Skip to main content
PMC home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2017 Mar 1.
Published in final edited form as: Addict Behav. 2015 Dec 15;54:52–58. doi: 10.1016/j.addbeh.2015.12.008

Prevalence and correlates of a lifetime cannabis use disorder among pregnant former tobacco smokers

Rebecca L Emery a,*, Melissa P Gregory a,b, Jennifer L Grace a, Michele D Levine a
PMCID: PMC4713331  NIHMSID: NIHMS747281  PMID: 26717552

Abstract

Background

Following tobacco and alcohol, cannabis is the most commonly used substance during pregnancy. Given the high prevalence of concurrent cannabis and tobacco use as well as the health consequences associated with prenatal substance use, we sought to document the relative contributions of psychosocial and psychiatric factors commonly associated with cannabis use in predicting a lifetime cannabis use disorder (CUD) among women who had quit smoking tobacco as a result of pregnancy.

Methods

Pregnant former tobacco smokers (n = 273) enrolled in a larger randomized controlled trial for postpartum tobacco relapse prevention completed semi-structured psychiatric interviews and self-reported demographic, pregnancy, health, psychosocial, and tobacco use factors during their third trimester of pregnancy.

Results

In total, 14% (n = 38) of women met criteria for a lifetime CUD. The strongest predictors of a lifetime CUD were a history of having multiple psychiatric disorders (OR = 36.44; 95% CI = 5.03–264.27; p < 0.001) followed by a lifetime alcohol use disorder (OR = 3.54; 95% CI = 1.27–9.87; p < 0.05). In addition, more frequent attempts to quit smoking tobacco (OR = 1.12; 95% CI = 1.01–1.25; p < 0.05) and lower self-efficacy about weight management after quitting smoking tobacco (OR = 0.78; 95% CI = 0.62–0.97; p < 0.05) also were significantly associated with a lifetime CUD.

Conclusions

Women with a history of both cannabis and tobacco dependence may represent a subset of women who need more specialized treatment during the perinatal period to improve substance use outcomes.

Keywords: Pregnancy, cannabis use disorder, tobacco dependence, prevalence, correlates

1. Introduction

Cannabis is the most commonly used illicit substance in the United States and is the only illicit substance for which there have been appreciable increases in the prevalence of use across the past decade (Ansell et al., 2015; Caldeira et al., 2012). A 2013 nationwide survey conducted by the Substance Abuse and Mental Health Services Administration, found that the number of Americans 12 years of age and older reporting daily cannabis use has nearly doubled since 2002, with current prevalence rates estimated to be 7.5% (SAMHSA, 2012). Cannabis also has the highest rates of past year dependence of any illicit substance, with 1.6% of users meeting criteria for a cannabis use disorder (CUD). Several factors, including changes in legal status, perceptions of low risk associated with use, and availability have been linked to the increased rates of cannabis use (Cerda et al., 2012; Palamar et al., 2014). Despite the growing societal acceptance of cannabis, the long-term consequences of cannabis use remain a general public health concern, and the high prevalence of cannabis use during pregnancy is of particular interest given the adverse effects on both maternal and fetal health.

Cannabis use is associated with significant health complications (Volkow et al., 2014). The acute effects of cannabis intoxication include euphoria, tachycardia, conjunctival congestion, and anxiety as well as slowed reaction time and impaired memory (Iversen, 2009). Although the acute effects of cannabis typically subside several hours following administration, prolonged cannabis use has been linked to chronic respiratory diseases, cognitive dysfunction, and behavioral problems. Smoking cannabis exposes users to carbon monoxide, bronchial irritants, tumor promotors, and carcinogens, which in turn increase risk for respiratory diseases (Ashton, 2001), severe respiratory symptoms (Macleod et al., 2015), and cardiovascular events (Volkow et al., 2014). Cannabis use also has lasting effects on cognition and the regulatory networks of the brain (Filbey et al., 2009; Gilman et al., 2014), which can lead to memory impairments, deficiencies in attention, slowed reaction time, poor impulse control and increased hostility as well as difficulties with information processing, perceptual coordination, and motor performance (Gunn et al., 2015).

In addition to the general consequences of cannabis use, prenatal cannabis exposure presents specific problems for the developing fetus and has lasting effects on child development. Although prenatal cannabis use has been associated with reduced gestational length and a slowing of fetal growth, studies linking cannabis use during pregnancy to premature birth and low birth weight have been equivocal, with some studies reporting associations between prenatal cannabis use and decreased birth weight (El-Mohandes et al., 2003; Gray et al., 2010) and others reporting no relationship between prenatal cannabis use and low birth weight or premature birth (English et al., 1997; Fergusson et al., 2002). However, prenatal cannabis exposure consistently has been associated with disrupted sleep patterns (Dahl et al., 1995; Scher et al., 1988) and delayed cognitive development in early childhood (Day, et al., 1994) as well as with adolescent deficits in cognitive development (Fried and Watkinson, 1990; Richardson et al., 2002), attention (Fried & Watkinson, 2002), and executive functioning (Fried et al., 1998; Willford et al., 2001). Prenatal cannabis exposure also has been associated with greater delinquent behaviors (Day et al., 2011), higher rates of depression (Gray et al., 2005) and anxiety (Leech et al., 2006) and later drug abuse (Day et al., 2006) among adolescents. Thus, cannabis use during the perinatal period has adverse consequences for both maternal and child health.

Despite the consequences of prenatal substance use, prenatal cannabis use is common. Cannabis is the third most commonly used substance during pregnancy following tobacco and alcohol (El Marroun et al., 2008; Gilchrist et al., 1996; Havens et al., 2009). Although rates of cannabis use tend to decline during pregnancy (Bailey et al., 2008; Gilchrist et al., 1996), an estimated 11% of women continue to use cannabis during pregnancy, with over 16% of pregnant cannabis users reporting near daily use (Ko et al., 2015). Women who are younger, less educated, single, unemployed, socioeconomically disadvantaged, or belong to a racial or ethnic minority group are more likely to use cannabis during pregnancy (El Marroun et al., 2008; Ko et al., 2015) as are multigravid women and women with unplanned pregnancies (El Marroun et al., 2008). Importantly, women who use tobacco and cannabis concurrently are at particular risk of continuing to use both substances during pregnancy (El Marroun et al., 2008; Ko et al., 2015; Lester et al., 2001), and women with a history of CUD are nearly three times more likely to continue using cannabis during pregnancy than are women without such a history (El Marroun et al., 2008).

Given the high concurrence between cannabis and tobacco use, the rates of prenatal cannabis use, and the specific health consequences of prenatal cannabis use, we sought to document the prevalence of a lifetime CUD among women who had quit smoking tobacco as a result of pregnancy and to examine the relative contributions of psychosocial and psychiatric factors commonly associated with cannabis use in predicting a lifetime CUD. We focused on demographic, pregnancy, health, psychosocial, and tobacco use factors as well as lifetime psychiatric disorders as predictors of a lifetime CUD. We hypothesized that factors related to greater nicotine dependence and more severe psychiatric problems would be most strongly related to a lifetime CUD among pregnant former tobacco smokers.

2. Material and Methods

2.1 Participants and Procedures

The procedures for this study were approved by the University of Pittsburgh Institutional Review Board, and participants provided written informed consent. Participants were part of a larger randomized controlled trial investigating the efficacy of a postpartum tobacco relapse prevention intervention that included a specialized focus on women’s postpartum concerns about mood and weight (Levine et al., 2013). Participants were pregnant women who self-reported smoking cigarettes daily for at least 1 month during the 3 months prior to pregnancy, smoked at least 5 cigarettes per day before quitting, had not smoked cigarettes during the past 2 weeks, and were motivated to remain abstinent postpartum. Women reported demographic and pregnancy-related information and also completed measures of tobacco use and psychosocial factors during their third trimester of pregnancy between 34 and 38 weeks gestation. Participants also completed semi-structured psychiatric interviews at the time of study enrollment or after delivery. Psychiatric interviews that occurred prenatally (n = 214) were conducted between 34 and 41 weeks gestation (M = 36.24; SD = 1.61) and those that were conducted postpartum (n = 62) were completed within 55 weeks of delivery (M = 20.07; SD = 17.58).

2.2 Measures

2.2.1 Demographic factors

Women reported demographic information, including age, race, income, and education.

2.2.2 Pregnancy factors

Women reported pregnancy information, including parity, whether their current pregnancy was intentional, and whether they intended to breastfeed after delivery.

2.2.3 Health and psychosocial factors

Weight and height were measured and body mass index was calculated as weight in kilograms divided by height in meters squared. Current depressive symptoms were assessed using the Center for Epidemiological Studies-Depression Scale (CES-D)(Radloff, 1977), a 20-item measure shown to be less sensitive than other depression scales to somatic symptoms that are common during pregnancy (Coyle and Roberge, 1992). A CES-D score of 16 or greater is considered an indicator for clinically meaningful depressive symptoms (Radloff, 1977). Women also reported the degree to which they appraised situations as stressful in the past month using the 14-item Perceived Stress Scale (PSS)(Cohen et al., 1983). Past month sleep quality and disturbances were assessed using the 19-item Pittsburgh Sleep Quality Index (PSQI), for which a score greater than 5 is considered indicative of poor sleep (Buysse et al., 1989). The internal consistency coefficients for the CES-D, PSS, and PSQI in the present study were 0.77, 0.65, and 0.65, respectively.

2.2.4 Tobacco use factors

Women were asked to think back to the last time they had smoked cigarettes every day for at least one month and complete the Fagerstrom Test of Nicotine Dependence (FTND), a 6-item scale assessing the intensity of physical addiction to nicotine (Heatherton et al., 1991). The internal consistency coefficient for the FTND in the present study was 0.62. Women also provided information on the number of cigarettes they smoked daily prior to quitting, their age at smoking tobacco initiation, the age they began smoking tobacco daily, the number of years they had been smoking tobacco, the number of times they had attempted to quit smoking tobacco, and when they had quit smoking tobacco for their current pregnancy. Women further reported the extent to which they used tobacco smoking as a method for weight control using the 3-item Weight Control Smoking Scale (Pomerleau et al., 1993). Women also completed 6-item measures assessing self-efficacy about weight management after quitting smoking tobacco (Borrelli and Mermelstein, 1998) and smoking cessation-specific weight concerns (Borrelli and Mermelstein, 1998; Perkins et al., 2001).

2.2.5 Lifetime psychiatric disorders

Lifetime psychiatric disorders were assessed using the Structured Clinical Interview for DSM-IV-TR Axis I Disorders: Non-Patient Version (SCID-I/NP). Although the psychiatric diagnoses included in this study were assessed using DSM-IV-TR criteria, the categories of psychiatric disorders were conceptualized according to the DSM-5. Specifically, obsessive compulsive disorder and posttraumatic stress disorder were treated as individual categories of psychiatric disorders rather than included with the overall anxiety disorders. In addition, women were dichotomized according to whether they met criteria for more than one category of psychiatric disorder (i.e., mood disorders, anxiety disorders, obsessive compulsive disorder, posttraumatic stress disorder, eating disorders, or substance use disorders). That is, women were divided into those who met criteria for one or fewer categories of psychiatric disorders and those who met criteria for more than one category of psychiatric disorders. For example, women who met criteria for both lifetime cannabis and alcohol use disorders but did not meet criteria for any additional psychiatric disorders were classified as having a history of one category of psychiatric disorder (i.e., substance use disorder).

3. Statistical Analysis

Differences in the study variables of interest were compared between women who completed the SCID-I/NP during pregnancy or postpartum. Women were categorized according to the absence or presence of a lifetime CUD. Independent samples t-tests and chi-square analyses initially were used to assess differences in demographic, pregnancy, health, psychosocial, and tobacco use factors as well as lifetime psychiatric disorders between women with and without a lifetime CUD. To correct for multiple comparisons, a Bonferroni correction was applied.

The relative importance of each set of risk factors in predicting a lifetime CUD was assessed using hierarchical binary logistic regression models with forced entry. Each set of factors was stepped into the model in order of hypothesized strength of association with a lifetime CUD, beginning with the weakest association. Demographic factors were entered first (Step 1) followed by pregnancy factors (Step 2), health and psychosocial factors (Step 3), tobacco use factors (Step 4), and lifetime psychiatric disorders (Step 5). At each step, the improvement in model fit given the addition of the set of risk factors was assessed through a chi-square analysis of the difference in likelihood ratio statistics. Nagelkerke’s R2 statistic was obtained at each step to assess the proportion of total variance explained by each set of risk factors (Nagelkerke, 1991). Odds ratios (OR) with 95% confidence intervals (CI) were computed for each predictor by exponentiation of the logit coefficients.

4. Results

Of the 300 women enrolled, 273 completed the SCID-I/NP and were included in the current analysis. Women included (n = 273) did not differ from those excluded (n = 27) on any of the psychosocial or psychiatric factors of interest (ps > 0.11). Moreover, women who completed the SCID-I/NP during pregnancy (n = 214) and those who completed the SCID-I/NP postpartum (n = 62) did not differ in demographic factors (ps > 0.10), pregnancy factors (ps > 0.39), health and psychosocial factors (ps > 0.06), tobacco use factors (ps > 0.13), or rates of psychiatric disorders (ps > 0.29).

In total, 14% (n = 38) of women met criteria for a lifetime CUD. Descriptive characteristics among women with and without a lifetime CUD are displayed in Table 1. As shown, women with a lifetime CUD were more educated, began smoking tobacco at an earlier age, attempted to quit smoking tobacco more frequently, were more likely to use smoking tobacco as a method of weight control, and had lower self-efficacy about weight management after quitting smoking tobacco relative to women without a lifetime CUD (ps < 0.05). In addition, women with a lifetime CUD reported greater depressive symptoms and higher levels of perceived stress during their third trimester of pregnancy than did women without a lifetime CUD disorder (ps < 0.04). Finally, women with a lifetime CUD were more likely to have a lifetime alcohol use disorder and to have met criteria for additional categories of psychiatric disorders than were women without a lifetime CUD (ps < 0.0001). Specifically, only 17% (n = 39) of women without a lifetime CUD met criteria for multiple lifetime psychiatric disorders whereas 53% (n = 20) of women with a lifetime CUD met criteria for at least one additional psychiatric disorder beyond a substance use disorder. Indeed, nearly half of women with a lifetime CUD (45%; n = 17) met criteria for a mood disorder and one quarter (24%; n = 9) met criteria for an anxiety disorder. After adjusting for multiple comparisons using the Bonferroni correction (p = 0.002), a lifetime alcohol use disorder and a history of multiple lifetime psychiatric disorders were the only factors to remain significantly different between women with and without a lifetime CUD.

Table 1.

Differences in psychosocial and psychiatric factors between women with and without a lifetime cannabis use disorder (CUD)

Demographic Factors No CUD CUD p*
Age 24.89 ± 5.89 25.47 ± 4.65 0.56
% Black (n) 54% (127) 58% (22) 0.67
% Income ≤ $30,000 (n) 78% (184) 84% (32) 0.46
% Education ≤ high school degree (n) 49% (114) 32% (12) 0.05
% Single (n) 29% (68) 32% (12) 0.74
Pregnancy Factors
% Nulliparous (n) 39% (90) 37% (14) 0.85
% Unintended pregnancy (n) 72% (169) 74% (28) 0.82
% Intend to breastfeed (n) 66% (156) 76% (29) 0.22
Health and Psychosocial Factors
Body mass index 33.11 (7.68) 33.04 ± 7.67 0.96
Mood 14.76 (9.61) 19.47 ± 10.15 0.006
Stress 22.09 (8.38) 25.03 ± 6.50 0.04
Sleep 7.59 (4.08) 8.78 ± 3.45 0.09
Tobacco Use Factors
Nicotine dependence 3.22 ± 2.04 3.16 ± 2.12 0.86
Age at first cigarette 15.18 ± 3.31 14.03 ± 2.49 0.04
Age daily smoker 16.91 ± 3.74 16.01 ± 2.38 0.16
Years smoking 8.25 ± 5.89 9.57 ± 4.61 0.19
Cigarettes smoked daily 10.90 ± 9.86 10.70 ± 7.64 0.90
Quit attempts 3.27 ± 3.39 5.21 ± 4.38 0.01
Weight control smoker 1.76 ± 2.20 2.84 ± 2.33 0.006
Weight self-efficacy 6.48 ± 5.58 5.58 ± 2.16 0.02
% Quit prior to second trimester (n) 68% (159) 71% (27) 0.68
Lifetime Psychiatric Disorders
% Alcohol use disorder (n) 18% (42) 55% (21) < 0.0001
% Obsessive compulsive disorder (n) 2% (4) 3% (1) 0.69
% Posttraumatic stress disorder (n) 7% (17) 11% (4) 0.49
% Mood disorders (n) 31% (73) 45% (17) 0.10
% Anxiety disorders (n) 18% (43) 24% (9) 0.43
% Eating disorders (n) 1% (3) 5% (2) 0.09
% History of >1 category of psychiatric disorder (n) 17% (39) 53% (20) < 0.0001
Open in a new tab

Note: Bolded values are significant.

*

After adjusting for multiple comparisons using the Bonferroni correction (p = 0.002), the only findings to remain significant were a history of alcohol use disorder and a history of >1 category of psychiatric disorder.

Results from the hierarchical binary logistic regression analysis are shown in Table 2. Demographic factors (Step 1) explained 4.6% (R2 = 0.046) of the total variance in the model, which was not statistically significant (p = 0.25). Education was the only factor in Step 1 significantly related to a lifetime CUD, such that women with more years of education were more likely to have a lifetime CUD than were less educated women (OR = 2.32; 95% CI = 1.05–5.15; p < 0.05). Although the inclusion of pregnancy factors (Step 2), health and psychosocial factors (Step 3), and tobacco use factors (Step 4) explained an additional 1.1% (R2 = 0.057), 5.5% (R2 = 0.112), and 8.8% (R2 = 0.200) of the total variance, respectively, the proportion of the total variance explained was not statistically different from that explained by demographic factors alone (ps > 0.09). In addition, there were no statistically significant predictors of a lifetime CUD disorder in Steps 2 through 4.

Table 2.

Hierarchical binary logistic regression analysis assessing psychosocial and psychiatric correlates of a lifetime cannabis use disorder

Demographic Factors Step 1 Step 2 Step 3 Step 4 Step 5
Age 1.02 (0.95–1.09) 1.02 (0.95–1.09) 1.02 (0.95–1.10) 1.06 (0.93–1.21) 1.07 (0.92–1.24)
Black 1.43 (0.64–3.18) 1.44 (0.63–3.28) 1.36 (0.59–3.17) 2.07 (0.76–5.63) 2.42 (0.76–7.70)
Income ≤ $30,000 0.55 (0.18–1.67) 0.55 (0.18–1.70) 0.52 (0.16–1.65) 0.52 (0.15–1.75) 0.43 (0.10–1.78)
Education ≤ high school degree 2.32 (1.05–5.15)* 2.16 (0.97–4.83) 2.06 (0.91–4.67) 2.15 (0.88–5.27) 1.57 (0.58–4.26)
Single 0.87 (0.40–1.88) 0.80 (0.37–1.77) 0.74 (0.33–1.67) 0.77 (0.32–1.86) 0.56 (0.20–1.56)
Pregnancy Factors
Nulliparous 1.17 (0.51–2.66) 1.16 (0.49–2.74) 1.05 (0.42–2.66) 0.92 (0.31–2.79)
Unintended pregnancy 1.02 (0.45–2.30) 1.21 (0.52–2.84) 1.07 (0.43–2.65) 1.83 (0.67–5.00)
Intend to breastfeed 1.74 (0.73–4.18) 1.84 (0.76–4.47) 1.54 (0.59–4.03) 2.75 (0.89–8.54)
Health and Psychosocial Factors
Body mass index 0.99 (0.94–1.04) 0.98 (0.93–1.04) 0.96 (0.90–1.03)
Mood 1.05 (1.00–1.11) 1.03 (0.97–1.09) 1.02 (0.95–1.09)
Stress 0.99 (0.93–1.06) 1.00 (0.93–1.08) 0.98 (0.90–1.07)
Sleep 1.04 (0.94–1.15) 1.03 (0.92–1.14) 1.04 (0.92–1.18)
Tobacco Use Factors
Nicotine dependence 1.03 (0.81–1.31) 0.95 (0.73–1.24)
Age at first cigarette 0.88 (0.72–1.08) 0.87 (0.71–1.08)
Age daily smoker 0.96 (0.81–1.14) 0.95 (0.80–1.13)
Years smoking 0.97 (0.84–1.11) 0.89 (0.76–1.05)
Cigarettes smoked daily 0.99 (0.94–1.03) 1.01 (0.95–1.06)
Quit attempts 1.08 (0.98–1.18) 1.12 (1.01–1.25)*
Weight control smoker 1.17 (0.99–1.40) 1.15 (0.93–1.42)
Weight self-efficacy 0.85 (0.70–1.02) 0.78 (0.62–0.97)*
Quit prior to second trimester 0.76 (0.30–1.91) 0.58 (0.19–1.72)
Lifetime Psychiatric Disorders
Alcohol use disorder 3.54 (1.27–9.87)*
Obsessive compulsive disorder 0.19 (0.01–5.06)
Posttraumatic stress disorder 0.53 (0.08–3.75)
Mood disorders 0.21 (0.04–1.03)
Anxiety disorders 0.23 (0.04–1.37)
Eating disorders 1.70 (0.08–34.52)
History of >1 category of psychiatric disorder 36.44 (5.03–264.27)**
Open in a new tab

Note: Bolded values are significant;

*

p < 0.05;

**

p < 0.001

As predicted, a history of multiple categories of psychiatric disorders and a lifetime alcohol use disorder were strongly associated with a lifetime CUD. Lifetime psychiatric disorders (Step 5) explained an additional 18.9% (R2 = 0.389) of the total variance (p < 0.0001), and four individual risk factors were shown to be predictive of a lifetime CUD in the final step. Specifically, women who met criteria for more than one category of psychiatric disorder and women with a lifetime alcohol use disorder respectively were 36.44 (95% CI = 5.03–264.27; p < 0.001) and 3.54 (95% CI = 1.27–9.87; p < 0.05) times more likely to have a lifetime CUD than were women without such histories. In addition, having a greater number of tobacco quit attempts (OR = 1.12; 95% CI = 1.01–1.25; p < 0.05) and lower self-efficacy about weight management after quitting smoking tobacco (OR = 0.78; 95% CI = 0.62–0.97; p < 0.05) also were predictive of a lifetime CUD.

5. Discussion

Women with a history of CUD as well as those who use cannabis and tobacco concurrently are at elevated risk of continuing to use both substances during pregnancy and postpartum (El Marroun et al., 2008; Ko et al., 2015; Lester et al., 2001). Given the adverse effects of prenatal cannabis use, we aimed to document rates of CUD and to identify characteristics of women who may be likely to use cannabis during the perinatal period by examining psychosocial and psychiatric correlates of a lifetime CUD among pregnant former tobacco smokers. The lifetime prevalence of cannabis dependence in the present sample was nearly double that of the national average for similarly aged individuals (Haberstick et al., 2014), with 14% (n = 38) of women meeting criteria for a lifetime CUD. Consistent with prior research (Peters et al., 2014), the strongest predictors of a lifetime CUD were a history of multiple categories of psychiatric disorders beyond substance use disorder followed by a lifetime alcohol use disorder. In addition, more frequent attempts to quit using tobacco and lower self-efficacy about weight management after quitting smoking tobacco also were significantly associated with a lifetime CUD.

These results indicate that a history of problematic cannabis use among pregnant former tobacco smokers is associated with several characteristics that may themselves have implications for treatment efforts aimed at reducing substance use during the perinatal period. It is well established that individuals with a greater incidence of psychiatric illness (Ball et al., 2006) as well as those with polysubstance dependence (Dutra et al., 2008; Haney et al., 2013; Stapleton et al., 2009) have poor treatment outcomes during substance use interventions. Moreover, women who concurrently use cannabis, tobacco, and alcohol are among those at highest risk of continuing to use at least one substance during pregnancy (Passey et al., 2014). Thus, the finding that women with a history of cannabis and tobacco dependence were also likely to have met criteria for a lifetime alcohol use disorder and to have a history of additional psychiatric disorders beyond substance use disorders suggests that women with these characteristics may have greater difficulty quitting substances during pregnancy and may be vulnerable to relapse postpartum. Indeed, the history of polysubstance dependence in conjunction with the high number of failed attempts to quit using tobacco among pregnant former tobacco smokers with a lifetime CUD is indicative of a tendency toward addictive behavior and difficulties remaining abstinent.

Women with lower weight management self-efficacy also had a greater likelihood of having a lifetime CUD than did those with higher weight management self-efficacy. Although it remains unclear how weight management self-efficacy relates to a lifetime CUD generally, it has been well documented that concern about postcessation weight gain prevents many women tobacco smokers from initiating quit attempts and is commonly related to relapse following tobacco cessation (Clark et al., 2006; Jeffery et al., 2000; Veldheer et al., 2014). Thus, the low weight management self-efficacy found among women with a lifetime CUD may have specific implications for treatment efforts aimed at reducing tobacco use. Although we did not find additional psychosocial or psychiatric factors predictive of a lifetime CUD in the final adjusted model, it is worth noting that initial group comparisons showed that women with a lifetime CUD were more educated, began smoking tobacco at an earlier age, were more likely to use tobacco as a method of weight control, and had poorer psychological adjustment at the end of their pregnancy, as evidenced by high levels of depressive symptoms and perceived stress, than did women without such a history. However, differences in these factors were not as robust as those related to psychiatric disorders as these group differences did not persist after adjusting for multiple comparisons.

Despite being associated with characteristics commonly related to poor substance use outcomes, women with a lifetime CUD in the present study successfully quit smoking tobacco before or during their third trimester of pregnancy. Thus, pregnancy may offer a unique window of opportunity for substance use treatment to capitalize on women's natural motivation to stop using substances for healthy fetal development. However, substance use interventions focused on reducing the use of cannabis (Eisen et al., 2000; Elk et al., 1998; Haug et al., 2004; Mullins et al., 2004; O'Neill et al., 1996; Schuler et al., 2002; Svikis et al., 1997; Yonkers et al., 2012) or tobacco (McBride et al., 1999; Ratner et al., 2000; Reitzel et al., 2010; Severson et al., 1997) during the perinatal period largely have been ineffective at promoting sustained abstinence. Among the general population, individuals who use both cannabis and tobacco report more severe symptoms of dependence for both substances (Agrawal and Lynskey, 2009; Patton et al., 2005; Timberlake et al., 2007) and are at risk for additional substance use problems (Allsop et al., 2012). In addition, concurrent cannabis and tobacco use is associated with poor treatment outcomes during both cannabis (Haney et al., 2013; Moore and Budney, 2001) and tobacco interventions (Patton et al., 2005; Stapleton et al., 2009), with some evidence suggesting that individuals may compensate for abstinence from one substance by increasing their use of the other (Allsop et al., 2012). Although interventions adapted to address concurrent cannabis and tobacco use have shown initial efficacy (Becker et al., 2014; Becker et al., 2013; Gulliver et al., 2015), there is a need for more effective treatment approaches. The current data suggest that a focus on other psychosocial and psychiatric issues may further improve interventions targeted at reducing comorbid tobacco and cannabis use, particularly among women who quit smoking tobacco during pregnancy.

There are several limitations to this study. First, no information on cannabis use at the time of assessment was collected, making it impossible to determine whether women continued to use cannabis during pregnancy or used cannabis and tobacco concurrently. Second, a subset of women completed psychiatric interviews postpartum rather than during pregnancy. However, the psychosocial and psychiatric factors of interest were not affected by the timing of this assessment. Third, information on lifetime psychiatric disorders and tobacco use were collected retrospectively, which prevents conclusions to be drawn regarding the directionality of these associations. Fourth, data were drawn from women seeking treatment to prevent tobacco relapse who also were motivated to remain abstinent postpartum. Thus, these findings may only pertain to a selective subset of pregnant women and may not be generalizable to other populations of pregnant women.

In summary, the present study documents a high rate of lifetime CUD among pregnant former smokers and provides insight into psychosocial and psychiatric correlates of a lifetime CUD among this population. Findings demonstrate that women with a history of both cannabis and tobacco dependence tend to have a high incidence of psychiatric disorders, a history of alcohol dependence, more frequent attempts to quit using tobacco, and lower self-efficacy to manage their weight without smoking tobacco. Given that each of these factors is independently related to poor treatment outcomes during substance use interventions among the general population, women with a history of cannabis and tobacco dependence may represent a subset of women who need more specialized treatment during the perinatal period to improve substance use outcomes.

Highlights.

  • In total, 14% of women met criteria for a lifetime cannabis use disorder (CUD).

  • A history of multiple psychiatric disorders and alcohol use disorder predicted CUD.

  • Greater quit attempts and lower weight management self-efficacy also predicted CUD.

Acknowledgments

Role of Funding Sources: This work was supported by NIDA (R01 DA021608 to MDL) and NHLBI (T32 HL07560 to RLE). Neither NIDA nor NHLBI had a role in the design and conduct of the study, collection, management, analysis or interpretation of the data, preparation of the manuscript for publication, or decision to submit the manuscript for publication.

Footnotes

Contributors: MDL developed the concept for the study. RLE, MPG, and JLG conducted the literature review and wrote the initial draft of the manuscript. RLE conducted the statistical analysis under the advice of MDL. All authors contributed to the critical revision of the manuscript for important intellectual content and have approved the final manuscript.

Conflict of Interest: No conflict declared.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

References

  1. Agrawal A, Lynskey MT. Tobacco and cannabis co-occurrence: does route of administration matter? Drug Alcohol Depend. 2009;99:240–247. doi: 10.1016/j.drugalcdep.2008.08.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Allsop DJ, Copeland J, Norberg MM, Fu S, Molnar A, Lewis J, Budney AJ. Quantifying the clinical significance of cannabis withdrawal. PLoS One. 2012;7:e44864. doi: 10.1371/journal.pone.0044864. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Ansell EB, Laws HB, Roche MJ, Sinha R. Effects of marijuana use on impulsivity and hostility in daily life. Drug Alcohol Depend. 2015;148:136–142. doi: 10.1016/j.drugalcdep.2014.12.029. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Ashton CH. Pharmacology and effects of cannabis: a brief review. Brit J Psychiat. 2001;178:101–106. doi: 10.1192/bjp.178.2.101. [DOI] [PubMed] [Google Scholar]
  5. Bailey JA, Hill KG, Hawkins JD, Catalano RF, Abbott RD. Men’s and women’s patterns of substance use around pregnancy. Birth. 2008;35:50–59. doi: 10.1111/j.1523-536X.2007.00211.x. [DOI] [PubMed] [Google Scholar]
  6. Ball SA, Carroll KM, Canning-Ball M, Rounsaville BJ. Reasons for dropout from drug abuse treatment: symptoms, personality, and motivation. Addict Behav. 2006;31:320–330. doi: 10.1016/j.addbeh.2005.05.013. [DOI] [PubMed] [Google Scholar]
  7. Becker J, Haug S, Sullivan R, Schaub MP. Effectiveness of different web-based interventions to prepare co-smokers of cigarettes and cannabis for double cessation: a three-arm randomized controlled trial. J Med Internet Res. 2014;16:3–26. doi: 10.2196/jmir.3246. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Becker J, Hungerbuehler I, Berg O, Szamrovicz M, Haubensack A, Kormann A, Schaub MP. Development of an integrative cessation program for co-smokers of cigarettes and cannabis: demand analysis, program description, and acceptability. Subst Abuse Treat Prev Policy. 2013;8:1–12. doi: 10.1186/1747-597X-8-33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Borrelli B, Mermelstein R. The role of weight concern and self-efficacy in smoking cessation and weight gain among smokers in a clinic-based cessation program. Addict Behav. 1998;23:609–622. doi: 10.1016/S0306-4603(98)00014-8. [DOI] [PubMed] [Google Scholar]
  10. Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiat Res. 1989;28:193–213. doi: 10.1016/0165-1781(89)90047-4. [DOI] [PubMed] [Google Scholar]
  11. Caldeira KM, O'Grady KE, Vincent KB, Arria AM. Marijuana use trajectories during the post-college transition: health outcomes in young adulthood. Drug Alcohol Depend. 2012;125:267–275. doi: 10.1016/j.drugalcdep.2012.02.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Cerda M, Wall M, Keyes KM, Galea S, Hasin D. Medical marijuana laws in 50 states: investigating the relationship between state legalization of medical marijuana and marijuana use, abuse and dependence. Drug Alcohol Depend. 2012;120:22–27. doi: 10.1016/j.drugalcdep.2011.06.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Clark MM, Hurt RD, Croghan IT, Patten CA, Novotny P, Sloan JA, Dakhil SR, Croghan GA, Wos EJ, Rowland KM, Bernath A, Morton RF, Thomas SP, Tschetter LK, Garneau S, Stella PJ, Ebbert LP, Wender DB, Loprinzi CL. The prevalence of weight concerns in a smoking abstinence clinical trial. Addict Behav. 2006;31:1144–1152. doi: 10.1016/j.addbeh.2005.08.011. [DOI] [PubMed] [Google Scholar]
  14. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav. 1983;24:385–396. doi: 10.2307/2136404. [DOI] [PubMed] [Google Scholar]
  15. Coyle CP, Roberge JJ. The psychometric properties of the Center for Epidemiological Studies-Depression Scale (CES-D) when used with adults with physical disabilities. Psychol Health. 1992;7:69–81. doi: 10.1080/08870449208404296. [DOI] [Google Scholar]
  16. Dahl RE, Scher MS, Williamson DE, Robles N, Day N. A longitudinal study of prenatal marijuana use. effects on sleep and arousal at age 3 years. Arch Pediatr Adolesc Med. 1995;149:145–150. doi: 10.1001/archpedi.1995.02170140027004. [DOI] [PubMed] [Google Scholar]
  17. Day NL, Goldschmidt L, Thomas CA. Prenatal marijuana exposure contributes to the prediction of marijuana use at age 14. Addiction. 2006;101:1313–1322. doi: 10.1111/j.1360-0443.2006.01523.x. [DOI] [PubMed] [Google Scholar]
  18. Day NL, Leech SL, Goldschmidt L. The effects of prenatal marijuana exposure on delinquent behaviors are mediated by measures of neurocognitive functioning. Neurotoxicol Teratol. 2011;33:129–136. doi: 10.1016/j.ntt.2010.07.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Dutra L, Stathopoulou G, Basden SL, Leyro TM, Powers MB, Otto MW. A meta-analytic review of psychosocial interventions for substance use disorders. Am J Psychiat. 2008;165:179–187. doi: 10.1176/appi.ajp.2007.06111851. [DOI] [PubMed] [Google Scholar]
  20. Eisen M, Keyser-Smith J, Dampeer J, Sambrano S. Evaluation of substance use outcomes in demonstration projects for pregnant and postpartum women and their infants: findings from a quasi-experiment. Addict Behav. 2000;25:123–129. doi: 10.1016/S0306-4603(98)00116-6. [DOI] [PubMed] [Google Scholar]
  21. El-Mohandes A, Herman AA, Nabil El-Khorazaty M, Katta PS, White D, Grylack L. Prenatal care reduces the impact of illicit drug use on perinatal outcomes. J Perinatol. 2003;23:354–360. doi: 10.1038/sj.jp.7210933. [DOI] [PubMed] [Google Scholar]
  22. El Marroun H, Tiemeier H, Jaddoe VW, Hofman A, Mackenbach JP, Steegers EA, Verhulst FC, van den Brink W, Huizink AC. Demographic, emotional and social determinants of cannabis use in early pregnancy: the Generation R study. Drug Alcohol Depend. 2008;98:218–226. doi: 10.1016/j.drugalcdep.2008.05.010. [DOI] [PubMed] [Google Scholar]
  23. Elk R, Mangus L, Rhoades H, Andres R, Grabowski J. Cessation of cocaine use during pregnancy: effects of contingency management interventions on maintaining abstinence and complying with prenatal care. Addict Behav. 1998;23:57–64. doi: 10.1016/S0306-4603(97)00020-8. [DOI] [PubMed] [Google Scholar]
  24. English DR, Hulse GK, Milne E, Holman CDJ, Bower CI. Maternal cannabis use and birth weight: a meta-analysis. Addiction. 1997;92:1553–1560. doi: 10.1111/j.1360-0443.1997.tb02875.x. [DOI] [PubMed] [Google Scholar]
  25. Fergusson DM, Horwood LJ, Northstone K. Maternal use of cannabis and pregnancy outcome. Brit J Obstet Gynec. 2002;109:21–27. doi: 10.1111/j.1471-0528.2002.01020.x. [DOI] [PubMed] [Google Scholar]
  26. Filbey FM, Schacht JP, Myers US, Chavez RS, Hutchison KE. Individual and additive effects of the CNR1 and FAAH genes on brain response to marijuana cues. Neuropsychopharmacol. 2009;35:967–975. doi: 10.1038/npp.2009.200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Fried PA, Watkinson B. 36- and 48-month neurobehavioral follow-up of children prenatally exposed to marijuana, cigarettes, and alcohol. [Accessed July 5, 2015];J Dev Behav Pediatr. 1990 11:49–58. http://www.ncbi.nlm.nih.gov/pubmed/2324288. [PubMed] [Google Scholar]
  28. Fried PA, Watkinson B, Gray R. Differential effects on cognitive functioning in 9- to 12-year olds prenatally exposed to cigarettes and marihuana. Neurotoxicol Teratol. 1998;20:293–306. doi: 10.1016/S0892-0362(97)00091-3. [DOI] [PubMed] [Google Scholar]
  29. Gilchrist LD, Hussey JM, Gillmore MR, Lohr MJ, Morrison DM. Drug use among adolescent mothers: prepregnancy to 18 months postpartum. J Adolescent Health. 1996;19:337–344. doi: 10.1016/S1054-139X(96)00052-3. [DOI] [PubMed] [Google Scholar]
  30. Gilman JM, Kuster JK, Lee S, Lee MJ, Kim BK, Makris N, van der Kouwe A, Blood AJ, Breiter HC. Cannabis use is quantitatively associated with nucleus accumbens and amygdala abnormalities in young adult recereational users. J Neurosci. 2014;34:5529–5538. doi: 10.1523/JNEUROSCI.4745-13.2014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Gray Day NL, Leech S, Richardson GA. Prenatal marijuana exposure: effect on child depressive symptoms at ten years of age. Neurotoxicol Teratol. 2005;27:439–448. doi: 10.1016/j.ntt.2005.03.010. [DOI] [PubMed] [Google Scholar]
  32. Gray Eiden RD, Leonard KE, Connors GJ, Shishler S, Huestis MA. Identifying prenatal cannabis exposure and effects of concurrent tobacco exposure on neonatal growth. Clin Chem. 2010;56:1442–1450. doi: 10.1373/clinchem.2010.147876. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Gulliver A, Farrer L, Chan JK, Tait RJ, Bennett K, Calear AL, Griffiths KM. Technology-based interventions for tobacco and other drug use in university and college students: a systematic review and meta-analysis. Addict Sci Clin Pract. 2015;10 doi: 10.1186/s13722-015-0027-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Gunn JKL, Rosales CB, Center KE, Nunez AV, Gibson SJ, Ehiri JE. The effects of prenatal cannabis exposure on fetal development and pregnancy outcomes: a protocol. Brit Med J. 2015;5:1–5. doi: 10.1136/bmjopen-2014-007227. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Haberstick BC, Young SE, Zeiger JS, Lessem JM, Hewitt JK, Hopfer CJ. Prevalence and correlates of alcohol and cannabis use disorders in the United States: results from the national longitudinal study of adolescent health. Drug Alcohol Depend. 2014;136:158–161. doi: 10.1016/j.drugalcdep.2013.11.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Haney M, Bedi G, Cooper ZD, Glass A, Vosburg SK, Comer SD, Foltin RW. Predictors of marijuana relapse in the human laboratory: robust impact of tobacco cigarette smoking status. Biol Psychiat. 2013;73:242–248. doi: 10.1016/j.biopsych.2012.07.028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Haug NA, Svikis DS, Diclemente C. Motivational enhancement therapy for nicotine dependence in methadone-maintained pregnant women. Psychol Addict Behav. 2004;18:289–292. doi: 10.1037/0893-164X.18.3.289. [DOI] [PubMed] [Google Scholar]
  38. Havens JR, Simmons LA, Shannon LM, Hansen WF. Factors associated with substance use during pregnancy: results from a national sample. Drug and Alcohol Depend. 2009;99:89–95. doi: 10.1016/j.drugalcdep.2008.07.010. [DOI] [PubMed] [Google Scholar]
  39. Heatherton TF, Kozlowski LT, Frecker RC, Fagerstrom KO. The fagerstrom test for nicotine dependence: a revision of the fagerstrom tolerance questionnaire. Brit J Addict. 1991;86:1119–1127. doi: 10.1111/j.1360-0443.1991.tb01879.x. [DOI] [PubMed] [Google Scholar]
  40. Iversen LL. The Science of Marijuana, 2nd edn. Brit J Clin Pharmacol. 2009;67:268–268. doi: 10.1111/j.1365-2125.2008.03355.x. [DOI] [Google Scholar]
  41. Jeffery RW, Hennrikus DJ, Lando HA, Murray DM, Liu JW. Reconciling conflicting findings regarding postcessation weight concerns and success in smoking cessation. Health Psychol. 2000;19:242–246. doi: 10.1037/0278-6133.19.3.242. [DOI] [PubMed] [Google Scholar]
  42. Ko JY, Farr SL, Tong VT, Creanga AA, Callaghan WM. Prevalence and patterns of marijuana use among pregnant and nonpregnant women of reproductive age. Am J Obstet Gynecol. 2015 doi: 10.1016/j.ajog.2015.03.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Leech SL, Larkby CA, Day R, Day NL. Predictors and correlates of high levels of depression and anxiety symptoms among children at age 10. J Am Acad Child Psy. 2006;45:223–230. doi: 10.1097/01.chi.0000184930.18552.4d. [DOI] [PubMed] [Google Scholar]
  44. Lester BM, ElSohly M, Wright LL, Smeriglio VL, Verter J, Bauer CR, Shankaran S, Bada HS, Walls HC, Huestis MA, Finnegan LP, Maza PL. The Maternal Lifestyle Study: drug use by meconium toxicology and maternal self-report. Pediatrics. 2001;107:309–317. doi: 10.1159/000207491. [DOI] [PubMed] [Google Scholar]
  45. Levine MD, Marcus MD, Kalarchian MA, Cheng Y. Strategies to Avoid Returning to Smoking (STARTS): a randomized controlled trial of postpartum smoking relapse prevention interventions. Contemp Clin Trials. 2013;36:565–573. doi: 10.1016/j.cct.2013.10.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Macleod J, Robertson R, Copeland L, McKenzie J, Elton R, Reid P. Cannabis, tobacco smoking, and lung function: a cross-sectional observational study in a general practice population. Brit J Gen Pract. 2015;65:e89–e95. doi: 10.3399/bjgp15X683521. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. McBride CM, Curry SJ, Lando HA, Pirie PL, Grothaus LC, Nelson JC. Prevention of relapse in women who quit smoking during pregnancy. Am J Public Health. 1999;89:706–711. doi: 10.2105/AJPH.89.5.706. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Moore BA, Budney AJ. Tobacco smoking in marijuana-dependent outpatients. J Subst Abuse. 2001;13:583–596. doi: 10.1016/S0899-3289(01)00093-1. [DOI] [PubMed] [Google Scholar]
  49. Mullins SM, Suarez M, Ondersma SJ, Page MC. The impact of motivational interviewing on substance abuse treatment retention: a randomized control trial of women involved with child welfare. J Subst Abuse Treat. 2004;27:51–58. doi: 10.1016/j.jsat.2004.03.010. [DOI] [PubMed] [Google Scholar]
  50. Nagelkerke NJ. A note on a general definition of the coefficient of determination. Biometrika. 1991;78:691–692. doi: 10.1093/biomet/78.3.691. [DOI] [Google Scholar]
  51. O'Neill K, Baker A, Cooke M, Collins E, Heather N, Wodak A. Evaluation of a cognitive-behavioural intervention for pregnant injecting drug users at risk of HIV infection. Addiction. 1996;91:1115–1125. doi: 10.1046/j.1360-0443.1996.91811154.x. [DOI] [PubMed] [Google Scholar]
  52. Palamar JJ, Ompad DC, Petkova E. Correlates of intentions to use cannabis among US high school seniors in the case of cannabis legalization. Int J Drug Policy. 2014;25:424–435. doi: 10.1016/j.drugpo.2014.01.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Passey ME, Sanson-Fisher RW, D'Este CA, Stirling JM. Tobacco, alcohol and cannabis use during pregnancy: clustering of risks. Drug Alcohol Depend. 2014;134:44–50. doi: 10.1016/j.drugalcdep.2013.09.008. [DOI] [PubMed] [Google Scholar]
  54. Patton GC, Coffey C, Carlin JB, Sawyer SM, Lynskey M. Reverse gateways? Frequent cannabis use as a predictor of tobacco initiation and nicotine dependence. Addiction. 2005;100:1518–1525. doi: 10.1111/j.1360-0443.2005.01220.x. [DOI] [PubMed] [Google Scholar]
  55. Perkins KA, Marcus MD, Levine MD, D’Amico D, Miller A, Broge M, Ashcom J, Shiffman S. Cognitive-behavioral therapy to reduce weight concerns improves smoking cessation outcome in weight-concerned women. J Consult Clin Psychol. 2001;69:604–613. doi: 10.1037/0022-006X.69.4.604. [DOI] [PubMed] [Google Scholar]
  56. Peters EN, Schwartz RP, Wang S, O'Grady KE, Blanco C. Psychiatric, psychosocial, and physical health correlates of co-occurring cannabis use disorders and nicotine dependence. Drug Alcohol Depend. 2014;134:228–234. doi: 10.1016/j.drugalcdep.2013.10.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Pomerleau CS, Ehrlich E, Tate JC, Marks JL, Flessland KA, Pomerleau OF. The female weight-control smoker: a profile. J Subst Abuse. 1993;5:391–400. doi: 10.1016/0899-3289(93)90007-X. [DOI] [PubMed] [Google Scholar]
  58. Radloff L. The CES-D scale: A self-report depression scale for research in the general population. Appl Psych Meas. 1977;1:385–401. doi: 10.1177/014662167700100306. [DOI] [Google Scholar]
  59. Ratner PA, Johnson JL, Bottorff JL, Dahinten S, Hall W. Twelve-month follow-up of a smoking relapse prevention intervention for postpartum women. Addict Behav. 2000;25:81–92. doi: 10.1016/S0306-4603(99)00033-7. [DOI] [PubMed] [Google Scholar]
  60. Reitzel LR, Vidrine JI, Businelle MS, Kendzor DE, Costello TJ, Li Y, Daza P, Mullen PD, Velasquez MM, Cinciripini PM, Cofta-Woerpel L, Wetter DW. Preventing postpartum smoking relapse among diverse low-income women: a randomized clinical trial. Nicotine Tob Res. 2010;12:326–335. doi: 10.1093/ntr/ntq001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Richardson GA, Ryan C, Willford J, Day NL, Goldschmidt L. Prenatal alcohol and marijuana exposure: effects on neuropsychological outcomes at 10 years. Neurotoxicol Teratol. 2002;24:309–320. doi: 10.1016/S0892-0362(02)00193-9. [DOI] [PubMed] [Google Scholar]
  62. SAMHSA. Results from the 2011 National Survey on Drug Use and Health: Mental Health Findings. Rockville, MD: Substance Abuse and Mental Health Services Administration; 2012. NSDUH Series H-45. [DOI] [Google Scholar]
  63. Scher MS, Richardson GA, Coble PA, Day NL, Stoffer DS. The effects of prenatal alcohol and marijuana exposure: disturbances in neonatal sleep cycling and arousal. Pediatr Res. 1988;24:101–105. doi: 10.1203/00006450-198807000-00023. [DOI] [PubMed] [Google Scholar]
  64. Schuler ME, Nair P, Black MM. Ongoing maternal drug use, parenting attitudes, and a home intervention: effects on mother-child interaction at 18 months. [Accessed July 5, 2015];J Dev Behav Pediatr. 2002 23:87–94. doi: 10.1097/00004703-200204000-00004. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3143381. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Severson HH, Andrews JA, Lichtenstein E, Wall M, Akers L. Reducing maternal smoking and relapse: long-term evaluation of a pediatric intervention. Prev Med. 1997;26:120–130. doi: 10.1006/pmed.1996.9983. [DOI] [PubMed] [Google Scholar]
  66. Stapleton JA, Keaney F, Sutherland G. Illicit drug use as a predictor of smoking cessation treatment outcome. Nicotine Tob Res. 2009;11:685–689. doi: 10.1093/ntr/ntp050. [DOI] [PubMed] [Google Scholar]
  67. Svikis DS, Lee JH, Haug NA, Stitzer ML. Attendance incentives for outpatient treatment: effects in methadone- and nonmethadone-maintained pregnant drug dependent women. Drug Alcohol Depend. 1997;48:33–41. doi: 10.1016/S0376-8716(97)00101-4. [DOI] [PubMed] [Google Scholar]
  68. Timberlake DS, Haberstick BC, Hopfer CJ, Bricker J, Sakai JT, Lessem JM, Hewitt JK. Progression from marijuana use to daily smoking and nicotine dependence in a national sample of U.S. adolescents. Drug Alcohol Depend. 2007;88:272–281. doi: 10.1016/j.drugalcdep.2006.11.005. [DOI] [PubMed] [Google Scholar]
  69. Veldheer S, Yingst J, Foulds G, Hrabovsky S, Berg A, Sciamanna C, Foulds J. Once bitten, twice shy: concern about gaining weight after smoking cessation and its association with seeking treatment. Int J Clin Pract. 2014;68:388–395. doi: 10.1111/ijcp.12332. [DOI] [PubMed] [Google Scholar]
  70. Volkow ND, Baler RD, Compton WM, Weiss SRB. Adverse health effects of marijuana use. New Engl J Med. 2014;370:2219–2227. doi: 10.1056/NEJMra1402309. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Willford J, Richardson GA, Leech S, Day N. Prenatal alcohol or marijuana exposure differentially affects executive functions in adolescents. Neurotoxicol Teratol. 2001;23:286. [Google Scholar]
  72. Yonkers K, Forray A, Howell H, Gotman N, Kershaw T, Rounsaville B, Carroll K. Motivational enhancement therapy coupled with cognitive behavioral therapy versus brief advice: a randomized trial for treatment of hazardous substance use in pregnancy and after delivery. Gen Hosp Psychiatry. 2012;34:439–449. doi: 10.1016/j.genhosppsych.2012.06.002.Motivational. [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES