Nicotine dependence syndrome scale pdf

A single overall score based on the first principal component, NDSS-T, was retained as a single core measure of dependence. The five factor scores showed differential patterns of correlations with external validators, supporting the multidimensionality of the measure. In study 2, we revised the NDSS to expand some subscales and administered it to smokers in a cessation study.

The same five factors were extracted, the internal reliability of some subscales was improved, and the factor scores again showed associations with dependence-relevant validators, which were largely maintained when we controlled for FTQ scores.

In study 3, with 91 smokers in a cessation trial, we established that the test-retest reliability of the subscales was adequate. Thus, the NDSS presents a valid multidimensional assessment of nicotine dependence that may expand on current measures.

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Article Navigation. Close mobile search navigation Article Navigation. Volume 6. Article Contents Abstract. University of Pittsburgh. Correspondence: Saul Shiffman, Ph.

Oxford Academic. Those with three symptoms during the same twelve month period fulfill the diagnosis of nicotine dependence in DSM-IV. We also used other measures of cigarette smoking including 1 number of cigarettes smoked per day during heaviest period of smoking, and 2 maximum amount of cigarettes smoked in a hour period when we examined the correlations of factors and the sum score.

In the analysis of linear and logistic regression we computed robust estimators of variance and used the clustering option in Stata Williams, to control for possible lack of independence of observations of subjects who came from the same family. The heritability of nicotine dependence measured by the NDSS was analyzed by using quantitative genetic methods based on linear structural modeling.

Thus, a greater similarity for MZ twins compared with DZ NIH-PA Author Manuscript twins gives support to the hypothesis that genetic transmission is a component of importance, under the assumption that MZ and DZ share to the same extent their trait-relevant environmental experiences Boomsma et al. Environmental factors are divided into those shared by a twin pair shared environment and factors unique to each twin individual unique environmental effects , which also includes measurement error.

The correlations for the shared environmental factors are one and for unique environmental factors 0 within both MZ and DZ twin pairs. Heritability refers to the total part of the phenotypic variance attributable to genetic influences, and comprises both additive effects of individual alleles at loci influencing a particular phenotype, and non-additive effects, reflecting interactions between alleles at the same locus dominance or between alleles across loci epistasis.

We estimated the proportions of trait variance accounted for by additive genetic factors A , by shared environmental factors C and by factors not shared unique with the co-twins E , so called ACE-model.

The ACE sex-limitation model was selected as a starting point of the modeling based on twin correlations. Full model was fitted including ACE effects for nicotine dependence and the correlations between the genetic and environmental components affecting that phenotype.

We used Stata statistical software, version 9. Page 6 3. Results 3. Loadings over 0. It had sixteen items with loading values 0. Highest loadings were items which described smoking drive i. The factor had eight items with loading values 0. Items included continuously smoking with little interruption and a fixed pattern of smoking. The third factor had six items with factor loadings of 0. The factor structure was derived from using all observations. To test for the possibility that the interdependence of family members may be affecting the factor structure, we ran the factor analysis so that there was only one person per family.

The factor structure and sum scale alpha values were virtually the same as in the original analysis. Also, the factor structure and sum scale alpha values in the sample consisting of current smokers only i. The varimax and the oblique factors correlated also highly with each other correlations of factors were 0. We also tested the five factor solution presented by Shiffman et al.

These results are somewhat below commonly-accepted standards for a well fitting model. Byrne, The NDSS sum score had a mean of For some analyses we categorized the sum score into five categories 14—24, 25—32, 33—41, 42—54, 55—70 sum score values.

Page 7 3. Test-retest reliability In the retest sample the mean age of ever smoked responders was 55 years SD 6. No significant mean differences emerged when the NDSS-T of the initial and the repeated questionnaires were tested by paired t-test, either overall or stratified by sex or a median split on age.

Correlation with other measures of dependence We examined the strength of the association of the five NDSS sum score categories with a diagnosis of DSM-IV nicotine dependence using logistic regression.

Age was a significant predictor of dependence such that older participants were not as dependent as younger ones. However, sex was not a significant predictor of dependence.

Table 3 shows the correlations of different measurements with the sum score and three factors. In all cases, as expected, the highest correlations were seen for the sum score. Logistic regression analyses Table 5 showed that all three factors were strongly associated with DSM-IV nicotine dependence. The ORs for the factors when entered jointly were only slightly less than when entered individually. The greater correlations in MZ compared to DZ pairs is evidence for genetic influences.

After starting with an ACE-model, the common environment C effect could be dropped from the model and thus an AE-model fit the data best. Additive genetic variance was 0. Due to a different pattern of correlations Table 6 among women we also tested an ADE-model and dropped dominance D effect from the model. The AE-model was again the best fitting model. Genetic sex-limitation modeling showed no differences in the genetic architecture of the second and third factors sub-scores between regularly smoking men and women no evidence for sex-specific genetic effects.

The NDSS was developed to assess multiple dimensions derived from a contemporary conceptualization of nicotine dependence constructs Shiffman et al. This suggests that the composition and smoking behaviour of the study samples may give different factor structures of the NDSS.

Differences in the number of subscales might be due to different sample sizes, population heterogeneity, different degrees of nicotine dependence of participants and differences in age distribution.

Our recently collected data is based on earlier collected years years years or — population-based data, including twin pairs which both co-twins were current or former smokers.

Page 9 of NDSS nicotine dependence. For these and other reasons, it is not surprising that we do not observe a similar factor structure as in other studies.

In any case, the analysis suggest that the factor structure reported by Shiffman et al. Further studies in randomly selected population samples of smokers are needed. Overall, the instrument worked quite similarly in Finland as in the U. The sum scale based on 14 items provides an easily computable score of overall nicotine dependence, which differentiated well persons with low and high risk of nicotine dependence when assessed against DSM-IV criteria.

DSM diagnoses have been shown to be associated with heavier smoking and to predict persistence of smoking Breslau et al. The problem is that DSM criteria are not specifically tailored to nicotine dependence. These measures assess different aspects of nicotine dependence Breslau and Johnson, ;Moolchan et al. While the heritability of dependence can only be assessed among persons who have smoked, heritability of initiation of smoking can be assessed among all persons in the population. This is generally the case in recent studies Kendler et al.

Genetic two stage modeling permits inclusion of information from never smokers to the analysis Heath et al. Earlier genetic studies on nicotine dependence have shown fairly high heritability estimates by different measurements varying from 0. Heritability of NDSS sum score was 0. In genetic modelling, the AE-model fit the data best for overall score, and the second and third factors.

Page 10 the correlations for MZ and DZ pairs were almost identical. This is quite surprising, and suggests that the core dependence symptoms of craving, withdrawal, and excess priority given to use may be less heritable than more peripheral factors such as escalation in use and continuity NIH-PA Author Manuscript of use. This surprising finding seems at odds with the estimated heritability of the FTQ Kendler at al. We could not analyse DSM-IV based nicotine dependence due to the limited number of twin pairs; the power of the twin study for binary traits is much lower than for continuous traits Neale and Miller, Given the relatively modest sample size of twins, and in particular the small number of female pairs, the power to detect either common environmental effects or non-additive effects was not very large.

Our data include opposite sex twin pairs, and thus we were able to examine sex limitation effects in the heritability of smoking behavior. We did not find evidence for sex differences, but the power to detect sex-specific effects was limited. Hence, it is possible that in larger samples, such effects will be detected in future studies of NDSS.

However, other twin studies of nicotine dependence generally have not shown such effects, and the AE model has been the optimal model also in those Kendler et al. The variation of heritability is not surprising taking into account that the role of genetic factors varies with population, time and place Kendler et al.

The measures examined in the study were self-report assessments using standardized instruments, not clinical tests. However, the internal consistency and test-retest reliability of NDSS were good. The subjects in the study were mostly middle aged and older, so they have long history of smoking and their smoking behavior might be relatively well established.

Smokers in the present study are also the survivors as in this age range, smokers are starting to die off differentially. Age was significant predictor of dependence such that older participants were not as dependent as younger ones. A limitation of Shiffman et al. This limits generalizability and also presumably limits the range of variability in nicotine dependence that could be observed and analyzed. More recently, Clark et al. The prior validation studies relied primarily on variance in other psychometric assessments within a single sample to validate the NDSS.

An alternative approach to validation is the extreme-groups or criterion-group design, in which subject groups that are known to represent extremes on the relevant dimension are contrasted on the measure under consideration Manterola et al. In other words, the validity of the NDSS can also be assessed by determining whether it robustly distinguishes nicotine-dependent and non-dependent smokers.

It has been demonstrated that chippers derive normal amounts of nicotine from cigarettes Shiffman et al. Chippers also differ from regular smokers in their family history of smoking Shiffman, , suggesting that genetic factors may protect them from dependence.

Thus, chippers appear to be relatively free of nicotine dependence, and measures of nicotine dependence can be validated by their ability to robustly distinguish chippers from regular smokers. In this study, we assess the ability of the NDSS and its subscales to discriminate chippers and regular smokers, and the ability of the scales to make independent and incremental contributions to such discrimination.

The NDSS was previously validated through correlations with other variables in a group that varied continuously in dependence Shiffman et al. Finally, we also assessed the concurrent validity of NDSS scales within the chippers group, where restriction of range should make such discriminations challenging. Previous analyses in Shiffman et al. However, the ability of the NDSS to assess variations in dependence at the low range of dependence has not been tested.

Being able to assess differences in dependence at the low end of the dependence continuum would be important for characterizing the full spectrum of smokers, which may be useful for assessing dependence early in its development. Two hundred and fifty-three smokers male and female participated in the study. This is the full sample reported previously in Sayette et al.

Seventy-seven percent of the sample was Caucasian, Selection criteria were applied at screening. Smokers who were trying to quit were excluded. Participants had to be between the ages of 21 and 35; this restriction was based on the need to constrain age-related variation in response-time on a cognitive task, which was key to the underlying study in Sayette et al.

Both groups had to report smoking at these rates for at least 2 years continuously see Shiffman et al. Empirically, on average, chippers smoked 4. On average, chippers smoked an average of 4. Other selection criteria are described in Sayette et al. The groups did not differ on ethnic makeup or on reported income, but showed modest differences on other variables. As described in Sayette et al.

These measures were completed prior to starting the experiment described in Sayette et al. Because the groups had been defined and selected based on different smoking rates, in scoring the FTQ, we used a variant scoring that did not include smoking rate in the scoring.

Other self-report measures included self-ratings of addiction and a composite rating of difficulty abstaining for various intervals ranging from an hour to a day see Shiffman et al. To contrast the groups, we relied primarily on logistic regression, with group membership as the dependent variable and NDSS scales and other variables as predictors.

We report odds ratios and their confidence intervals as the primary statistics. We also report the results of logistic regressions, summarized using receiver operating curve ROC analysis Hanley and McNeil, The ROC curve plots the sensitivity and specificity of the measure as a discriminator of subject status, and the area under the curve can be interpreted as the percentage of pairwise case comparisons that would allow one to correctly discriminate a chipper and a regular smoker based on the score Hanley and McNeil, Analysis of demographics had revealed some differences in the composition of the groups.

As these differences were presumed to be inherent in the groups, they were not controlled in subsequent analyses, which were meant to assess the ability of the NDSS to distinguish the groups, regardless of their composition.

We also assessed the association between NDSS scales and other variables within the chippers group, by conducting multivariate regressions using the NDSS scales as predictors of several dependence-relevant variables: smoking rate, FTQ scores, difficulty abstaining, and self-rated addiction. We report subscale statistics as well as the total amount of variance accounted for R 2 by all of the NDSS subscales in aggregate, as a measure of the total predictive power across all subscales. Table 1 shows the mean NDSS scores for chippers and regular smokers, along with odds ratios based on logistic regression.

Each NDSS subscale significantly discriminated the two groups. In other words, every 1-point increase in NDSS-T scores increased the odds that the respondent was a regular smoker fold. Indeed, the area under the ROC curve was 0. Among the subscales, the Drive scale was the most robust correlate of group membership, with an odds ratio of 6. The area under the ROC curve for Drive was 0. Each of the other subscales also significantly discriminated the groups. The weakest effect was seen for the priority subscale, still a significant discriminator with an odds ratio of 1.

Logistic regression plots showing the relationship between NDSS scales X -axis, showing the observed range of scores for each scale and the probability of being in the regular smoker group Y -axis. These data demonstrate that each NDSS subscale significantly distinguishes chippers and regular smokers. To test the independent contributions of the scales, we conducted a multivariate logistic regression including all the NDSS scales.

Table 1 shows the resulting odds ratios and significance. In this analysis, Priority has a positive odds ratio of 1. Exploratory analyses not reported in detail reveal that Priority is pushed out of the model by Drive, which apparently captures its associations with the group difference. Table 1 also shows the means and group statistics for the FTQ.

The FTQ significantly discriminated the groups, with an odds ratio of 4. Only the incremental contribution of the Priority scale was not significant. To test whether the NDSS could capture the limited variance within the chippers group, we assessed the within-group correlations between NDSS scores and other measures of dependence. We conducted multivariate regression equations, predicting selected dependence-relevant measures from the five NDSS subscales, entered simultaneously.

Results are displayed in Table 2. Among chippers, NDSS scales were significantly associated with all of the measures tested. Drive was the only significant multivariate predictor. All NDSS scales made significant unique contributions. We also examined correlates of the number of days per week the subjects smoked. Associations between NDSS scales and indicators of dependence, within the chippers group. The model fit is summarized by R 2 for each model.

We also examined the within-group associations with FTQ scores. FTQ was associated with within-group variation in all variables examined. However, the magnitude of associations was considerably smaller than that for the NDSS scales. Table 2 also shows that adding the FTQ to the NDSS models did not generally increase the variance accounted for, another indicator that the FTQ did not contain additional relevant variance.

Using an extreme-groups design contrasting chippers non-dependent smokers and regular, relatively heavy smokers, this study demonstrated the validity of the NDSS and each of its constituent subscales as measures of nicotine dependence. Furthermore, each of these NDSS measures discriminated the groups even when we controlled for a valid and widely used measure of nicotine dependence, the FTQ.