A Three-Year Autoregressive Cross-Lagged Panel Analysis on Nicotine Dependence and Average Smoking / 대한의료정보학회지

OBJECTIVES: 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. METHODS: 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 Fagerstrom Test for Nicotine Dependence. To examine the relationship between nicotine dependence and average smoking, an autoregressive cross-lagged model was explored in the study. RESULTS: 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. CONCLUSIONS: 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.

A Three-Year Autoregressive Cross-Lagged Panel Analysis on Nicotine Dependence and Average Smoking / 대한의료정보학회지

OBJECTIVES: 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. METHODS: 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 Fagerstrom Test for Nicotine Dependence. To examine the relationship between nicotine dependence and average smoking, an autoregressive cross-lagged model was explored in the study. RESULTS: 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. CONCLUSIONS: 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.

Application of Social Network Analysis to Health Care Sectors / 대한의료정보학회지

OBJECTIVES: This study aimed to examine the feasibility of social network analysis as a valuable research tool for indicating a change in research topics in health care and medicine. METHODS: Papers used in the analysis were collected from the PubMed database at the National Library of Medicine. After limiting the search to papers affiliated with the National Institutes of Health, 27,125 papers were selected for the analysis. From these papers, the top 100 non-duplicate and most studied Medical Subject Heading terms were extracted. NetMiner V.3 was used for analysis. Weighted degree centrality was applied to the analysis to compare the trends in the change of research topics. Changes in the core keywords were observed for the entire group and in three-year intervals. RESULTS: The core keyword with the highest centrality value was "Risk Factor," followed by "Molecular Sequence Data," "Neoplasms," "Signal Transduction," "Brain," and "Amino Acid Sequence." Core keywords varied between time intervals, changing from "Molecular Sequence Data" to "Risk Factors" over time. "Risk Factors" was added as a new keyword and its social network was expanded. The slope of the keywords also varied over time: "Molecular Sequence Data," with a high centrality value, had a decreasing slope at certain intervals, whereas "SNP," with a low centrality value, had an increasing slope at certain intervals. CONCLUSIONS: The social network analysis method is useful for tracking changes in research topics over time. Further research should be conducted to confirm the usefulness of this method in health care and medicine.