Examining the Association between Substance Use Disorder Treatment and Smoking Cessation Chang Shu1 Benjamin Lê Cook2* 1. Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA 2. Center for Multicultural Mental Health Research, Cambridge Health Alliance, Cambridge, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA *Correspondence should be addressed to Benjamin Lê Cook, Ph.D., M.P.H., Center for Multicultural Mental Health Research, 120 Beacon Street, 4th Floor, Somerville, MA, 02143, USA, Email: [email protected] Running head: Substance Use Treatment and Smoking Word count: 2884 Declarations of interest: No conflicts of interest. This work was supported by the William F. Milton Fund
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Abstract Aims: To examine rates of smoking cessation among persons with last year substance use disorder (SUD) and how these rates differ among those that have ever received SUD treatment, those that have never received treatment, and those that received last year outpatient and/or inpatient treatment. Design: Cross-sectional study based on 2009-2012 National Survey on Drug Use and Health (NSDUH). Setting and Participants: A total of 12,796 adult lifetime smokers with any last year SUD. The sample is representative of the non-institutionalized U.S. adult population. Measurements: We described smoking cessation rates by type of SUD and SUD treatment. We used a logistic regression model identifying the association between smoking cessation and lifetime SUD treatment, adjusting for confounders. We also estimated models identifying the association between smoking cessation and last year outpatient and inpatient SUD treatment. Findings: Multivariate models identified significantly lower odds of quitting among those with lifetime SUD treatment (OR=0.561, p-value80%) [6], a rate of 80% for those with cocaine abuse or dependence [7], and a rate of approximately 75% for those with alcohol dependence [8, 9]. Deaths of persons with SUD are more likely to be due to a tobacco-related cause rather than alcohol or other drug-related causes [10, 11]. The National Institutes of Health (NIH) stress the benefits of quitting for persons with SUD is high and have called for more research efforts concerning this population [12, 13]. Given the high correlation of smoking and SUD, and the long term effects of smoking on physical health, the SUD treatment setting appears to be an opportune location to discuss smoking cessation with patients. Smoking prevalence reported in SUD treatment settings is very high, with 65% or higher of SUD treatment patients reporting tobacco use [14]. Historically, SUD treatment centers have tended to de-prioritize tobacco use in treatment settings, residential facilities, and housing [15], with one study showing that
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among units within agencies participating in the National Drug Abuse Treatment Clinical Trials Network, 69% of the units offered no smoking cessation treatment [16]. Possible explanations suggested by past study authors are that most SUD service providers do not prioritize smoking cessation as part of treatment [16], and that behavioral and/or pharmacological cessation interventions have not been integrated into routine clinical care [17]. This inactivity may also be attributed to substance provider’s impressions that the introduction of smoking cessation treatment and potential moderation may adversely affect an individual’s SUD treatment. Contrary to providers’ concerns, evidence has shown that quitting smoking does not negatively impact SUD treatment outcomes [18, 19]. Building upon prior research, we assess smoking cessation rates among individuals in SUD treatment in the U.S., and how the relationship between cessation and SUD treatment depends on the type of substance used. We build upon prior studies demonstrating that drug treatment may lead to increased cigarette use [20], and that changes in smoking behaviors are associated with substance use treatment outcomes [21, 22]. We scale these research questions up to analyze a nationally representative sample of respondents with last year SUD and focus specifically on smoking cessation as an outcome rather than a predictor. We analyze the 2009–2012 National Survey on Drug Use and Health (NSDUH) to (1) compare smoking cessation rates by receipt of lifetime SUD treatment and substance use type; (2) measure the association of smoking cessation and lifetime SUD treatment after adjustment for sociodemographic and clinical factors; and (3) measure the association of smoking cessation and last year SUD outpatient and inpatient treatment after adjustment for sociodemographic and clinical factors.
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Methods Participants The 2009-2012 National Survey on Drug Use and Health (NSDUH) provides U.S. national estimates of smoking prevalence, illicit drug and alcohol use, and information on SUD treatment and socio-demographic characteristics. We analyzed adults 18 and older identified as having any last year alcohol or illicit drug use dependence or abuse (called substance use disorder or SUD from here forward). We used four years of NSDUH data to increase the robustness of estimates, leading to a final sample of 12,796 adult lifetime smokers with any last year SUD. Sampling design and weights were incorporated into all estimates to accurately weight the four years of pooled nationally representative data[23, 24], and to provide accurate variance calculations. An individual is characterized as a lifetime smoker if he or she had 100 or more cigarettes in his/her lifetime. Illicit drug or alcohol abuse or dependence in the past year is determined by a series of questions based on criteria specified in the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) [25]. Substances are categorized into alcohol, marijuana, cocaine, heroin, and other illicit drugs.
Measures Dependent Variable: Similar to prior studies [26, 27], we define quitting smoking as an ever-smoker (100+ lifetime cigarettes) that did not smoke cigarettes during the past 30 days. Independent Variables of Interest: The first independent variable of interest, any lifetime SUD treatment, indicates treatment for SUD in a hospital, rehabilitation center, community mental health center, emergency room, physician’s office, jail or prison, but not
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treatment in a self-help group (e.g., Alcoholics Anonymous). To identify differences in smoking cessation by treatment location, we also assessed the association between quitting smoking and receiving last year inpatient SUD treatment (hospitalization or residential inpatient facility) and outpatient SUD treatment (outpatient rehabilitation facility, mental health center, emergency room, doctor's office, or prison/jail). Unfortunately, the timing of these variables differs from the first independent variable of interest because lifetime outpatient and inpatient SUD treatment was unavailable. We constructed mutually exclusive categories for the past year SUD treatment variable with the following response categories: patients receiving both inpatient and outpatient treatment, inpatient only, outpatient only, or none. Other Independent Variables: To adjust for cessation differences by substance use category, we used the following indicators of any abuse or dependence of the following substances: alcohol, cocaine, heroin, marijuana, and other substances (a category that includes prescription pain relievers, hallucinogens, inhalants, sedatives, stimulants, and tranquilizers). These indicators are 0/1 variables of each type of substance allowing for an individual to be categorized as having abuse or dependence to multiple substances. We also adjusted for SUD severity (abuse or dependence) and severity of mental illness (mild, moderate, serious), the latter being calculated using a prediction formula developed by SAMHSA combining data from the K-6 and the WHO-Disability Assessment Schedule (WHODAS) impairment scale [28]. NSDUH respondents of any race claiming to be of Latino or Hispanic origin were identified as Hispanic. Others were classified as black, white, Asian or Pacific Islanders, or other race by their responses to the question about race. Other socio-demographic characteristics are age, marital status, an indicator of any criminal activity (any arrest for
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breaking the law not counting minor traffic violations, any last year parole or probation), insurance status (Medicare, Medicaid/Children's Health Insurance Program, private insurance, uninsured, other), employment status, rural area indicator, education (less than high school, high school graduate, some college, college graduate) and income related to the federal poverty level (FPL) (100-125% of FPL, 125-200% of FPL, 200-400% of FPL, and 400%+ of FPL).
Statistical Methods We examined summary statistics for the dependent and independent variables, comparing individuals with and without lifetime SUD treatment using chi-square tests for categorical variables and t-tests for continuous variables. Bonferroni corrections were used in order to identify appropriate thresholds for statistical significance in the presence of multiple comparisons [29]. We also compared quitting rates by lifetime SUD treatment and type of substance use using chi-square tests. Next, we estimated a logistic regression model of smoking cessation conditional on lifetime SUD treatment and the sociodemographic and clinical characteristics described above. To investigate the influence of outpatient and inpatient treatment on smoking cessation, we estimated a similar logistic regression model of smoking cessation conditional on type of SUD treatment (both inpatient and outpatient settings, inpatient only, outpatient only and no treatment). All analyses used sampling weights to provide estimates that are representative of the U.S., non-institutionalized Latino population. Standard errors were estimated using svy commands in Stata 13 software[30] which accounts for the survey sampling design. To
This article is protected by copyright. All rights reserved.
address missing data in variables other than smoking or smoking cessation in the NSDUH (less than 1% missing on the indicator of any interaction with the criminal justice system), we implemented multiple imputation methods using the mi procedure in Stata. This technique creates five complete datasets, imputes missing values using a chained equations approach, analyzes each dataset, and uses standard rules to combine estimates and adjust standard errors for the uncertainty due to imputation [31, 32].
Sensitivity analyses We conducted sensitivity analyses to address the possibility that the lifetime SUD treatment reported by the respondent occurred after quitting and therefore was unlikely to influence tobacco cessation. Due to the lack of specificity of the exact time of SUD treatment in relation to quitting (because of the categorization rather than exact measurement of age), we used the upper and lower bounds of the age category to create an upper and lower bound on the time since treatment. First, we conducted sensitivity analyses re-estimating regression models using an upper bound on time since treatment (including more quitters in the analysis) and a lower bound on time since treatment (including fewer quitters in the analysis).
Results Among the 12,796 adult smokers with past year SUD, respondents who never received SUD treatment (29%) were more likely to quit than those who did receive SUD treatment (16%, p
This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/add.12879
This article is protected by copyright. All rights reserved.
Abstract Aims: To examine rates of smoking cessation among persons with last year substance use disorder (SUD) and how these rates differ among those that have ever received SUD treatment, those that have never received treatment, and those that received last year outpatient and/or inpatient treatment. Design: Cross-sectional study based on 2009-2012 National Survey on Drug Use and Health (NSDUH). Setting and Participants: A total of 12,796 adult lifetime smokers with any last year SUD. The sample is representative of the non-institutionalized U.S. adult population. Measurements: We described smoking cessation rates by type of SUD and SUD treatment. We used a logistic regression model identifying the association between smoking cessation and lifetime SUD treatment, adjusting for confounders. We also estimated models identifying the association between smoking cessation and last year outpatient and inpatient SUD treatment. Findings: Multivariate models identified significantly lower odds of quitting among those with lifetime SUD treatment (OR=0.561, p-value80%) [6], a rate of 80% for those with cocaine abuse or dependence [7], and a rate of approximately 75% for those with alcohol dependence [8, 9]. Deaths of persons with SUD are more likely to be due to a tobacco-related cause rather than alcohol or other drug-related causes [10, 11]. The National Institutes of Health (NIH) stress the benefits of quitting for persons with SUD is high and have called for more research efforts concerning this population [12, 13]. Given the high correlation of smoking and SUD, and the long term effects of smoking on physical health, the SUD treatment setting appears to be an opportune location to discuss smoking cessation with patients. Smoking prevalence reported in SUD treatment settings is very high, with 65% or higher of SUD treatment patients reporting tobacco use [14]. Historically, SUD treatment centers have tended to de-prioritize tobacco use in treatment settings, residential facilities, and housing [15], with one study showing that
This article is protected by copyright. All rights reserved.
among units within agencies participating in the National Drug Abuse Treatment Clinical Trials Network, 69% of the units offered no smoking cessation treatment [16]. Possible explanations suggested by past study authors are that most SUD service providers do not prioritize smoking cessation as part of treatment [16], and that behavioral and/or pharmacological cessation interventions have not been integrated into routine clinical care [17]. This inactivity may also be attributed to substance provider’s impressions that the introduction of smoking cessation treatment and potential moderation may adversely affect an individual’s SUD treatment. Contrary to providers’ concerns, evidence has shown that quitting smoking does not negatively impact SUD treatment outcomes [18, 19]. Building upon prior research, we assess smoking cessation rates among individuals in SUD treatment in the U.S., and how the relationship between cessation and SUD treatment depends on the type of substance used. We build upon prior studies demonstrating that drug treatment may lead to increased cigarette use [20], and that changes in smoking behaviors are associated with substance use treatment outcomes [21, 22]. We scale these research questions up to analyze a nationally representative sample of respondents with last year SUD and focus specifically on smoking cessation as an outcome rather than a predictor. We analyze the 2009–2012 National Survey on Drug Use and Health (NSDUH) to (1) compare smoking cessation rates by receipt of lifetime SUD treatment and substance use type; (2) measure the association of smoking cessation and lifetime SUD treatment after adjustment for sociodemographic and clinical factors; and (3) measure the association of smoking cessation and last year SUD outpatient and inpatient treatment after adjustment for sociodemographic and clinical factors.
This article is protected by copyright. All rights reserved.
Methods Participants The 2009-2012 National Survey on Drug Use and Health (NSDUH) provides U.S. national estimates of smoking prevalence, illicit drug and alcohol use, and information on SUD treatment and socio-demographic characteristics. We analyzed adults 18 and older identified as having any last year alcohol or illicit drug use dependence or abuse (called substance use disorder or SUD from here forward). We used four years of NSDUH data to increase the robustness of estimates, leading to a final sample of 12,796 adult lifetime smokers with any last year SUD. Sampling design and weights were incorporated into all estimates to accurately weight the four years of pooled nationally representative data[23, 24], and to provide accurate variance calculations. An individual is characterized as a lifetime smoker if he or she had 100 or more cigarettes in his/her lifetime. Illicit drug or alcohol abuse or dependence in the past year is determined by a series of questions based on criteria specified in the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) [25]. Substances are categorized into alcohol, marijuana, cocaine, heroin, and other illicit drugs.
Measures Dependent Variable: Similar to prior studies [26, 27], we define quitting smoking as an ever-smoker (100+ lifetime cigarettes) that did not smoke cigarettes during the past 30 days. Independent Variables of Interest: The first independent variable of interest, any lifetime SUD treatment, indicates treatment for SUD in a hospital, rehabilitation center, community mental health center, emergency room, physician’s office, jail or prison, but not
This article is protected by copyright. All rights reserved.
treatment in a self-help group (e.g., Alcoholics Anonymous). To identify differences in smoking cessation by treatment location, we also assessed the association between quitting smoking and receiving last year inpatient SUD treatment (hospitalization or residential inpatient facility) and outpatient SUD treatment (outpatient rehabilitation facility, mental health center, emergency room, doctor's office, or prison/jail). Unfortunately, the timing of these variables differs from the first independent variable of interest because lifetime outpatient and inpatient SUD treatment was unavailable. We constructed mutually exclusive categories for the past year SUD treatment variable with the following response categories: patients receiving both inpatient and outpatient treatment, inpatient only, outpatient only, or none. Other Independent Variables: To adjust for cessation differences by substance use category, we used the following indicators of any abuse or dependence of the following substances: alcohol, cocaine, heroin, marijuana, and other substances (a category that includes prescription pain relievers, hallucinogens, inhalants, sedatives, stimulants, and tranquilizers). These indicators are 0/1 variables of each type of substance allowing for an individual to be categorized as having abuse or dependence to multiple substances. We also adjusted for SUD severity (abuse or dependence) and severity of mental illness (mild, moderate, serious), the latter being calculated using a prediction formula developed by SAMHSA combining data from the K-6 and the WHO-Disability Assessment Schedule (WHODAS) impairment scale [28]. NSDUH respondents of any race claiming to be of Latino or Hispanic origin were identified as Hispanic. Others were classified as black, white, Asian or Pacific Islanders, or other race by their responses to the question about race. Other socio-demographic characteristics are age, marital status, an indicator of any criminal activity (any arrest for
This article is protected by copyright. All rights reserved.
breaking the law not counting minor traffic violations, any last year parole or probation), insurance status (Medicare, Medicaid/Children's Health Insurance Program, private insurance, uninsured, other), employment status, rural area indicator, education (less than high school, high school graduate, some college, college graduate) and income related to the federal poverty level (FPL) (100-125% of FPL, 125-200% of FPL, 200-400% of FPL, and 400%+ of FPL).
Statistical Methods We examined summary statistics for the dependent and independent variables, comparing individuals with and without lifetime SUD treatment using chi-square tests for categorical variables and t-tests for continuous variables. Bonferroni corrections were used in order to identify appropriate thresholds for statistical significance in the presence of multiple comparisons [29]. We also compared quitting rates by lifetime SUD treatment and type of substance use using chi-square tests. Next, we estimated a logistic regression model of smoking cessation conditional on lifetime SUD treatment and the sociodemographic and clinical characteristics described above. To investigate the influence of outpatient and inpatient treatment on smoking cessation, we estimated a similar logistic regression model of smoking cessation conditional on type of SUD treatment (both inpatient and outpatient settings, inpatient only, outpatient only and no treatment). All analyses used sampling weights to provide estimates that are representative of the U.S., non-institutionalized Latino population. Standard errors were estimated using svy commands in Stata 13 software[30] which accounts for the survey sampling design. To
This article is protected by copyright. All rights reserved.
address missing data in variables other than smoking or smoking cessation in the NSDUH (less than 1% missing on the indicator of any interaction with the criminal justice system), we implemented multiple imputation methods using the mi procedure in Stata. This technique creates five complete datasets, imputes missing values using a chained equations approach, analyzes each dataset, and uses standard rules to combine estimates and adjust standard errors for the uncertainty due to imputation [31, 32].
Sensitivity analyses We conducted sensitivity analyses to address the possibility that the lifetime SUD treatment reported by the respondent occurred after quitting and therefore was unlikely to influence tobacco cessation. Due to the lack of specificity of the exact time of SUD treatment in relation to quitting (because of the categorization rather than exact measurement of age), we used the upper and lower bounds of the age category to create an upper and lower bound on the time since treatment. First, we conducted sensitivity analyses re-estimating regression models using an upper bound on time since treatment (including more quitters in the analysis) and a lower bound on time since treatment (including fewer quitters in the analysis).
Results Among the 12,796 adult smokers with past year SUD, respondents who never received SUD treatment (29%) were more likely to quit than those who did receive SUD treatment (16%, p
