program showed that it successfully prevented

relapse among parents who had called a Quitline and were at least 24-hours quit (Jackson et al.

2016). These studies seem to support our hypothesis that offspring smoking status/risk can serve

as a motivator for parental behavior change, with benefits for the parent and child.

Alternatively, our data could also imply that for some families, reducing SHSe would not

have a positive effect on child smoking. One potential explanation for this could be the idea

discussed by Darling and Cumsille (2003) that parental smoking creates an environment that

makes smoking more likely for a child (through a combination of modeling, perceived

acceptability of behavior, environmental factors), but there still needs to be a ‘trigger event’

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(Darling and Cumsille 2003). Children with parents who smoke may be more likely to both

encounter a trigger event and react to this event by increasing smoking than children with non-

smoking parents. In this case, exclusively addressing parental smoking may not be sufficient to

reduce child smoking as the susceptibility to a trigger event may remain. For example, an

intervention that solely focused on promoting parental smoking cessation (without focusing on

child smoking) was not effective at reducing child smoking onset (Schuck et al. 2015). Overall,

parental smoking cessation is always a desired outcome, but some parents may not be ready to

quit and interventions that address both child SHSe and smoking may be the most advantageous

to reduce child risk of current and future tobacco exposure.

Additionally, in some cases, increases in parent-reported child SHSe were accompanied

by increases in child smoking. Interestingly, our results also suggest that decreases in parent-

reported child SHSe were associated with decreases in child smoking, suggesting that the risk

incurred from parental modeling may be modifiable. These patterns of change were found when

both the linear and quadratic slopes of parent-reported child SHSe were compared with the linear

slope of child smoking and are commensurate with the concept of observational learning, and

also smoking contagion, of smoking behavior among families. This suggests that SHSe reduction

interventions, including interventions that promote parent cessation and/or aim to reduce child

SHSe, have the potential to have a direct positive effect on child health through reductions in

child SHSe and smoking. As proposed earlier, interventions that capitalize on the fact that child

SHSe and smoking appear to change together and explicitly target both child SHSe and smoking

may hold the most potential for breaking the intergeneration transmission of smoking. However,

it is important to note that the current study did not examine whether the relationship between

child smoking and SHSe was moderated by other variables that are risk factors for child smoking

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(e.g., access to tobacco); therefore, more research is needed to inform what types of interventions

would be most effective.

Despite the contribution of this paper, there are limitations. While we were able to

examine how changes in parent-reported child SHSe and smoking were correlated and provide

hypotheses about why these change patterns were observed, we lacked data to confirm what

factors influenced the identified patterns. This study could not control for other influences on

SHSe and child smoking; future studies should examine other variables that may affect child

SHSe or smoking. Due to multicollinearity and lack of power, we were unable to evaluate

potential differences in these patterns based on child asthma status. Notably, the patterns of

results were similar across the models that included study group and the multiple group model

that was stratified by asthma status. Future studies could explore potential differences in these

relationships between children with and without asthma. The fact that all parents were enrolled

in a smoking intervention may limit the generalizability of our findings; however, the study was

advertised as a health education intervention, parents did not have to want to quit smoking to

participate, and the study design allowed us to effectively examine our hypothesis about tandem

changes in parent-reported child SHSe and smoking, something that may be have been difficult

in an observational study. Our study also had some limitations to our measurement. First,

because parents reported on child SHSe and their own smoking behavior, common method

variance may have influenced our results. Second, although measuring child SHSe was

hypothesized to be a better proxy of parental modeling than parent smoking status alone, there is

still uncertainty surrounding if the child witnessed the parent smoking. Third, the correlation

between parent-reported child SHSe and objectively measured child SHSe was low (r = .28).

This correlation reflects the association between parent reports of child SHSe based on all

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possible sources of SHSe (i.e., exposure from all people in all places) with the objective measure

of child SHSe based on a dosimeter children wore for one week at baseline. This correlation may

be low for several reasons. It is possible that the objective estimate of child SHSe underestimated

the child’s exposure to SHS compared to parent reports because the dosimeter was not worn

consistently or was placed somewhere where it could not efficiently gather nicotine.

Alternatively, the low correlation may reflect parents’ difficulty with accurately reporting on

their child’s exposure to SHSe for situations when the parent is not present. While our study

provides preliminary support for a relationship between changes in child SHSe and smoking,

future research should confirm these results by examining these relationships with measurement

approaches less at risk for bias.

Conclusion

This is the first study to examine the longitudinal, bidirectional relationships between

parent-reported child SHSe and child self-reported smoking. Parent-reported child SHSe and

child smoking changed in tandem across time. The results may inform tobacco interventions that

have the potential to reduce both child SHSe and child smoking among children at increased risk

due to their exposure to parental smoking. Further research could aim to confirm the identified

change patterns and explore variables that predict which patterns of change are most likely for

families.

Acknowledgments

The authors thank Kristoffer S. Berlin, Ph.D. for his guidance.

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Ethical approval: All procedures performed in studies involving human participants were in

accordance with the ethical standards of the institutional and/or national research committee and

with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

20

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Table 1. Participant Characteristics at Baseline (n = 222).

M (SD) n (%)

Parent Age 39.95 (8.88)

Parent sex (Female) 193 (86.9%)

Parent race/ethnicity

White, non-Hispanic 112 (50.5%)

Black, non-Hispanic 58 (26.1%)

Hispanic 30 (13.5%)

Other 22 (9.9%)

Parent- completed education beyond high school 93 (41.9%)

Income below $25,000 139 (62.6%)

Parent cigarettes smoked per day 15.55 (9.62)

Parent- Fagerstrom Test for Nicotine Dependence 4.77 (2.36)

Other smokers lived in the home 98 (44.1%)

Total home smoking ban in place 91 (41.0%)

Child Age 11.56 (2.98)

Child Sex (Female) 114 (51.4%)

Child Baseline Smoking Status (Never smoker)1 122 (86.5%)

1 This is the valid percent considering missing data.

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Table 2. Correlation matrix and descriptive statistics. 1 2 3 4 5 6 7 8 9 10 11

1. Baseline child smoking

- 0.80*** 0.93*** 0.78*** 0.68*** 0.32*** 0.28** 0.39*** 0.08 0.30*** -0.06

2. 2-month child smoking

- 0.69*** 0.83*** 0.78*** 0.28** 0.30** 0.37*** 0.04 0.35*** 0.07

3. 4-month child smoking

- 0.86*** 0.84*** 0.09 0.11 0.26* -0.06 0.40*** 0.11

4. 6-month child smoking

- 0.90*** 0.26** 0.22* 0.39*** 0.10 0.40*** 0.15

5. 12-month child smoking

- 0.29** 0.30** 0.29** 0.09 0.24* 0.07

6. Baseline SHSe - 0.54*** 0.38*** 0.46*** 0.04 0.15*

7. 4-month SHSe - 0.62*** 0.61*** .08 0.18*

8. 6-month SHSe - 0.53*** 0.16 0.16*

9. 12-month SHSe - 0.05 0.19**

10. Child age - 0.20**

11. Study Group -

Mean 1.30 1.42 1.32 1.27 1.45 4.30 2.68 2.45 2.09 11.56 -

Standard Deviation 0.90 1.09 0.97 0.88 1.17 3.74 3.14 2.09 3.00 2.98 -

Notes: n’s range from 58-222. These n’s are small due to listwise deletion. In the Mplus models, the full information maximum likelihood (FIML) procedure was used to address missing data. * p <.05,** p <.01,*** p <.001.

Table 3. Results from the Latent Growth Curve Models (LGCM) for Child SHSe and Smoking.

Models χ2 (df) CFI SRMR BIC AIC

Child SHSe LGCM

1. Intercept only 110.23 (26)***

.71 .08 4060.24 4019.40

2. Lineara 53.47 (22)***

.89 .05 4028.17 3973.73

3. Quadraticb 24.51 (16) 0.97 .03 4031.95 3957.10

Child Smoking LGCM

4. Intercept onlyc 108.33 (42)***

.81 .10 1191.89 1149.08

5. Lineard 49.03 (36) .96 .05 1148.04 1085.46

6. Quadraticc,d 51.15 (30)** .94 .05 1177.17 1094.84

Parallel Process Growth Model

7. Quadratic Child SHSe and Linear Child Smoking b, e

136.36 (65)***

.91 .05 5189.30 5012.36

8. Quadratic Child SHSe and Linear Child Smoking (only controlling for child age and parent quit) b, e, f

125.16 (57)***

.90 .06 5150.58 5007.67

Notes: All models controlled for study group, child age (time invariant covariates) and parent quit status (time varying covariate), unless otherwise stated. A non-significant χ2 and the following values were indicative of good model fit: > .95 for CFI, < .08 for SRMR 32,33. A BIC change of 10 or more was interpreted as strong evidence for improved model fit 38. * p <.05,** p <.01,*** p <.001.a Variance of the linear slope factor was constrained to 0.b The residual variances for the observed child SHSe were constrained to be equal.c Residual variance for the observed 4 month child smoking was constrained to 0..d Residual variance for the observed 12 month child smoking was constrained to 0.e Residual variances for the observed 4 and 12 month child smoking were constrained to be equal. The residual covariances were constrained to be equal. Autoregressive correlations were constrained to be equal. f Due to multicollinearity between child age and study group, model only controlled for child age and parent quit status.

Figure 1. Conceptual diagram of parallel process growth model.

Figure 1. Scatterplot of linear slope values for child smoking and child SHSe.

Quadrant 1 (n = 6, 2.7%): SHSe in-creased, smoking decreased

Quadrant 2 (n = 66, 29.7%): SHSe in-creased, smoking increased

Quadrant 3 (n = 95, 42.8%): SHSe de-creased, smoking increasedQuadrant 4 (n = 55, 24.8%): SHSe de-creased, smoking decreased

MSHSe = -13.36

MSmoking = -0.08

MSHSe = -7.19

MSmoking = 0.23

MSHSe = 3.84

MSmoking = 0.35MSHSe = 2.95

MSmoking = -0.13