台灣地區男性戒菸的多階段隨機模型之探討

Abstract

研究目的 有關台灣地區戒菸行為模式，過去研究雖多有探討，但是鮮少對不同階段之間提出新的解釋模型以及影響因子之探討。本研究目的有二：(1)參考跨理論行為模式，提出適合台灣地區戒菸門診中多階段戒菸模型(持續抽菸、猶豫戒菸及戒菸成功)，(2)探討戒菸族群在菸癮戒除過程中，由抽菸期轉移至心存猶豫以及戒菸成功等階段的影響因子。 材料與方法 本研究所使用的資料主要來自國民健康局自2003年起各縣市所推動的門診戒菸計劃，此計劃透過醫療院所以及社區衛生所辦理門診戒菸來協助民眾戒菸，對於18歲以上之成人，尼古丁成癮度達4分以上或平均一天吸10支菸以上，願意接受簽約之醫療院所所提供之戒菸治療，共收案2,791人。本研究將戒菸過程定義為三個分期，分別為抽菸期(Smoking Phase)、猶豫期(Hesitation Phase)及戒菸成功期(Quitting Smoking)。根據本研究架構所定義的多階段分期可知本研究之依變項包括(1)戒菸猶豫期以及(2)戒菸成功。在自變項部分，我們利用門診戒菸記錄表與電訪所收集之資料，作為本研究各階段相關因子的定義，概分為個案戒菸經驗、個案健康狀況、個案菸癮相關因子、周遭親友疾病經驗，與自覺健康與效益等因素。由於門診戒菸記錄表並未追蹤戒菸成果，本結果設計一問卷進行電話訪談收集民眾戒菸成果、自覺健康、親友疾病經驗、對門診戒菸的記憶以及門診戒菸的效益訊息。 在資料分析方面，本研究利用羅吉斯迴歸模式（Logistic Regression Model）分析影響是否參加第二次以上的戒菸門診及是否戒菸成功兩種二元事件的各別影響因子，並利用比例危險迴歸模式（Proportional Hazards Regression Model）分析在考慮最後一次接受戒菸門診到戒菸成功時所花費時間的影響因子，最後利用多階段模式（Multi-state Model）量化個人在抽菸與意圖戒菸之間行為的轉移及兩種狀態其分別進展至戒菸成功的轉移率。 結果 本研究之研究族群共2,791人，分別來自基隆市(n=125人)、台北縣(n=1,124人)、台南縣(n=1,308人)、連江縣(馬祖)(n=123人)及台東縣(n=111人)等五個縣市在2004年至2006年間參加戒菸門診的民眾，其中主要以台北縣及台南縣為主，各佔40.3%及46.9%。 在影響戒菸猶豫期之相關因子方面包括有個案年齡 (OR=1.01, 95% CI: 1.00-1.01, p=0.0009)、是否罹患過肝病 (OR=1.64, 95% CI: 1.09-2.47, p=0.0189)、在禁菸區會感到難受 (OR=1.21, 95% CI: 1.03-1.41, p=0.0172)，以及戒菸門診提供單位 (OR=1.67, 95% CI: 1.26-2.21, p=0.0004)。 影響戒菸成功相關因子有越年邁者越容易戒菸成功 (OR=1.05, 95% CI=1.03-1.08, P<.0001)，菸齡在15(含)年以下者相對戒菸成功比較高 (OR=6.03, 95% CI=2.27-16.04, P=0.0003)，自覺健康較同年齡者佳者，有較高的相對戒菸成功比 (OR=7.14, 95% CI=3.22-15.83, P<.0001)，自己罹患有糖尿病者，相對戒菸勝算比為3.02 (95% CI=1.08-8.44, P<0.0349)，因自己有健康問題而參加門診戒菸者，相對戒菸成功比為2.23倍，(95% CI=0.99-5.00, P<0.0520)。 以比例危險迴歸模式進行多變項分析，其中菸齡的解釋力最大，菸齡小於15年者其戒菸速率為大於者之4.45倍（95%信賴區間：1.93-10.29）。再者為本身為糖尿病患者，其戒菸速率為非糖尿病患者之2.71倍（95%信賴區間：1.25-5.89）。接著為自覺健康狀態，其中自覺健康比較好者的戒菸速率為自覺健康比較差者的2.34倍（95%信賴區間：1.53-3.58），覺得沒有差者約為自覺健康比較差者的四成（95%信賴區間：0.24-0.57）。當嚴重生病、臥病在床時不會抽菸者為仍會抽菸者之1.89倍（95%信賴區間：1.20-2.97）。年齡每增加一歲其戒菸成功速率增為1.04倍(95%信賴區間：1.02-1.06)。 利用連續時間馬可夫模式估計個案在抽菸期、猶豫期及戒菸期之間，估計結果顯示個案從抽菸期轉變到猶豫期的轉移速率約為每天1.85次（95%信賴區間：0-7.24），由猶豫期返回抽菸期的速率則為每天0.64次（95%信賴區間：0-2.50），由抽菸期和猶豫期分別進展至戒菸期的轉移速率則分別為每天2.69

Objectives: Despite a number of studies addressing smoking cessation outpatient service (SCOS) in Taiwan, very few studies have been conducted to address factors associated with different stages of smoking behaviour change or to propose a new model to accommodate such a multi-state process. The present study aimed (1) to propose a new stage of change model to interpret the behaviour of outpatient smoking cessation in Taiwan and (2) to assess relevant factors associated with different behaviour change states, from smoking phase either to hesitation phase or to quitting smoking. Methods: The study subjects (n=2,791) were enrolled from the 2004-2006 SCOS in five Taiwan Counties and a telephone interview design. By defining state of change with three phases related to smoking quitting process, including current smoking phase, hesitation phase, and quitting smoking phase were proposed. Independent variables used in our analyses included (1) basic information of smokers with their quitting smoking experiences (2) personal health status (diseases) (3) nicotine dependent factors (4) diseases histories of their friends and relatives (5) perceived health status. We used logistic regression models to elucidate the effects of relevant factors on whether to have more than two visits of SCOS or whether to quit smoking successfully respectively. The proportional hazards regression model was used to analyze the effects of relevant risk factors on time to smoking cessation. Finally, we applied the multi-state models to quantify the transitions between smoking, hesitation to quit, and quitting from the two states. Results: Factors associated with hesitation phase include age, personal diseases with liver diseases, feeling discomfort in the non-smoking area, those who received the smoking cessation outpatient service from the public health centers, were identified as significant predictors. Factors associated with quitting smoking included age, smoking age, perceiving better health than peers, having diabetes mellitus, and attending the smoking cessation clinic due to personal perceived illness. Results of proportional hazards regression model found those who were more likely to quit smoking were elderly, short duration of smoking age, having diabetes mellitus, perceiving better health than peers, or stopping smoking when being ill in bed. In the continuous-time Markov model, the transition rate from smoking to hesitation phase was estimated as 1.85 per day (95% CI: 0-7.24), and the estimated regression rate from hesitation phase to smoking was 0.64 per day (95% CI: 0-2.50). The daily successful quitting rates from smoking and from hesitation phase in the previous time were estimated as 2.69x10-4 (95% CI: 1.87 x10-4-3.50 x10-4) and 2.44 x10-4 (95% CI: 1.18 x10-4-3.71 x10-4), respectively. In terms of annual quitting rates, the figures were 0.0982 (95% CI: 0.0684-0.1279) and 0.0891 (95% CI: 0.0432-0.1353), respectively. Those perceived healthier has 3.18 times higher quitting rate compared with those perceived worse or equal health. The corresponding figure from hesitation phase was 16.27 (95% CI: 3.74-71.28). Heavy smokers with desire of smoking which having serious diseases had 1.41 (95% CI: 0.98-2.02) times higher transition rate from smoking phase to hesitation phase. The corresponding figure for the regression from hesitation phase back to smoking phase was 1.64 (95% CI: 1.15-2.35). Conclusions: We proposed a new model of multi-state stochastic process for quitting smoking for Taiwanese males who using smoking cessation outpatient service. This multi-state model included smoking phase, hesitation phase and quitting smoking. Using different regression models, the results found those who were more likely to quit smoking were elderly, perceiving better health than peers, having diabetes mellitus, or stopping smoking when being ill in bed. Such a novel multi-state model may be applied to aid men in any phases to quit smoking.