Predictors of retention in a multicomponent treatment for smokers

Abstract

Background:

There is a lack of knowledge about factors that promote or hinder retention of smokers in treatment.

Objective:

The aim of this study was the identification of variables that predict retention of smokers who received a multicomponent treatment against smoking.

Method:

Participants (n = 79) simultaneously received pharmacological and psychological treatment, including an intervention phase prior to the date of smoking cessation. They were evaluated periodically in their abstinence, depressive and anxious symptoms, and were randomly assigned to three treatment conditions (nicotine patch, bupropion or nicotine patch + bupropion). Eighteen variables were grouped into four categories (demographic, consumption pattern, mood and treatment). Data were analyzed using student's t test and X², for inclusion into a multivariate logistic regression model.

Results:

Results indicate that age of onset of regular tobacco consumption, secondary education and bupropion pharmacological treatment are significant in relation to the retention of smokers to smoking treatment.

Discussion:

The reported “age of onset” correlates with treatment retention (OR = 1.545, 95 % CI = 1.175-2.032). This variable has not previously been reported in the literature, and taking it into account in the design of prevention and treatment for smoking could increase their success.

Keywords:
Tobacco smoking; treatment; retention; transdermal nicotine patch; bupropion

Introduction

Tobacco smoking is the main risk factor associated with preventable mortality worldwide¹; there are currently one billion smokers in the world, of which approximately 650 million will die of tobacco smoking². It is estimated that tobacco smoking will be responsible for 6.5 million deaths in low and medium income countries by 2030³. According to the National Survey of Addictions, 21.7% of Mexican population smokes and 8.9% smokes daily⁴. Data is alarming considering that around 60,000 people die each year in Mexico due to smoking-related diseases causing loss of 10-15 years of productive life⁵.

According to the U.S. Department of Health and Human Services, the risk of smoking-related illnesses and mortality decrease through cessation⁶. Therefore, cessation at age 30 results in a gain about 10 years of life expectancy⁷.

Although there are various treatment options for abstinence, lack of retention in treatment may hinder its success and a number of variables have been identified to be related to it. Curtin et al.⁸ found that smokers with major depression had higher attrition rates than individuals with less depression symptoms. Another study found that pregnant smokers who were single, used drugs and had lower education were less likely to stay in treatment⁹.

Furthermore, lower levels of depressed mood, fewer ‘pros’ for smoking¹⁰, early initiation of treatment after the intake session¹¹, low rates of smoking, having children, being female, being married and poor general health¹² are predictors of attendance to treatment.

Identifying the predictors of treatment adherence allows to develop strategies to decrease attrition rates and improve treatment success¹³.

Objective

In this study, we sought to identify predictors of retention in a multicomponent smoking cessation treatment, as a secondary analysis of results from a previous publication¹⁴.

Method

Participants

Inclusion criteria for the smoking cessation treatment were as follows: smoking 10 or more cigarettes a day, and ages 18 to 65. Exclusion criteria were as follow: use or abuse of another psychoactive substance (including psychiatric medications), diagnosis of mental disorders and conditions, pregnancy, lactation, hypersensitivity or presence of any medical condition with contraindications to the pharmacological treatment. Criteria were corroborated by participants self-report.

A total of 136 smokers who participated voluntarily in a smoking cessation treatment were recruited at the Center for Prevention of Addiction at the Psychology School of the National Autonomous University of Mexico (UNAM) from September to December 2009. Of those participants, 79 met the criteria for participation in the study and received psychological and pharmacological treatment. Due to the secondary nature of this study, the data was obtained from the same participants as the main publication¹⁴.

Instruments

Beck Depression Inventory (BDI)

A 21 question, multiple choice self-report inventory that measures depression severity. It has content, construct and concurrent validity, and it was standardized for Mexican population¹⁵.

Beck Anxiety Inventory (BAI)

A 21 question, multiple choice self-report inventory for measuring severity of anxiety. It was standardized for Mexican population with high internal consistency and construct validity¹⁶.

Fagerström Test for Nicotine Dependence (FTND)

A standard instrument of six items to assess nicotine addiction and classifies smokers in three degrees of dependence. This is the most used scale for dependence worldwide¹⁷.

Clinical history form

A questionnaire was designed to assess sociodemographic information (age, sex, marital status and education), and consumption patterns: age of onset, number of years smoking, amount of cigarettes, number of cessation attempts, reasons to quit, etc.

Procedure

Research protocol was reviewed and approved by the Research Ethics Committee of the UNAM addiction research macro project: “Development of New Models for Prevention and Treatment of Addictive Behaviors”. Eligible subjects were interviewed to assess whether they met inclusion criteria and were given a written consent sheet. Those who met criteria and agreed to continue in the study were evaluated with BDI, BAI, FTND and the Clinic History Form, and were randomly allocated in one of the three treatment settings (Transdermal nicotine patches [TNP] pharmacological therapy, bupropion pharmacological therapy, and TNP and bupropion pharmacological therapy). All treatment settings included sessions of cognitive-behavioral psychological therapy (CBPT). The first two sessions (one per week) were aimed to assess their cognitions about tobacco smoking and their specific pattern of addiction, respectively. At the same time, patients changed their usual cigarettes for 0.1 mg low nicotine cigarettes (LNC) and gradually reduced their daily cigarette consumption until abstinence was reached at the beginning of the third week of treatment. Participants received two more weekly sessions of CBPT that focused on strategies to prevent or deal with specific tobacco smoking related situations and to prevent relapse. Pharmacological treatment began on the first session and continued for three months. During this period, participants were monitored by abstinence biomarkers and psychological indicators related to mood [for more details on the treatment see¹⁸].

Data analysis

Treatment retention was considered whenever a participant completed the active phase of treatment (equal to or greater than 90 days of monitoring).

Data analysis was conducted in two phases, taking into account the recommendations of Hosmer and Lemeshow¹⁹. First, we identified the predictors of retention, then we evaluated binary associations of continuous and categorical variables using Student's t and chi square (X²) respectively. We used a cutoff for inclusion in the multivariate model of p < 0.25. Subsequently, we conducted a multivariate logistic regression that included the variables obtained in the previous analysis. Data analysis were performed using SPSS version 19.

Results

Sample consisted of 45 men and 34 women, whose average age was 42.6 years old (SD 11.17), 45% reported being married and 38% being single; more than half of the sample had undergraduate or graduate studies (60%). The main reason for wanting to quit smoking was related to health problems (76%). Out of the 79 patients, 55 (69%) were maintained throughout the treatment. Tables 1 and 2 show the features of participants who remained in the active phase of treatment and of participants who abandoned it.

Table 1
Participants characteristics

Variables		Retention to treatment		Treatment abandonment		Bivariate analysis
		(n = 55)		(n = 24)
		Frequency	%	Frequency	%
Sex						X2(1) = 0.68
	Male	33	60	12	40
	Female	22	64.7	12	35.3
Marital Status						X2(3) = 10.18*
	Single	15	50	15	50
	Married	28	77.7	8	22.3
	Cohabiting	5	83.3	1	16.7
	Divorced	7	100	0	0
Education						X2(4) = 5.89*
	Primary	0	0	1	100
	Secondary	7	50	7	50
	High School	13	76.4	4	23.6
	Graduate	21	72.4	8	27.6
	Post graduate	14	77.7	4	22.3
Living with other smokers						X2(1) = 0.13
	Yes	46	69.6	20	30.4
	No	9	75	3	25
Reasons to quit smoking						X2(2) = 0.46
	Health problems	41	68.3	19	31.7
	Family problems	9	69.2	4	30.8
	Economic problems	1	100	0	0
MDE at some point in life						X2(1) = 0.62
	Yes	30	73.1	11	26.9
	No	22	64.7	12	35.3
Type of Treatment						X2(2) = 3.23*
	TNP	18	62	11	38
	Bupropion	14	63.3	8	36.7
	TNP + Bupropion	23	82.1	5	17.9

• MDE: Major Depressive Episode; TNP: Transdermal Nicotine Patch.
• ^*
  p < 0.25.

Table 2
Assessed variables

Variables		Retention to treatment		Treatment abandonment		Bivariate analysis
		(n = 55)		(n = 24)
Sociodemographics		Mean	SD	Mean	SD
	Age	43.71	11.27	40.04	10.72	t(45) = −1.37*
Consumption pattern
	Age of first cigarette	15.69	4.63	14.35	3.12	t(60) = −1.48*
	Age of onset of regular smoking	20.98	7.3	17.09	2.39	t(76) = −2.49*
	Number or years smoking	23.38	11.69	22.04	10.3	t(49) = −0.51
	Cigarettes per day	14.07	8.09	15.54	7.25	t(48) = 0.79
	Years smoking the current amount of cigarettes	7.45	8.17	4.67	4.77	t(77) = −1.55*
	Number of quitting attempts	2.59	3.03	2	2.8	t(43) = −0.81
	FTND score	3.56	2.25	4.13	2.39	t(39) = 0.96
Health status
	Anxiety level	10.68	7.84	11.57	7.53	t(43) = 0.46
	Depression level	9.24	6.34	9.39	6.28	t(41) = 0.09

• ^*
  p < 0.25.

Bivariate analysis between treatment retention and demographic, consumption, mood and treatment variables, resulted in seven variables that met the criteria for inclusion in the multivariate regression analysis: treatment modality (X²₍₂₎ = 3.23, p = 0.199), education (X²₍₄₎ = 5.89, p = 0.207), marital status (X²₍₃₎ = 10.18, p = 0.017), age (t₍₄₅₎ = −1.37, p = 0.175), age of first cigarette consumption (X²₍₆₀₎ = −1.48, p = 0.142), age of onset of regular consumption (X²₍₇₆₎ = −2.49, p = 0.015) and years consuming the current number of cigarettes (X²₍₇₇₎ = −1.55, p = 0.124).

Table 3 shows the multivariate regression model for treatment retention, which indicates that adherence to treatment increases when the age of onset of regular consumption is higher (OR = 1.545, 95% CI = 1.175 – 2.032). Secondary education decreases retention to treatment compared to postgraduate education (OR = 0.018, 95% CI = 0.001 – 0.365); likewise bupropion intake was less likely promote retention when compared the combined drug therapy (TNP and bupropion) (OR = 0.085, 95%, IC = 0.01 – 0.745). The model was statistically significant (X²₍₁₁₎ = 47.4, p = 0.000).

Table 3
Logistic Regression Model of treatment retention

Variables		B	SE	Wald	p	OR	95% CI
							Low	High
Age of onset of regular smoking		0.435	0.14	9.67	0.002*	1.545	1.175	2.032
Years smoking the current amount of cigarettes		0.1	0.061	2.671	0.102	1.105	0.98	1.297
Education
	Primary	47.257	43595.7	0	0.999	0	0	.
	Secondary	-4.037	1.545	6.828	0.009*	0.018	0.001	0.365
	High School	-0.371	1.328	0.078	0.78	0.69	0.051	9.317
	Graduate	-0.022	1.023	0	0.983	0.978	0.132	7.259
	Post graduate	.	.	.	.	.	.	.
Type of treatment
	TNP	0.147	0.944	0.024	0.876	1.158	0.182	7.364
	Bupropion	-2.463	1.106	4.956	0.026*	0.085	0.01	0.745
	NP + Bupropion	.	.	.	.	.	.	.
Marital status
	Single	21.441	12727.8	0	0.999	0	0	.
	Married	18.759	12727.8	0	0.999	0	0	.
	Cohabiting	3.324	21145	0	1	27.764	0	.
	Divorced	.	.	.	.	.	.	.

• TNP: Transdermal Nicotine Patch.
• ^*
  p < 0.25.

The proportion of variance explained was Nagelkerke R² = 0.649. The model has a specificity of 69.6% and a sensitivity of 90.9%; with these values we obtained an overall successful prediction of 84.6%. The fit of the model was tested using the Hosmer and Lemeshow test (X²(8) = 3.014, p = 0.933).

Discussion

Multivariate regression results suggest that higher age of onset of regular consumption increases the probability of adherence to treatment. On the contrary, secondary education and pharmacological treatment with bupropion reduce the probability of treatment retention. These results are noteworthy since only three variables provided relevant information of a total of 18 sociodemographic, consumption pattern, mood and treatment variables.

Age of onset of regular consumption is a variable that has not been reported previously as a predictor of retention to treatment. In the study sample, we found that average age of onset in patients that finished treatment was 20.98 years old, while age of onset of patients that did not finish treatment was 17.09 years and also were the group who consumed more cigarettes per day and had higher dependence levels.

Additionally, the finding that secondary education is a risk predictor of treatment abandonment in comparison with higher education is in agreement with previous reports⁹. Educational institutions plays a dual role in the onset of substance use; as a protective factor that provides knowledge in the area of health promotion and life skills to deal with social pressure²⁰, and as a risk factor favoring drug use among young people who decide to experience new sensations and achieve insertion into a social circle through the substances, sidelining academic performance and consequently increasing the likelihood of school dropout^21-23. It is not unlikely that people with secondary education or lower have a smaller repertoire of health related information and of coping skills related to smoking situations. The greater rate of treatment retention of participants with higher education could be explained by the latter.

Bupropion has been recognized as an effective smoking cessation treatment that maintains long-term abstinence in several clinical trials²⁴, however, treatment success largely relies on retention to treatment. It has previously been reported that several patients abandon pharmacological treatment and stop taking the drug voluntarily due to the fear of adverse reactions, their perception of lack of improvement and the belief that the drug was unnecessary, excessive or that it could cause addiction²⁵. In order to avoid the introduction of a confounding factor, i.e., the cessation of bupropion intake by the participants, we provided sufficient information during the evaluation session about the characteristics of the drugs used in this research.

Several variables have been associated to retention to treatment for smoking cessation, such as age, sex, marital status^9,11,12, nicotine dependence level, depressive symptoms and a major depressive episode at some point in life⁸.

Although 76% of our patients reported health problems in their clinic history, this information was not corroborated with additional studies. It was not possible to assess this variable in the multivariate regression model because we did not find and association with treatment retention in the bivariate analysis; this is similar to the results reported by Lee et al.¹⁰ who did not find either an association between health state and retention to treatment in Latin American populations. For future research in this subject we suggest the conduction of a thorough medical evaluation to confirm patients self-reports and to classify them more accurately to determine if health status is associated or not with treatment retention.

Other variables such as anxiety symptoms, family support, environmental barriers, number of attempts to quit smoking and living with other smokers, have been associated with tobacco smoking and were considered in this study, but since they showed no relation to retention to treatment, they were not evaluated in the multivariate logistic regression model. A possible explanation for these results is the small sample size and consequently, the lack of statistical power of the tests used. We propose to continue studies with these variables that take into account other treatment modalities and bigger sample sizes to promote statistical power²⁶.

Due to the efforts of clinicians and researchers to increase retention to treatment for substance abuse, it has been identified that most dropouts occur mainly during the first 30 days^27-30 as a result of patient, therapist and context variables^31,32; however, this information does not come from smoking cessation treatments themselves, therefore it is of vital interest to identify critical periods of treatment abandonment in smoking cessation patients. This will clarify the variables related to retention to treatment and will stimulate adherence to treatment and improve the tobacco abstinence prognosis.

The main limitations of this study were: (i) sample size, because it does not allow the generalization of our results to other contexts, (ii) the psychological treatment that complemented all the pharmacological treatment conditions because it may be a confounding factor that hinder the assessment of the associations, (iii) the unavailability of information regarding the patients’ reasons for treatment abandonment, and (iv) the lack of measurements of plasma drug concentration because it is unknown whether the specific effect of these drugs are related to treatment adherence or abandonment.

• Institution where the study was conducted: National Autonomous University of Mexico.
• Research funding
  Funding for this work was provided by UNAM through the “Development of New Models for Prevention and Treatment of Addictive Behaviors” macro project.

Acknowledgments

Authors acknowledge and are thankful of Fabiola Jael García-Anguiano for her support in editing this manuscript.

References

• ¹
  World Health Organization. WHO Report on the Global Tobacco Epidemic 2008: the MPOWER package. Geneva: World Health Organization; 2008.
• ²
  Mackay J, Eriksen M, Shafey O. The Tobacco Atlas. Atlanta: American Cancer Society; 2006.
• ³
  Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 2006;3(11):e442.
• ⁴
  Reynales-Shigematsu L, Guerrero-López C, Lazcano-Ponce E, Villatoro-Velázquez J, Medina-Mora M, Fleiz-Bautista C. Encuesta Nacional de Adicciones 2011: Reporte de Tabaco. México: Instituto Nacional de Psiquiatría Ramón la Fuente Muñiz; 2012.
• ⁵
  Waters H, Sáenz de Miera B, Ross H, Reynales Shigematsu LM. La economía del tabaco y los impuestos al tabaco en México. París: Unión Internacional contra la Tuberculosis y Enfermedades Respiratorias; 2010.
• ⁶
  US Department of Health and Human Services. The health consequences of smoking: a report of the Surgeon General. Atlanta, GA: US 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; 2004.
• ⁷
  Doll R, Hill AB. The mortality of doctors in relation to their smoking habits: a preliminary report. BMJ. 1954;1(4877):1451-5.
• ⁸
  Curtin L, Brown RA, Sales SD. Determinants of attrition from cessation treatment in smokers with a history of major depressive disorder. Psychol Addict Behav. 2000;14(2):134-42.
• ⁹
  El-Khorazaty MN, Johnson AA, Kiely M, El-Mohandes AAE, Subramanian S, Laryea HA, et al. Recruitment and retention of low-income minority women in a behavioral intervention to reduce smoking, depression, and intimate partner violence during pregnancy. BMC Public Health. 2007;7(3):233.
• ¹⁰
  Lee CS, Hayes RB, McQuaid EL, Borrelli B. Predictors of retention in smoking cessation treatment among Latino smokers in the Northeast United States. Health Educ Res. 2010; 25(4):687-97.
• ¹¹
  Kalkhuis-Beam S, Stevens SL, Baumritter A, Carlson EC, Pletcher JR, Rodriguez D, et al. Participant- and study-related characteristics predicting treatment completion and study retention in an adolescent smoking cessation trial. J Adolesc Health. 2011;49(4):371-8.
• ¹²
  Wagner EH, Schoenbach VJ, Orleans CT, Grothaus LC, Saunders KW, Curry S, et al. Participation in a smoking cessation program: a population-based perspective. Am J Prev Med. 1990;6(5):258-66.
• ¹³
  Snow WM, Connett JE, Sharma S, Murray RP. Predictors of attendance and dropout at the Lung Health Study 11-year follow-up. Contemp Clin Trials. 2007;28(1):25-32.
• ¹⁴
  Moreno-Coutiño A, García-Anguiano F, Ruiz-Velasco S, Medina-Mora ME. Assessment of depressive symptoms in severe smokers with minimal-mild depressive symptomatology receiving pre-smoking abstinence for integrated treatment: a randomized clinical trial. Salud Ment. 2015;38(6):433-9.
• ¹⁵
  Jurado S, Villegas ME, Méndez L, Rodríguez F, Loperena V, Varela R. La estandarización del Inventario de Depresión de Beck para los residentes de la Ciudad de México. Salud Ment. 1998;21(3):26-31.
• ¹⁶
  Robles R, Varela R, Jurado S, Páez F. Versión mexicana del Inventario de Ansiedad de Beck: propiedades psicométricas. Rev Mex Psicol. 2001;18(2):211-8.
• ¹⁷
  Heatherton TF, Kozlowski LT, Frecker RC, Fagerström KO. The Fagerström Test for Nicotine Dependence: a revision of the Fagerström Tolerance Questionnaire. Br J Addict. 1991;86(9):1119-27.
• ¹⁸
  Moreno-Coutiño AB, Hernández-Delgado MA, García-Anguiano FJ, Díaz-Ayala D, Reidl-Martínez LM, Medina-Mora Icaza ME. Manual del Tratamiento Integral Pre-abstinencia contra el tabaquismo para fumadores con sintomatología depresiva. México: Universidad Nacional Autónoma de México; 2011.
• ¹⁹
  Hosmer Jr DW, Lemeshow S. Applied logistic regression. US: John Wiley & Sons; 2004.
• ²⁰
  Laespada T, Iraurgi I, Aróstegi E. Factores de riesgo y protección frente al consumo de drogas: hacia un modelo explicativo del consumo de drogas en jóvenes de la CAPV. Universidad de Deusto; 2004.
• ²¹
  Anaya-Ocampo R, Arillo-Santillán E, Sánchez-Zamorano LM, Lazcano-Ponce E. [Poor school performance associated with tobacco persistence among Mexican student]. Salud Publica Mex. 2006;48:s17-29.
• ²²
  López S, Rodríguez-Arias JL. Factores de riesgo y de protección en el consumo de drogas en adolescentes y diferencias según edad y sexo. Psicothema. 2010;22(4):568-73.
• ²³
  Nuño-Gutiérrez BL, Alvarez-Nemegyei J, Madrigal-De León EA, Tapia-Curiel A. [Factors associated to tobacco-smoking pattern in school teenagers]. Rev Med Inst Mex Seguro Soc. 2007;46(1):19-26.
• ²⁴
  Hughes JR, Stead LF, Lancaster T. Antidepressants for smoking cessation. Cochrane Database Syst Rev. 2014;(1):CD000031.
• ²⁵
  Organización Mundial de la Salud. Tratamiento farmacológico de los trastornos mentales en la atención primaria de salud. Organización Panamericana de la Salud; 2010.
• ²⁶
  Murphy KR, Myors B, Wolach A. Statistical power analysis: a simple and general model for traditional and modern hypothesis tests. New York: Routledge; 2014.
• ²⁷
  Baekeland F, Lundwall L. Dropping out of treatment: a critical review. Psychol Bull. 1975;82(5):738-83.
• ²⁸
  De Leon G. Retention in drug-free therapeutic communities. NIDA Res Monogr. 1991;106:218-44.
• ²⁹
  Silberfeld M, Glaser FB. Use of the life table method in determining attrition from treatment. J Stud Alcohol. 1978;39(9):1582-90.
• ³⁰
  Swett Jr C, Noones J. Factors associated with premature termination from outpatient treatment. Psychiatr Serv. 1989;40(9):947-51.
• ³¹
  Martin Alfonso L, Grau Abalo J. La investigación de la adherencia terapéutica como un problema de la psicología de la salud. Psicol y Salud. 2014;14(1):89-99.
• ³²
  Rodríguez Marín J. Efectos de la interacción entre el profesional sanitario y el paciente. Satisfacción del paciente. Cumplimiento de las prescripciones terapéuticas. In: Rodríguez Marin J, editor. Psicología social de la salud. Madrid: Síntesis; 1995. p. 151-60.

Publication Dates

• Publication in this collection
  Nov-Dec 2016

History

• Received
  23 Feb 2016
• Accepted
  28 Oct 2016