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RESEARCH PAPER
Acculturation, Depression, and Smoking Cessation: a trajectory pattern recognition approach
1
University of Massachusetts, Boston, Boston, USA
2
University of Massachusetts Dartmouth and Medical School Dartmouth, Dartmouth, USA
3
Department of Computer and Information Science, College of Engineering, University of Massachusetts Dartmouth, Dartmouth, USA
4
Division of Biostatistics and Health Services Research Department of Quantitative Health Sciences, University of Massachusetts Medical School, Dartmouth, USA
5
Hunter College, City University of New York, New York, USA
6
University of California San Diego, Deparetment of Psychiatry, La Jolla, USA
Submission date: 2016-07-28
Acceptance date: 2017-07-06
Publication date: 2017-07-24
Corresponding author
Hua Fang
University of Massachusetts Dartmouth and Medical School Dartmouth, Dartmouth, MA 02747, USA
University of Massachusetts Dartmouth and Medical School Dartmouth, Dartmouth, MA 02747, USA
Tob. Induc. Dis. 2017;15(July):33
KEYWORDS
culturally adaptive interventiontrajectory pattern recognitionmultiple imputationfuzzy clusteringMIFuzzylongitudinalsmoking cessationacculturationdepression
ABSTRACT
Background:
Korean Americans are known for a high smoking prevalence within the Asian American population. This study examined the effects of acculturation and depression on Korean Americans’ smoking cessation and abstinence.
Methods:
This is a secondary data analysis of a smoking cessation study that implemented eight weekly individualized counseling sessions of a culturally adapted cessation intervention for the treatment arm and a standard cognitive behavioral therapy for the comparison arm. Both arms also received nicotine patches for 8 weeks. A newly developed non-parametric trajectory pattern recognition model (MI-Fuzzy) was used to identify cognitive and behavioral response patterns to a smoking cessation intervention among 97 Korean American smokers (81 men and 16 women).
Results:
Three distinctive response patterns were revealed: (a) Culturally Adapted (CA), since all identified members received the culturally adapted intervention; (b) More Bicultural (MB), for having higher scores of bicultural acculturation; and (c) Less Bicultural (LB), for having lower scores of bicultural acculturation. The CA smokers were those from the treatment arm, while MB and LB groups were from the comparison arm. The LB group differed in depression from the CA and MB groups and no difference was found between the CA and MB groups. Although depression did not directly affect 12-month prolonged abstinence, the LB group was most depressed and achieved the lowest rate of abstinence (LB: 1.03%; MB: 5.15%; CA: 21.65%).
Conclusions:
A culturally adaptive intervention should target Korean American smokers with a high level of depression and a low level of biculturalism to assist in their smoking cessation.
Trial registration:
NCT01091363. Registered 21 March 2010.
Korean Americans are known for a high smoking prevalence within the Asian American population. This study examined the effects of acculturation and depression on Korean Americans’ smoking cessation and abstinence.
Methods:
This is a secondary data analysis of a smoking cessation study that implemented eight weekly individualized counseling sessions of a culturally adapted cessation intervention for the treatment arm and a standard cognitive behavioral therapy for the comparison arm. Both arms also received nicotine patches for 8 weeks. A newly developed non-parametric trajectory pattern recognition model (MI-Fuzzy) was used to identify cognitive and behavioral response patterns to a smoking cessation intervention among 97 Korean American smokers (81 men and 16 women).
Results:
Three distinctive response patterns were revealed: (a) Culturally Adapted (CA), since all identified members received the culturally adapted intervention; (b) More Bicultural (MB), for having higher scores of bicultural acculturation; and (c) Less Bicultural (LB), for having lower scores of bicultural acculturation. The CA smokers were those from the treatment arm, while MB and LB groups were from the comparison arm. The LB group differed in depression from the CA and MB groups and no difference was found between the CA and MB groups. Although depression did not directly affect 12-month prolonged abstinence, the LB group was most depressed and achieved the lowest rate of abstinence (LB: 1.03%; MB: 5.15%; CA: 21.65%).
Conclusions:
A culturally adaptive intervention should target Korean American smokers with a high level of depression and a low level of biculturalism to assist in their smoking cessation.
Trial registration:
NCT01091363. Registered 21 March 2010.
REFERENCES (60)
1.
U.S. Department of Health and Human Services. The health consequences of smoking─50 years of progress. A report of the surgeon general: executive summary. Rockville: DPHH Services, Office of the Surgeon General; 2014.
2.
University of California Los Angeles Center for Health Research Policy. (2016). California, health interview survey: adult survey, 2001–2015. Los Angeles, CA: UCLA. 2016. http://healthpolicy.ucla.edu/c.... Accessed 3 Mar 2017.
3.
Leistikow BN, Chen M, Tsodikov A. Tobacco smoke overload and ethnic, state, gender, and temporal cancer mortality disparities in Asian-Americans and Pacific islander-Americans. Prev Med. 2006;42:430–4.
4.
Centers for Disease Control and Prevention. Smoking-attributable mortality, years of potential life lost, and productivity losses --- United States, MMWR. 2008;57(45):1226-1228.2000—2004.
5.
Caraballo RS, Yee SL, Gfroerer J, Mirza SA. Adult tobacco use among racial and ethnic groups living in the United States, 2002-2005. Public Health Research, Practice and Policy. 2008;5(3):1–9.
6.
Brugge D, Dejong W, Hyde J, Le Q, Shih CS, Wong A, Tran A. Development of targeted message concepts for recent Asian immigrants about secondhand smoke. J Commun Health. 2002;7:25–37.
7.
Liu JJ, Wabnitz C, Davidson E, Bhopal RS, White M, Johnson MR, Netto G, Sheikh A. Smoking cessation interventions for ethnic minority groups—a systematic review of adapted interventions. Prev Med. 2013;57:765–75.
8.
Perez-Stable EJ, Marin G, Posner SF. Ethnic comparison of attitudes and beliefs about 20 cigarette smoking. J Gen Intern Med. 1998;13:167–74.
9.
Fang CY, Ma GX, Miller SM, Tan Y, Su X, Shive S. A brief smoking cessation intervention for Chinese and Korean American smokers. Prev Med. 2006;43:321–4.
10.
McDonnell DD, Kazinets Z, Lee HJ, Moskowitz JM. An internet-based smoking cessation program for Korean Americans: results from a randomized controlled trial. Nicotine Tob Res. 2011;13:336–43.
11.
Zhu S-H, Cummins SE, Wong S, Gamst AC, Tedeschi GJ, Reyes-Nocon J. The effects of a multilingual telephone quitline for Asian smokers: a randomized controlled trial. J Natl Cancer Inst. 2012;104:299–310.
12.
Kim SS, Kim SH, Fang H, Kwon S, Shelley D, Ziedonis DM. A culturally adapted smoking cessation intervention for Korean Americans: a mediating effect of perceived family norm toward quitting. J Immigr Minor Health. 2015;17:1120–9.
13.
Kim SS, Ziedonis D, Chen K. Tobacco use and dependence in Asian Americans: a review of the literature. Nicotine Tob Res. 2007;9(2):169–84.
14.
Lee S, Sobal J, Frongillo EA. Acculturation and health in Korean Americans. Soc Sci Med. 2000;51:159–73.
15.
Ji M, Hofstetter RC, Hovell M, et al. Smoking cessation patterns and predictors among adult Californians of Korean descent. Nicotine Tob res. 2005;7(1):59–69.
16.
Brown SA, Vik PW, Patterson TL, Grant I, Schuckit MA. Stress, vulnerability and adult alcohol relapse. J Stud Alcohol Drugs. 1995;56:538–45.
17.
McClave AK, Dube SR, Strine TW, Kroneke K, Caraballo RS, Mokdad AH. Associations between smoking cessation and anxiety and depression among U.S. adults. Addict Behav. 2009;34:491–7.
18.
Tsoh JY, Lam JN, Delucchi KL, Hall SM. Smoking and depression in Chinese Americans. American Journal of Medical Science. 2003;326:187–91.
19.
Wiecha J, Lee V, Hodgkins J. Patterns of smoking, risk factors for smoking, and smoking cessation among Vietnamese men in Massachusetts (United States). Tob Control. 1998;7:27–34.
20.
Hitsman B, Papandonatos GD, McChargue DE, DeMott A, Herrera MJ, Spring B, Borrelli B, Niaura R. Past major depression and smoking cessation outcome: a systematic review and meta-analysis update. Addiction. 2013;108:294–306.
21.
Weinberger AH, Pilver CE, Desai RA, Mazure CM, McKee SA. The relationship of major depressive disorder and gender to changes in smoking for current and former smokers: longitudinal evaluation in the U.S. population. Addiction. 2012;107:1847–56.
22.
Zvolensky MJ, Farris SG, Leventhal AM, Ditre JW, Schmidt MB. Emotional disorders and smoking: relations to quit attempts and cessation strategies among treatment-seeking smokers. Addict Behav. 2015;40:126–31.
23.
Pomerleau OF, Pomerleau CS, Mehringer AM, Snedecor SM, Ninowski R, Sen A. Nicotine dependence, depression, and gender: characterizing phenotypes based on withdrawal discomfort, response to smoking, and ability to abstain. Nicotine Tob Res. 2005;7:91–102.
24.
Husky MM, Mazure CM, Paliwal P, McKee SA. Gender differences in the comorbidity of smoking behavior and major depression. Drug Alcohol Depen. 2008;93:176–9.
25.
Poulin C, Hand D, Boudreau B, Santor D. Gender differences in the association between substance use and elevated depressive symptoms in a general adolescent population. Addiction. 2005;100:525–35.22.
26.
Luk JW, Tsoh JY. Moderation of gender on depression and smoking in Chinese Americans. Addict Behav. 2010;35:1040–3.
27.
Yun WJ, Shin MH, Kweon SS, Rhy SY, Rhee JA. Association of smoking status, cumulative smoking, duration of smoking cessation, age of starting smoking, and depression in Korean adults. BMC Public Health. 2012;12:724.
28.
Kim SS, Kim SH, Seward G, Fortuna L, McKee S. Korean American women’s experiences with smoking and factors associated with their quit intentions. ISRN Addiction. 2013; doi:10.1155/2013/796570.
29.
Park MB, Kim C-B, Nam EW, Hong KS. Does South Korea have hidden female smokers: discrepancies in smoking rates between self-reports and urinary cotinine level. BMC Womens Health. 2014;14:156.
30.
Borrelli B, Bock B, King T, Pinto B, Marcus BH. The impact of depression on smoking cessation in women. Am J Prev Med. 1996;12:378–87.
31.
Leventhal AM, Waters AJ, Boyd S, Moolchan ET, Lerman C, Pickworth WB. Gender differences in acute tobacco withdrawal: effects on subjective, cognitive, and physiological measures. Exp Clin Psychopharm. 2007;15:21–36.
32.
Xu J, Azizian A, Monterosso J, Domier CP, Brody AL, Fong TW, London ED. Gender effects on mood and cigarette craving during early abstinence and resumption of smoking. Nicotine Tob Res. 2008;10:1653–61.
33.
Zhang Z, Fang H, Wang H. A new MI-based visualization aided validation index for mining big longitudinal web trial data. IEEE Access. 2016;4:2272–80. doi:10.1109/ACCESS.2016.2569074.23.
34.
Zhang Z, Fang H, Wang H. Multiple imputation based clustering validation (MIV) for big longitudinal trial data with missing values in e-health. J Med Syst. 2016;40(6):1–9.
35.
Fang H, Johnson C, Stopp C, Espy KA. A new look at quantifying tobacco exposure during pregnancy using fuzzy clustering. Neurotoxicol Teratol. 2011;33:155–65.
36.
Fang H, Dukic V, Pickett KE, Wakschlag L, Espy KA. Detecting graded exposure effects: a report on an East Boston pregnancy cohort. Nicotine Tob Res. 2012;14:1115–20.
37.
Kent D, Hayward RA. Limitations of applying summary results of clinical trials to individual patients. JAMA. 2011;298:1209–12.
38.
Sculpher M. Subgroups and heterogeneity in cost-effectiveness analysis. PharmacoEconomics. 2008;26:799–806.
39.
Anderson KG, Ramo DE, Brown SA. Life stress, coping and comorbid youth: an examination of the stress-vulnerability model for substance relapse. J Psychoactive Drugs. 2006;38:255–62.
40.
Dagher A, Tannenbaum B, Hayashi T, Pruessner JC, McBride D. An acute psychosocial stress enhances the neural response to smoking cues. Brain Res. 2009;1293:40–8.
41.
Schoedel KA, Hoffmann EB, Rao Y, Sellers EM, Tyndale RF. Ethnic variation in CYP2A6 and association of genetically slow nicotine metabolism and smoking in adult Caucasians. Pharmacogenetics. 2004;14:615–26.
43.
Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50:179–211.
44.
Suinn RM, Ahuna C, Khoo G. The Suinn-Lew Asian self-identity acculturation scale: concurrent and factorial validation. Educ Psychol Meas. 1992;52:1041–6.
45.
Heatherton TF, Kozlowski LT, Frecker RC, Fagerström KO. The Fagerström test for nicotine dependence: a revision of the Fagerström tolerance questionnaire. Brit J Addict. 1991;86:1119–27.
46.
Babor TF, Higgins-Biddle JC, Saunders JB, Monteiro MG. The alcohol use disorders identification test: guidelines for use in primary care. 2nd ed. Geneva: World Health Organization; 2001.
47.
Radloff LS. The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1:385–401.
48.
Cho MJ, Kim KH. Use of the Center for Epidemiologic Studies Depression (CES-D) scale in Korea. J Nerv Ment Dis. 1998;186:304–10.
49.
McKee SA, O’Malley SS, Salovey P, Krishnan-Sarin S, Mazure CM. Perceived risks and benefits of smoking cessation: gender-specific predictors of motivation and treatment outcome. Addict Behav. 2005;30:423–35.
50.
Kim SS, Kim SH, Gulick EE. Cross-cultural validation of smoking abstinence self-efficacy scale with Korean-American men. Issues Ment Health Nurs. 2009;30:122–30.
51.
Hughes JR, Keely JP, Niaura RS, Ossip-Klein DJ, Richmond RL, Swan GE. Measures of abstinence in clinical trials: issues and recommendations. Nicotine Tob Res. 2003;5:13–25.
52.
Murray RP, Connett JE, Buist AS, Gerald LB, Eichenhorn MS. Experience of black subjects in the lung health study smoking cessation intervention program. Nicotine Tob Res. 2001;3:375–82.
53.
Voorhees CC, Stillman FA, Swank RT, Heagerty PJ, Levine DM, Becker DM. Heart, body, and soul: impact of church-based smoking cessation interventions on readiness to quit. Prev Med. 1996;25:277–85.
54.
SAS Institute Inc. The GLIMMIX procedure. In: SAS/STAT® 9.2 User’s Guide. Cary: the Author; 2013.
55.
Fang H, Espy KA, Rizzo ML, Stopp C, Wiebe CA, Stroup WW. Pattern recognition of longitudinal trial data with nonignorable missingness: an empirical case study. Int J Inf Technol Decis Mak. 2009;8(3):491–513.
56.
Fang H, Rizzo ML, Wang H, Espy KA, Wang Z. A new nonlinear classifier with a penalized signed fuzzy measure using effective genetic algorithm. Pattern Recogn. 2010;43(4):1393–401.
57.
Fang H, Zhang Z. An enhanced visualization method to aid behavioral trajectory Patten recognition infrastructure for big longitudinal data. IEEE Trans Big Data. 2017; doi:10.1109/TBDATA.2017.2653815.
58.
Fang H. MIFuzzy clustering for incomplete longitudinal data in smart health. Smart Health. 2017; doi:10.1016/j.smhl.2017.04.002.
59.
Sun HQ, Guo S, Chen DF, et al. Family support and employment as predictors of smoking cessation success: a randomized, double-blind, placebo-controlled trial of nicotine sublingual tablets in Chinese smokers. Am J Drug Alcohol Ab. 2009;35:183–8.
60.
Williams JB, Gibbon M, First MB, et al. The structured clinical interview for DSM-III-R (SCID). II. Multisite test-retest reliability. Arch Gen Psychiat. 1992;49:630–6.
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