Diapositiva 1

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1 1 The comorbidities may be one of the predictors of smoking cessation in Taiwan Jing-Yi Huang, Hsiu-Wen Kuo, Chih-Dao Chen Department of family medicine, Far Eastern Memorial Hospital

2 2010 年可歸因於 20 大主要危險因子之疾病負擔 - 男性失能調整存活人年 (DALY)(%) 依 2012 年 Lancet 期刊研究指出, 導致全球死亡的前 20 種健康風險因子中, 分析各因子之失能調整損失人年 (DALY) 顯示, 居首位者為菸害, 約占 8.5%, 表示菸害造成過早死亡, 折壽最多, 其中菸害所造成的人年損失中, 又以心血管疾病死亡年數所占最多, 約超過一半二代戒菸服務經費來自菸品健康福利捐 2

3 2010 年可歸因於 20 大主要危險因子之疾病負擔 - 女性失能調整存活人年 (DALY)(%) 二代戒菸服務經費來自菸品健康福利捐 3

十大死因長期趨勢 : 菸害相關疾病持續威脅國人 4 150 age-standardized mortality rate (1/100,000) 130 十大死因皆與吸菸有關 :6

每十萬人口死亡人數惡性腫瘤, 130.2 110 吸菸嚼檳榔與肥胖, 三者共構成更強大的慢性病威脅 90 70 50 30 10 (10) 心臟病, 50.2 腦血管疾病, 30.

4 十大死因長期趨勢 : 菸害相關疾病持續威脅國人 age-standardized mortality rate (1/100,000) 130 十大死因皆與吸菸有關 :6 個直接相關 4 個間接相關 ( 癌症心臟病腦血管疾病糖尿病肺炎慢性阻塞性疾病 ) 99 年約 20,024 人死於菸害, 每 25 分鐘即有 1 人死於菸害單位 : 每十萬人口死亡人數惡性腫瘤, 吸菸嚼檳榔與肥胖, 三者共構成更強大的慢性病威脅 (10) 心臟病, 50.2 腦血管疾病, 30.4 糖尿病, 26.0 肺炎, 24.7 事故傷害, 23.7 慢性阻塞性肺病, 15.3 慢性肝病及肝硬化, 14.8 腎炎腎病症後群及腎病變, 12.5 自殺, 11.8 年 4

5 5

6 菸害與各疾病死亡之相對危險性 (Relative Risk; RR 值 ) 6 一手菸 RR 值死因男性女性癌症氣管肺支氣管癌喉癌唇口腔及咽部癌症食道癌膀胱癌腎臟及腎盂癌症胰臟癌心血管疾病主動脈瘤腦血管疾病 (35-64 歲 ) 冠狀動脈心臟病 (35-64 歲 ) 粥狀動脈硬化呼吸道疾病支氣管炎肺氣腫慢性呼吸道阻塞二手菸 RR 值死因男性女性暴露家庭二手菸者肺癌冠狀動脈心臟病腦血管疾病暴露職場二手菸者肺癌冠狀動脈心臟病腦血管疾病二手菸之其他危害 : 低體重兒嬰兒猝死症中耳炎氣喘發作肺炎白血病等資料來源 : 美國疾病管制署 (CDC) 可歸因於吸菸死亡罹病及經濟成本評估 SAMMEC (Smoking-Attributable Mortality, Morbidity, and Economic Costs,CPS-II ) 6

7 Introduction 7 Cigarette smoking causes more than 480,000 deaths each year in the United States. This is about one in five deaths. Smoking causes about 90% (or 9 out of 10) of all lung cancer deaths in men and women. About 80% (or 8 out of 10) of all deaths from chronic obstructive pulmonary disease (COPD) are caused by smoking. Smoking can cause cancer almost anywhere in your body. Cigarette smoking increases risk for death from all causes in men and women. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2014 [accessed 2015 Aug 17].

8 In Taiwan, in all 3.5 million of adult cigarette smokers, more than 40% smokers had made at least one attempt to quit during their smoking careers. However, only about 8.6% of self-quitters were still abstinent 6 months later. Therefore, it is very important to know why so many smoking had attempts to quit but there was only few of success. 8

9 9 A series of review studies had submitted some predictors of smoking cessation. For example, the longest previous quit attempt and not having any other smokers in the household is a strong predictor of success. Smokers with recurrent major depressive disorder or those who were treated for depression were significantly less likely to be abstinent of smoking. Two indicators of nicotine dependence (smoking within 30 min of waking up and higher cotinine levels) reduced the odds of quitting was noted. Alcoholism is a negative prognostic factor for successful smoking cessation. Common predictors of smoking cessation in clinical practice (Pasquale Caponnetto*, Riccardo Polosa, accepted 24 February 2008, ELSEVIER)

10 10 Other studies showed that only measures of nicotine dependence are consistently predictive of success of those attempts. Gender, age and marital status and educational level are not related consistently to quit attempts or quit success across countries. In addition, the affluence of income, education, or employment status were not found to be predictive. Predictors of attempts to stop smoking and their success in adult general population samp a systematic review, Eleni Vangeli1, Australia, 8 July 2011, Addiction, 106,

11 11 On the other hand, there was also studies compared with sustained quitters and relapses. The relapses were more likely to report symptoms of emotional distress (anxiety(p=0.014), depression(p=0.003) and insomnia(p<0.001)) and higher levels of nicotine dependence(p<0.001), to drink more alcohol (drinking amounts larger than 15 grams per day (P<.001)), and to report more medical conditions, such as myocardial infarction (P=.02) and higher rates of back (P<.01) and joint pain (P=.02). (Am J Public Health. 2008;98: doi: /ajph )

Predictor 12 Predictor Relation Ref longest previous quit and not having any other smokers in the household attempt positive Pasquale Caponnetto, Riccardo Polosa, 2008.

12 Predictor 12 Predictor Relation Ref longest previous quit and not having any other smokers in the household attempt positive Pasquale Caponnetto, Riccardo Polosa, recurrent major depressive disorder or those who were treated for depression negative Pasquale Caponnetto, Riccardo Polosa, nicotine dependence (smoking within 30 min of waking up and higher cotinine levels) negative Pasquale Caponnetto, Riccardo Polosa, U.S. Department of Health and Human Services, Alcoholism negative Pasquale Caponnetto, Riccardo Polosa, Gender, age, marital status, educational level, affluence of income, education, and employment status not related U.S. Department of Health and Human Services, 2014.

13 Therefore, only when we know more about the predictors of smoking cessation, the more we can do to help the smokers to quit. There was also other comorbidities may influence smoking cessation. However less studies had analyzed. 13

14 Hypothesis 14 Co morbidity is a predictor of smoking cessation successful rate.

15 Study Aim 15 This study examined not only demographic variables but also the relationships between comorbidities and smoking cessation. We wanted to know whether the common comorbidities to influence smoking cessation successful rate. If we understand that there was other comorbidities influence smoking cessation successful rate, we had better chance to help the smokers.

16 Method 16 The retrospective study enrolled 1182 Taiwanese smokers included who went fareast hospital Family Medicine department for smoking cessation with counseling or drugs between 2014/07 to 2015/03. After medication or counseling was given, we had followed up their smoking status 6months later by telephone or coming back to our department.

17 Inclusion and exclusion criteria 17 At baseline in our study, all individuals in our outpatient department smoked at least 10 cigarettes per day or Fagerstrom Test for Nicotine Dependence (FTND)>4 points. All individuals selected for our samples were exposed to the key behavior of having made quit attempt and decided to quit.

18 We excluded men with missed visits during the timeframe or didn t catch the telephone that was used to define their quit status. Individuals who were expired during the trial were also excluded from our sample. After exclusion, we enrolled 682 participants in this study. 594 male and 88 female was noted. 18

19 Case collection flowchart 19 Met inclusion criteria (n=1182) Final enrolled cases (n=682) Excluded criteria (n=500): 1. with missed visits during the timeframe 2. didn t catch the telephone that was used to define their quit status Success in quit smoking cases (n=369) Failure in quit smoking cases (n=313)

20 Outcome measures 20 primary outcome: 7 day point prevalence quit rate at 6 months. (it means no smoking during the previous 7 days at the 6 month follow up) Secondary outcome: demographic variables influence successful rate, compare with the previous series of studies. Do point prevalence and prolonged abstinence measures produce sim results in smoking cessation studies? A systematic review John R. Hughes, M.D, Nicotine Tob Res Jul; 12(7):

21 Independent Variables demographic variables gender, age, marital status and education smoking history total cigarettes per day at baseline, pack years, years smoked and FTND and CO value alcohol use Y/N chronic medical condition DM, HTN, COPD, CAD, AMI, peptic ulcer, GAD

22 the chronic medical conditions We also recorded the chronic medical conditions by diagnosis code(icd code) of participants who went to hospital during 2014/ /03 with their agreement. The common comorbidities may influence the quitting attempt : diabetes mellitus, hypertension, cardiovascular disease, myocardial infarction, peptic ulcer, chronic obstructive pulmonary disease and general anxiety disorders

23 Outcome-1 Our primary outcome showed that the 7 day point prevalence quit rate at 6 month follow up was about 53.9%(369/682). The more higher of nicotine dependence level (FTND and CO level), the more likely to fail smoking succession. Those who were married or suffered from AMI had higher rates of smoking cessation.

24 Outcome-2 After adjusting other independent variables that smoking cessation was significantly predicted by marital status (OR:1.66, CI: , p=0.01), moderate and severe FTND score (OR: 0.4, CI: p=<0.001; OR: 0.25, CI: p=<0.001), and comorbid AMI (OR: 1.95, CI: , p=0.03). CO level was not included due to 242 missing data.

28 Conclusion The outcome showed that comorbid AMI, marital satus and moderate and severe FTND score affect the smoking cession successful rate. The result can be apply to specific groups to increase smoking cession successful rate when the recourses were limited. The further study should be continuous for more costeffective smoking cessation model.

29 Limitations 29 The comorbidities was not clarified into 2 group: acute or chronic medical condition. And it may causes the bias of predicting smoking cessation. AMI patients had higher rate of smoking cessation may due to its emergent condition. Smoking status was depends on the follow-up call visit question: Have you ever smoke during the past 7 days? instead of asking whether the participants had not smoked at all (6 full months of not smoking) or had stopped at some time during the interval (<6 months of not smoking).

30 Thanks for attention 30

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