Previous studies have been limited to the use of cross sectional data to identify the relationships between nicotine dependence and smoking. Therefore, it is difficult to determine a causal direction between the two variables. The purposes of this study were to 1) test whether nicotine dependence or average smoking was a more influential factor in smoking cessation; and 2) propose effective ways to quit smoking as determined by the causal relations identified.
This study used a panel dataset from the central computerized management systems of community-based smoking cessation programs in Korea. Data were stored from July 16, 2005 to July 15, 2008. 711,862 smokers were registered and re-registered for the programs during the period. 860 of those who were retained in the programs for three years were finally included in the dataset. To measure nicotine dependence, this study used a revised Fagerström Test for Nicotine Dependence. To examine the relationship between nicotine dependence and average smoking, an autoregressive cross-lagged model was explored in the study.
The results indicate that 1) nicotine dependence and average smoking were stable over time; 2) the impact of nicotine dependence on average smoking was significant and vice versa; and 3) the impact of average smoking on nicotine dependence is greater than the impact of nicotine dependence on average smoking.
These results support the existing data obtained from previous research. Collectively, reducing the amount of smoking in order to decrease nicotine dependence is important for evidence-based policy making for smoking cessation.
In Korea, the highest point in the country's adult smoking rate was in 1980 (male, 63.5%; female, 12.6%); currently, (as of June 2011), 39.0% of adult males and 1.8% of adult females smoke. The decrease in smoking prevalence can be attributed to the combined influences of the national health promotion law legislated in 1995 and the increased cost of cigarettes. Specifically, in December 2004, powerful non-smoking policies and supports for smoking cessation were enacted in the nation including prohibited non-smoking areas, smoking cessation clinics in community health centers, telephone counseling for smoking cessation and non-smoking campaigns for public and health education.
Since July 21, 2011, the Ministry of Health and Welfare (MOHW) of Korea has been planning to accelerate implementation of the national comprehensive policy planning which includes the price and non-price policy to drastically reduce the high prevalence of smoking as compared with the Organization for Economic Co-operation and Development (OECD) countries.
While smokers recognize that the habit is injurious to health, smoking cessation is difficult because of nicotine addiction contained in cigarettes [
Rigotti [
A study conducted by Lee and Seo [
Lessov-Schlaggar et al. [
In summary, the majority of previous studies have been limited to the sole use of cross sectional data to identify the relationships between nicotine dependence and smoking. Therefore, with analyses conducted at one selected time point, it is difficult to test and determine a causal direction between the two variables. This study used a panel dataset which contains information regarding attempts for quitting smoking accumulated for three years. The purposes of this study were to 1) test whether nicotine dependence or average smoking was a more influential factor in smoking cessation; and 2) propose effective ways to quit smoking as determined by the causal relations identified.
This study used a three-year panel dataset obtained from the central computerized management systems for the smoking cessation programs of the community health centers in the nation. Data were stored from July 16, 2005 until July 15, 2008. 711,862 smokers were registered and re-registered for the programs for the period. 860 of those who were retained for three years in the programs were finally included in the dataset. For the study, success in smoking cessation means smokers did not smoke cigarettes for the past consecutive six months. The rate of success in smoking cessation each year is as follows: 32.7% (1st), 29.7% (2nd), 12.9% (3rd). The majority of the smokers were men (n = 800, 93%). Average age of the smokers was 49.2 years (standard deviation = 12.8 years). About 90% (n = 770) had health insurance. Half (n = 369) lived in smaller cities (
To measure nicotine dependence, a revised Fagerström Test for Nicotine Dependence (FTND) was used, which is currently in wide use on a global level [
In the study, average smoking was operationalizing as the number of cigarettes smoked a day for the entire smoking period until the smokers visited the smoking cessation clinic in the community health centers.
To examine the longitudinal relationships between nicotine dependence and smoking, an autoregressive cross-lagged (ARCL) model was explored. The core of the ARCL is that the value at the time of [t] is explained by the value at the time of the previous point, [t-1] [
Homogeneity means that "statistical properties of any one part of an overall dataset are the same as any other part" [
Homogeneity of paths is conducted to test the regression coefficients for each observed variable is stable over time. It can be tested by comparing if the impact of the variable (regression coefficient) at the time of [t-1] on the variable at the time of [t] is same with the impact of the variable (regression coefficient) at the time of [t] on the variable at the time of [t+1]. The regression coefficient can be labeled as stability coefficient [
To test homogeneity of error covariance is to equally constrain covariance between the errors of the variables. In order to evaluate the goodness-of-fit, χ2 tests and the goodness-of-fit indices were considered. Among the various fit indices, incremental fit indices such as Comparative Fit Index (CFI), Normed Fit Index (NFI), Tucker-Lewis Index (TLI), and absolute indices such as Goodness Fir Index (GFI), Root Mean Square Error of Approximation (RMSEA) were used for the study [
In general, if the values of incremental fit indices such as the CFI are larger than 0.9, it means that the model fit is good [
For the analyses of this study, SPSS ver. 19.0 was used for descriptive statistics and average analysis. To analyze the ARCL model, AMOS ver. 20.0 (IBM, New York, USA) for the structural equation modeling was employed.
The research questions (RQ) of this study are as follows:
RQ 1: Is nicotine dependence stable over the time period? RQ 2: Is average smoking stable over the time period? RQ 3: Is the impact of nicotine dependence on average smoking significant? RQ 4: Is the impact of average smoking on nicotine dependence significant?
The ARCL model was employed to test the above research questions (
Nicotine dependence and average smoking over the time period are described in
Prior to analyzing the ARCL model, correlations between nicotine dependence and average smoking were analyzed.
To test homogeneity of paths and error covariance, the following six competitive models were developed:
Model 1: No constraint to the base model Model 2: Homogeneity constraint model to the autoregressive coefficient (A) of nicotine dependence Model 3: Homogeneity constraint model to the autoregressive coefficient (B) of average smoking Model 4: Homogeneity constraint model to the cross-regression coefficient (C) of nicotine dependence Model 5: Homogeneity constraint model to the cross-regression coefficient (D) of average smoking Model 6: Homogeneity constraint model to the error coefficient (E) of nicotine dependence and average smoking
To ascertain the optimal model of the above six models, statistical analyses were sequentially conducted, and its results were compared. Each model was nested; therefore, χ2 tests were viewed as appropriate for the analyses. However, to increase the reliability of the statistical results, other goodness-of-fit indices such as CFI, GFI, NFI, TLI, RMSEA were applied because χ2 tests has been recognized as problematic when related to sample size sensitivity [
Although homogeneity constraints were added to the paths A-E, the fit indices were not getting worse (
A latent growth curve model was developed to explore trajectory changes of nicotine dependence and average smoking. It was noted that both nicotine dependence and average smoking were proved to be linear transformation models (
In
Trajectory changes of nicotine dependence showed a linear model which had decreased for the three-year period. The mean intercept of nicotine dependence was 5.454, and it showed a steady decline of 0.247 on average over the period (
Trajectory changes of average smoking also showed a linear model which had decreased for the three-year period. The mean intercept of average smoking was 21.758, and it showed a steady decline of 0.817 on average over the period. The values of the intercept and the variance of slope were statistically significant. The mean intercept of average smoking had a significant difference between individuals. The slope also had a significant difference between individuals. In addition, the coefficient of correlation between the intercept and the slope was -0.398 indicating that the higher the intercept of average smoking, the lower rate of smoking decline.
This study was designed to examine the causal relations between nicotine dependence and average smoking for the three year period applying the ARCL to longitudinal data. Results from this study are as follows:
First, nicotine dependence measured at each point was stable over time. Nicotine dependence at the previous point significantly influenced nicotine dependence at future time points. Stated in other terms, nicotine dependence of smokers was an ongoing phenomenon, not a temporary one. Second, average smoking was stable over time. Therefore, average smoking was similar to nicotine dependence. Third, the impact of nicotine dependence on average smoking was significant. Data suggest that average smoking and nicotine dependence had strongly positive relations over time. In addition, the impact of nicotine dependence on average smoking had not changed over time. Lastly, the impact of average smoking on nicotine dependence was significant. Results obtained from this panel data analysis indicate that as average smoking increases, nicotine dependence also increases. These findings remained stable for the three-year period. The impact of average smoking on nicotine dependence was greater than the impact of nicotine dependence on average smoking; therefore, average smoking was the main cause to increase nicotine dependence.
Limitations of this study were as follows:
First, this study used a total score, not an individual item, of the FTND to measure nicotine dependence. Therefore, it was not possible to analyze reliability of the FTND each year. Instead, reliability of a total score of the FTND each year was presented in this study. Second, the models proposed and tested in this study showed the goodness-of fit indices, however, this result does not mean that the study models include the most influential variables on smoking cessation. Therefore, future research is necessary to further establish the relationship between nicotine dependence and average smoking. Additional studies including more influential variables theoretically valid and empirically tested in previous research will lead to the development of new approaches to success in smoking cessation.
In summary, these results support the existing data obtained from previous research that explored the relationships between nicotine dependence and average smoking [
This manuscript was prepared by the support of the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (NRF-2011-413-G0006). The authors acknowledge the contribution from the Ministry of Health and Welfare (MOHW) of Korea, which operated the national smoking cessation program at the community health centers in the nation. The content of this manuscript does not represent the official views of the MOHW.
No potential conflict of interest relevant to this article was reported.
Autoregressive cross-lagged model of nicotine dependence and the average smoking.
Linear model of nicotine dependence.
Linear model of average smoking.
Changes of nicotine dependence over the time period.
General characteristics (n = 860)
Values are presented as number (%).
Descriptive statistics of nicotine dependence and average smoking (n = 860)
SD: standard deviation.
Correlation between nicotine dependence and average smoking
a
Comparison of the autoregressive cross-lagged models
GFI: Goodness Fir Index, NFI: Normed Fit Index, CFI: Comparative Fit Index, TLI: Tucker-Lewis Index, RMSEA: Root Mean Square Error of Approximation.
Parameter estimates of the base model and the ARCL Model 6
ARCL: autoregressive cross-lagged, B: non-standardized coefficients, β: standardized coefficients, CR: critical ratio.
a
Results from the ARCL model of nicotine dependence and average smoking
ARCL: autoregressive cross-lagged, NFI: Normed Fit Index, CFI: Comparative Fit Index, TLI: Tucker-Lewis Index, RMSEA: Root Mean Square Error of Approximation.
a
Intercept of nicotine dependence and path coefficients of slope
B: non-standardized coefficients, β: standardized coefficients, SE: standard error, CR: critical ratio.
a