Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2024 Jan 23.
Published in final edited form as: Subst Use Misuse. 2023 Jan 23;58(3):444–453. doi: 10.1080/10826084.2023.2167495

Psychopathic traits are associated with lifetime history of nicotine dependence among incarcerated offenders

J Michael Maurer 1,3, Bethany G Edwards 1,2, Carla L Harenski 1, Kent A Kiehl 1,2,4
PMCID: PMC9970823  NIHMSID: NIHMS1871971  PMID: 36683568

Abstract

Individuals scoring high on psychopathy engage in problematic patterns of alcohol and illicit substance use. However, our understanding regarding the association between psychopathy and nicotine use remains limited, which is surprising, given the detrimental consequences associated with such use. Previous studies have observed significant correlations between psychopathic traits (particularly Factor 2 scores assessing lifestyle/behavioral and antisocial traits from the Psychopathy Checklist - Revised [PCL-R]) and increased frequency of nicotine use. However, no study has investigated whether individuals scoring high on psychopathy are characterized by problematic patterns of nicotine use, including lifetime history of nicotine dependence. The current study aimed to address this gap, specifically investigating whether PCL-R scores were associated with higher total scores from the Fagerström Test for Nicotine Dependence (FTND). Across both incarcerated men and women, PCL-R total, Factor 2, and Facet 3 (measuring lifestyle/behavioral psychopathic traits) scores were positively correlated with FTND total scores. Additionally, across both samples, hierarchical linear regression analyses revealed these same psychopathy scores remained associated with higher FTND total scores when controlling for additional covariate measures (e.g., age, severity of alcohol and illicit substance use, race, ethnicity, and IQ). Though associated with small effect sizes, our results support the notion that lifestyle/behavioral psychopathic traits represent a general risk factor for engaging in risky behavior associated with deleterious health consequences, including nicotine use. Our results hold implications for the development of treatment approaches, designed to reduce problematic levels of substance use among individuals scoring high on psychopathy.

Keywords: psychopathy, nicotine dependence, incarcerated sample

Introduction

Nicotine use is a serious public health concern associated with detrimental health consequences, including cancer (Grando, 2014), yet it remains common in the United States. In 2019, roughly 34 million adults (around 10% of the nation) smoked cigarettes (Cornelius et al., 2020). Compared to individuals in the general community, individuals involved in the criminal justice system exhibit higher rates of nicotine use and nicotine-related problems, including dependence (Winkleman et al., 2019). In fact, an epidemiological study performed by Vaughn et al. (2012) observed that justice-involved individuals were more likely to meet criteria for nicotine dependence compared to individuals from the general population, even when controlling for other important variables associated with increased nicotine use, including age, gender, race, income, and education. Other variables evident in incarcerated individuals (e.g., higher rates of impulsivity, sensation seeking, and negative affect) have also been previously associated with increased nicotine use (Flory & Manuck, 2009; Halberstadt et al., 2021). These variables also characterize individuals scoring high on psychopathy (Hare, 2003; Hicks et al., 2004; Maurer et al., 2021), a population characterized by high incarceration rates (Hare, 2003; Verona & Vitale, 2018). However, to date, the relationship between psychopathic traits and problematic nicotine use is not well-understood.

Psychopathy comprises a constellation of interpersonal, affective, and behavioral traits (Hare, 2003) and among incarcerated individuals, is commonly assessed using the Hare Psychopathy Checklist – Revised (PCL-R; Hare, 2003). Compared to individuals scoring low on the PCL-R, those scoring high on the PCL-R are more likely to use and abuse alcohol and illicit substances (Hemphill et al., 1994; Miller & Lynam, 2003) and meet criteria for substance dependence (Hemphill et al., 1994; Smith & Newman, 1990). Psychopathic traits, measured via the PCL-R, can be separated into two dimensions reflective of interpersonal and affective traits (PCL-R Factor 1) and lifestyle/behavioral and antisocial traits (PCL-R Factor 2; Hare, 2003; Harpur et al., 1989; Kennealy et al., 2007). PCL-R Factor 2 scores, rather than PCL-R Factor 1 scores, are consistently associated with more severe patterns of alcohol and illicit substance use (see Taylor & Lang, 2006 for a comprehensive review). However, fewer studies have investigated the association between psychopathic traits and nicotine use, despite evidence for higher global prevalence and disease burden for nicotine compared to other forms of substance use (Peacock et al., 2018).

Consistent with other forms of substance use, increased frequency of nicotine use, measured via the Survey on Alcohol and Drug Use (SADU; Backman et al., 1991), has been associated with PCL-R Factor 2 scores, rather than Factor 1 scores, among incarcerated offenders (Hicks et al., 2010; Kennealy et al., 2007; Patrick et al., 2005). The SADU assesses frequency of nicotine use, asking participants about their frequency of nicotine use throughout their lifetime, 12 months prior to incarceration, and 30 days prior to incarceration. While frequency of nicotine use is significantly correlated with nicotine dependence, individuals can still engage in high rates of nicotine use without being physically dependent to nicotine (Dierker et al., 2007). Therefore, there exists the possibility that psychopathy scores, particularly, Factor 2, may be associated with increased frequency of nicotine use, but not problematic patterns of nicotine use, including dependence. Here, we attempted to address this gap, investigating the association between psychopathic traits (assessed using total, factor, and facet scores from the PCL-R) and lifetime history of nicotine dependence (assessed via total scores from the Fagerström Test for Nicotine Dependence [FTND; Heatherton et al., 1991]) among two samples of incarcerated offenders: adult men and women. Rather than simply measuring frequency of nicotine use, higher FTND total scores reflect one’s physical addiction to nicotine (Heatherton et al., 1991), with items pertaining to compulsive use (i.e., finding it difficult to refrain from smoking in places where it is forbidden) and dependence (i.e., smoking even when physically sick).

The current study had two aims: First, we investigated whether psychopathy scores were significantly correlated with lifetime history of nicotine dependence (i.e., higher FTND total scores) across two samples of incarcerated offenders. We hypothesized that PCL-R Factor 2 scores, rather than Factor 1 scores, would be positively correlated with FTND total scores, consistent with previous research suggesting that PCL-R Factor 2 represents a general risk factor for problematic substance use (Coid et al., 2009; Hare, 2003; Taylor & Lang, 2006). Second, we aimed to extend upon previously published studies by performing hierarchical linear regression analyses, investigating whether PCL-R scores remained associated with higher FTND total scores when controlling for additional variables associated with increased nicotine use (e.g., participant’s age, severity of alcohol and illicit substance use, self-reported race and ethnicity, and intelligence quotient [IQ]). These additional covariate measures may be especially important to control for, as Patrick et al. (2005) observed that PCL-R Factor 2 scores were no longer significantly correlated with frequency of nicotine use when controlling for comorbid use of alcohol and illicit substance use. As individuals scoring high on psychopathy are more likely to engage in poly-substance use compared to those scoring low on psychopathy (Hemphill et al., 1994; Smith & Newman, 1990), and comorbid substance use is associated with higher rates of nicotine use (Goodwin et al., 2014), it remains to be seen whether psychopathy scores remain significantly associated with problematic patterns of nicotine use when accounting for additional covariates, including substance use severity measures. We hypothesized that PCL-R Factor 2 scores would remain associated with higher FTND total scores when controlling for additional covariate measures. By further delineating our understanding regarding psychopathic traits and nicotine dependence, this may aid in the further development of specialized treatments designed to reduce heightened rates of problematic substance use among individuals scoring high on psychopathy.

Method

Participants

Participants in the current study included n = 1,444 incarcerated offenders (n = 1,046 men and n = 398 women) recruited from correctional facilities in the states of New Mexico and Wisconsin. We investigated the association between psychopathic traits and nicotine use among incarcerated offenders, as justice-involved individuals are characterized by higher rates of psychopathy compared to community dwelling individuals. In fact, 8 – 17% of incarcerated women (Verona & Vitale, 2018) and 15 – 25% of incarcerated men (Hare, 2003) meet criteria for psychopathy compared to ~1% of community individuals (Neumann & Hare, 2008). Additionally, incarcerated individuals exhibit higher rates of problematic nicotine use, including dependence, compared to individuals recruited from the general community (Vaughn et al., 2012; Winkleman et al., 2019). By recruiting incarcerated offenders, we hoped to better delineate the association between psychopathic traits and problematic patterns of nicotine use. Participants included in the current study ranged from 18 to 64 years of age, with men ranging from 18 to 62 years of age (M = 33.46, SD = 8.40) and women ranging from 19 to 64 years of age (M = 36.39, SD = 8.79). Regarding NIH race and ethnicity classifications, men self-identified as American Indian or Alaskan Native (n = 53, 5.1%), Asian (n = 5, 0.5%), Black or African American (n = 226, 21.6%), Native Hawaiian or other Pacific Islander (n = 2, 0.2%), White (n = 720, 68.8%), or more than one race (n = 13, 1.2%). Additionally, n = 27 men (2.6%) chose not to self-disclose their race. Women included in the current study self-identified as American Indian or Alaskan Native (n = 32, 8%), Asian (n = 1, 0.3%), Black or African American (n = 35, 8.8%), Native Hawaiian or other Pacific Islander (n = 3, 0.8%), White (n = 317, 79.6%), or more than one race (n = 4, 1%). An additional n = 6 women (1.5%) chose not to self-disclose their race. Regarding ethnicity, participants self-identified as Hispanic/Latinx (n = 326 men [31.2%], n = 179 women [45%]) or non-Hispanic/Latinx (n = 700 men [66.9%], n = 219 women [55%]); an additional n = 20 men (1.9%) chose not to self-report their ethnicity.

Volunteer research participants provided written informed consent and were informed they were allowed to discontinue participating in the study at any point, without consequence. Participants were compensated at a rate consistent with the hourly labor wage of the correctional facility at which they were housed. This research was approved by several review boards, including the Ethical and Independent Review Services and the University of Wisconsin - Madison and the Office for Human Research Protections.

Assessments

Psychopathic traits were assessed using the PCL-R (Hare, 2003), an expert-administered rating scale consisting of a semi-structured interview and review of collateral information, including institutional files. Each of the twenty items of the PCL-R are scored as either a zero (does not apply), one (applies somewhat), or two (definitely applies), with total scores potentially ranging from zero to 40. For men, PCL-R total scores ranged from 3.2 to 38 (M = 22.77, SD = 7.04, α = .82) and for women, PCL-R total scores ranged from three to 37.9 (M = 20.01, SD = 6.61, α = .82). Independent double-ratings were conducted on approximately 10% of PCL-R interviews, resulting in excellent rater agreement (ICC = .96, p < .001). In addition to PCL-R total scores, we included PCL-R factor and facet scores in analyses performed, with Factor 1 measuring interpersonal psychopathic traits (Facet 1, including traits such as pathological lying and conning and manipulative behavior) and affective psychopathic traits (Facet 2, measuring traits such as a lack of empathy, guilt, and remorse). Factor 2 of the PCL-R assesses lifestyle/behavioral psychopathic traits (Facet 3, including impulsivity and irresponsibility) and antisocial psychopathic traits (Facet 4, assessing traits such as criminal versatility and poor behavioral controls).

Lifetime history of nicotine dependence was assessed using the total score derived from the FTND (Heatherton et al., 1991). The FTND is a six-item self-report measure assessing various aspects of nicotine use, including frequency of nicotine use (i.e., the number of cigarettes smoked per day), compulsive use, and nicotine dependence. The FTND has been previously utilized in samples of incarcerated offenders (e.g., Bock et al., 2013; Cropsey & Kristeller, 2003; Lantini et al., 2015; Voglewede & Noel, 2004). Since cigarettes were considered contraband at the correctional facilities where data collection occurred and participants were not permitted to smoke, participants were asked to respond to the FTND assessment based on when they were last permitted to smoke cigarettes (i.e., pre-incarceration). FTND total scores can range from zero to 10, with higher scores indicating stronger physical dependence to nicotine. Among men, the mean FTND total score was 4.11 (SD = 2.44, range: 0 – 10, α = .59) and among women, the mean FTND total score was 4.48 (SD = 2.37, range: 0 – 10, α = .58). The low internal consistency of the FTND is consistent with previously published studies in which participants were allowed to smoke (e.g., Cronbach’s alpha values of .33 (Foster et al., 2014), .47 (Salgado-Garcia et al., 2013), .55 (John et al., 2004), .56 (Payne et al., 1994), and .61 (Heatherton et al., 1991)). Low internal consistency of the FTND has also been observed among incarcerated offenders who were not permitted to smoke (.61 as reported in Cropsey & Kristeller, 2003; Voglewede & Noel, 2004). Oh et al. (2019) suggest that the FTND may suffer from low internal consistency due to the small number of overall items (six total) and high proportion of dichotomous, binary yes/no responses (four total). Despite suffering from low internal consistency, the FTND exhibits good to excellent test-retest reliability (Carmo & Pueyo, 2002; Etter et al., 1999; Haddock et al., 1999), suggesting that the FTND should still be considered a valid assessment for nicotine dependence, despite certain limitations associated with the assessment.

Severity of alcohol and illicit substance use was assessed using a modified version of the Addiction Severity Index (ASI; McLellan et al., 1992), where years of regular use were summed across all substances that the participant reported using regularly (i.e., three or more times per week for a minimum period of one month). Substances assessed included alcohol, cannabis, heroin, cocaine, methamphetamine, other amphetamines (e.g., Adderall), hallucinogens, inhalants, methadone, and other opiates/analgesics (e.g., OxyContin). Nicotine use was not assessed via this assessment, and therefore, did not contribute to substance use severity scores. Years of regular use were then divided by the participant’s age (to control for opportunity to use), multiplied by 100, and a square root transformation was applied to correct for skewness. This operational definition of substance use severity has been incorporated in previous studies by our research group (Edwards et al. 2021). Substance use severity scores ranged from 0 to 20.32 (M = 7.63, SD = 3.36) for men and 0 to 17.37 (M = 8.45, SD = 3.05) for women.

Full-scale IQ was estimated from the Vocabulary and Matrix Reasoning subtests from the Wechsler Adult Intelligence Scale – 3rd Edition (Wechsler, 1997) or Wechsler Abbreviated Scale of Intelligence – 2nd Edition (Wechsler, 2011). IQ was included as a covariate measure in analyses, serving as a proxy measure for socioeconomic status (SES). As data collection occurred across multiple correctional facilities, measures of SES were not available for all participants. However, IQ scores were available for all participants; previous studies have reported significant associations between SES and IQ (von Stumm & Plomin, 2015), specifically Vocabulary and Matrix Reasoning subtests (Kroeff et al., 2020). Full-scale IQ scores ranged from 70 to 137 for men (M = 96.83, SD = 12.74) and 70 to 123 for women (M = 95.18, SD = 10.23). Descriptive statistics for PCL-R scores, FTND total scores, and covariate measures are reported in Table 1.

Table 1.

Descriptive Statistics for PCL-R scores, Covariate Measures, and FTND total scores

Adult Men Adult Women
Variable Mean SD Range Mean SD Range
PCL-R Total 22.77 7.04 3.2 – 38 20.01 6.61 3 – 37.9
PCL-R Factor 1 7.37 3.76 0 – 16 5.60 3.50 0 – 16
PCL-R Factor 2 13.10 3.70 2 – 20 12.37 3.70 0 – 20
PCL-R Facet 1 2.59 2.06 0 – 8 2.04 1.77 0 – 8
PCL-R Facet 2 4.77 2.27 0 – 8 3.56 2.25 0 – 8
PCL-R Facet 3 6.26 2.17 0 – 10 6.07 2.19 0 – 10
PCL-R Facet 4 6.77 2.48 0 – 10 6.26 2.29 0 – 10
FTND Total 4.11 2.48 0 – 10 4.48 2.37 0 – 10
Substance Use Severity 7.63 2.44 0 – 20.32 8.45 3.05 0 – 17.37
Age 33.46 3.36 18.06 – 62.64 36.39 8.79 19.17 – 64.79
IQ 96.83 12.74 70 – 137 95.18 10.23 70 – 123
Open in a new tab

Note. PCL-R refers to total, factor, and facet scores derived from the Hare Psychopathy Checklist – Revised (PCL-R; Hare, 2003). FTND total refers to the total score from the Fagerström Test for Nicotine Dependence (FTND; Heatherton et al., 1991). Substance use severity refers to years of regular alcohol and illicit substance use assessed via a modified version of the Addiction Severity Index (ASI; McLellan et al., 1992). IQ refers to full-scale IQ estimated from the Vocabulary and Matrix Reasoning subtests from the WAIS-III (Wechsler, 1997) or WASI-II (Wechsler, 2011).

Statistical Analyses

First, correlation analyses were performed, separately for men and women, investigating whether PCL-R scores (i.e., PCL-R total, factor, or facet scores) were significantly positively correlated with FTND total scores. Significant correlations reflected those which survived a modified Bonferroni multiple comparison correction (i.e., .05/7, or p < .007). Additional correlations between PCL-R scores, covariate measures, and FTND total scores are included within our Supplemental Materials. Additionally, hierarchical linear regression analyses were performed, separately for men and women, where we investigated whether PCL-R total, factor, or facet scores remained significantly associated with higher FTND total scores when controlling for covariates known to relate to increased nicotine use and psychopathy (e.g., participant’s age, severity of alcohol and illicit substance use, self-identified race and ethnicity1, and IQ scores). Therefore, for both men and women, three separate hierarchical linear regression analyses were performed, with the dependent measure assessing problematic nicotine use (i.e., FTND total scores). Predictor variables included covariate measures (entered in Step 1) and psychopathy scores (entered in Step 2). For each hierarchical linear regression analysis performed, the covariate measures entered in Step 1 remained the same. However, in Step 2, different psychopathy scores were entered in each regression analysis, to allow for the understanding of which specific psychopathy scores remained significantly associated with higher FTND total scores, while controlling for covariate measures. Specifically, in Regression #1, PCL-R total scores were entered in Step 2, whereas in Regressions #2 and #3, PCL-R factor scores (i.e., PCL-R Factor 1 and 2 scores) or PCL-R facet scores (i.e., PCL-R Facet 1, 2, 3, and 4 scores) were entered in Step 2, respectively. In these hierarchical linear regression analyses, significant effects reflected those which survived a modified Bonferroni multiple comparison correction (i.e., .05/3, or p < .017)2.

Results

Correlation Analyses

Among men, significant correlations emerged between psychopathy total, factor, and facet scores and FTND total scores. For example, FTND total scores were positively correlated with PCL-R total scores (r = .13, p < .001, 95% CI: .07 - .19), Factor 2 scores (r = .15, p < .001, 95% CI: .09 - .21), and Facet 3 scores (r = .18, p < .001, 95% CI: .11 - .24). Additionally, among women, similar correlations emerged between variables. For example, FTND total scores were positively correlated with PCL-R total scores (r = .21, p < .001, 95% CI: 11 - .31), Factor 2 scores (r = .26, p < .001, 95% CI: .16 - .35), Facet 3 scores (r = .24, p = .001, 95% CI: .14 - .34), and Facet 4 scores (r = .20, p < .001, 95% CI: .10 - .30). Though statistically significant, when consulting Cohen (1988)’s standard for effect sizes (i.e., r’s .10 from to .29 reflecting small effect sizes), correlations between psychopathy scores and nicotine dependence scores reflect small effect sizes for both men and women.

Hierarchical Linear Regression Analyses

When controlling for participant’s age, severity of alcohol and illicit substance use, self-identified race and ethnicity, and full-scale IQ scores, psychopathy scores remained significantly associated with higher FTND total scores for both men and women (see Tables 2 and 3). Specifically, among men, PCL-R total scores (β = .10, p = .001, f2 = .01), Factor 2 scores (β = .14, p < .001, f2 = .01), and Facet 3 scores (β = .12, p = .002, f2 = .01) remained associated with higher FTND total scores when controlling for these covariate measures. Similarly, among women, PCL-R total scores (β = .19, p < .001, f2 = .03), Factor 2 scores (β = .30, p < .001, f2 = .06), and Facet 3 scores (β = .21, p = .001, f2 = .03) remained associated with higher FTND total scores when controlling for covariate measures. Important to note, while statistically significant effects emerged, these hierarchical linear regression results reflect small effect sizes (i.e., f2 of .02 to .15 reflect small effect sizes, see Cohen, 1988)3,4 .

Table 2.

Hierarchical Linear Regression with PCL-R scores and Covariate Measures Predicting FTND Total Scores in Adult Men

B (SE B) 95% CI β part r ΔR2
Regression #1
Step 1 .12
Substance Use 0.16 (0.02) [.16, .28] .22 .21
Age 0.02 (0.01) [.01, .12] .07 .06
Ethnicity −1.29 (0.18) [−.31, −.18] −.25 −.22
American Indian −1.12 (0.34) [−.16, −.04] −.10 −.10
Asian 0.02 (1.04) [−.06, .06] .00 .00
Black −0.91 (0.20) [−.22, −.09] −.15 −.14
Native Hawaiian5 0.82 (1.64) [−.04, .07] .02 .02
More than One Race −0.65 (0.70) [−.09, .03] −.03 −.03
IQ 0.01 (0.01) [−.04, .09] .03 .02
Step 2 .01
PCL-R Total 0.04 (.01) [.04, .17] .10 .10
Regression #2
Step 1 .11
Substance Use 0.16 (0.02) [.15, .28] .21 .20
Age 0.02 (0.01) [−.00, .12] .06 .06
Ethnicity −1.30 (0.18) [−.31, −.18] −.25 −.23
American Indian −1.13 (0.34) [−.16, −.04] −.10 −.10
Asian 0.03 (1.04) [−.06, .06] .00 .00
Black −0.89 (0.21) [−.22, −.08] −.15 −.13
More Than One Race −0.65 (0.71) [−.09, .03] −.03 −.03
IQ 0.00 (0.01) [−.04, .08] .02 .02
Step 2 .02
PCL-R Factor 1 −0.02 (0.03) [−.10, .05] −.03 −.02
PCL-R Factor 2 0.10 (0.03) [.07, .22] .14 −.12
Regression #3
Step 1 .12
Substance Use 0.16 (0.02) [.16, .28] .21 .21
Age 0.02 (0.01) [−.00, .12] .06 .06
Ethnicity −1.33 (0.18) [−.32, −.19] −.25 −.23
American Indian −1.29 (0.35) [−.18, −.05] −.12 −.11
Asian 0.03 (1.04) [−.06, .06] .00 .00
Black −0.91 (0.21) [−.22, −.09] −.15 −.14
More Than One Race −0.67 (0.70) [−.09, .03] −.03 −.03
IQ 0.00 (0.01) [−.05, .08] .01 .01
Step 2 .02
PCL-R Facet 1 −0.00 (0.05) [−.08, .07] −.00 −.00
PCL-R Facet 2 −0.05 (0.04) [.12, .03] −.05 −.03
PCL-R Facet 3 0.13 (0.04) [.04, .19] .12 .09
PCL-R Facet 4 0.08 (0.04) [.01, .15] .08 .07
Open in a new tab

Note. Bolded values reflect statistically significant effects (i.e., survived a modified Bonferroni multiple comparison correction of .05/3, or p < .017). Ethnicity refers to either Hispanic/Latino or non-Hispanic/Latino, with higher scores representing participants who self-identified as Hispanic/Latino. As self-identified race included multiple response options, race was dummy coded to allow for its inclusion within hierarchical linear regression analyses. Specifically, White was chosen as the reference category, and all other options (e.g., American Indian/Alaskan Native, Asian, Black or African American, Native Hawaiian or other Pacific Islander, or more than one race) were included in the regression model as separate dummy coded variables. This allows for the regression coefficients for each dummy coded group to be interpreted as the effect for that category compared to the reference group. The 95% CI refers to the confidence interval referring to standardized β values, not unstandardized B values. Three separate hierarchical linear regression analyses were performed, each with the same dependent measure (i.e., FTND total score) and covariates entered in Step 1. In Step 2, different psychopathy scores were entered in each of the three regressions (i.e., either PCL-R total scores [Regression #1], PCL-R factor scores [Regression #2], or PCL-R facet scores [Regression #3] scores), to identify which specific psychopathic traits were associated with FTND total scores, while controlling for covariate measures.

Table 3.

Hierarchical Linear Regression with PCL-R scores and Covariate Measures Predicting FTND Total Scores in Adult Women

B (SE B) 95% CI β part r ΔR2
Regression #1

Step 1
.06
Substance Use 0.12 (0.04) [.05, .25] .15 .15
Age 0.00 (0.01) [−.09, .11] .01 .01
Ethnicity −0.61 (0.26) [−.24, −.02] −.13 −.12
American Indian −1.01 (0.45) [−.22, −.01] −.11 −.11
Asian −3.86 (2.32) [−.18, .02] −.08 −.08
Black −0.71 (0.44) [−.19, .02] −.09 −.08
Native Hawaiian −1.38 (1.35) [−.15, .05] −.05 −.05
More Than One Race −1.73 (1.18) [−.17, .03] −.08 −.07
IQ −0.00 (0.01) [−.11, .10] −.00 −.00
Step 2 .03
PCL-R Total 0.07 (0.02) [.09, .30] .19 .18
Regression #2
Step 1 .06
Substance Use 0.11 (0.04) [.04, .24] .14 .14
Age 0.00 (0.01) [−.10, .11] .01 .00
Ethnicity −0.61 (0.26) [−.24, −.02] −.13 −.12
American Indian −0.95 (0.46) [−.21, −.01] −.11 −.11
Asian −3.87 (2.32) [−.18, .01] −.09 −.09
Black −0.75 (0.44) [−.19, .01] −.09 −.09
Native Hawaiian −1.43 (1.35) [−.15, .05] −.05 −.05
More Than One Race −1.77 (1.18) [−.17, .02] −.08 −.08
IQ −0.00 (0.01) [−.11, .09] −.01 −.01
Step 2 .06
PCL-R Factor 1 −0.06 (0.04) [−.20, .02] −.09 −.08
PCL-R Factor 2 0.19 (0.04) [.18, .42] .30 .24
Regression #3
Step 1 .06
Substance Use 0.11 (0.04) [.04, 24] .14 .14
Age 0.00 (0.01) [−.09, .11] .01 .01
Ethnicity −0.61 (0.26) [−.24, −.02] −.13 −.12
American Indian −0.95 (0.46) [−.21, −.01] −.11 −.10
Asian −3.89 (2.32) [−.18, .01] −.09 −.09
Black −0.74 (0.44) [−.19, .01] −.09 −.09
Native Hawaiian −1.41 (1.36) [−.15, .05] −.05 −.05
More Than One Race −1.76 (1.18) [−.17, .02] −.08 −.08
IQ −0.00 (0.01) [−.11, .09] −.01 −.01
Step 2 .06
PCL-R Facet 1 0.03 (0.08) [−.10, .14] .02 .02
PCL-R Facet 2 −0.14 (0.07) [−.25, −.01] −.13 −.10
PCL-R Facet 3 0.23 (0.07) [.09, .34] .21 .17
PCL-R Facet 4 0.14 (0.06) [.02, .26] .13 .14
Open in a new tab

Note. Bolded values reflect statistically significant effects (i.e., survived a modified Bonferroni multiple comparison correction of .05/3, or p < .017). Ethnicity refers to either Hispanic/Latina or non-Hispanic/Latina, with higher scores representing participants who self-identified as Hispanic/Latina. As self-identified race included multiple response options, race was dummy coded to allow for its inclusion within hierarchical linear regression analyses. Specifically, White was chosen as the reference category, and all other options (e.g., American Indian/Alaskan Native, Asian, Black or African American, Native Hawaiian or other Pacific Islander, or more than one race) were included in the regression model as separate dummy coded variables. This allows for the regression coefficients for each dummy coded group to be interpreted as the effect for that category compared to the reference group. The 95% CI refers to the confidence interval referring to standardized β values, not unstandardized B values. Three separate hierarchical linear regression analyses were performed, each with the same dependent measure (i.e., FTND total score) and covariates entered in Step 1. In Step 2, different psychopathy scores were entered in each of the three regressions (i.e., either PCL-R total scores [Regression #1], PCL-R factor scores [Regression #2], or PCL-R facet scores [Regression #3] scores), to identify which specific psychopathic traits were associated with FTND total scores, while controlling for covariate measures.

Discussion

Previously published studies have observed significant correlations between PCL-R Factor 2 scores and frequency of nicotine use among incarcerated offenders (Hicks et al., 2010; Kennealy et al., 2007; Patrick et al., 2005). Here, we extend upon previous research, identifying significant associations between psychopathy scores and problematic patterns of nicotine use (assessed via FTND total scores measuring lifetime history of nicotine dependence), across two independent samples of incarcerated offenders. Specifically, across both samples, PCL-R total, Factor 2, and Facet 3 scores were associated with higher FTND total scores in correlation analyses. Further, these same psychopathy scores remained significantly associated with higher FTND total scores in hierarchical linear regression analyses when controlling for other covariate measures related to increased nicotine use (e.g., participant’s age, severity of alcohol and illicit substance use, self-identified race and ethnicity, and IQ scores). As reported in our Supplemental Materials, similar results were observed in multiple regression analyses performed, without controlling for the influence of additional covariate measures. While the results obtained in the current study reflect small effect sizes (in both correlation and hierarchical linear regression analyses), they help improve our understanding regarding the association between psychopathic traits and nicotine use. Specifically, in addition to engaging in higher frequency of nicotine use (Hicks et al., 2010; Kennealy et al., 2007; Patrick et al., 2005), individuals scoring high on psychopathy, particularly lifestyle/behavioral and antisocial psychopathic traits, are characterized by higher rates of problematic patterns of nicotine use, including lifetime history of nicotine dependence. Therefore, while associated with small effect sizes, the results obtained in the current study further support the notion that PCL-R Factor 2 represents a general risk factor for problematic forms of substance use (Coid et al., 2009; Hare, 2003; Taylor & Lang, 2006), including nicotine dependence.

Individuals scoring high on psychopathy typically engage in higher rates of substance use to experience the associated pleasurable effects (Cope et al., 2014). Despite being as addictive as other drugs, cigarettes are typically rated as less pleasurable compared to other substances of abuse (Kozlowski et al., 1989). As such, individuals scoring high on psychopathy may engage in higher levels of problematic nicotine use for additional reasons beyond pleasure. For example, negative emotionality (e.g., depression, anxiety, and other mood disturbances) has been previously associated with increased nicotine use (Elkins et al., 2006; Lechner et al., 2018). Furthermore, compared to PCL-R Factor 1 scores, PCL-R Factor 2 scores have been previously associated with higher rates of negative emotionality (Harpur et al., 1989; Hicks & Patrick, 2006; Patrick, 1994; Skeem et al., 2007; Verona et al., 2001). Individuals may engage in nicotine use to help reduce negative emotionality, as side effects associated with nicotine use include increased relaxation, reduced anxiety, and relief from stress (Benowitz, 2010). Therefore, individuals scoring high on psychopathy, particularly Factor 2, may engage in problematic patterns of nicotine use to reduce symptoms related to negative emotionality.

In general, there remains skepticism whether individuals scoring high on psychopathy can be successfully treated. For example, after completing substance use treatment, individuals scoring high on psychopathy continue to be characterized by high rates of substance use (Messina et al., 1999; Smith & Newman, 1990). However, improved treatment outcomes have been observed when targeting specific symptomatology among individuals scoring high on psychopathy. For example, when specifically targeting negative emotionality, individuals scoring high on psychopathy have exhibited clinical improvements, including increased motivation and adherence to substance use treatment (Cecero et al., 1999; Reardon et al., 2002; Woody et al., 1985). Individuals scoring high on PCL-R Factor 2 (Edwards, Carre, & Kiehl, 2019), as well as individuals characterized by increased nicotine use (Elkins et al., 2006; Lechner et al., 2018), are characterized by higher rates of negative emotionality. As such, targeting negative emotionality may lead to improved outcomes for those with comorbid psychopathic traits and nicotine use. Supporting this notion, therapeutic approaches which have targeted negative emotionality have been associated with higher rates of cigarette cessation (Brown et al., 2008; Gifford et al., 2004). Therefore, while speculative, individuals scoring high on PCL-R Factor 2 and exhibiting problematic patterns of substance use, including nicotine use, may benefit from treatment approaches targeting specific psychological vulnerabilities, including negative emotionality, such as Dialectical Behavior Therapy (DBT). For example, treatment protocols like DBT may help to reduce Factor 2 traits and problematic levels of substance use, including nicotine use, by way of improving emotion regulation (Cooperman et al., 2019; McMain et al., 2001).

Compared to other substances of abuse, nicotine use is often overlooked by researchers (Ziedonis et al., 2006); however, higher rates of nicotine use are associated with deleterious outcomes, consistent with other forms of substance use (Friedman, 1998). Specifically, the use of nicotine, either on its own (Lewis et al., 2015), or in combination with other forms of substance use (Peters et al., 2014; Saatcioglu & Erim, 2009), has been associated with an increased propensity to engage in violent and antisocial behavior. Additionally, consistent with other drugs (Dowden & Brown, 2002), increased nicotine use is predictive of future recidivism (Nelson et al., 2015; Terranova et al., 2021). Previous research suggests that the combination of psychopathic traits, along with problematic substance use, is the strongest predictor of future antisocial behavior (Steadman et al., 2000). While this previous study did not investigate whether the combination of psychopathic traits and nicotine use was associated with violent outcomes, the results obtained in the current study suggest that PCL-R Factor 2 scores represent a general risk factor for problematic substance use, including nicotine dependence. As such, individuals scoring high on PCL-R Factor 2 and meeting criteria for nicotine dependence (and/or other forms of substance dependence), likely represent a high-risk sample of individuals, who continually and reliably engage in severe antisocial behavior.

In addition to severe antisocial behavior, lifestyle/behavioral psychopathic traits are related to increased risk-taking associated with detrimental health consequences. For example, in a sample of undergraduate students, erratic lifestyle psychopathic traits, assessed via the Self-Report Psychopathy Scale (Paulhus et al., 2015), were associated with an increased frequency of engaging in risky behavior with health-related consequences, including engaging in binge drinking and unprotected sexual activity, not wearing sunscreen while outside, and eating high cholesterol foods (Hosker-Field et al., 2016). While this study was performed with community participants, these results suggest that individuals scoring high on lifestyle/behavioral psychopathic traits may engage in activities which increase their chance of future health-related problems, including increased rates of sexually transmitted infections, skin cancer, and high cholesterol and diabetes. Supporting this notion, individuals scoring high on psychopathy have been characterized by a higher number of health-related problems, including high cholesterol and blood pressure, compared to individuals scoring low on psychopathy (Beaver et al., 2014). Furthermore, a study by Vaurio et al. (2018) suggest that not only are individuals scoring high on psychopathy characterized by a higher mortality rate compared to those scoring low on psychopathy, but those scoring high on psychopathy were more likely to die from lung disease (13.64%) compared to those scoring low on psychopathy (4.11%). Individuals engaging in increased nicotine use are characterized by higher rates of lung disease compared to non-smokers (Mishra et al., 2015). While Vaurio et al. (2018) did not measure levels of cigarette or nicotine use, the results of the current study suggest that individuals scoring high on psychopathy, especially lifestyle/behavioral psychopathic traits, may continue to engage in problematic levels of nicotine use, despite health-related consequences.

Limitations

It is important to note limitations associated with the current study. First, while we included covariate measures (e.g., participant’s age, severity of alcohol and illicit substance use, self-identified race and ethnicity, and IQ scores) in hierarchical linear regression analyses, we did not include other measures associated with increased nicotine use, including impulsivity, to limit multicollinearity in models. Impulsivity is an item included within the scoring criteria for the PCL-R and has been associated with increased substance use (Moeller & Dougherty, 2002). However, as PCL-R Factor 2 and Facet 3 scores were associated with higher FTND nicotine dependence scores, it remains unclear whether results were specifically driven by lifestyle/behavioral psychopathic traits, rather than impulsivity. Additionally, as previously discussed in our Method section, the FTND used to assess nicotine dependence in the current study suffers from low internal consistency (de Meneses-Gaya et al., 2009; Sharma et al., 2021). As such, our results should be considered with this limitation in mind. Future researchers should incorporate the use of other measures assessing nicotine dependence, including the Cigarette Dependence Scale (Etter et al., 2003), to see if similar results are obtained using measures associated with higher internal consistency (Cronbach’s alpha of .84 in Etter et al., 2003). Lastly, we investigated the relationship between psychopathic traits and nicotine dependence via traditional cigarette smoking. There has been a national increase in the use of electronic cigarettes, particularly among young adults (Cullen et al., 2019). While the use of electronic cigarettes is believed to be less harmful compared to the use of traditional cigarettes, electronic cigarette use remains associated with harmful side effects, including respiratory symptoms (McConnell et al., 2016). As such, future research should investigate the association between psychopathic traits and electronic cigarette dependence, particularly among younger adults.

Conclusions

Among two samples of incarcerated adults (i.e., men and women), we observed that psychopathic traits were associated with lifetime history of nicotine dependence. Specifically, we observed that PCL-R total, Factor 2, and Facet 3 scores were positively correlated with nicotine dependence scores, operationalized via FTND total scores. Furthermore, the association between nicotine dependence and these same psychopathy scores remained significant when controlling for other covariate measures, including participant’s age, severity of substance use history, self-identified race and ethnicity, and IQ scores in hierarchical linear regression analyses. Our results extend upon previous research, which simply investigated the association between psychopathic traits and frequency of nicotine use, by identifying significant associations between psychopathy scores and FTND total scores. Though the results in our study represent small effect sizes (in correlation and hierarchical linear regression analyses), our results support the notion that PCL-R Factor 2 and Facet 3 scores represent general risk factors for engaging risky behavior associated with deleterious health consequences, including problematic patterns of substance use. Furthermore, the results of the current study may help inform treatment intervention approaches designed to reduce problematic levels of substance use among individuals with elevated psychopathic traits.

Supplementary Material

Supp 1

Footnotes

1

As race is a categorical variable, this variable was dummy coded to allow for its inclusion within hierarchical linear regression analyses. The number of groups here was five (K – 1), with White being chosen as the reference group, as the majority of participants self-identified as White.

2

While some correlations between variables remained consistent across gender, other correlations were not consistent across gender (e.g., correlations between FTND total scores and participant’s age). Therefore, we also report unadjusted associations between FTND total scores and PCL-R total, factor, and facet scores, without the influence of covariate measures, in our Supplemental Materials.

3

While women in the current study were characterized by larger effect sizes compared to men, across gender, small effect sizes were observed, via correlation and hierarchical linear regression analyses.

4

As reported in our Supplemental Materials, unadjusted associations were also investigated between psychopathy scores and FTND total scores, without the influence of covariate measures. In these analyses performed, consistent effects were observed as those reported in the main text (i.e., PCL-R total, Factor 2, and Facet 3 scores were associated with higher FTND total scores for both men and women).

5

In Regressions #2 and #3, participants who self-identified as Native Hawaiian or Pacific Islander (n = 2) were not included in hierarchical linear regression analyses, as these participants did not have PCL-R Factor 2 and Facet 4 scores, due to too many omitted items to correctly calculate factor and facet scores.

References

  1. Backman JG, Johnston LD, & O’Malley PM (1991). Monitoring the Future Project after 17 years: Design and Procedures Institute for Social Research; Ann Arbor, MI. Paper 33. [Google Scholar]
  2. Beaver KM, Nedelec JL, da Silva Costa C, Poersch AP, Stelmach MC, Freddi MC, ... & Boccio C (2014). The association between psychopathic personality traits and health-related outcomes. Journal of Criminal Justice, 42(5), 399–407. [Google Scholar]
  3. Benowitz NL (2010). Nicotine addiction. The New England Journal of Medicine, 362, 2295–2303. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bock B, Lopes CE, van den Berg JJ, Roberts MB, Stein LAR, Martin RA, ... & Clarke JG (2013). Social support and smoking abstinence among incarcerated adults in the United States: a longitudinal study. BMC Public Health, 13(1), 1–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Brown RA, Palm KM, Strong DR, Lejuez CW, Kahler CW, … & Gifford EV (2008). Distress tolerance treatment for early-lapse smokers: Rationale, program description, and preliminary findings. Behavior Modification, 32(3), 302–332. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Carmo JT, & Pueyo AA (2002). Adaptation into Portuguese for the Fagerström Test for Nicotine Dependence (FTND) to evaluate the dependence and tolerance for nicotine in Brazilian smoker. Revista Brasileira de Medicina, 59(1), 73–80. [Google Scholar]
  7. Cecero JJ, Ball SA, Tennen H, Kranzler HR, & Rounsaville BJ (1999). Concurrent and predictive validity of antisocial personality disorder subtyping among substance abusers. The Journal of Nervous & Mental Disease, 187(8), 478–486. [DOI] [PubMed] [Google Scholar]
  8. Cohen J (1988). Statistical power analysis for the behavioral sciences Routledge. [Google Scholar]
  9. Coid J, Yang M, Ullrich S, Roberts A, Moran P, Bebbington P, … & Hare R (2009). Psychopathy among prisoners in England and Wales. International Journal of Law and Psychiatry, 32(3), 134–141. [DOI] [PubMed] [Google Scholar]
  10. Cooperman NA, Rizvi SL, Hughes CD, & Williams JM (2018). Field test of a dialectical behavior therapy skills training-based intervention for smoking cessation and opioid relapse prevention in methadone treatment. Journal of Dual Diagnosis, 15(1), 67–73. [DOI] [PubMed] [Google Scholar]
  11. Cope LM, Vincent GM, Jobelius JL, Nyalakanti PK, Calhoun VD, & Kiehl KA (2014). Psychopathic traits modulate brain responses to drug cues in incarcerated offenders. Frontiers in Human Neuroscience, 8, 87. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Cornelius ME, Wang TW, Jamal A, Loretan CG, & Neff LJ (2020). Tobacco product use among adults – United States 2019. Morbidity and Mortality Weekly Report, 69(46), 1736–1742. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Cropsey KL, & Kristeller JL (2003). Motivational factors related to quitting smoking among prisoners during a smoking ban. Addictive behaviors, 28(6), 1081–1093. [DOI] [PubMed] [Google Scholar]
  14. Cullen KA, Gentzke AS, Swadley MD, Chang JT, Anic GM, … & King BA (2019). e-Cigarette use among youth in the United States. JAMA, 322(1), 2095–2103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Dierker LC, Donny E, Tiffany S, Colby SM, Perrine N, Clayton RR, & Tobacco Etiology Research Network. (2007). The association between cigarette smoking and DSM-IV nicotine dependence among first year college students. Drug and Alcohol Dependence, 86(2–3), 106–114. [DOI] [PubMed] [Google Scholar]
  16. de Meneses-Gaya C, Zuardi AW, de Azevedo Marques JM, Souza RM, Loureiro SR, & Crippa JAS (2009). Psychometric qualities of the Brazilian versions of the Fagerström Test for Nicotine Dependence and the Heaviness of Smoking Index. Nicotine & Tobacco Research, 11(10), 1160–1165. [DOI] [PubMed] [Google Scholar]
  17. Dowden C, & Brown SL (2002). The role of substance abuse factors in predicting recidivism: A meta analysis. Psychology, Crime & Law, 8(3), 243–264. [Google Scholar]
  18. Edwards BG, Carre JR, & Kiehl KA (2019). A review of psychopathy and Cluster B personality traits and their neural correlates in female offenders. Biological Psychology, 148, 107740. [DOI] [PubMed] [Google Scholar]
  19. Elkins IJ, King SM, McGue M, & Iacono WG (2006). Personality traits and the development of nicotine, alcohol, and illicit drug disorders: Prospective links from adolescence to young adulthood. Journal of Abnormal Psychology, 115(1), 26–39. [DOI] [PubMed] [Google Scholar]
  20. Etter JF, Duc TV, & Perneger TV (1999). Validity of the Fagerstrom test for nicotine dependence and of the Heaviness of Smoking Index among relatively light smokers. Addiction, 94(2), 269–281. [DOI] [PubMed] [Google Scholar]
  21. Etter JF, Le Houezec J, & Perneger TV (2003). A self-administered questionnaire to measure dependence on cigarettes: The Cigarette Dependence Scale. Neuropsychopharmacology, 28, 359–370. [DOI] [PubMed] [Google Scholar]
  22. Flory JD, & Manuck SB (2009). Impulsiveness and cigarette smoking. Psychosomatic Medicine, 71(4), 431–437. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Foster DW, Zvolensky MJ, Garey L, Ditre JW, & Schmidt NB (2014). Relationships between drinking motives and smoking expectancies among daily smokers who are also problem drinkers. Journal of dual diagnosis, 10(3), 118–129. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Friedman AS (1998). Substance use/abuse as a predictor to illegal and violent behavior: A review of the relevant literature. Aggression and Violent Behavior, 3(4), 339–355. [Google Scholar]
  25. Gifford EV, Kohlenberg BS, Hayes SC, Antonuccio DO, Piasecki MM, Rasmussen-Hall ML, & Palm KM (2004). Acceptance-based treatment for smoking cessation. Behavior Therapy, 35(4), 689–705. [DOI] [PubMed] [Google Scholar]
  26. Goodwin RD, Scheffer CE, Chartrand H, Bhaskaran J, Hart CL, Sareen J, & Bolton J (2014). Drug use, abuse, and dependence in the persistence of nicotine dependence. Nicotine & Tobacco Research, 16(12), 1606–1612. [DOI] [PubMed] [Google Scholar]
  27. Grando SA (2014). Connections of nicotine to cancer. Nature Reviews Cancer, 14(6), 419–429. [DOI] [PubMed] [Google Scholar]
  28. Haddock CK, Lando H, Klesges RC, Talcott GW, & Renaud EA (1999). A study of the psychometric and predictive properties of the Fagerström Test for Nicotine Dependence in a population of young smokers. Nicotine & Tobacco Research, 1(1), 59–66. [DOI] [PubMed] [Google Scholar]
  29. Halberstadt AL, Skrzynski CJ, Wright AGC, & Creswell KG (2021). Predicting smoking and nicotine dependence from the DSM-5 alternative model for personality pathology. Personality Disorders: Theory, Research, and Treatment [DOI] [PMC free article] [PubMed]
  30. Hare RD (2003). Manual for the Revised Psychopathy Checklist (2nd Ed.). Toronto, Ontario, Canada: Multi-Health Systems. [Google Scholar]
  31. Harpur TJ, Hare RD, & Hakstian AR (1989). Two-factor conceptualization of psychopathy: Construct validity and assessment implications. Psychological Assessment: A Journal of Consulting and Clinical Psychology, 1(1), 6–17. [Google Scholar]
  32. Heatherton TF, Kozlowski LT, Frecker RC, & Fagerstrom K-O (1991). The Fagerström Test for Nicotine Dependence: A revision of the Fagerstrom Tolerance Questionnaire. British Journal of Addiction, 86(9), 1119–1127. [DOI] [PubMed] [Google Scholar]
  33. Hemphill JF, Hart SD, & Hare RD (1994). Psychopathy and substance use. Journal of Personality Disorders, 8(3), 169–180. [Google Scholar]
  34. Hicks BM, & Patrick CJ (2006). Psychopathy and negative emotionality: Analyses of suppressor effects reveal distinct relations with emotional distress, fearfulness, and anger-hostility. Journal of Abnormal Psychology, 115(2), 276–287. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Hicks BM, Vaidyanathan U, & Patrick CJ (2010). Validating female psychopathy subtypes: Differences in personality, antisocial and violent behavior, substance abuse, trauma, and mental health. Personality Disorders: Theory, Research, and Treatment, 1(1), 38–57. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Hosker-Field AM, Molnar DS, & Book AS (2016). Psychopathy and risk taking: Examining the role of risk perception. Personality and Individual Differences, 91, 123–132. [Google Scholar]
  37. John U, Meyer C, Schumann A, Hapke U, Rumpf HJ, Adam C, ... & Lüdemann J (2004). A short form of the Fagerström Test for Nicotine Dependence and the Heaviness of Smoking Index in two adult population samples. Addictive behaviors, 29(6), 1207–1212. [DOI] [PubMed] [Google Scholar]
  38. Kennealy PJ, Hicks BM, & Patrick CJ (2007). Validity of factors of the Psychopathy Checklist - Revised in female prisoners: Discriminant relations with antisocial behavior, substance abuse, and personality. Assessment, 14(4), 323–340. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Kozlowski LT, Wilkinson DA, Skinner W, Kent C, Franklin T, & Pope M (1989). Comparing tobacco cigarette dependence with other drug dependencies: Greater or equal ‘difficulty quitting’ and ‘urges to use’, but less ‘pleasure’ from cigarettes. JAMA, 261(6), 898–901. [DOI] [PubMed] [Google Scholar]
  40. Kroeff CDR, Yates DB, Mendonca Filho EJD, Bandeira DR, & Silva MAD (2020). Intelligence and socioeconomic context on childhood: Comparisons by place of residence and school type. Paideia (Ribeirao Preto), 30. [Google Scholar]
  41. Lantini R, Van Den Berg JJ, Roberts MB, Bock BC, Stein LAR, Parker DR, ... & Clarke JG (2015). Characteristics of smoking used cigarettes among an incarcerated population. Psychology of Addictive Behaviors, 29(1), 254. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Lechner WV, Gunn RL, Minto A, Philip NS, Brown RA, Uebelacker LA, Price LH, & Abrantes AM (2018). Effects of negative affect, urge to smoke, and working memory performance (n-back) on nicotine dependence. Substance Use & Misuse, 53(7), 1177–1183. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Lewis AS, Oberleitner LMS, Morgan PT, Picciotto MR, & McKee SA (2015). Association of cigarette smoking with interpersonal and self-directed violence in a large community-based sample. Nicotine & Tobacco Research, 18(6), 1456–1462. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Maurer JM, Tirrell PS, Anderson NE, Rodriguez SN, Caldwell MF, Van Rybroek GJ, & Kiehl KA (2021). Dimensions of impulsivity related to psychopathic traits and dimensions of homicidal behavior among incarcerated male youth offenders. Psychiatry Research, 303, 114094. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. McConnell R, Barrington-Trimis JL, Wang K, Urman R, Hong H, … & Berhane K (2016). Electronic cigarette use and respiratory symptoms in adolescents. American Journal of Respiratory and Critical Care, 195(8), 1043–1049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. McLellan AT, Kushner H, Metzger D, Peters R, Smith I, Grissom G, Pettinati H, & Argeriou M (1992). The fifth edition of the Addiction Severity Index. Journal of Substance Abuse Treatment, 9(3), 199–213. [DOI] [PubMed] [Google Scholar]
  47. McMain S, Korman LM, & Dimeff L (2001). Dialectical behavior therapy and the treatment of emotion dysregulation. Journal of Clinical Psychology, 57(2), 183–196. [DOI] [PubMed] [Google Scholar]
  48. Messina NP, Wish ED, & Nemes S (1999). Therapeutic community treatment for substance abusers with antisocial personality disorder. Journal of Substance Abuse Treatment, 17(1–2), 121–128. [DOI] [PubMed] [Google Scholar]
  49. Miller JD, & Lynam DR (2003). Psychopathy and the five-factor model of personality: A replication and extension. Journal of Personality Assessment, 81(2), 168–178. [DOI] [PubMed] [Google Scholar]
  50. Mishra A, Chaturvedi P, Datta S, Sinukumar S, Joshi P, & Garg A (2015). Harmful effects of nicotine. Indian Journal of Medical and Paediatric Oncology, 36(01), 24–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Moeller FG, & Barratt ES (2001). Psychiatric aspects of impulsivity. The American Journal of Psychiatry, 158(11), 1783–1793. [DOI] [PubMed] [Google Scholar]
  52. Nelson SE, Belkin K, LaPlante DA, Bosworth L, & Schaffer HJ (2015). A prospective study of psychiatric comorbidity and recidivism among repeat DUI offenders. Archives of Scientific Psychology, 3(1), 8–17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Neumann CS, & Hare RD (2008). Psychopathic traits in a large community sample: Links to violence, alcohol use, and intelligence. Journal of Consulting and Clinical Psychology, 76(5), 893–899. [DOI] [PubMed] [Google Scholar]
  54. Oh H, Im B, & Seo W (2019). Comparisons of the stages and psychosocial factors of smoking cessation and coping strategies for smoking cessation in college student smokers: Conventional cigarette smokers compared to dual smokers of conventional and e‐cigarettes. Japan Journal of Nursing Science, 16(4), 345–354. [DOI] [PubMed] [Google Scholar]
  55. Patrick CJ (1994). Emotion and psychopathy: Startling new insights. Psychophysiology, 31, 319–330. [DOI] [PubMed] [Google Scholar]
  56. Patrick CJ, Hicks BM, Krueger RF, & Lang AR (2005). Relations between psychopathy facets and externalizing in a criminal offender sample. Journal of Personality Disorders, 19(4), 339–356. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Paulhus DL, Neumann CS, & Hare RD (2015). Self-Report Psychopathy Scale – III Multi-Health Systems: Toronto. [Google Scholar]
  58. Payne TJ, Smith PO, McCracken LM, McSherry WC, & Antony MM (1994). Assessing nicotine dependence: A comparison of the Fagerström Tolerance Questionnaire (FTQ) with the Fagerström Test for Nicotine Dependence (FTND) in a clinical sample. Addictive behaviors, 19(3), 307–317. [DOI] [PubMed] [Google Scholar]
  59. Peacock A, Leung J, Larney S, Colledge S, Hickman M, … & Degenhardt L (2018). Global statistics on alcohol, tobacco and illicit drug use: 2017 status report. Addiction, 113(10), 1905–1926. [DOI] [PubMed] [Google Scholar]
  60. Peters EN, Schwartz RP, Wang S, O’Grady KE, & Blanco C (2014). Psychiatric, psychosocial, and physical health correlates of co-occurring cannabis use disorders and nicotine dependence. Drug and Alcohol Dependence, 134, 228–234. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Reardon ML, Cukrowicz KC, Reeves MD, & Joiner TE (2002). Duration and regularity of therapy attendance as predictors of treatment outcome in an adult outpatient population. Psychotherapy Research, 12(3), 273–285. [Google Scholar]
  62. Saatcioglu O, & Erim R (2009). Aggression among male alcohol-dependent inpatients who smoke cigarettes. The Journal of Psychology, 143(6), 615–624. [DOI] [PubMed] [Google Scholar]
  63. Salgado-García FI, Cooper TV, & Taylor T (2013). Craving effect of smoking cues in smoking and antismoking stimuli in light smokers. Addictive Behaviors, 38(10), 2492–2499. [DOI] [PubMed] [Google Scholar]
  64. Sharma MK, Suman LN, Srivastava K, Suma N, & Vishwakarma A (2021). Psychometric properties of Fagerstrom Test of Nicotine Dependence: A systematic review. Industrial Psychiatry Journal, 30(2), 207. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Skeem J, Johansson P, Andershed H, Kerr M, & Louden JE (2007). Two subtypes of psychopathic violent offenders that parallel primary and secondary psychopathy. Journal of Abnormal Psychology, 116(2), 395–409. [DOI] [PubMed] [Google Scholar]
  66. Smith SS, & Newman JP (1990). Alcohol and drug abuse-dependence disorders in psychopathic and nonpsychopathic criminal offenders. Journal of Abnormal Psychology, 99(4), 430–439. [DOI] [PubMed] [Google Scholar]
  67. Steadman HJ, Silver E, Monohan J, Appelbaum P, Clark Robbins P, Mulvey EP, Grisso T, Roth LH, & Banks S (2000). A classification tree approach to the development of actuarial violence risk assessment tools. Law and Human Behavior, 24, 83–100. [DOI] [PubMed] [Google Scholar]
  68. Taylor J, & Lang AR (2006). Psychopathy and substance use disorders. In Patrick CJ (Ed.), Handbook of Psychopathy (pp. 495–511). New York: Guilford Press. [Google Scholar]
  69. Terranova C, Forza G, Beccegato E, Ruggeri A, Viel G, Viero A, & Montisci M (2021). Cigarette smoking as a predictor of male DUI recidivism. International Journal of Environmental Research and Public Health, 18(20), 10761. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Vaughn MG, DeLisi M, Beaver KM, Perron BE, & Abdon A (2012). Toward a criminal justice epidemiology: Behavioral and physical health of probationers and parolees in the United States. Journal of Criminal Justice, 40, 165–173. [Google Scholar]
  71. Vaurio O, Repo‐Tiihonen E, Kautiainen H, & Tiihonen J (2018). Psychopathy and mortality. Journal of forensic sciences, 63(2), 474–477. [DOI] [PubMed] [Google Scholar]
  72. Verona E, Patrick CJ, & Joiner TE (2001). Psychopathy, antisocial personality, and suicide risk. Journal of Abnormal Psychology, 110(3), 462–470. [DOI] [PubMed] [Google Scholar]
  73. Verona E, & Vitale J (2018). Psychopathy in women: Assessment, manifestations, and etiology. In Patrick CJ (Ed.), Handbook of Psychopathy (2nd Ed., pp. 509–528). New York, NY: Guilford. [Google Scholar]
  74. Voglewede JP, & Noel NE (2004). Predictors of current need to smoke in inmates of a smoke-free jail. Addictive behaviors, 29(2), 343–348. [DOI] [PubMed] [Google Scholar]
  75. von Stumm S, & Plomin R (2015). Socioeconomic status and the growth of intelligence from infancy through adulthood. Intelligence, 48, 30–36. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Wechsler D (1997). WAIS-III: Wechsler Adult Intelligence Scale (3rd Edition). The Psychological Corporation, San Antonio, TX. [Google Scholar]
  77. Wechsler D (2011). Wechsler Abbreviated Scale of Intelligence – Second Edition (WASI-II) San Antonio, TX: NCS Pearson. [Google Scholar]
  78. Winkleman TNA, Diaz Vickery K, & Busch AM (2019). Tobacco use among non-elderly adults with and without criminal justice involvement in the past year: United States, 2008–2016. Addiction Science & Clinical Practice, 14(1), 1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Woody GE, McLellan AT, Luborsky L, & O’Brien CP (1985). Sociopathy and psychotherapy outcomes. Archives in General Psychiatry, 42(11), 1081–1086. [DOI] [PubMed] [Google Scholar]
  80. Ziedonis DM, Guydish J, Williams J, Steinberg M, & Foulds J (2006). Barriers and solutions to addressing tobacco dependence in addiction treatment programs. Alcohol Research & Health, 29(3), 228–235. [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supp 1
NIHMS1871971-supplement-Supp_1.docx (20.9KB, docx)

RESOURCES