The Relative Contribution of Metacognitive Beliefs and Expectancies to Drinking Behaviour

Marcantonio M. Spada; Giovanni B. Moneta; Adrian WellsAlcohol Alcohol.  2007;42(6):567-574.  ©2007 Oxford University Press

Posted 12/20/2007

Abstract and Introduction

Abstract

Aim: Alcohol expectancies refer to the effects of alcohol use anticipated by an individual. Metacognitive beliefs about alcohol use are a specific form of alcohol expectancy relating to the beliefs individuals hold about the effects of alcohol on cognition and emotion.
Method: A community sample of 355 individuals completed measures of alcohol expectancies, metacognitive beliefs about alcohol use, and drinking behaviour.
Results: Correlation analyses indicated that alcohol expectancies and metacognitive beliefs about alcohol use were positively correlated with drinking behaviour. Structural regression modelling revealed that three of the four facets of metacognitive beliefs about alcohol use were independent contributors to drinking behaviour, and that, when controlling for such beliefs, only negative social performance alcohol expectancies explained additional variance in drinking behaviour.
Conclusions: These results add to the argument that there is a value in differentiating between metacognitive beliefs about alcohol use and alcohol expectancies in predicting drinking behaviour.

Introduction

Expectancies refer to a person's evaluation of an anticipated outcome (Tolman, 1932). This evaluation is understood to be primarily of an 'if-then' nature; if a certain event is presented, then a certain event is expected to follow (Bolles, 1972). Alcohol expectancies, therefore, refer to an individual's explicit or implicit set of beliefs about the effects of drinking alcohol (Brown, 1985; Brown et al., 1987). Alcohol expectancies are believed to reflect memories arising from various forms of learning (Del Boca et al., 2002).

According to Social Learning Theory (e.g. Maisto et al., 1999) and Outcome Expectancy models (e.g. Jones et al., 2001) information about the link between drinking behaviours and specific outcomes is learned. The concept of alcohol expectancies thus provides a theoretical framework for understanding a person's motivation to drink and can help explain problematic drinking behaviour.

The construct of alcohol expectancies is multidimensional, and includes both positive and negative effects of alcohol use (Leigh and Stacy, 1993). Positive alcohol expectancies (e.g. 'Drinking will make me relax') refer to the drinker's perception of the positive outcomes of drinking, and have been shown to be associated to alcohol consumption (Christiansen et al., 1989; Darkes and Goldman, 1993; Dunn and Goldman, 1996; Goldman et al., 1999). Negative alcohol expectancies (e.g. 'When I drink I have problems driving') refer to the expected negative outcomes that occur as a result of drinking and have been found, overall, to be less reliably associated to alcohol use (Christiansen et al., 1989; Stacy et al., 1990; McNally and Palfai, 2001). Indeed some studies have found this construct to be associated with lower levels of alcohol use (Leigh, 1989; Weirs et al., 1997; Kilbey et al., 1998; Sharkansky and Finn, 1998), and a lower probability for relapse after treatment (Eastman and Norris, 1982; Jones and McMahon, 1994), while other studies have reported a positive association with heavy drinking (Mann et al., 1987; McMahon et al., 1994) or no association to alcohol use (Southwick et al., 1981; Fromme et al., 1993).

The emergence of cognitive theories of psychopathology (e.g. Beck, 1976) has led to a growing interest in the characteristics of cognition and its regulation. One particular line of theoretical work has emphasized the role of metacognitive beliefs as central to the development and persistence of dysfunction (Wells, 2000). Metacognitive beliefs refer to the information individuals hold about their own cognition and internal states, and about coping strategies that impact on both (Brown, 1987; Wells and Matthews, 1994, 1996; Wells, 2000). Examples of information individuals hold about their own cognition and internal states may include beliefs concerning the significance of particular types of thoughts, e.g. 'It is bad to think thought X' or 'I need to control thought X'. Examples of information individuals hold about coping strategies that impact on cognition and internal states may include beliefs such as 'Smoking will help me get things sorted out in my mind' or 'Worrying will help me solve the problem'. According to the metacognitive theoretical tenet (Wells and Matthews, 1994, 1996; Wells, 2000) these beliefs are fundamental in predisposing individuals to develop response patterns to thoughts and internal events that are characterized by heightened self-focused attention, recyclical thinking patterns, avoidance and thought suppression, threat monitoring and maladaptive behaviours. Research evidence appears to support this contention, as metacognitive beliefs have been implicated in a variety of psychological problems including depression (Papageorgiou and Wells, 2003), generalized anxiety disorder (Cartwright-Hatton and Wells, 1997; Wells and Carter, 2001), hypochondriasis (Bouman and Meijer, 1999), obsessive-compulsive disorder (Emmelkamp and Aardema, 1999), post-traumatic stress disorder (Reynolds and Wells, 1999), procrastination (Spada et al., 2006a), psychosis (Morrison et al., 2000), smoking dependence (Spada et al., 2007) and test anxiety (Spada et al., 2006c).

Recent research undertaken by Spada and colleagues (Spada and Wells, 2005, 2006a,b; Spada et al., 2006b) has looked at the role of metacognition in alcohol use, identifying specific positive and negative metacognitive beliefs about alcohol use and finding these constructs to be associated with drinking behaviour. Positive metacognitive beliefs about alcohol use can be conceptualized as a specific form of expectancy relating to the use of alcohol as a means of controlling and regulating cognition and emotion. Examples of positive metacognitive beliefs about alcohol use may include: 'Drinking makes me think more clearly' (problem-solving), 'Drinking helps me to control my thoughts' (thought control), 'Drinking helps me focus my mind' (attention regulation), 'Drinking reduces my self-consciousness' (self-image control), 'Drinking reduces my anxious feelings' (emotion regulation). From a metacognitive standpoint such beliefs are thought to play a central role in motivating individuals to engage in alcohol use as a means of cognitive-emotional regulation (Spada and Wells, 2006a).

A key difference between positive alcohol expectancies and positive metacognitive beliefs about alcohol use is that the former do not explicitly distinguish between cognitive and metacognitive belief domains. Indeed, in existing positive alcohol expectancies measures some items appear to tap into the social-cognitive domain (e.g. 'Drinking will make me have a good time'; 'Drinking will give me pleasant physical effects'; 'Drinking will make me more outgoing') while others appear to tap into the metacognitive domain (e.g. 'Drinking will take away my negative mood and feelings'; 'Drinking allows me to take my mind off problems'). Furthermore, none of the current positive alcohol expectancies measures clearly identify beliefs concerning the usefulness of alcohol as a cognitive control and self-regulation tool (i.e. specific beliefs regarding problem-solving, thought control, attention regulation, and self-image control arising from alcohol use).

Negative metacognitive beliefs about alcohol use concern the perception of lack of executive control over alcohol use (e.g. 'My drinking persists no matter how I try to control it'), and the judgement of the negative impact of alcohol use on cognitive functioning (e.g. 'Drinking will damage my mind'). From a metacognitive standpoint, such beliefs are thought to play a crucial role in the perpetuation of alcohol use (Spada and Wells, 2006a) by becoming activated during, and following a drinking episode, and triggering negative emotional states that compel a person to drink more. Negative alcohol expectancies differ from such beliefs inasmuch as they mainly measure general negative outcomes arising from alcohol use (e.g. 'I get a hangover'; 'I feel guilty'). Therefore, there is some overlap between metacognitive beliefs about alcohol use and alcohol expectancies but this is limited to beliefs regarding the effects of alcohol on emotional self-regulation.

In view of the potential role of metacognitive beliefs in drinking behaviour, and the metacognitive theoretical tenet (Wells and Matthews, 1994, 1996; Wells, 2000) that such beliefs should contribute significantly to psychopathology, additional research is required that may contribute to our knowledge of the relative contribution of alcohol expectancies and metacognitive beliefs about alcohol use to drinking behaviour. In this paper, we report a study aimed at comparing the two constructs. We hypothesized that positive and negative metacognitive beliefs about alcohol use would explain additional variance in drinking behaviour when compared to alcohol expectancies.

Method

Participants

A community sample of 355 individuals (211 females and 142 males) participated in the study. Two hundred and forty participants were undergraduate students from London universities, and 115 were professionals. The professionals were primarily university and health services employees. For purposes of inclusion in this study the participants were required to speak English and to be at least 18 years of age. The mean age and standard deviation (SD) for the total sample were 28.8 and 9.6 years respectively (age range 18-64 years). Of the total sample, 75% were white, 12% were black, 10% were from Indian, Chinese or other Asian origins, and the remaining 3% were from mixed ethnic background. The sample was largely middle-class. The participants reported drinking a mean number of 28.9 units per week (SD = 28.7). Their mean score on the Alcohol Use Disorders Identification Test (AUDIT) was 9.3 (SD = 6.4) indicating moderate problem drinking behaviour.

Measures

The following measures were used:

The Alcohol Outcome Expectancies Scale (AOES). AOES is a 34-item measure developed to assess alcohol expectancies (Leigh and Stacy, 1993). It was designed to address limitations of previous alcohol expectancies measures (Leigh and Stacy, 1993). AOES measures positive and negative alcohol expectancies and consists of two factors, positive and negative alcohol effects. Each factor has four sub-categories; the positive factor includes: social facilitation, fun, sex, and tension reduction. The negative factor includes: social performance, emotions, physical, and cognitive performance. The measure is scored using a 6-point likelihood scale with the end points of 'no chance' to 'certain to happen'. Participants are asked to rate how likely the consequences listed are to take place if they drank alcohol. AOES possesses good test-retest reliability, discriminant and convergent validity (Leigh and Stacy, 1993).

The Positive Alcohol Metacognitions Scale (PAMS). PAMS is a 12-item measure developed to assess positive metacognitive beliefs about alcohol use (Spada and Wells, 2006b). It consists of two factors: (i) positive metacognitive beliefs about emotional self-regulation; and (ii) positive metacognitive beliefs about cognitive self-regulation. Examples of items relating to emotional self-regulation include: 'Drinking makes me feel more relaxed', 'Drinking reduces my anxious feelings', and 'Drinking reduces my self-consciousness'. Examples of items relating to cognitive self-regulation include: 'Drinking makes me think more clearly', 'Drinking helps me to control my thoughts', and 'Drinking helps me focus my mind'. The measure is scored using a four-point likelihood scale with the end points of 'do not agree' to 'agree very much'. Participants are asked to rate how much they agree with the statements listed.

The original item pool for PAMS was gathered from a semi-structured interview with problem drinkers (Spada and Wells, 2006a) and transcripts of cognitive therapy conducted with outpatients. PAMS was initially constructed and factor-analysed in a community sample (N = 261) and its factor structure was replicated in a clinical sample (N = 80) (Spada and Wells, 2006b). Results from these studies suggest that PAMS is dimensional and possesses good internal reliability in both community (α = 0.88 for PAMS total, α = 0.81 for factor 1, and α = 0.87 for factor 2) and clinical (α = 0.84 for PAMS total, α = 0.77 for factor 1, and α = 0.81 for factor 2) populations (Spada and Wells, 2006b). Mean PAMS scores at testing and retesting over an 8-week period in a community sample (N = 50) indicate acceptable test-retest reliability for both factors (factor 1: rho = 0.75, P < 0.0005; factor 2: rho = 0.65, P < 0.0005), suggesting that they possess relatively stable characteristics (Spada and Wells, 2006b). PAMS factors 1 and 2 have been found to predict problem drinking independently of trait anxiety in a community population (N = 138) (Spada and Wells, 2006b). PAMS factor 1 has been found to predict problem drinking independently of anxiety and depression in a clinical population (N = 80) (Spada and Wells, 2006b). PAMS factor 2 has been found to predict classification as a problem drinker independently of emotion in a mixed community and clinical population (N = 163) (Spada and Wells, 2006b).

The Negative Alcohol Metacognitions Scale (NAMS). NAMS is a 6-item measure developed to assess negative metacognitive beliefs about alcohol use (Spada and Wells, 2006b). It consists of two factors: (i) negative metacognitive beliefs about uncontrollability; and (ii) negative metacognitive beliefs about cognitive harm. Items relating to uncontrollability include: 'I have no control over my drinking', 'My drinking persists no matter how I try to control it', and 'Drinking controls my life'. Items relating to cognitive harm include: 'If I cannot control my drinking I will cease to function', 'Drinking will damage my mind', and 'Drinking will make me lose control'. The measure is scored using a 4-point likelihood scale with the end points of 'do not agree' to 'agree very much'. Participants are asked to rate how much they agree with the statements listed.

The original item pool for NAMS was gathered from a semi-structured interview with problem drinkers (Spada and Wells, 2006a) and transcripts of cognitive therapy conducted with outpatients. NAMS was initially constructed and factor-analysed in a community sample (N = 261) and its factor structure was replicated in a clinical sample (N = 80) (Spada and Wells, 2006b). Results from these studies suggest that NAMS is dimensional and possesses good internal reliability in both community (α = 0.74 for NAMS total, α = 0.68 for factor 1, and α = 0.72 for factor 2) and clinical (α = 0.87 for PAMS total, α = 0.87 for factor 1, and α = 0.83 for factor 2) populations (Spada and Wells, 2006b). Mean NAMS scores at testing and retesting over an 8-week period in a community sample (N = 50) indicate acceptable test-retest reliability for factor 2, but poor test-retest reliability for factor 1 (factor 1: rho = 0.42, P = 0.001; factor 2: rho = 0.68, P < 0.0005) (Spada and Wells, 2006b). NAMS factor 1 has been found to predict problem drinking independently of trait anxiety in a community population (N = 138) (Spada and Wells, 2006b). NAMS factor 1 has been found to predict problem drinking independently of anxiety and depression in a clinical population (N = 80) (Spada and Wells, 2006b). NAMS factor 1 has been found to predict classification as a problem drinker independently of emotion in a mixed community and clinical population (N = 163) (Spada and Wells, 2006b).

The Quantity Frequency Scale (QFS). QFS is a measure of alcohol consumption levels, with items assessing the dimensions of quantity and frequency of alcohol beverages consumed over a period of 30 days (Cahalan et al., 1969). This measure consists of three questions ('have you been drinking any beer/wine/spirits over the last 30 days?'; 'about how often do you consume beer/wine/spirits?'; and 'about how much beer/wine/spirits did you drink on a typical day when you drink beer/wine/spirits?'). These are repeated for each of the major alcohol beverage categories (beer, wine and distilled spirits). The total scores from the different alcohol beverage categories are then added together and an estimated daily (or weekly) level of alcohol consumption can be computed. This instrument has been extensively used and possesses good validity and reliability (Hester and Miller, 1995).

The Alcohol Use Disorders Identification Test (AUDIT). AUDIT was developed as a screening tool by the World Health Organisation (WHO) for early identification of problem drinkers (Babor et al., 1992). AUDIT consists of ten questions regarding recent alcohol consumption, alcohol dependence symptoms and alcohol-related problems. Respondents are asked to choose one out of a maximum of five statements: (per question) that most applies to their use of alcohol beverages over the past year. Responses are scored from zero to four in the direction of problem drinking. The summary score for the total AUDIT ranges from zero, indicating no presence of problem drinking behaviour, to 40 indicating marked levels of problem drinking behaviour and alcohol dependence. The threshold for indicating possible problem drinking pathology is a score of eight. This instrument has been extensively used and possesses good validity and reliability (Hester and Miller, 1995).

Procedure

Participants were informed that all data provided in the questionnaires would be treated with the strictest confidence and that participation in the research project was entirely voluntary (i.e. they could withdraw at any time if they so wished). Instructions for completing the questionnaires were given verbally and in writing. Participants completed the AOES, PAMS, NAMS, QFS and AUDIT measures on one occasion.

Results

Data Description

Descriptive statistics for all study variables are shown in Table 1 . Cronbach's alpha coefficients for problem drinking, all positive alcohol expectancies (social facilitation, fun, sex, and tension reduction), all negative alcohol expectancies (social performance, emotions, physical, and cognitive performance) and positive metacognitive beliefs about alcohol use (emotional and cognitive self-regulation) exceeded 0.70 and were thus satisfactory. The Cronbach's alpha coefficients for negative metacognitive beliefs about alcohol use (uncontrollability and cognitive harm) were lower (0.61 and 0.64, respectively) but still acceptable in consideration of the small number of items contributing to the measures' scores.

An inspection of histograms and skewness coefficients showed that several measures were positively skewed. Since we aimed to run structural regression modelling we transformed these measures using a square-root transformation (Tabachnick and Fidell, 1996). This was successful in achieving normality for all measures.

Two-tailed Pearson Product-Moment correlations showed that problem drinking was positively correlated with alcohol use as evidenced in the literature. All alcohol expectancies were positively correlated with one another, as found in previous validation studies. All metacognitive beliefs about alcohol use had low to moderate positive correlations with one another (with the exception of positive metacognitive beliefs about cognitive self-regulation and negative metacognitive beliefs about cognitive harm which were uncorrelated) as found in the preliminary validation studies of these measures (Spada and Wells, 2006b). In all, these patterns of correlations support the convergent validity of the measures of drinking behaviour, alcohol expectancies, and metacognitive beliefs about alcohol use.

All alcohol expectancies and all metacognitive beliefs about alcohol use were positively correlated with both measures of drinking behaviour. Moreover, with the exception of negative metacognitive beliefs about cognitive harm, which were uncorrelated with fun and sex alcohol expectancies, and positive metacognitive beliefs about emotional self-regulation which were uncorrelated with physical alcohol expectancies, alcohol expectancies and metacognitive beliefs about alcohol use were positively correlated with one another. These findings indicate the need of adopting structural regression modelling in order to disentangle the contributions of each construct.

Structural Regression Modelling

The relative contribution of alcohol expectancies and metacognitive beliefs about alcohol use to drinking behaviour was examined using structural regression modelling (e.g. Kline, 1998). Drinking behaviour was defined as a latent dependent variable that is indirectly measured by AUDIT and QFS. All alcohol expectancies and metacognitive beliefs about alcohol use were defined as directly measurable predictors of drinking behaviour. The goodness-of-fit of the models was assessed by the chi-square, comparative fit index (CFI), and root mean square error of approximation (RMSEA). The chi-square tests the overall fit of the model to the data. Strictly, a model fits if the chi-square is non-significant, provided that there is enough statistical power. The CFI measures how well the model fits compared to a null model. This index ranges from 0-1, with a value of 0.95 or higher, indicating satisfactory fit (Hu and Bentler, 1999). The RMSEA is a measure of discrepancy between the model and the data adjusted for degrees of freedom. This index ranges from 0-1, with a value below 0.05 indicating good fit (Hu and Bentler, 1999). The models were estimated using the software LISREL 8.8 (Jöreskog and Sörbom, 1996).

In the first model, drinking behaviour was regressed only on the eight alcohol expectancies. The fit of the model was satisfactory (chi-square = 6.07, df = 7, P = 0.53, CFI = 1.00, RMSEA = 0.00). The model accounted for 40% of the variance in drinking behaviour. Figure 1(a) shows the path diagram with the estimated standardized path coefficients. Negative social performance alcohol expectancies were the only significant predictor of drinking behaviour. In all, the model confirms that alcohol expectancies are contributors to drinking behaviour, but suggests that the eight measures of this construct might excessively overlap with one another.

Figure 1. 

Structural regression models of drinking behaviour, defined as a latent dependent variable indirectly measured by AUDIT and QFS, on: (a) positive alcohol expectancies (AOEQ 1-4) and negative alcohol expectancies (AOEQ 5-8); and (b) metacognitive beliefs about alcohol use (PAMS1, PAMS2, NAMS1, and NAMS2). Both sets of variables are defined as directly measurable predictors. N = 355. The figure shows the standardized path coefficients and their significance levels for each of the two separate models, indicating that: (1) the negative social performance facet of alcohol expectancies significantly predicted drinking behaviour; and (2) all four facets of metacognitive beliefs about alcohol use significantly predicted drinking behaviour. AUDIT refers to the Alcohol Use Disorders Identification Test. QFS refers to the Quantity Frequency Scale. AOEQ refers to alcohol expectancies, with the following facets: (1) positive social facilitation, (2) positive fun, (3) positive sex, (4) positive tension reduction, (5) negative social performance, (6) negative emotions, (7) negative physical, and (8) negative cognitive performance. PAMS1 refers to positive metacognitive beliefs about emotional self-regulation. PAMS2 refers to positive metacognitive beliefs about cognitive self-regulation. NAMS1 refers to negative metacognitive beliefs about uncontrollability. NAMS2 refers to negative metacognitive beliefs about cognitive harm. *P < 0.05; **P < 0.01.

     

In the second model, drinking behaviour was regressed only on the four metacognitive beliefs about alcohol use. The fit of the model was almost identical to that of the previous model (chi-square = 2.69, df = 3, P = 0.44, CFI = 1.00, RMSEA = 0.00). The model accounted for 43% of the variance in drinking behaviour. Figure 1(b) shows the path diagram with the estimated standardized path coefficients. All four metacognitive beliefs about alcohol use were significant predictors of drinking behaviour. In all, the model suggests that the measures of this construct are sufficiently distinct from one another, and each one of them is an independent contributor to drinking behaviour.

In the third model, drinking behaviour was regressed on both alcohol expectancies and metacognitive beliefs about alcohol use. The fit of the model was slightly better than that of the previous two models (chi-square = 8.24, df = 11, P = 0.69, CFI = 1.00, RMSEA = 0.00). The model accounted for 54% of the variance in drinking behaviour. Figure 2 shows the path diagram with the estimated standardized path coefficients. Three of the four metacognitive beliefs about alcohol use (PAMS factors 1 and 2, and NAMS factor 1) were significant predictors of drinking behaviour. Of the eight alcohol expectancies only negative social performance alcohol expectancies was a significant predictor of drinking behaviour. In all, the model corroborates that three metacognitive beliefs are an independent contributor to drinking behaviour, and suggests that, when controlling for metacognitive beliefs, only negative social performance alcohol expectancies explain additional variance in drinking behaviour.

Figure 2. 

Structural regression model of drinking behaviour, defined as a latent dependent variable indirectly measured by AUDIT and QFS, on positive alcohol expectancies (AOEQ 1-4), negative alcohol expectancies (AOEQ 5-8) and metacognitive beliefs about alcohol use (PAMS1, PAMS2, NAMS1, and NAMS2). Both sets of variables are defined as directly measurable predictors. N = 355. The figure shows the standardized path coefficients and their significance levels, indicating that three of the four facets of metacognitive beliefs about alcohol use (PAMS1 and 2, and NAMS1) and the negative social performance facet of alcohol expectancies significantly predicted drinking behaviour. AUDIT refers to the Alcohol Use Disorders Identification Test. QFS refers to the Quantity Frequency Scale. AOEQ refers to alcohol expectancies, with the following facets: (1) positive social facilitation, (2) positive fun, (3) positive sex, (4) positive tension reduction, (5) negative social performance, (6) negative emotions, (7) negative physical, and (8) negative cognitive performance. PAMS1 refers to positive metacognitive beliefs about emotional self-regulation. PAMS2 refers to positive metacognitive beliefs about cognitive self-regulation. NAMS1 refers to negative metacognitive beliefs about uncontrollability. NAMS2 refers to negative metacognitive beliefs about cognitive harm. *P < 0.05; **P < 0.01.

     

Discussion

The aim of the present study was to investigate the relative contribution of alcohol expectancies and metacognitive beliefs about alcohol use to drinking behaviour. Structural regression modelling revealed that three of the four metacognitive beliefs about alcohol use (positive metacognitive beliefs about emotional self-regulation, positive metacognitive beliefs about cognitive self-regulation, and negative metacognitive beliefs about uncontrollability) were independent contributors to drinking behaviour, and that, when controlling for such beliefs, only negative social performance alcohol expectancies explained additional variance in drinking behaviour. These results are consistent with our hypothesis that metacognitive beliefs about alcohol use account for individual differences in drinking behaviour over and above the construct of alcohol expectancies.

The present results add to a growing body of data that has demonstrated links between metacognition and drinking behaviour (Spada and Wells, 2005, 2006a,b; Spada et al., 2006b). The findings also indicate that alcohol expectancies and metacognitive beliefs about alcohol use as assessed by existing measures are, to a degree, distinct constructs, and that metacognitive beliefs about alcohol use play an important role in predicting drinking behaviour beyond that of alcohol expectancies. The key similarity between metacognitive beliefs about alcohol use and alcohol expectancies is that the positive dimensions of both constructs capture what are essentially motivations for alcohol use. A crucial difference, however, is that positive alcohol expectancies do not explicitly distinguish between cognitive and metacognitive belief domains. This is an important distinction, because, according to metacognitive theory, the key markers of psychopathology are beliefs pertaining to the metacognitive rather than cognitive domain (Wells, 2000). Furthermore, while there may be a partial overlap between the content of positive metacognitive beliefs about emotional self-regulation and some items in the positive alcohol expectancies scales, none of the current expectancy measures clearly identifies beliefs concerning the usefulness of alcohol as a cognitive self-regulation tool. With respect to the negative dimensions of both scales, whereas alcohol expectancies mainly measure general negative outcomes arising from alcohol use, metacognitive beliefs about alcohol use specifically tap into the perception of lack of executive control over alcohol use, and the impact of alcohol use as a coping strategy on cognitive functioning. The findings of this study highlight the importance of developing the measurement of beliefs about alcohol use into well-separated cognitive and metacognitive dimensions.

The findings of this study also suggest that Wells' metacognitive framework (Wells, 2000) might be used to develop a conceptualization of maladaptive drinking behaviour. Such an approach may help further our understanding of cognitive factors involved in cause and maintenance of excessive alcohol use and complement social learning and outcome expectancy models. From a metacognitive standpoint, positive metacognitive beliefs about alcohol use motivate individuals to engage in alcohol use as a means to regulate internal states. During, and following a drinking episode, individuals appraise their alcohol use as both uncontrollable and dangerous through the activation of negative metacognitive beliefs. The activation of these beliefs, in turn, leads to an escalation in negative emotions, further locking the person into the vicious cycle of drinking.

In line with this metacognitive conceptualization of emotional dysfunction (Wells and Matthews, 1994, 1996), both positive and negative metacognitive beliefs about alcohol use were found to predict drinking behaviour. This suggests that believing that alcohol use is an effective strategy for controlling thoughts, reducing self-consciousness, solving problems, and managing emotion may be fundamental to the initiation of drinking behaviour. Conversely, during and following a drinking episode, the activation of beliefs regarding the lack of executive control over alcohol use may trigger negative emotional states that compel a person to drink more.

These results of this study also have a number of possible clinical implications. In terms of assessment, information could be gathered not only in relation to alcohol expectancies, but also to associated metacognitive beliefs about alcohol use. With respect to treatment, the modification of metacognitive beliefs about alcohol use (e.g. through cost-benefit analyses and/or verbal re-attribution) may supplant interventions aimed at restructuring alcohol expectancies. Finally, in case of relapse of problem drinking behaviour, it may be helpful to derive and illustrate the role of metacognitive beliefs about alcohol use in the given episode.

The present results are preliminary in nature and they must be considered with regard to design limitations. Social desirability, self-report biases, and poor recall may have contributed to errors in self-report measurements. A cross-sectional design was adopted and this may only be suggestive of a causal inference. Future studies should, therefore, employ longitudinal designs. Furthermore, the sample consisted largely of middle class university students, and potential confounders such as, socio-economic status and education were not controlled for. Thus, while the present findings can be generalized to drinking behaviour they will need to be verified by examining individuals from a wider age range and controlling for background variables. Most importantly, future research will have to ascertain whether metacognitive beliefs about alcohol use or alcohol expectancies are a better predictor of problem drinking in clinical samples.

Despite these limitations this study has demonstrated that there is a value in differentiating between metacognitive beliefs about alcohol use and alcohol expectancies.

Table 1. Means, Standard Deviations, Cronbach's Alphas, and Two-tailed Pearson Product-moment Correlations of the Study Variables

Variables	X	SD	Alpha	1	2	3	4	5	6	7	8	9	10	11	12	13
Drinking behaviour
1. Problem drinking (AUDIT)	9.27	6.39	0.91
2. Alcohol use (QFS)	28.95	28.73	N/A	0.79**
Alcohol outcome expectancies
3. Positive social facilitation (AOEQ1)	22.60	7.02	0.89	0.46**	0.34**
4. Positive fun (AOEQ2)	23.50	6.73	0.86	0.42**	0.36**	0.76**
5. Positive sex (AOEQ3)	13.58	5.58	0.90	0.41**	0.35**	0.62**	0.54**
6. Positive tension reduction (AOEQ4)	10.55	3.51	0.80	0.44**	0.34**	0.72**	0.68**	0.51**
7. Negative social performance (AOEQ5)	6.09	3.66	0.89	0.55**	0.49**	0.43**	0.40**	0.43**	0.44**
8. Negative emotions (AOEQ6)	6.25	2.93	0.80	0.40**	0.33**	0.32**	0.14**	0.28**	0.35**	0.56**
9. Negative physical (AOEQ7)	13.10	4.42	0.72	0.19**	0.15**	0.28**	0.18**	0.16**	0.24**	0.35**	0.49**
10. Negative cognitive performance (AOEQ8)	18.19	6.20	0.70	0.31**	0.27**	0.50**	0.42**	0.33**	0.42**	0.43**	0.45**	0.50**
Metacognitive beliefs
11. Positive emotional self-regulation (PAMS1)	17.93	5.47	0.80	0.39**	0.27**	0.64**	0.53**	0.46**	0.51**	0.26**	0.17**	0.09	0.23**
12. Positive cognitive self-regulation (PAMS2)	7.83	3.49	0.82	0.50**	0.43**	0.49**	0.41**	0.47**	0.45**	0.33**	0.30**	0.21**	0.24**	0.31**
13. Negative uncontrollability (NAMS1)	4.10	1.70	0.61	0.51**	0.46**	0.26**	0.20**	0.28**	0.28**	0.34**	0.35**	0.22**	0.23**	0.15**	0.43**
14. Negative cognitive harm (NAMS2)	4.77	2.18	0.64	0.30**	0.27**	0.15**	0.06	0.08	0.15**	0.30*	0.32**	0.23**	0.24**	0.21**	0.04	0.48**

N = 355.
*P < 0.05;
**P < 0.01.

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A., Christiansen B. A., Goldman M. S. Alcohol expectancy questionnaire: an instrument for the assessment of adolescent and adult alcohol expectancies. Journal of Studies on Alcohol (1987) 48:483-491. Cahalan D., Cisin I., Crossley H. American Drinking Practices: A National Survey of Drinking Behaviors and Attitudes, Rutgers Center for Alcohol Studies, Monograph no. 6 (1969) New Brunswick, NJ: College University Press. Cartwright-Hatton S., Wells A. Beliefs about worry and intrusions: the meta-cognitions questionnaire and its correlates. Journal of Anxiety Disorders (1997) 11:279-315. Christiansen B. A., Smith G. T., Roehling P. V., Goldman M. S. Using alcohol expectancies to predict adolescent drinking behavior after one year. Journal of Consulting and Clinical Psychology (1989) 57:93-99. Darkes J., Goldman M. S. Expectancy challenge and drinking reduction: Experimental evidence for a mediational process. Journal of Consulting and Clinical Psychology (1993) 61:334-353. Del Boca F. K., Darkes J., Goldman M. S., Smith G. Advancing the expectancy concept via the interplay between theory and research. Alcoholism-Clinical and Experimental Research (2002) 26:926-935. Dunn M. E., Goldman M. S. Empirical modeling of an alcohol expectancy memory network in elementary-school children as a function of grade and risk status. Experimental and Clinical Psychopharmacology (1996) 4:209-217. Eastman C., Norris H. Alcohol dependence, relapse, and self-identity. Journal of Studies on Alcohol (1982) 43:1214-1231. Emmelkamp P. M. G., Aardema A. Metacognitions, specific obsessive-compulsive beliefs and obsessive compulsive behaviour. In: Clinical Psychology & Psychotherapy, 6 (1999) Chichester: Wiley. 139-145. Special issue, Metacognition and Cognitive Behaviour Therapy. Fromme K., Stroot E. A., Kaplan D. Comprehensive effects of alcohol: development and psychometric assessment of a new expectancy questionnaire. Psychological Assessment (1993) 5:19-26. Goldman M. S., Del Boca F. K., Darkes J. Alcohol expectancy theory: the application of cognitive neuroscience. In: Psychological Theories of Drinking and Alcoholism -- Blane H., Leonard K., eds. (1999) New York: Guilford Press. Hester R. K., Miller W. R. Handbook of Alcoholism Treatment Approaches: Effective Alternatives (1995) Needham Heights, MA: Allyn & Bacon. Hu L., Bentler P. M. Cutoff criteria for fit indices in covariance structure analysis: conventional versus new alternatives. Structural Equation Modeling (1999) 6:1-55. Jones B. T., McMahon J. Negative and positive alcohol expectancies as predictors of abstinence after discharge from a residential treatment program: a one-month and three-month follow-up study in men. Journal of Studies on Alcohol (1994) 55:543-548. Jones B. T., Corbin W., Fromme K. A review of expectancy theory and alcohol consumption. Addiction (2001) 96:57-72. Jöreskog K. G., Sörbom D. LISREL 8: User's Reference Guide (1996) Chicago, IL: Scientific Software International, Inc. Kilbey M. M., Downey K., Breslau N. Predicting the emergence and persistence of alcohol dependence in young adults: the role of expectancy and other risk factors. Experimental and Clinical Psychopharmacology (1998) 6:149-156. Kline R. B. Principles and Practice of Structural Equation Modeling (1998) New York: Guilford Press. Leigh B. C. Attitudes and expectancies as predictors of drinking habits: a comparison of three scales. Journal of Studies on Alcohol (1989) 50:432-440. Leigh B. C., Stacy A. W. Alcohol outcome expectancies: Scale construction and predictive utility in higher order confirmatory models. Psychological Assessment (1993) 5:216-229. Maisto S. A., Carey K. B., Bradizza C. M. Social learning theory. In: Psychological Theories of Drinking and Alcoholism -- Goldman M. S., Del Boca F. K., Darkes J., eds. (1999) New York: Guilford Press. Mann L. M., Chassin L., Sher K. J. Alcohol expectancies and risk for alcoholism. Journal of Consulting and Clinical Psychology (1987) 55:411-417. McMahon J., Jones B. T., O'Donnell P. Comparing positive and negative alcohol expectancies in male and female social drinkers. Addiction Research (1994) 1:349-365. McNally A. M., Palfai T. P. Negative emotional expectancies and readiness to change among college student binge drinkers. Addictive Behaviors (2001) 26:721-734. Morrison A. P., Wells A., Nothard S. Cognitive factors in predisposition to auditory and visual hallucinations. British Journal of Clinical Psychology (2000) 39:67-78. Papageorgiou C., Wells A. An empirical test of a clinical metacognitive model of rumination and depression. Cognitive Therapy and Research (2003) 27:261-273. Reynolds M., Wells A. The Thought Control Questionnaire -- psychometric properties in a clinical sample, and relationships with PTSD and depression. Psychological Medicine (1999) 29:1089-1099. Sharkansky E. J., Finn P. P. Outcome expectancies and disinhibition on ad-lib alcohol consumption. Journal of Studies on Alcohol (1998) 59:198-206. Southwick L., Steele C. M., Marlatt G. A., Lindell M. Alcohol-related expectancies: defined by phase of intoxication and drinking experience. Journal of Consulting and Clinical Psychology (1981) 49:713-721. Spada M. M., Wells A. Metacognitions, emotion and alcohol use. Clinical Psychology & Psychotherapy (2005) 12:150-155. Spada M. M., Wells A. Metacognitions about alcohol use in problem drinkers. Clinical Psychology & Psychotherapy (2006a) 13:138-143. Spada M. M., Wells A. Metacognitive beliefs about alcohol use: development and validation of two self-report scales (2006b) (manuscript under review). Spada M. M., Hiou K., Nikcevic A. V. Metacognitions, emotions and procrastination. Journal of Cognitive Psychotherapy (2006a) 20:319-326. Spada M. M., Zandvoort M., Wells A. Metacognitions in problem drinkers. Cognitive Therapy and Research (2006b) published electronically 20/10/2006. Spada M. M., Nikcevic A. V., Moneta G. B., Ireson J. Metacognition as a mediator of the effect of test anxiety on surface approach to studying. Educational Psychology (2006c) 26:1-10. Spada M. M., Nikcevic A. V., Moneta G. B., Wells A. Metacognition as a mediator of the relationship between emotion and smoking dependence. Addictive Behaviors (2007) published electronically 23/01/2007. Stacy A. W., Widaman K. F., Marlatt G. A. Expectancy models of alcohol use. Journal of Personality and Social Psychology (1990) 58:918-928. Tabachnick B. G., Fidell L. S. Using Multivariate Statistics (1996) London: Harper Collins Publishers. Tolman E. C. Purposive Behavior in Animals and Men (1932) New York: Appleton-Century-Crofts. Weirs R. W., Hoogeveen K. J., Sergeant J. A., Gunning W. B. High-and low-dose alcohol-related expectancies and the differential associations with drinking in male and female adolescents and young adults. Addiction (1997) 92:871-888. Wells A. Emotional Disorders and Metacognition: Innovative Cognitive Therapy (2000) Chichester: Wiley. Wells A., Matthews G. Attention and Emotion. A Clinical Perspective (1994) Hove: Erlbaum. Wells A., Matthews G. Modelling cognition in emotional disorder: the S-REF model. Behaviour Research and Therapy (1996) 34:881-888. Wells A., Carter K. Further tests of a cognitive model of generalized anxiety disorder: metacognitions and worry in GAD, panic disorder, social phobia, depression, and non patients. Behavior Therapy (2001) 32:85-102.

Reprint Address

Marcantonio M. Spada, Clinical and Health Psychology Research Centre, School of Human and Life Sciences, Roehampton University, Whitelands College, Holybourne Avenue, London SW15 4JD, UK. Tel: +44 (0)20 8392 3559; E-mail: M.Spada@roehampton.ac.uk .

Marcantonio M. Spada,¹ Giovanni B. Moneta,² Adrian Wells<sup>3

1</sup>Roehampton University, UK
²London Metropolitan University, UK
³University of Manchester, UK

Tags: Drinking Behaviour, Alcohol
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Buprenorphine Offers Alternative to Methadone for Opioid Dependence

Buprenorphine for the Treatment of Opioid Dependence

Boothby L, Doering PL
Am J Health Syst Pharm. 2007;64:266-272

Study Summary

Methadone is frequently used to treat opioid dependence, but it has some limitations: As a Schedule II controlled substance, it can only be prescribed in the hospital setting or at methadone clinics, and it has potential to be abused by patients. This study examined the effectiveness of buprenorphine as a substitute treatment for opioid dependence.

The authors conducted a Medline search of clinical trials involving buprenorphine and buprenorphine-naloxone, both Schedule III controlled substances that can be prescribed by approved physicians in outpatient settings. On the basis of the available literature, they found both buprenorphine and buprenorphine-naloxone to be as effective as methadone, with similar adverse-effect profiles. However, because naloxone's narcotic-antagonistic properties help prevent abuse, they concluded that buprenorphine-naloxone is the preferred treatment for maintenance therapy.

Viewpoint

Treatment for opioid dependence is complex. Due to the restrictions and the potential for abuse that surround methadone treatment, buprenorphine in combination with naloxone appears to be an effective and convenient option.

The Drug Addiction Treatment Act of 2000 (DATA 2000) allows qualified physicians to treat opioid dependence with Schedule III, IV, or V opioids in office-based practices.^[1] Buprenorphine and buprenorphine/naloxone are the first drugs to meet the DATA 2000 criteria. In order for a physician to prescribe these agents for opioid dependence, however, he or she must get a DATA 2000 waiver. Furthermore, compounded versions of buprenorphine cannot be used in these cases.

A patient with an opioid addiction who is admitted to a hospital for a primary medical problem other than the addiction may be administered opioid agonists (eg, methadone, buprenorphine) to prevent opioid withdrawal that would complicate the primary medical problem. In such cases, the prescribing physician does not need a DATA 2000 waiver, nor does the dispensing pharmacist need to verify the physician's authority to prescribe those medications.

However, in the outpatient setting, a physician must be qualified under DATA 2000 to prescribe buprenorphine or buprenorphine-naloxone. All prescriptions for these agents must contain the physician's DEA number. If the prescription does not contain a unique DEA number, the pharmacist must check to make sure that the prescribing physician has a valid waiver by either:

checking the Substance Abuse and Mental Health Services Administration (SAMHSA) online physician locator at www.buprenorphine.samhsa.gov/bwns_locator/index.html; or calling SAMHSA at 1-866-287-2728; or contacting the prescribing physician directly and requesting a fax of his or her DEA registration certificate.

If the physician is not registered, the pharmacist must ask him or her whether he or she has requested a waiver from SAMHSA. The prescription can then be dispensed if the physician has submitted, in good faith, a written application to SAMHSA for permission to dispense controlled narcotics for maintenance or detoxification treatment.^[2]

Abstract

References

Substance Abuse and Mental Health Services Administration. Buprenorphine. Available at: http://buprenorphine.samhsa.gov/ Accessed March 13, 2007. Substance Abuse and Mental Health Services Administration. Frequently asked questions about buprenorphine and the Drug Addiction Treatment Act of 2000 (DATA 2000). Available at: http://buprenorphine.samhsa.gov/faq.html#2 Accessed March 13, 2007.

Jacqueline H. Kostick, PharmD, clinical pharmacist; medical writer/editor, Caremark, Specialty Pharmacy Theracom, Rockville, Maryland

Disclosure: Jacqueline H. Kostick, PharmD, has disclosed that she is employed as a contractor for, and owns stock in, Caremark.

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Cigarette Smoking Among Adults

Cigarette Smoking Among Adults -- United States, 2006

MMWR. 2007;56(44):1157-1161. ©2007 Centers for Disease Control and Prevention (CDC)

Posted 12/04/2007

Introduction

One of the national health objectives for 2010 is to reduce the prevalence of cigarette smoking among adults to ≤12% (objective 7-1a).^[1] To assess progress toward achieving this objective, CDC analyzed data from the 2006 National Health Interview Survey (NHIS). This report summarizes the results of that analysis, which indicated that in 2006, approximately 20.8% of U.S. adults were current cigarette smokers. This prevalence had not changed significantly since 2004,^[2] suggesting a stall in the previous 7-year (1997--2004) decline in cigarette smoking among adults in the United States. In addition, the findings indicated that persons with a diagnosis of a smoking-related chronic disease have a significantly higher prevalence of being a current smoker than persons with other chronic diseases or persons with no chronic disease. To reduce smoking prevalence further in the United States, comprehensive, evidence-based approaches for preventing smoking initiation and increasing cessation, including clinical interventions for populations at high risk, need to be fully implemented.^[3]

The 2006 NHIS adult core questionnaire, containing questions on cigarette smoking and cessation attempts, was administered by in-person interview to a nationally representative sample of 24,275 persons in the noninstitutionalized U.S. civilian population aged ≥18 years; the overall response rate was 70.8%. To classify smoking status, respondents were asked, "Have you smoked at least 100 cigarettes in your entire life?"; Those who answered "yes" were asked, "Do you now smoke cigarettes every day, some days, or not at all?" Ever smokers were defined as those who reported having smoked at least 100 cigarettes during their lifetimes. Current smokers were those who had smoked at least 100 cigarettes during their lifetimes and, at the time of the interview, reported smoking every day or some days. Former smokers were those who reported smoking at least 100 cigarettes during their lifetimes but currently did not smoke. Never smokers were those who reported never having smoked 100 cigarettes during their lifetimes. Among current cigarette smokers, making at least one cessation attempt during the preceding year was defined as a "yes" response to the question, "During the past 12 months, have you stopped smoking for more than one day because you were trying to quit smoking?" Respondents were categorized as having a chronic disease if they answered "yes" to any one of a series of questions about 42 chronic diseases (i.e., "Have you ever been told by a doctor or other health professional that you had...?"); of these chronic diseases, 16 were considered to be smoking related*.^[4] Data were adjusted for nonresponse and weighted to provide national estimates of cigarette smoking prevalence. Because the distribution of smoking-related morbidity varies by age, estimates of current, former, and never smokers by chronic disease status were age adjusted to the 2000 U.S. adult population; 95% confidence intervals were calculated using statistical analysis software to account for the survey's multistage probability sample design. Statistical significance was determined by non-overlapping confidence intervals.

In 2006, an estimated 20.8% (45.3 million) of U.S. adults were current cigarette smokers; of these, 80.1% (36.3 million) smoked every day, and 19.9% (9.0 million) smoked some days. Among current cigarette smokers, an estimated 44.2% (19.9 million) had stopped smoking for more than 1 day during the preceding 12 months because they were trying to quit. Of the estimated 91 million persons who had smoked at least 100 cigarettes during their lifetimes (i.e., ever smokers), 50.2% (45.7 million) had quit smoking at the time of the interview.

The prevalence of current cigarette smoking varied substantially among population subgroups. By sex, prevalence was higher among men (23.9%) than women (18.0%) ( Table 1 ). Among racial/ethnic groups, Asians had the lowest prevalence (10.4%). Hispanics had a significantly lower prevalence of smoking (15.2%) than American Indians/Alaska Natives (32.4%), non-Hispanic blacks (23.0%), and non-Hispanic whites (21.9%).

Prevalence also varied by level of education. Smoking prevalence was highest among adults who had earned a General Educational Development (GED) diploma (46.0%) and those with 9--11 years of education (35.4%); overall, smoking prevalence decreased as education level increased. By age group, adults aged 18--24 years and 25--44 years had the highest prevalence of smoking (23.9% and 23.5%, respectively). The prevalence of current smoking was higher among adults living below the federal poverty level (30.6%) than among those at or above this level (20.4%).

Before 2006, certain population subgroups already had achieved smoking prevalences that were lower than the national health objective of 12%, and the prevalences remained low in 2006. These included Hispanic (10.1%) and Asian (4.6%) women, women with undergraduate (8.4%) or graduate (5.8%) degrees, men with undergraduate (10.8%) or graduate (7.3%) degrees, and women aged ≥65 years (8.3%).

In 2006, the age-adjusted prevalence of current smoking was 36.9% among persons with a smoking-related chronic disease and 19.3% among those without a chronic disease ( Table 2 ). Current smoking prevalence was higher among persons with smoking-related cancers (other than lung cancer) (38.8%), coronary heart disease (CHD) (29.3%), and stroke (30.1%) than among persons without chronic diseases, and nearly half (49.1%) of U.S. adults with emphysema and 41.1% of those with chronic bronchitis were current smokers. With the exception of persons who had a stroke, persons with any smoking-related chronic disease were significantly less likely to have never smoked than those with other chronic diseases (53.5%) or no chronic disease (64.3%). Persons with lung cancer (17.9%) and emphysema (22.3%) were least likely to be never smokers.

Reported by: VJ Rock, MPH, A Malarcher, PhD, JW Kahende, PhD, K Asman, MSPH, C Husten, MD, R Caraballo, PhD, Office on Smoking and Health, National Center for Chronic Disease Prevention and Health Promotion, CDC.

Editorial Note

Cigarette smoking remains the leading preventable cause of disease and death in the United States, resulting in approximately 438,000 deaths annually.^[5] The prevalence of cigarette smoking remained relatively unchanged during the early 1990s but gradually decreased from 1997 (24.7%) to 2004 (20.9%) (Figure). This report indicates that the prevalence of current smoking among U.S. adults in 2006 (20.8%) was not significantly different from the prevalence in 2004 (20.9%), suggesting a stall in previous declines. This lack of a decrease in cigarette use during 2 years might be a result of several factors. Most notably, funding for comprehensive state programs for tobacco control and prevention decreased by 20.3% from 2002 to 2006.^[6] and tobacco-industry marketing expenditures nearly doubled from 1998 ($6.7 billion) to 2005 ($13.1 billion).^[7] In 2005, approximately 81% ($10.6 billion) of tobacco-industry marketing expenditures were related to discounting strategies (e.g., coupons, two-for-one offers, or promotional discounts for retailers or wholesalers)^[7] that reduce the impact of increases in the unit price of tobacco, which are effective in preventing initiation of smoking and increasing cessation.^†

Figure 1.

Among smokers who already have a smoking-related chronic disease, those who quit have a lower risk for death from the disease than those who continue smoking.^[8] Smokers who quit have a slower rate of decline in lung function and a lower incidence of bronchitis, emphysema, and other respiratory conditions than persons who continue to smoke.^[8] Among smokers with CHD, those who quit have a lower risk for further CHD-related morbidity and mortality than those who continue to smoke.^[8] In addition, smokers who have cancer and who continue smoking during treatment decrease treatment effectiveness, overall survival prognosis, and quality of life and increase the risk for having another malignancy or comorbid condition.^[9] The continuation of smoking among those who have smoking-related chronic diseases described in this report highlights the need for health-care providers to emphasize the importance of quitting. Health-care providers should repeatedly offer intensive smoking-cessation interventions to all of their patients, especially those with smoking-related chronic diseases who continue to smoke.

The findings in this report are subject to at least three limitations. First, estimates of cigarette smoking are based on self-report and are not validated by biochemical tests. However, self-reported population-based data on current smoking status have high validity when compared with measured serum cotinine levels.^[10] Second, the NHIS questionnaire is administered in English and Spanish only, which might have resulted in imprecise estimates for certain racial/ethnic subgroups because of language barriers. Third, the small NHIS samples for certain population groups (e.g., American Indians/Alaska Natives) resulted in unstable single-year estimates with large confidence intervals.

Since the 1960s, smoking prevalence in the United States has decreased substantially (Figure); however, recent data suggest that declines in both adolescent and adult smoking prevalence might be stalling. Cigarette smoking continues to result in substantial costs. The economic costs of smoking in the United States are estimated at $167 billion annually ($92 billion in productivity losses from premature death and $75.5 billion in health-care expenditures).^[5] In 2007, the Institute of Medicine concluded that funding comprehensive tobacco-control programs at levels recommended by CDC and regulations designed to foster policy innovations are essential strategies that should be implemented to reduce tobacco use.^[3]

* Cigarette smoking has been identified by the Surgeon General as a cause of selected malignant neoplasms, cardiovascular diseases, and respiratory diseases.^[4] Smoking-related chronic diseases include 1) cancers: lung; bladder; cervix; esophagus; kidney; larynx-windpipe; mouth, tongue, or lip; pancreas; stomach; and throat-pharynx; 2) cardiovascular diseases: coronary heart disease, angina pectoris, heart attack, and stroke; and 3) respiratory diseases: emphysema and chronic bronchitis.
^† CDC. The guide to community preventive services: tobacco. Available at http://www.thecommunityguide.org/tobacco.

Table 1. Estimated Percentage of Persons Aged ≥ Years Who Were Current Smokers,* by Sex and Selected Characteristics -- National Health Interview Survey, United States, 2006

Table 2. Estimated Age-adjusted Prevalence of Current Smokers,* Former Smokers† and Never Smokers§ Among U.S. Adults Aged ≥ 18 Years, by Chronic Disease Status -- National Health Interview Survey, United States, 2006

References

US Department of Health and Human Services. Healthy people 2010 (conference ed, in 2 vols). Washington, DC: US Department of Health and Human Services; 2000. Available at http://www.health.gov/healthypeople. CDC. Cigarette smoking among adults---United States, 2004. MMWR 2005;54:1121--4. Institute of Medicine. Ending the tobacco problem: a blueprint for the nation. Washington, DC: The National Academies Press; 2007. US Department of Health and Human Services. The health consequences of smoking: a report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, CDC; 2004. CDC. Annual smoking-attributable mortality, years of potential life lost, and economic costs---United States, 1997--2001. MMWR 2005;54:625--8. Campaign for Tobacco-Free Kids, American Lung Association, American Cancer Society, American Heart Association. A broken promise to our children: the 1998 state tobacco settlement eight years later. Washington, DC: Campaign for Tobacco-Free Kids; 2006. Available at http://www.tobaccofreekids.org/reports/settlements/2007/fullreport.pdf. Federal Trade Commission. Cigarette report for 2004 and 2005. Washington, DC: Federal Trade Commission; 2007. Available at http://www.ftc.gov/reports/tobacco/2007cigarette2004-2005.pdf. US Department of Health and Human Services. The health benefits of smoking cessation: a report of the Surgeon General. Rockville, MD: US Department of Health and Human Services, CDC; 1990. Gritz ER, Fingeret MC, Vidrine DJ, Lazev AB, Mehta NV, Reece GP. Successes and failures of the teachable moment: smoking cessation in cancer patients. Cancer 2006;106:17--27. Caraballo RS, Giovino GA, Pechacek TF, Mowery PD. Factors associated with discrepancies between self-reports on cigarette smoking and measured serum cotinine levels among persons aged 17 years or older: third National Health and Nutrition Examination Survey, 1988--1994. Am J Epidemiol 2001;153:807--14.

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