In the United States, maternal SDP continues to be a major public health concern. Rates of SDP vary by individual state, but on average, 12.3% of women report SDP (Tong et al., Reference Tong, Dietz, Morrow, D’Angelo, Farr, Rockhill and England2013). Despite a large literature suggesting undesirable outcomes in children exposed to SDP and warnings encouraging women to stop smoking while pregnant, the decrease in prevalence of SDP during recent years has been non-significant (13.3% in 2000 to 12.3% in 2010; Tong et al., Reference Tong, Dietz, Morrow, D’Angelo, Farr, Rockhill and England2013). SDP is associated with multiple adverse birth-related outcomes, such as pre-term delivery (Castles et al., Reference Castles, Adams, Melvin, Kelsch and Boulton1999; Shah & Bracken, Reference Shah and Bracken2000), increased risk for spontaneous abortion (Castles et al., Reference Castles, Adams, Melvin, Kelsch and Boulton1999), and lower birth weight (e.g., Benjamin-Garner & Stotts, Reference Benjamin-Garner and Stotts2013; Knopik et al., Reference Knopik, Marceau, Palmer, Smith and Heath2015 is in press; Kuja-Halkola et al., Reference Kuja-Halkola, D’Onofrio, Larsson and Lichtenstein2014; Marceau et al., Reference Marceau, Palmer, Neiderhiser, Smith and Knopik2015 is still under review). It has also been associated with prenatal ischemia–hypoxia (see Smith et al., Reference Smith, Schmidt-Kastner, McGeary, Kaczorowski and Knopik2015 is still under review), respiratory disease (Cook & Strachan, Reference Cook and Strachan1999), cancer later in life (Doherty et al., Reference Doherty, Grabowski, Hoffman, Ng and Zelikoff2009), and a host of neurodevelopmental and behavioral outcomes (see Knopik, Reference Knopik2009, for a review). Findings also suggest that there are a variety of placental complications linked to prenatal exposure to cigarette smoke (e.g., alterations to the development and function of the placenta; Einarson & Riordan, Reference Einarson and Riordan2009), which could effectively translate into a number of sequelae (e.g., intrauterine growth retardation and later behavioral problems; Huizink & Mulder, Reference Huizink and Mulder2006; Joya et al., Reference Joya, Manzano, Álvarez, Mercadal, Torres, Salat-Batlle and Garcia-Algar2014; Knopik, Reference Knopik2009).
Until fairly recently, studies of the effects of SDP did not take into account more precise measurement of parental characteristics or genetic/familial influences that might also contribute to the SDP-outcome relationship. Some early attempts to do this included modeling maternal and paternal psychopathology in twin studies (e.g., Knopik et al., Reference Knopik, Sparrow, Madden, Bucholz, Hudziak, Reich and Heath2005; Maughan et al., Reference Maughan, Taylor, Caspi and Moffitt2004; Thapar et al., Reference Thapar, Fowler, Rice, Scourfield, van den Bree, Thomas and Hay2003) and children of twin studies (D’Onofrio et al., Reference D’Onofrio, Turkheimer, Eaves, Corey, Berg, Solaas and Emery2003; Knopik et al., Reference Knopik, Heath, Jacob, Slutske, Bucholz, Madden and Martin2006). The twin design can offer considerable knowledge in the genetic etiology of, not only outcomes of interest (e.g., attention deficit hyperactivity disorder [ADHD]), but also risk factors (e.g., SDP; see Agrawal et al., Reference Agrawal, Knopik, Pergadia, Waldron, Bucholz, Heath and Martin2008 for genetic etiology of SDP; D’Onofrio et al., Reference D’Onofrio, Turkheimer, Eaves, Corey, Berg, Solaas and Emery2003, Reference D’Onofrio, Van Hulle, Waldman, Rodgers, Harden, Rathouz and Lahey2008). It can also determine whether genetic effects differ in two environments; however, twin models may only partially control for how genetic factors may impact outcomes, since they assume that the specified environments represent ‘true’ or ‘pure’ environmental risk factors that are free from genetic influences (i.e., that there is no gene-environment correlation; Caspi et al., Reference Caspi, Taylor, Moffitt and Plomin2000; D’Onofrio et al., Reference D’Onofrio, Turkheimer, Eaves, Corey, Berg, Solaas and Emery2003; Purcell & Koenen, Reference Purcell and Koenen2005). Further, the prenatal environment is an obligatory shared environment in the twin design (i.e., differential exposure between members of the twin pair is challenging, if not impossible, to determine). Classical twin studies, even those that add explicit measures of the environment, are also not able to delineate the actions involved in intergenerational processes (D’Onofrio et al., Reference D’Onofrio, Turkheimer, Eaves, Corey, Berg, Solaas and Emery2003). Twin studies that have examined the association of SDP and behavioral outcomes suggest that, once genetic and environmental risks were controlled for, the effects of SDP on conduct disorder were reduced (Maughan et al., Reference Maughan, Taylor, Caspi and Moffitt2004); however, despite this reduction for conduct problems, SDP explained a small but significant amount of the variance above and beyond genetic effects on ADHD (e.g., Knopik et al., Reference Knopik, Sparrow, Madden, Bucholz, Hudziak, Reich and Heath2005; Thapar et al., Reference Thapar, Fowler, Rice, Scourfield, van den Bree, Thomas and Hay2003).
The Children-of-Twins (COT) design, which has also been used to investigate SDP-outcome associations, can begin to elucidate the role that specific environments (such as prenatal exposure) play in the etiology of psychological and behavioral phenomena (D’Onofrio et al., Reference D’Onofrio, Turkheimer, Eaves, Corey, Berg, Solaas and Emery2003), while studying intergenerational associations with fewer assumptions than the twin-family design. In the case of prenatal exposure, it allows one to begin to disentangle genetic, prenatal exposure, and other environmental effects on offspring outcomes. It also offers the additional advantage of including offspring sibling pairs that may differ in their amounts and/or timing of prenatal exposure (Knopik, Reference Knopik2009). However, there are relatively few COT datasets and even fewer that include detailed assessment of SDP. Results from COT designs addressing the SDP-outcome association yielded a pattern of results consistent with maternal SDP having an independent effect on offspring birth weight (D’Onofrio et al., Reference D’Onofrio, Turkheimer, Eaves, Corey, Berg, Solaas and Emery2003) and ADHD (Knopik et al., Reference Knopik, Heath, Jacob, Slutske, Bucholz, Madden and Martin2006), even after controlling for potential confounders (e.g., genetic transmission, other environmental factors, and other covariates).
The ability to begin to disentangle genetic and environmental intergenerational transmission in the domain of SDP is critical for understanding the magnitude of risk that SDP carries as this can have real implications for future research, intervention, and prevention efforts. It is unlikely that a single design will provide the answers to the complicated nature of the association between SDP and subsequent outcomes, given methodological limitations and the risk factor under consideration (SDP which, in twin offspring, will not differ). Thus, as a general call to the field, Knopik (Reference Knopik2009) suggested the importance of a comprehensive approach to studying SDP effects, while also acknowledging the role of genetic influences. D’Onofrio et al. (Reference D’Onofrio, Lahey, Turkheimer and Lichtenstein2013) later called this a ‘comprehensive family-based quasi-experimental approach’. The case-crossover design (or within-mother, between-pregnancy design used in the MO-MATCH) is an example of one such approach, which examines siblings discordant for prenatal exposure to SDP. A strength of the case-crossover design is that associations of SDP with a child outcome can be examined both within a family and between families, and results can strengthen our inferences about causality in associations of SDP and child outcomes. The between-family association can be considered consistent with the historically standard comparison of looking at offspring of mothers who smoke during pregnancy relative to offspring of mothers who do not. This comparison does not control for influences that siblings share (i.e., genetic and familial effects) that might confound the SDP-outcome association; however, it does allow replication of historical associations found in the literature. Conversely, the strongest support for a causal influence of SDP on a child outcome (e.g., child ADHD) occurs when there is a within-family association: the exposed sibling has more ADHD symptoms than the unexposed sibling. Because the design controls for mother- and family-level characteristics, and to a certain extent genetic influences, the within-family association is the strongest evidence that SDP may be causally linked with the outcome.
At the time that MO-MATCH was considered for funding and ultimately funded, there were only two studies that considered relatives differentially exposed to prenatal smoking (D’Onofrio et al., Reference D’Onofrio, Van Hulle, Waldman, Rodgers, Harden, Rathouz and Lahey2008; Lambe et al., Reference Lambe, Hultman, Torrang, MacCabe and Cnattingius2006). These studies drew their samples from larger projects that were not necessarily designed to examine SDP, but which had useful data with which to examine these associations. Results suggested that for school performance (Lambe et al., Reference Lambe, Hultman, Torrang, MacCabe and Cnattingius2006) and conduct problems (D’Onofrio et al., Reference D’Onofrio, Van Hulle, Waldman, Rodgers, Harden, Rathouz and Lahey2008), the observed associations between these outcomes and SDP might not be causal. However, findings for ADHD problems, as measured by the Child Behavior Checklist (CBCL; Achenbach, Reference Achenbach1978), were less clear (D’Onofrio et al., Reference D’Onofrio, Van Hulle, Waldman, Rodgers, Harden, Rathouz and Lahey2008). Their comparison of siblings who differed in SDP exposure showed a small but significant effect of SDP on ADHD problems, a finding consistent with Knopik et al. (Reference Knopik, Heath, Jacob, Slutske, Bucholz, Madden and Martin2006), who used a COT approach.
Over the course of this data collection effort, there has been a surge of interest in the prenatal environment (e.g., D’Onofrio et al., Reference D’Onofrio, Singh, Iliadou, Lambe, Hultman, Grann and Lichtenstein2010, Reference D’Onofrio, Lahey, Turkheimer and Lichtenstein2013; Gaysina et al., Reference Gaysina, Fergusson, Leve, Horwood, Reiss, Shaw and Harold2013; Knopik, Reference Knopik2009; Kuja-Halkola et al., Reference Kuja-Halkola, D’Onofrio, Larsson and Lichtenstein2014; Rice et al., Reference Rice, Harold, Bolvin, Hay, Van den Bree and Thapar2009; Thapar et al., Reference Thapar, Rice, Hay, Boivin, Langley, van den Bree and Harold2009; see also upcoming special issue of Behavior Genetics on the prenatal environment). The majority of studies to date have used phenotypes pulled from medical records and national databases. These sources are incredibly useful and offer large sample sizes, but there is a need for purposefully designed deep phenotyping that will allow for more nuanced questions to address the complex nature of the SDP-outcome association. With purposefully designed studies, we can begin to address questions concerning why mothers change their behaviors between pregnancies, a question that, as of yet, has not been available in larger registry-based datasets. More detailed phenotyping allows researchers to approach these questions from angles that are not supported with the larger medical records-based dataset. As one example, incorporating an extensive laboratory-based neuropsychological testing battery offers the potential advantage that laboratory-based measures may be relatively free from bias, whereas checklists, rating scales, and structured interviews can be influenced by a number of factors (Nichols & Waschbusch, Reference Nichols and Waschbusch2004). It also affords the investigation of multiple aspects of the ADHD-related behavior spectrum. For instance, research has suggested that children with ADHD exhibit impairments in executive function and processing speed in real-world activities as well as in neuropsychological testing, and that cognitive deficits detected by standardized neuropsychological testing are related to performance difficulties in real-world activities (e.g., Lawrence et al., Reference Lawrence, Houghton, Douglas, Durkin, Whiting and Tannock2004). Further, youth exhibit different types of ADHD-related behaviors in the classroom than at home. This observation lends credence to the collection of both parent- and teacher-reported symptoms, each of which are considered valid and provide important insights into how and when children experience ADHD-related impairments (e.g., Lahey et al., Reference Lahey, Applegate, McBurnett, Biederman, Greenhill, Hynd and Richters1994).
Overview of the Current Study
The MO-MATCH study is an ongoing data collection collaboration between Rhode Island Hospital/Brown University and Washington University in St Louis. The Institutional Review Boards of Rhode Island Hospital, Washington University, and the State of Missouri Department of Health and Senior Services approved the study. MO-MATCH started as a pilot project in 2004 to determine the feasibility of locating, contacting, and interviewing a specific group of mothers of Missouri-born children. These mothers had, according to birth records (BR), reported smoking during one pregnancy but not during another pregnancy. This within-mother, between-pregnancy design (or sibling comparison design) provides the best possible methodological control for many stable maternal and familial confounding factors (e.g., heritable and socio-demographic characteristics of the mother that predict increased probability of SDP). The design also controls for differences between mothers who do and do not smoke during pregnancy; differences that might otherwise artifactually create, or alternatively mask, an association between SDP and child outcomes. The overarching goals of this project are: (1) to critically investigate prenatal environmental influences on child attention problems and associated learning and cognitive deficits, and (2) to provide a foundation for later characterization of the molecular epidemiology of effects of SDP and offspring attention, learning, and cognition.
Study Hypotheses and Aims
There were three primary aims for this study. Aim 1 was a procedural aim to enroll and interview a new sample of mothers, identified via BR, and to confirm full sibling pairs discordant for maternal SDP. Following maternal diagnostic telephone interview about each child (assessing pregnancy and life events surrounding pregnancy, early life exposures, and child behavior), these families completed in-depth neuropsychological lab assessment of children and parents (including general cognitive ability, memory, reading, receptive language, executive function). Full sib-ship will be verified via DNA from samples collected from parents and discordant sibling pairs. DNA has currently been isolated and will be stored for future informed molecular analyses. Aim 2 was to assess the effects of SDP on offspring neurocognitive outcomes (especially memory, executive function, lexicon, and reading), capitalizing on the unique within-mother, between-pregnancy comparison. The first hypothesis of Aim 2 was that, when genetic and familial effects are not modeled, SDP will be associated with significant childhood deficits in tests (particularly memory, executive function, receptive language, and reading) that tap brain regions shown in basic science studies to be affected by prenatal nicotine exposure. The second hypothesis of Aim 2 was that sibling comparisons will show attenuated associations, suggesting partial confounding due to influences that siblings share. Aim 3 was to assess the effects of SDP on ADHD, as measured by multiple assessments (see Table 2) and multiple reporters (parent and teacher). We hypothesized that (a) significantly greater ADHD symptoms will be found in children who have been exposed to SDP when considering the standard between-mother comparison; and (b) within-mother, between-pregnancy (or sibling comparison) will suggest, at least for maternal reported ADHD variables, partial confounding due to effects that siblings share.
Recruitment Methods
The literature on SDP heavily guided our decision-making process for recruitment, and the target age range of the offspring in our design was a large deciding factor for our entire recruitment and assessment protocol. Given that ADHD is a clinically significant condition with very clear public health implications, and that neuropsychological alterations, such as executive function, memory, reading, and language, found in school-age populations, are also of public health concern due to the consequences and special services involved, we chose our target offspring age range as 8–15 years of age. Our justification for this age range is as follows: (1) the uncertainty of findings in infants/toddlers (see Knopik, Reference Knopik2009 for a review) provide a rationale for our focus on an older age group, where apparent deficits are seen more robustly; (2) our research team has considerable experience with assessment administration, clinical treatment, and research with children of the proposed age range; (3) two of our primary outcomes of interest, ADHD and reading, can be adequately studied in this age group (i.e., DSM-IV ADHD criteria suggest that children exhibit behaviors before the age of 7 years, thus they will be through the period of risk for first symptom onset by the time they enter our study; most standard and validated reading assessments are designed for children with a minimum age of 8 years); and (4) the proposed sampling scheme will develop a cohort which, in the future, can be followed longitudinally through periods of increased externalizing symptoms and substance use initiation, abuse, and dependence.
Families were identified using BR obtained from the Missouri Department of Health and Senior Services Bureau of Health Informatics. BR in Missouri, for birth years 1998–2005 (to target children aged 8–15 years at the time of testing over the project period), were examined for mothers who changed smoking behavior between two pregnancies. Over 4000 mothers were identified. In order to locate and track these families, our research team had permission to use BR, drivers license records, marriage, and divorce records to assist in locating the best possible contact information for each potential family. These records were supplemented using commercial databases.
Candidate mothers were sent a letter prior to attempts to contact that family by telephone, to introduce them to the study and to tell them that they would be contacted for a brief (10 min) screening interview to determine eligibility. The letter stressed voluntary participation and included a phone number at which families choosing not to participate, or those having concerns or questions, could contact project staff. Attempts were then made to reach the biological mother of the children. Once contacted, the voluntary nature of the study was again explained, and the mother was also told that she could choose not to answer any or all of the questions.
After 1,520 initial screening interviews to verify the primary inclusion criteria obtained through the BR (i.e., mom smoked during one pregnancy but not the other), only 27% of mothers agreed via screening with the BR (the majority [57%] reported smoking during both pregnancies, and 16% reported not smoking for both pregnancies). Once BR information was verified, additional exclusionary criteria were assessed. Families were excluded at this point if: (1) mothers failed to understand the elements of informed consent; (2) English was not the primary language spoken in the home; (3) if the children had a history of head trauma, neurological disorders, or uncorrected visual, or auditory acuity deficits. Additional criteria that precluded participation were: (4) the children did not have the same biological father (as based on maternal report at this point in the protocol); (5) the mother did not have custody of the children; and (6) if one of the children was deceased. Based on evidence (e.g., Morales-Suarez-Varela et al., Reference Morales-Suarez-Varela, Bille, Christensen and Olson2006) suggesting that offspring of non-smokers who used nicotine substitutes (NRT) during pregnancy are at increased risk for congenital malformations, mothers who reported using nicotine substitutes in the ‘non-smoking’ pregnancy were also excluded.
Once initial eligibility (i.e., mother smoked during one pregnancy but not during another) was confirmed through the screening interview (N = 413), brief information about the study was given with opportunity to answer questions, and the mother was invited to complete a diagnostic interview. One hundred and seventeen women declined to participate in the study, after the screening interview, and mothers (N = 70) were excluded due to the additional eligibility criteria (criteria 4–6) listed above. If the mother agreed to participate, a respondent booklet containing consent documents and a 10-page packet containing a brochure about future genetic research was mailed to the participant and her family, and an appointment was made for a consent interview (to take place before the diagnostic interview) in which these materials were reviewed with the participants. Once verbal consent was obtained, the maternal telephone diagnostic interviews could take place. At the time that data collection ended for the current project, 173 mothers had consented and completed the entire protocol.
Once the mother was recruited, permission was sought from the mother to contact the birth father. The mother was asked about the birth father's identity (which could confirm BR information, if it was recorded), information about how to locate the birth father, and any potential concerns. Birth fathers were only included if the mother agreed to providing contact information and released his information to the study. We recruited 54% of fathers from our 173 families (N = 94). Biological paternity will be confirmed via DNA.
In addition to maternal diagnostic interviews in which mothers provide information about their pregnancies and their children's behavior, mothers, fathers (when available), and children traveled to our research offices to complete in-person family laboratory neuropsychological testing. Prior to starting the lab-based session, consent forms for the lab-based session and saliva samples were reviewed and participant understanding was confirmed by project staff. Consent forms (and assent forms for children) were then signed. Mothers also signed a release providing the names of their children's teachers so that project staff could contact teachers to complete questionnaires regarding certain child behaviors. At the completion of the laboratory assessment, the session was concluded and participants were debriefed.
Sample
Mother-reported data was available on 344 pregnancies and father-reported data was available on 181 pregnancies. Demographic information regarding parent age, child age, education, and marital status are provided in Table 1. The mean age of mothers and fathers at the time of interview was 39.83 years (SD = 5.62) and 44.04 years (SD = 6.34), respectively. Parents were primarily of Caucasian ancestry (96%, n = 250; three individuals refused to provide ancestral information). In a comparison to census data for the state of Missouri (census.missouri.edu), the MO-MATCH sample appears consistent with the demographics of the state in terms of marriage rates (75% census vs. 77% MO-MATCH). MO-MATCH parents show slightly lower rates of stopping at a high school education (31.6% census vs. 18–20% MO-MATCH) and completing at least ‘some graduate school’ (26.2% census vs. 17–22% MO-MATCH), but are consistent with rates of completing ‘some college’. The MO-MATCH sample appears to have lower rates of children and families served by food stamps (20–30% in the state of Missouri and 11% in MO-MATCH). MO-MATCH shows higher rates of tobacco usage than state averages, which is expected given the nature of sample selection. Additional sample characteristics are shown in Table 2.
TABLE 1 Sample Demographics
N = 344 pregnancies from 173 families. Total Ns vary because of randomly missing data.
TABLE 2 Sample Characteristics
aMAGIC: Missouri Assessment of Genetics Interview for Children-Parent Interview; SWAN: Strengths and Weaknesses of Attention-Deficit/Hyperactivity Disorder; and Normal Behavior Scale.
bFrom MAGIC-Adult on Self interview.
Of the 173 participating families, 94 fathers provided data. We examined possible differences between families where fathers did versus did not participate, using Wilcoxon–Mann–Whitney tests (e.g., non-parametric analog to independent samples t tests) on demographic and study variables. Specifically, we tested for differences where fathers did versus did not participate for: marital status; maternal age; maternal employment status; maternal education; age difference between siblings; the most prevalent maternal diagnoses: nicotine dependence and alcohol abuse; and child 1 and child 2's age, sex, birth weight, SDP severity, and ADHD symptoms (a total of 17 tests). There were only two differences (fewer than expected by chance) in mothers’ age and marital status. The remainder of demographic variables and all study variables did not differ for families where fathers did versus did not participate, χ 2 < 2.91, p > .05. Mothers were slightly older in families where fathers participated (M = 41.18, SD = 5.36) than in families where fathers did not participate (M = 38.17, SD = 5.66), χ 2 = 25.09, p < .05. Second, families where fathers participated were more likely to have a ‘married’ status, whereas families with fathers who did not participate had a higher proportion of ‘divorced’ status, χ 2 = 12.89, p < .05 (families where fathers participated: married = 95.45%, never married = 2.27%, divorced = 2.27%; families where fathers did not participate: married = 54.67%, never married = 6.67%, divorced = 30.67%, separated = 5.33%, widowed = 2.67%).
Assessment
This MO-MATCH protocol was carefully chosen to be standard and comprehensive, while also targeting behavioral/cognitive deficits linked to brain regions that have been proposed to be negatively affected by prenatal nicotine exposure. To select phenotypes, we relied on both basic science (i.e., animal) models as well as human studies to identify phenotypes that are suggested to be influenced by maternal SDP. Animal studies (see Knopik, Reference Knopik2009, for a review) have shown a clear effect of prenatal nicotine on offspring behavior. Several studies indicate that chronic prenatal nicotine exposure in rats and mice results in increased receptor density of fetal and neonatal cerebral nicotine acetylcholine receptors (nAChRs; e.g., Slotkin, Reference Slotkin1998; Van de Kamp & Collins, Reference Van de Kamp and Collins1994). Up-regulation of the nAChRs during development is conclusive evidence that the cell has experienced chronic nicotinic stimulation. The long-term effects of this up-regulation remain unclear (Ernst et al., Reference Ernst, Moolchan and Robinson2001); although the proposed mode of action suggests that this stimulation results in premature onset of cell differentiation, at the expense of replication, leading to (1) brain cell death, (2) structural changes in regional brain areas, and (3) altered neurotransmitter systems (i.e., norepinephrine, dopamine, and serotonin; Slikker et al., Reference Slikker, Xu, Levin and Slotkin2005). Such alterations could translate to deficits in later learning, memory, behavior, and development. Differences in developmental profiles of receptor binding between species and strains suggest that genetic factors regulate the maturation of the nicotinic receptor (Van de Kamp & Collins, Reference Van de Kamp and Collins1994). These genetic factors may explain inter-individual differences in sensitivity to the effects of in utero exposure to nicotine (Ernst et al., Reference Ernst, Moolchan and Robinson2001).
However, the rat, mouse, and other model organism brains are very different from the human brain. Moreover, model organism studies tend to focus only on prenatal nicotine (and can be very controlled in their exposures), whereas in human studies, the fetus is exposed to over 4,000 chemicals in the average cigarette, including a large amount of other toxic components, such as carbon monoxide, ammonia, nitrogen oxide, lead, and other metals (Huizink & Mulder, Reference Huizink and Mulder2006). Human studies have been somewhat variable, particularly in studies examining behavioral outcomes (see Knopik, Reference Knopik2009, for a review); however, some of the most robust associations reported tend to be in the externalizing spectrum (i.e., ADHD and conduct disorder). Further, at the time that this project began, there were only a few genetically informed studies considering the role of SDP in child and adolescent behavior, but the ones that were published focused on ADHD and related disruptive behavior. Considered together, animal studies provided a clear biological pathway for possible prenatal nicotine associations with learning and behavior, and human studies tend to converge on an association between SDP and externalizing behaviors (before considering genetic effects). We applied this information to aid in the selection of the primary phenotypes under investigation and the covariates that would be included.
The MO-MATCH assessment includes questionnaires (parent-report and teacher-report), maternal telephone diagnostic interviews, in-person interviews, an extensive neuropsychological testing battery, and collection of DNA. BR data for both children are also available as they are the initial means of finding potential participants. The interviews were semi-structured diagnostic interviews, which allowed project staff to establish rapport with mothers, and computer-assisted personal interviews that were completed privately by mothers and fathers to facilitate confidentiality and honest responses. The incorporation of detailed phenotypic parental assessment (interview and lab-based) increases our ability to identify the potential confounding effects of heritable and socio-demographic characteristics that may create false-positive findings in between-family comparisons. Most studies do not control for the fact that prenatal exposures may be correlated with parental behaviors that could also act as important risk factors that are in turn transmitted to their offspring. A listing of the measures administered is presented in Table 3. Reliability information for key measures is presented in Table 4 and supporting the literature identifying the major domains for which SDP effects have been reported is shown in Table 5.
Of primary importance to the study aims, both the semi-structured telephone diagnostic interview (Missouri Assessment of Genetics Interview for Children––Parent on Child [MAGIC-Parent on Child]; Todd et al., Reference Todd, Joyner, Heath, Neuman and Reich2003) and the personal computerized interviews completed by mothers and fathers (MAGIC-Adult on Self; Todd et al., Reference Todd, Joyner, Heath, Neuman and Reich2003) assess detailed assessment of SDP. The MAGIC-Adult on Self also includes questions regarding tobacco exposure during and after pregnancy, and assesses tobacco use by mother, father, or significant other, and any other person living in the household such as a relative or older sibling, during and after pregnancy into the first five years of the child's life. The questions ask about specific time periods, including overall pregnancy, first, second, and third trimester, first six months of child's life, second six months of child's life, and second through fifth years of life. The total number of smokers in the home during these periods is also assessed.
The protocol includes maternal and teacher report of child externalizing behavior (e.g., ADHD), lab-based assessment of major domains of child cognition, lexicon, memory, executive function, fine motor, and reading, complementary lab-based assessment of these domains in parents, as well as assessment of parental psychopathology. The protocol also includes assessment of environmental risks through which parental psychopathology and related prenatal, and possibly perinatal, substance use are hypothesized to impact child outcome, within-family risks (prenatal substance use, prenatal care); and between-family environmental risks (socio-demographic disadvantage). While this protocol has been carefully selected to be comprehensive, we focus this project on four key domains where we expect, based on basic animal work and existing human work, to find true deficits: memory, executive function, reading/lexicon, and ADHD (see Table 5 for domains and supporting the literature).
TABLE 3 Summary of Core Assessments
I = Maternal Interview; Q = Questionnaire; L = Lab; M = Mother; F = Father; C = Child; O = Observer, T = Teacher; MAGIC = Missouri Assessment for Genetics Interview for Children; GORT-IV = Gray Oral Reading Test — Fourth edition.
TABLE 4 Published Reliability Data for Key Constructs in the Assessment Protocol
WISC-IV-INT: Wechsler Intelligence Scale for Children (Short Form); PPVT-III: Peabody Picture Vocabulary Test-III; CELF-4: Clinical Evaluation of Language CMS: Children's Memory Scale; DKEFS: Delis Kaplan Executive Function System; Logan: Logan Stop Task; Fundamentals — Fourth Edition; WJ-III: Woodcock Johnson-III; GORT: Gray Oral Reading Test — Fourth edition; CBCL: Child Behavior Checklist; TRF: Teacher's Report Form.
TABLE 5 Measure-Linked Domains, Component Key Constructs, and Empirical Basis for Testing
WISC: Wechsler Intelligence Scale for Children (Short Form); CMS: Children's Memory Scale; DKEFS: Delis Kaplan Executive Function System; Logan: Logan Stop Task; PPVT-III: Peabody Picture Vocabulary Test-III; CELF-4: Clinical Evaluation of Language Fundamentals — Fourth edition; WJ-III: Woodcock Johnson-III; GORT: Gray Oral Reading Test — Fourth edition; CBCL: Child Behavior Checklist; TRF: Teacher's Report Form; SWAN: Strengths and Weaknesses of Attention-Deficit/Hyperactivity Disorder and Normal Behavior Scale.
Retrospective Report of SDP
The first paper using MO-MATCH data (Knopik et al., under review) examined rates of agreement across BR report of SDP, retrospective maternal report of SDP, and retrospective paternal report of SDP. Specifically, we compared reports for any SDP during pregnancy and for quantity of cigarettes smoked across each pregnancy. We then compared rates of agreement for changes in SDP behavior from one pregnancy to another. This is a key first step, as results will inform every subsequent model fitted to these data. Once predictive utility of various reports of SDP was determined, we then fit a sibling comparison model to examine the association between SDP and birth weight. This approach controls for genetic and familial influences that make the siblings similar and provides a test of whether SDP has an independent effect on birth weight once effects that siblings share are taken into account. Results showed high agreement between reporters and support the utility of retrospective report of SDP. Further, we replicated a causal association between SDP and birth weight, wherein SDP results in reduced birth weight even when accounting for genetic and familial confounding factors via a sibling comparison approach.
Plans for Future Research
Composite indices for each domain (see Table 4) will be constructed in order to pursue hypothesis testing. Further, in instances where we have multiple reporters, we will create composites consistent with the gold-standard in the clinical literature. When the associations between measures (for the same construct) are low-to-moderate, we will forego composites and analyze measures separately. The rich data collection allows for many avenues of further research. For example, in addition to the primary aims of this project, we will explore reasons why these sibling pairs differ in their exposure to SDP. More specifically, we investigate why these mothers changed their smoking behaviors from one pregnancy to another. Our assessment protocol included questions related to stressors and complications surrounding pregnancy that will aid our efforts in this domain. Another additional area of research will allow us to begin to explore the effects of maternal passive exposure to environmental tobacco exposure (ETS) during pregnancy, child's postnatal exposure to ETS, and possible contributions of paternal or household smoking in the pre, peri, and postnatal periods to offspring outcome.
Conclusion
The MO-MATCH study, while still in the early stages of data analysis, has the potential to be an important and distinctive asset to the field of prenatal exposure. Not only is this study among the first specifically designed US-based family studies (i.e., not a convenience sample pulled from a larger study designed for other purposes) to leverage the sibling comparison approach to prenatal smoke exposure, but it is also positioned to begin to characterize the molecular epidemiology of effects of SDP, both genetic and epigenetic, on carefully selected phenotypes such as offspring attention, learning, and cognition. Further, not only can this study begin to disentangle SDP effects from genetic/familial effects, it can also begin to achieve an accurate assessment of the magnitude of the association between SDP, ADHD, and neuropsychological outcomes. This more refined effect size is of great public health significance because this information could help clarify what are and are not potential causes of ADHD and other neuropsychological deficits seen in school-aged children, findings that can inform treatment providers and prevention efforts concerning how to treat the public health concern of SDP. The future of the MO-MATCH study may include tracking the sample into high school and early adulthood, with assessments of additional behavior problems and substance use as well as additional assessments of neighborhood constructs. As an invaluable complement to larger samples with less detailed phenotypic assessment, we are confident that the MO-MATCH data will provide a valuable scientific resource in the years ahead.
Acknowledgments
This work was supported by NIH grants: DA023134 (Knopik), DA17671 (Knopik), AA07728 AA07728 (Heath), AA09022 (Heath), AA11998 (Heath), HD049024 (Heath), AA017688 (Heath), AA021492 (Heath). Dr. Marceau is supported by T32 DA016184. Dr Palmer is supported by K01 AA021113 and L30 TR001045.
