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. Author manuscript; available in PMC: 2026 Jan 23.
Published in final edited form as: Exp Clin Psychopharmacol. 2019 Mar 21;27(6):569–577. doi: 10.1037/pha0000270

Anxiety Sensitivity and Smoking Outcome Expectancies among Spanish-speaking Latinx Adult Smokers

Michael J Zvolensky a,b,c, Jafar Bakhshaie a, Justin M Shepherd a, Lorra Garey a, Natalia Peraza a, Andres G Viana a, Jason T Brown d, Richard A Brown d,e
PMCID: PMC12826622  NIHMSID: NIHMS2132958  PMID: 30896240

Abstract

Smoking is among the most important health behaviors linked to premature death and disability among the Latinx population. Yet, there is limited understanding of whether transdiagnostic factors like anxiety sensitivity may help explain smoking expectancies among Spanish-speaking Latinx smokers. The present investigation evaluated anxiety sensitivity in regard to smoking outcome expectancy factors among a large sample of adult Latinx smokers. Participants were 363 Spanish-speaking Latinx daily smokers (58.7% female, Mage = 33.3 years, SD = 9.8). As expected, anxiety sensitivity was significantly related to expectancies of negative reinforcement and negative personal consequences. Anxiety sensitivity also was a significant predictor of smoking expectancies of appetite control and positive reinforcement. The present study provides novel empirical evidence that anxiety sensitivity explains a notable degree of variance in smoking outcome expectancies over the variance accounted for by a range of theoretically relevant covariates among Latinx smokers. These results highlight the clinical utility in assessing anxiety sensitivity among Latinx smokers and focusing greater attention on this construct in efforts to better understand cognitive-based smoking expectancies among this population.

Keywords: Anxiety Sensitivity, Smoking, Latinx, Smoking Expectancies, Comorbidity, Health, Tobacco

1. Introduction

The Latinx population is among the largest minority groups in the United States (Colby & Ortman, 2015). Approximately 16% of all Latinx in the US are current smokers (Caraballo & Lee, 2004; CDC, 2006). This group represents a major health disparity population given that they are less likely to have access to smoking cessation treatment and are at greater risk for smoking cessation failure (Caraballo & Lee, 2004; CDC, 2014; Levinson, Pérez-Stable, Espinoza, Flores, & Byers, 2004). Thus, smoking is among the most important health behaviors linked to premature death and disability among the Latinx population (Anderson & Smith, 2005). Given the prevalence and negative impact of smoking among Latinx smokers (Anderson & Smith, 2005; Caraballo & Lee, 2004; CDC, 2006), there is a need to expand research on smoking among this understudied group. One important area and largely neglected facet of research pertains to cognitive aspects of smoking.

For decades, smoking outcome expectancies have been among the most influential cognitive factors documented in relation to smoking and other addictive behavior (Brandon, Juliano, & Copeland, 1999). Smoking outcome expectancies reflect beliefs about the positive and negative expected consequences or effects of smoking (Gwaltney, Shiffman, Balabanis, & Paty, 2005). Negative expectancies include beliefs that smoking can cause personal physical harm (e.g., smoking increases risk of cancer). In contrast, positive expectancies reflect beliefs that smoking can improve mood states (e.g., smoking will uplift mood), negative reinforcement beliefs about smoking (e.g., smoking will dampen negative mood), and beliefs that smoking can be used to manage appetite or weight concerns (e.g., smoking will reduce food craving). Integrative theories and corresponding empirical work indicate that such outcome expectancies are related to numerous aspects of smoking, including being a proximal mediator of smoking motivation (Brandon et al., 1999; Goldman, Brown, & Christiansen, 1987; Marlatt & Donovan, 2005; Niaura et al., 1988), smoking initiation (Doran et al., 2013; Goldman et al., 1987), cigarette dependence (Copeland, Brandon, & Quinn, 1995; Myers, MacPherson, McCarthy, & Brown, 2003), tobacco withdrawal (Langdon & Leventhal, 2014; Wetter et al., 1994), and cessation outcome (Kenford et al., 2002; Wetter et al., 1999; Wetter et al., 1994).

Research focused on smoking expectancies among Latinx smokers is relatively modest. Of the work that has been completed, results suggest that Spanish-speaking smokers from Spain endorse both negative reinforcement and positive outcome expectancies for smoking, and other work on Latinx smokers in the US suggests that negative reinforcement and positive reinforcement expectancies are related to smoking withdrawal and relapse (Cepeda-Benito & Reig Ferrer, 2000; Reig-Ferrer & Cepeda-Benito, 2007; Vidrine et al., 2009). Additionally, some research suggests expectancies that ‘smoking will cause personal harm’ are among those most commonly endorsed among Latinx smokers (Marin, Marin, Perez-Stable, Otero-Sabogal, & Sabogal, 1990; Marin, Marin, Perez-Stable, Sabogal, & Otero-Sabogal, 1990). Despite the promise of existing work, there is a need to broaden and deepen our understanding of factors that may explain variability in smoking outcome expectancies among Latinx smokers.

There has been an increased amount of scientific attention focused on the role of affective vulnerability in terms of smoking behavior in general and among Latinx smokers specifically (Pérez-Stable, Marin, Marín, & Katz, 1990; Zvolensky et al., 2017). Broadly, past research has identified that both emotional vulnerability factors and psychiatric conditions are related to the prevalence of smoking, higher dependence on tobacco, decreased success in quitting smoking, heavier rates of smoking, and greater withdrawal and craving (Ziedonis et al., 2008). To delineate underlying mechanisms explaining relations between emotion vulnerability and smoking, one promising line of research has focused on anxiety sensitivity (Zvolensky & Bernstein, 2005). Anxiety sensitivity reflects the fear of anxiety and arousal-related sensations, which pertains to a relative stable cognitive predisposition for anxiety-depressive psychopathology (McNally, 2002). Anxiety sensitivity is theoretically and empirically distinguishable from anxiety symptoms and other negative affect states among non-Latinx Whites (Zvolensky, Kotov, Antipova, & Schmidt, 2005) and Latinx (Zvolensky et al., 2018). Among non-Latinx Whites, anxiety sensitivity is related to numerous aspects of smoking behavior, ranging from decreased quit success to greater perceived barriers for quitting (Leventhal & Zvolensky, 2015). Further, this body of work has found anxiety sensitivity often is associated with negative reinforcement smoking expectancies (Leventhal & Zvolensky, 2015). Lesser attention has been directed at expectancies for appetite-weight or positive reinforcement, although some work suggests positive associations (Garey et al., 2017).

Theoretically, anxiety sensitivity may be an important individual difference factor for smoking outcome expectancies among Latinx smokers. As has been observed among non-Latinx Whites (Garey et al., 2017), higher anxiety sensitivity may contribute to ‘dual’ beliefs about smoking among Latinx smokers, including positive expectancies (i.e., smoking helps mitigate aversive internal states and curbs appetite) and yet also yields negative personal consequences (e.g., respiratory irritation, physical illness; Marin et al., 1990; Marin, 1996). This phenomenon may result from smokers higher in anxiety sensitivity (a) experiencing a greater anxiolytic effect from smoking and, therefore, developing stronger beliefs for smoking to relieve negative mood symptoms and, concurrently, (b) being more hypervigilant about their health, body function, and bodily sensations, including desire to eat, coughing, and shortness of breath, which may lead to greater recognition of the appetite-weight suppression effect of smoking and harmful consequences of smoking. Also, to the extent that Latinx smokers higher relative to lower in anxiety sensitivity experience more depressed affect, it is possible that these emotional experiences may be related to smoking to engage in an alternative pleasurable activity (positive reinforcement).

Overall, the present investigation sought to evaluate the relevance of anxiety sensitivity in relation to smoking outcome expectancy factors among adult Latinx smokers. It was hypothesized that higher levels of anxiety sensitivity would be significantly related to smoking expectancies for negative reinforcement, appetite-weight control, and personal harm. It also was hypothesized that anxiety sensitivity would evince a significant, but relatively less robust, association with expectancies for positive reinforcement. In all models, we adjusted for several theoretically- and clinically-relevant covariates shown in past work to relate to smoking among Latinx smokers, including gender, income, education, number of medical conditions, non-alcohol drug use, alcohol consumption, and severity of anxiety symptoms (Escobedo, Anda, Smith, Remington, & Mast, 1990; Haynes, Harvey, Montes, Nickens, & Cohen, 1990; Navarro, 1996; Sabogal, Otero-Sabogal, Perez-Stable, Marin, & Marin, 1989; Vega, Zimmerman, Warheit, Apospori, & Gil, 1993).

2. Method

2.1. Participants

Participants were 363 Spanish-speaking Latinx daily smokers (58.7% female, Mage = 33.3 years, SD = 9.8). Eligible participants were current daily smokers between 18–64 years old. Exclusion criteria included being younger than age 18, non-Latinx, a non-Spanish speaker, and an inability to give informed, voluntary, written consent to participate. Most subjects reported being born in Mexico (35.8%), 22.6% born in the United States, 17.4% born in Puerto Rico, 9.1% born in Cuba, 5.5% born in South America, 2.2% born in Dominican Republic, 2.2% born in Central America, and 5.2% reported being born in other regions. In terms of education, 18.2% of participants reported less than 6 years of education, 15.2% 6–11 years, 24.5% 12 years (completion of high school), and 42.1% reported more than 12 years. The median income bracket fell within the range of $35,999 to $49,999. Participants were recruited across 32 states and the state-wide distribution was as follows: 21.90% California, 13.96% Texas. 13.65% New York, 12.06% Florida, 5.39% Arizona, 3.80%, Illinois, 3.17%, Pennsylvania, 2.85% New Jersey, 2.85% North Dakota, 2.53% Massachusetts, 1.90% Connecticut, 1.58% Michigan, 1.58% Ohio, 1.26% Virginia, 1.26% Washington, 1.26% Wisconsin, 0.95% Missouri, 0.63% Alabama, 0.63% Colorado, 0.63% Indiana, 0.63% Iowa, 0.63% Kentucky, 0.63% Minnesota, 0.63% New Mexico, 0.63% Oregon, 0.31% Delaware, 0.31% Georgia, 0.31% Hawaii, 0.31% Louisiana, 0.31% Oklahoma, 0.31% South Carolina, and 0.31% West Virginia. Participants reported being daily smokers for an average of 11.4 years (SD=9.7), smoked an average of 9.3 (SD=8.7) cigarettes per day, reported an average of 4.2 (SD=6.4) prior failed (self-defined ‘serious’) quit attempts and a median of 3 prior quit attempts. Based on Fagerström Test for Cigarette Dependence (Heatherton, Kozlowski, Frecker, & Fagerström, 1991), participants average cigarette dependence rate was 7.6 (SD=3.5), which reflects physical dependence on tobacco.

2.2. Measures

Anxiety Sensitivity Index-3 (ASI-3; Taylor et al., 2007).

The ASI-3 is an 18-item self-report measure of sensitivity to and fear of the potential negative consequences of anxiety-related symptoms and sensations (Taylor et al., 2007). Respondents are asked to indicate, on a 5-point Likert-type scale (0 = very little to 4 = very much), the degree to which they are concerned about these possible negative consequences (possible range 0–72). The ASI-3, derived in part from the original ASI (Reiss & McNally, 1985), has sound psychometric properties, including excellent internal consistency, predictive validity, and reliability among treatment-seeking smokers (Farris et al., 2015). Additionally, the factor structure and psychometric properties of the anxiety sensitivity construct has been supported with a variety of Latinx samples (Cintrón, Carter, Suchday, Sbrocco, & Gray, 2005; Jardin et al., 2018; Sandin, Chorot, & McNally, 1996; Zvolensky et al., 2003). As in past work among Latinx smokers (Zvolensky et al., in press), the total ASI-3 score was used in the present study (α = .97).

Short Form-Smoking Consequences Questionnaire (S-SCQ; Myers et al., 2003).

The S-SCQ is a 21-item self-report measure of cigarette smoking expectancies. Respondents are asked to indicate, on a 10-point Likert scale (0 = completely unlikely to 9 = completely likely), the likelihood of each smoking-related consequence occurring. The S-SCQ consists of four factors and has well demonstrated psychometric validity (Myers et al., 2003). We used each of the four factors as criterion variables in the current study: Negative Consequences; Positive Reinforcement; Negative Reinforcement; Appetite-Weight Control (α’s = .81 to .84)

Demographics Questionnaire.

Demographic data including gender, race, age, educational level (1= 6 years or less to 4= more than 12 years), and annual income (1= $0–$4,999 to 9= $100,000 or higher) was collected from participants. Gender, income, and education were included as covariates.

The Medical History and Present Medical Condition Questionnaire (Precision, 2012).

This questionnaire assesses for history of medical conditions and was used to measure the following: heart disease, high blood pressure, diabetes, respiratory disease, gastrointestinal disease, peripheral arterial disease, musculoskeletal disease, headache, genitourinary disease, anemia, oral disease, and pregnancy. The sum score of medical conditions was calculated and used as an index of number of medical problems.

Drug Abuse/Dependence Screener (Rost, Burnam, & Smith, 1993).

This questionnaire is a 3-item screener for commonly abused substances (e.g. cannabis, stimulants, sedatives, tranquilizers, cocaine, heroin, opiates, psychedelics). This measure has been successfully used in past studies among Spanish-speakers (Watkins, Paddock, Zhang, & Wells, 2006). The answer to the question “Have you ever used one of these drugs on your own more than 5 times in your life?” was used as an index of non-alcohol drug use.

The Alcohol Use Disorders Identification Test (AUDIT; Babor, Higgins-Biddle, Saunders, Monteiro, & WHO, 2001).

The AUDIT is a 10-item self-report measure to assess problematic alcohol use. Questions (e.g., “How often do you have a drink containing alcohol”) are rated on various scales from 0 (never) to 4 (4 or more times a week) and are summed to a total score as well as the three subscales (e.g. consumption). The AUDIT has strong psychometric properties including reliability and validity (Babor et al., 2001; Saunders, Aasland, Babor, De la Fuente, & Grant, 1993). In this study, the AUDIT-consumption subscale was used (α = .82). This subscale is used as a problem drinking screener (Bush, Kivlahan, McDonell, Fihn, & Bradley, 1998) and has been used successfully in past studies among Spanish-speakers (Cremonte, Ledesma, Cherpitel, & Borges, 2010).

The Hospital Anxiety and Depression Scale (HADS; Herrero et al., 2003).

The HADS is a 14-item self-report screening scale for anxiety and depression (Zigmond & Snaith, 1983). The HADS includes a 7-item anxiety subscale and a 7-item depression subscale. Each item is scored on a 4-point Likert scale from 0 (definitely as much or very often) to 3 (hardly at all or not at all). The Spanish version of HADS has demonstrated good psychometric properties among Spanish-speaking samples (Herrero et al., 2003). The anxiety subscale of HADS was used as a covariate in the current study. (α = .77).

2.3. Procedure

Participants were recruited across 32 U.S. states through Qualtrics, an online survey management system. Adults with a Qualtrics Panels account that endorsed current daily smoking and smoked tobacco within the past week were sent a survey advertisement. Respondents were screened for eligibility and directed to the anonymous online survey in Spanish. Participants provided informed consent prior to completing the survey, which took approximately 20 minutes. Participants could opt to receive their compensation in varying forms (e.g., cash-based incentives [i.e., gift cards], rewards miles, rewards points, etc.). Although the forms were different, the level of compensation remained was consistent across respondents and was typically 20% to 35% of the total cost per completed survey ($7.50). The study protocol was approved by the Institutional Review Board at the University of Houston.

2.4. Analytic Strategy

Analyses were conducted using SPSS version 24. First, sample descriptive statistics and zero-order correlations among study variables were analyzed. Second, to evaluate the incremental predictive power of anxiety sensitivity, four separate two-step hierarchical regressions were conducted for each of the criterion variables (i.e., Negative Consequences; Positive Reinforcement; Negative Reinforcement; Appetite-Weight Control). For all analyses, step 1 included covariates (gender, income, education, number of medical conditions, non-alcohol drug use, alcohol consumption, and anxiety symptoms), as these variables have been found to correlate with smoking among Latinx samples (Gregor & Borrelli, 2012; Noonan et al., 2016; Winkleby, Schooler, Kraemer, Lin, & Fortmann, 1995; Zvolensky, Bogiaizian, Salazar, Farris, & Bakhshaie, 2014). Male, female, and transgender variables were dummy coded to facilitate interpretation as recommended by Hardy (1993) and transgender served as the reference group. Following recommendations by Cohen, Cohen, West, and Aiken (2003), all dummy coded variables were included in the regression analyses in order to represent the total effect of gender. Step 2 included the anxiety sensitivity total score. Model fit for each of the steps was evaluated with the F statistic and an increase in variance accounted for as evidenced by a change in R2. Squared semi-partial correlations (sr2) were used as measures of effect size.

3. Results

3.1. Descriptive Statistics

Bivariate correlations are presented in Table 1. Anxiety sensitivity was positively associated with each of the criterion variables (rs ranging from .30 to .44; see Table 1). All smoking expectancies positively correlated (rs ranging from .64 to .89).

Table 1.

Bivariate correlations

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.
1. Male 1 −.989** .165** −.109* .118* .088 .153** .010 .095 .004 .079 .040 .148**
2. Female 1 −.167** .108* −.122* −.088 −.173** −.013 −.100 −.011 −.084 −.042 −.163**
3. Income 1 .105* −.030 −.043 .232** −.091 .153** .134* .152** .151** .188**
4. Education 1 −.169** −.157** −.242** −.125* −.057 −.027 −.067 −.062 −.190**
5. Medical Conditions 1 .145** .209** .272** .109* .081 .104* .070 .226**
6. Non-Alcohol Drug Use 1 .244** .297** .111* .075 .103* .066 .277**
7. Alcohol Consumption 1 .188** .290** .185** .291** .223** .464**
8. Anxiety Symptoms 1 .221** .040 .168** .134* .423**
9. Negative Consequences 1 .636** .856** .832** .417**
10. Positive Reinforcement 1 .791** .816** .304**
11. Negative Reinforcement 1 .888** .437**
12. Appetite-Weight Control 1 .388**
13. Anxiety Sensitivity 1
Mean/n 148 213 5.67 2.90 2.05 177 6.76 9.15 22.78 30.67 40.93 30.20 26.84
SD/% 40.8 58.7 2.32 1.14 2.60 48.8 3.30 4.90 8.55 10.06 13.25 9.82 20.04
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Note. N=363;

*

p < 0.05;

**

p < 0.01;

Male = % listed as male (Coded: 0 = transgender or female, 1 = male); Female= % listed as female (Coded: 0 = transgender or male, 1 = female); Non-Alcohol Drug Use = % listed as “yes” (Coded: 1 = no and 2 = yes); Medical Conditions = Total number of medical conditions as per the Medical History and Present Medical Condition Questionnaire (Precision, 2012); Non-Alcohol Drug Use = Non-alcohol drug use as per the Drug Abuse/Dependence Screener (Rost et al., 1993); Alcohol Consumption= AUDIT-Consumption subscale as per the Alcohol Use Disorders Identification Test (Babor et al., 2001); Anxiety Symptoms= Sum score of the anxiety subscale of the Hospital Anxiety and Depression Scale (Herrero et al., 2003); Negative Consequences, Positive Reinforcement, Negative Reinforcement, and Appetite-Weight Control = Subscales from the Smoking Consequences Questionnaire-Short Form (Myers et al., 2003); Anxiety Sensitivity = Total score from the Anxiety Sensitivity Index-3 (Taylor et al., 2007).

3.2. Primary Analyses

For negative consequences, step 1 of the model was significant (F(8, 354) = 6.469, p < .001, R2 = .13). Examining the individual predictors indicated that income (B = 0.406, SE = 0.195, p = .038, sr2 = .01), alcohol consumption (B = 0.579, SE = 0.146, p < .001, sr2 = .04) and anxiety symptoms (B = 0.354, SE = 0.103, p = .001, sr2 = .03) were significant predictors of negative consequences. In step 2, anxiety sensitivity was a significant predictor (B = 0.140, SE = 0.026, p < .001, sr2 = .07; see Table 2).

Table 2.

Hierarchical Regression Results

S-SCQ: Negative Consequences
Model F Statistic ΔR2 B SE t p-value sr 2
1 Male 6.469 .128 0.183 5.803 0.031 .975 0.000
Female −0.527 5.803 −0.091 .928 0.000
Income 0.406 0.195 2.080 .038 0.011
Education 0.112 0.396 0.284 .776 0.000
Medical Conditions 0.039 0.174 0.223 .824 0.000
Non-Alcohol Drug Use 0.051 0.914 0.056 .956 0.000
Alcohol Consumption 0.579 0.146 3.966 < .001 0.039
Anxiety Symptoms 0.354 0.103 3.437 .001 0.029
2 Anxiety Sensitivity 29.911 .068 0.140 0.026 5.469 < .001 0.068
S-SCQ: Positive Reinforcement
Model F Statistic ΔR2 B SE t p-value sr 2
1 Male 2.317 .050 −4.520 7.124 −0.634 .526 0.001
Female −3.612 7.123 −0.507 .612 0.001
Income 0.479 0.240 1.998 .047 0.011
Education 0.060 0.486 0.124 .901 0.000
Medical Conditions 0.213 0.214 0.995 .320 0.003
Non-Alcohol Drug Use 0.870 1.122 0.775 .439 0.002
Alcohol Consumption 0.436 0.179 2.431 .016 0.016
Anxiety Symptoms −0.010 0.126 −0.077 .939 0.000
2 Anxiety Sensitivity 25.152 .063 0.159 0.032 5.015 < .001 0.063
S-SCQ: Negative Reinforcement
Model F Statistic ΔR2 B SE t p-value sr 2
1 Male 5.468 .110 0.460 9.078 0.051 .960 0.000
Female −0.117 9.077 −0.013 .990 0.000
Income 0.607 0.306 1.985 .048 0.010
Education 0.017 0.619 0.028 .978 0.000
Medical Conditions 0.104 0.272 0.382 .703 0.000
Non-Alcohol Drug Use 0.230 1.430 0.161 .872 0.000
Alcohol Consumption 0.938 0.228 4.107 < .001 0.042
Anxiety Symptoms 0.371 0.161 2.303 .022 0.013
2 Anxiety Sensitivity 42.641 .096 0.258 0.039 6.530 < .001 0.096
S-SCQ: Appetite-Weight Control
Model F Statistic ΔR2 B SE t p-value sr 2
1 Male 3.484 .073 1.528 6.870 0.222 .824 0.000
Female 1.751 6.869 0.255 .799 0.000
Income 0.532 0.231 2.298 .022 0.014
Education −0.178 0.468 −0.381 .703 0.000
Medical Conditions 0.026 0.206 0.127 .899 0.000
Non-Alcohol Drug Use −0.130 1.082 −0.120 .905 0.000
Alcohol Consumption 0.512 0.173 2.964 .003 0.023
Anxiety Symptoms 0.243 0.122 1.990 .047 0.010
2 Anxiety Sensitivity 37.659 .089 0.185 0.030 6.137 < .001 0.089
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Note. Medical Conditions = Number of medical conditions as per the Medical History and Present Medical Condition Questionnaire (Precision, 2012); Non-Alcohol Drug Use = Non-alcohol drug use as per the Drug Abuse/Dependence Screener (Rost et al., 1993); Alcohol Consumptions = AUDIT-Consumption subscale as per the Alcohol Use Disorders Identification Test (Babor et al., 2001); Anxiety Symptoms = Anxiety as per the sum score of the anxiety subscale of the Hospital Anxiety and Depression Scale (Herrero et al., 2003); Anxiety Sensitivity = Anxiety Sensitivity as per the total score of the Anxiety Sensitivity Index-3 (Taylor et al., 2007) S-SCQ = Smoking Consequences Questionnaire-Short Form (Myers et al., 2003).

In terms of positive reinforcement expectancies, step 1 was significant (F(8, 354) = 2.317, p = .020, R2 = .05). Income (B = 0.479, SE = 0.240, p = .047, sr2 = .01) and alcohol consumption (B = 0.436, SE = 0.179, p = .016, sr2 = .02) were significant predictors. In step 2, anxiety sensitivity was a significant predictor (B = 0.159, SE = 0.032, p < .001, sr2 = .06; see Table 2).

For negative reinforcement, the overall model was significant (F(8, 354) = 5.468, p < .001, R2 = .11). Significant effects were evident in step 1 for income (B = 0.607, SE = 0.306, p = .048, sr2 = .01), alcohol consumption (B = 0.938, SE = 0.228, p < .001, sr2 = .04) and anxiety symptoms (B = 0.371, SE = 0.161, p = .022, sr2 = .01). In step 2, anxiety sensitivity was a significant predictor (B = 0.258, SE = 0.039, p < .001, sr2 = .10; see Table 2).

In predicting the appetite-weight control expectancies, the overall model was significant (F(8, 354) = 3.484, p = .001, R2 = .07) and significant effects emerged in step 1 for income (B = 0.532, SE = 0.231, p = .022, sr2 = .01) alcohol consumption (B = 0.512, SE = 0.173, p = .003, sr2 = .02) and anxiety symptoms (B = 0.243, SE = 0.122, p = .047, sr2 = .01). In step 2, anxiety sensitivity was a significant predictor (B = 0.185, SE = 0.030, p < .001, sr2 = .09; see Table 2).

4. Discussion

Latinx smokers are a health disparities group, which has been highly understudied relative to many other racial/ethnic populations (Escobedo et al., 1990). The present study sought to build upon past research by exploring the explanatory utility of anxiety sensitivity, a transdiagnostic construct, in relation smoking outcome expectancies. As expected, the global anxiety sensitivity construct was significantly related to expectancies of negative reinforcement and negative personal consequences. The size of the observed effects was moderate at 10% and 7% of variance, respectively. Further, these effects were evident above and beyond the variance accounted for by gender, income, education, number of conditions, non-alcohol drug use, alcohol consumption level, and anxiety symptoms, highlighting their potential clinical importance. These findings are in line with research focused on non-Latinx White smokers (Leventhal & Zvolensky, 2015) and extend them to a diverse sample of Latinx smokers. Additionally, these data add to the available (limited) literature on Latinx smokers by demonstrating anxiety sensitivity is a relatively robust clinical correlate of negative expectancies for smoking.

Anxiety sensitivity also was a significant predictor of smoking expectancies of appetite control (9% of variance) and positive reinforcement (6% of variance). Again, these significant effects were evident beyond the variance accounted for by the covariates at the earlier step in the model. Past work largely focused on non-Latinx Whites has generally found modest correlations between anxiety sensitivity and appetite-weight control expectancies and limited associations with positive reinforcement expectancies (Leventhal & Zvolensky, 2015). These novel findings suggest that among Latinx smokers, anxiety sensitivity may showcase relatively more robust relations with a wide array of expectancies for smoking. Theoretically, Latinx smokers more likely to evaluate internal sensations as personally dangerous may then experience lower positive mood (negative affect) or more intense emotional experiences. In fact, anxiety sensitivity was strongly associated with anxiety symptoms in the current sample (r = .42). Thus, it is possible that such emotional experiences may be related to smoking to curb emotional eating or be used as a life enhancing (pleasurable) activity, as has been evident in some non-Latinx smoking samples (Garey et al., 2017). Consistent with this type of formulation, non-smoking research among non-Latinx Whites has found anxiety sensitivity is related to emotional eating (Otto et al., 2016). Future research is needed to explore the complex relations that may be evident between anxiety sensitivity and positive outcome expectancies for smoking among Latinx smokers. For example, it may be useful to test whether emotion dysregulation mediates the relation between anxiety sensitivity and positive outcome expectancies among this group of smokers.

Although not a primary study aim, several additional observations warrant comment. First, we modeled several theoretically-relevant covariates that have been implicated in smoking among Latinx smokers in past work (Castro et al., 2013; Castro et al., 2012; Dominguez et al., 2015). Of these covariates, it is striking that greater relative income was associated with more positive and negative smoking outcome expectancies. These data suggest that greater financial resources may be linked to stronger beliefs about the consequences of smoking. Further, greater income was associated with greater anxiety sensitivity. Thus, it may be that higher income Latinx smokers are more worried about the negative consequences of anxiety-related sensations. Although lower relative income often tracks with more severe smoking and mental illness in the general population (e.g., Adler, Boyce, Chesney, Folkman, & Syme, 1993; Sareen, Afifi, McMillan, & Asmundson, 2011), the current data suggest that, at least among Latinx smokers, more complex relations between income and smoking and anxiety sensitivity may be operative. Future research is needed to expressly focus on the income-smoking and mental health relations among Latinx smokers. Second, across all models, greater alcohol consumption was related to positive and negative outcome expectancies. Thus, heavier drinking is reliably associated with smoking outcome expectancies among Latinx smokers. Some past work has highlighted the clinical importance of ‘dual’ smoking-alcohol users among the Latinx population (e.g., Adler et al., 1993; Escobedo, Reddy, & DuRant, 1997). Our work is in line with these findings and encourages future work to explore the mechanisms that may underline smoking-alcohol relations among this health disparity group.

From a clinical perspective, the findings of the current investigation could inform models and intervention programming for Latinx smokers. Most notably, the results suggest anxiety sensitivity is central to understanding smoking outcome expectancies among Latinx smokers and should be included in integrative models of smoking maintenance and relapse for this population. Further, it may be that reducing anxiety sensitivity is therapeutically beneficial for changing smoking outcome expectancies. Although some initial work has found that anxiety sensitivity reduction may be useful to help quit smoking (Zvolensky et al., 2014), it would be useful to evaluate whether similar tactics could change outcome expectancies. Such an approach would strengthen the therapeutic rationale for targeting anxiety sensitivity to change cognitive-based aspects of smoking behavior among Latinx smokers.

Limitations to the present study should be noted. Results were based upon a cross-sectional study design and cannot address causal relations. Therefore, future research could usefully employ alternative research designs, including laboratory and longitudinal models. Second, although the sample was collected from 32 States, the present data may not generalize to all Latinx smokers. Future research may benefit by sampling specific Latinx groups in greater depth (e.g., Mexican-origin smokers) to better understand population-specific factors in this heterogenous Latinx group. Finally, although we studied a wide range of clinically significant factors, we did not assess cultural-specific factors. Future research could therefore continue to explore other factors that may be relevant to Latinx smokers in the context of anxiety sensitivity and outcome expectancies for smoking (e.g., discrimination, acculturation; Marin et al., 1990).

Overall, the present study provides novel empirical evidence that anxiety sensitivity explains a notable degree of variance in smoking outcome expectancies among Latinx smokers. These findings were evident over the variance accounted for by a range of relevant covariates. Such results highlight the clinical utility in assessing anxiety sensitivity among Latinx smokers and focusing greater scientific attention on this construct in efforts to better understand cognitive-based smoking expectancies among this population.

Funding:

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Footnotes

Conflict of Interest: Richard A. Brown has equity ownership in Health Behavior Solutions, which is developing products for tobacco cessation. The terms of this arrangement have been reviewed and approved by the University of Texas at Austin in accordance with its policy on objectivity in research.

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