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Gender-specific profiles of tobacco use among non-institutionalized people with serious mental illness Johnson, Joy L; Ratner, Pamela A; Malchy, Leslie A; Okoli, Chizimuzo T; Procyshyn, Ric M; Bottorff, Joan L; Groening, Marlee; Schultz, Annette; Osborne, Marg Nov 30, 2010

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RESEARCH ARTICLE Open AccessGender-specific profiles of tobacco use amongnon-institutionalized people with seriousmental illnessJoy L Johnson1*, Pamela A Ratner1, Leslie A Malchy1, Chizimuzo TC Okoli1,2, Ric M Procyshyn3, Joan L Bottorff4,Marlee Groening1, Annette Schultz5, Marg Osborne1AbstractBackground: In many countries, smoking remains the leading preventable cause of death. In North America,reductions in population smoking levels are stabilising and, in recent years, those involved in tobacco controlprogramming have turned their attention to particular segments of society that are at greatest risk for tobacco use.One such group is people with mental illness. A picture of tobacco use patterns among those with mental illnessis beginning to emerge; however, there are several unanswered questions. In particular, most studies have beenlimited to particular in-patient groups. In addition, while it is recognised that men and women differ in relation totheir reasons for smoking, levels of addiction to nicotine, and difficulties with cessation, these sex and genderdifferences have not been fully explored in psychiatric populations.Methods: Community residents with serious mental illness were surveyed to describe their patterns of tobacco useand to develop a gender-specific profile of their smoking status and its predictors.Results: Of 729 respondents, almost one half (46.8%) were current tobacco users with high nicotine dependencelevels. They spent a majority of their income on tobacco, and reported using smoking to cope with theirpsychiatric symptoms. Current smokers, compared with non-smokers, were more likely to be: diagnosed with aschizophrenia spectrum disorder (rather than a mood disorder); male; relatively young; not a member of aracialised group (e.g., Aboriginal, Asian, South Asian, Black); poorly educated; separated or divorced; housed in aresidential facility, shelter, or on the street; receiving social assistance; and reporting co-morbid substance use.There is evidence of a gender interaction with these factors; in the gender-specific multivariate logistic regressionmodels, schizophrenia spectrum disorder versus mood disorder was not predictive of women’s smoking, nor waseducation, marital status or cocaine use. Women, and not men, however, were more likely to be smokers if theywere young and living in a residential facility.Conclusion: For men only, the presence of schizophrenia spectrum disorder is a risk factor for tobacco use. Otherfactors, of a social nature, contribute to the risk of smoking for both men and women with serious mental illness.The findings suggest that important social determinants of smoking are “gendered” in this population, thustobacco control and smoking cessation programming should be gender sensitive.* Correspondence: Joy.Johnson@ubc.ca1School of Nursing, University of British Columbia, T201 - 2211 WesbrookMall, Vancouver, BC, Canada V6T 2B5Full list of author information is available at the end of the articleJohnson et al. BMC Psychiatry 2010, 10:101© 2010 Johnson et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the CreativeCommons Attribution License (, which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly cited.BackgroundIn many countries, smoking remains the leadingpreventable cause of death. In North America, reduc-tions in smoking rates are stabilising and, in recentyears, those involved in tobacco control programminghave turned their attention to particular segmentsof society that are at greatest risk for tobacco use,especially people with mental illness.An appreciation of the high rate of tobacco use bythose with mental illness is emerging. In a USA popula-tion-based study of 4,441 respondents aged 15-54 years,Lasser and colleagues [1] reported that current smokingrates for those with no mental illness, lifetime mentalillness, or mental illness in the past month were 22.5%,34.8%, and 41.0%, respectively. The burden of tobaccouse appears to be disproportionally borne by those withmental illness. Dani and Harris reported that 7% ofAmericans have a mental illness, and that this relativelysmall group consumes 34% of all cigarettes sold in theUSA [2]. Those with mental illness are noted to have ahigher “all cause” mortality rate compared with the gen-eral population; although suicide and accidents contri-bute to the high rate, very high mortality rates due tocardiovascular disease are apparent [3].Those with serious mental illness (SMI) (i.e., thoseindividuals who require long-term treatment for theirillness) are at particular risk for tobacco use. Previousstudies have found very high smoking rates amongselected populations of people with SMI, including psy-chiatric outpatients [4], patients in state mental hospitalsin the USA, and patients in several other countries [5,6].There is some evidence that smoking rates vary by psy-chiatric diagnosis, with individuals with a diagnosis ofschizophrenia having the highest tobacco use rate [7].Sex and gender differences in tobacco use have beenthe focus of numerous studies. It is increasingly recog-nised that men and women differ in relation to theirreasons for smoking, levels of addiction to nicotine, anddifficulties with cessation. Some of these differences maybe attributed to social factors (gender) while others maybe attributable to biological factors (sex) [8]. These sexand gender differences have not been fully explored inpsychiatric populations.Although it is now recognised that substance use dis-orders are prevalent among people with SMI, tobaccouse is often not included in substance use screening [9],even though there are emerging links being madebetween tobacco use and other substance use and insome instances with antipsychotic medication use [10].There is limited understanding of whether those withSMI who use tobacco are also more likely to use othersubstances, and if so, which substances are most fre-quently used.A picture of tobacco use patterns among those withSMI is emerging; however, there are several unansweredquestions. In particular, much of the data collected havebeen limited to particular clinics or in-patient groups,and few researchers have disaggregated their data bygender. Given recent trends of deinstitutionalisation,further study is warranted of tobacco use patternsamong men and women living in the community withSMI. There also is a need to explore how tobacco usevaries by diagnosis, whether it differs by symptomatol-ogy and other substance use, and whether social-environmental factors are salient.The purpose of this study was to determine the rate oftobacco use among people with SMI accessing commu-nity-based mental health services, and to learn moreabout the factors associated with their tobacco use. Thespecific objectives of the research were to: (a) describethe profile of tobacco use among people with SMI, (b)determine whether tobacco use differs by psychiatricdiagnosis and by gender, and (c) determine the extentto which co-morbid substance use and social-environ-mental factors are associated with smoking status.MethodsWe conducted a cross-sectional survey in which we tar-geted all adults with SMI who received services fromcommunity-based mental health teams in Vancouver,Canada. The vast majority of non-institutionalised per-sons with a diagnosis of SMI, in this city, are followedby one of these teams (they provide services to almost6,000 people, more than 1% of Vancouver’s population).Each mental health team provides psychiatric assess-ment and comprehensive treatment through drop-inand outreach services for people in their catchmentarea. Services include medication management, indivi-dual and group therapy, rehabilitation, and education.Many clients receive additional support in the form ofrehabilitation programming or housing through con-tracted agencies.SampleWe sought to obtain a representative sample of peoplewith SMI receiving community mental health services.Because of confidentiality concerns (i.e., disclosure ofnames and diagnoses without consent), however, wewere not permitted to draw a random sample from thepopulation of people receiving services. Consequently,we recruited voluntary participants who were receivingservices from seven of the eight mental health teams.Eligible participants were individuals whose healthrecords were flagged as active and who received carefrom an adult care program. All study participants wereliving in the community and were able to communicateJohnson et al. BMC Psychiatry 2010, 10:101 2 of 12and be understood in English, Mandarin, Cantonese, orPunjabi.ProceduresThe research staff visited each community mental healthteam, provided information about the study, answeredquestions, and negotiated strategies to access eligibleparticipants. A research assistant recruited participantsat the mental health team offices during regular operat-ing hours. The participants were introduced to the sur-vey either through the reception desk personnel or theircase managers. The participants could “self refer” to theresearch staff in response to brochures and flyers avail-able in the office waiting areas. The research staffexplained the study in detail, obtained written, fullyinformed consent, and administered the questionnaire[11]. Upon completion of the questionnaire, the partici-pants received a $10 gift certificate for a local grocerystore. Data collection occurred between October 2005and October 2006, with each mental health teaminvolved for approximately 4-6 months.Ethical approvalEthical approval was obtained from the BehaviouralResearch Ethics Board of the University of BritishColumbia. Approval to conduct the research wasobtained from Vancouver Coastal Health, VancouverCommunity Health Service Delivery Area.MeasuresThe questionnaire, which included several scales anditems, requiring 20-45 minutes to complete, was admi-nistered by the research staff.DemographicsThe demographic items included: age ("What is yourbirth date?”), gender ("Do you identify as male, female,trans-gendered or other?”), and ethnic/cultural back-ground ("What would you say is your main ethnic orcultural background?”). The information from this itemwas used to create a “racialised group” variable ("no” or“yes”). The use of this term is meant to construe thebelief that racial classifications are socially constructedand embedded in Eurocentric notions of inferiority,colonization, and prestige [12]. In the study community,people who are Aboriginal, Asian, South Asian or Blacktend to be racialised, which has implications for theirhealth [13]. The other demographic variables included:marital status ("What is your current marital status?”),current living situation ("Who do you live with? Alone,with family, friend(s), group home, or other?”), andhousing type ("What kind of housing do you live in?”Independent, semi-independent, residential, shelter/hos-tel, no fixed address, other?), financial support ("In thelast month, where have you received money or financialsupport from? Earned income/paid work, socialassistance/welfare, disability benefits, unemploymentinsurance, pension, savings, alimony/child support,family contribution, panhandling, other”), disposableincome ("After paying for housing and food last month,how much money did you have to spend on yourself?”),and income “prioritizing strategies” ("When you have tomake decisions about spending money on cigarettes,have you ever chosen to give up anything so that youwould have enough tobacco? Have you given up buyingfood? Coffee? Bus fare? Rent? Medication? Anythingelse?”).Psychiatric DiagnosisNot all of the participants (15.1%) provided permissionto access their medical records. These individuals’ diag-nostic information was limited to a self-report of thepsychiatric diagnosis ("What is your diagnosis?”). Forthe remainder who provided consent (84.9%), informa-tion about their diagnoses was collected from theirexisting mental health team medical record. Oncereferred to a community mental health team, all clientsare assessed by one of the team’s psychiatrists. The psy-chiatrists typically base their diagnoses on findings of aone-hour assessment interview (that includes mentalstatus examination and case history). DSM IV criteriaare used to guide the diagnostic process. A diagnosis isrecorded at the time of the client’s intake to communitymental health services, and then modified as required.For the purposes of this study, the most current diagno-sis was recorded.For the purpose of the analysis, we classified the speci-fic diagnoses as schizophrenia spectrum disorders, mooddisorders, or anxiety disorders. A diagnosis of a schizo-phrenia spectrum disorder included schizophrenia andits subtypes, schizoaffective disorder, delusional disorder,or psychosis not otherwise specified. Mood disordersincluded diagnoses of bipolar disorder, major depres-sion, manic depression or dysthymia. Anxiety disordersincluded diagnoses of obsessive compulsive disorder,generalized anxiety disorder, and panic disorder.Psychiatric SymptomsPsychiatric symptoms were assessed with the BriefSymptom Inventory (BSI) [14], which has been validatedfor use with people living with schizophrenia and is pre-ferred over other scales of psychopathology because it isrelatively non-invasive, quick to administer, and suitablefor use by research staff [15]. The 18-item scale mea-sures anxiety (e.g., nervousness or shakiness inside),depression (e.g., feeling lonely), and general somaticsymptoms (e.g., feeling weak in parts of your body)using a 5-point scale to measure the extent of distressexperienced over the past week; the response optionswere: “not at all,” “a little bit,” “moderately,” “quite abit,” and “extremely.” The internal consistency for theJohnson et al. BMC Psychiatry 2010, 10:101 3 of 12Global Severity Index (GSI) has been reported to bestrong with a coefficient alpha of .89 [15]. In this study,the scale had a coefficient alpha of .92. We followed theprescribed BSI scoring method: the raw GSI score wascalculated by adding the 18 items [16]. If participantshad more than 2 item responses missing for any sub-scale, their scores were not calculated and the case wastreated as missing. When participants had 1 or 2 miss-ing items, values were imputed by rounding the mean ofthe completed items to the nearest whole number. TheGSI scores were standardized using T scores with amean of 50 and an SD of 10 to determine “caseness.”Those with GSI scores of 63 or greater were deemed tobe at positive risk for psychological distress [14,16].Tobacco Use PatternsSmoking status was determined by asking the partici-pants if they had “ever” smoked, whether they hadsmoked more than 100 cigarettes in their lifetime, whenthey smoked their last cigarette, and if they smokedevery day [17]. The participants were classified as non-smokers (had never smoked or smoked less than 100cigarettes), former smokers (had smoked more than 100cigarettes, but had not smoked in the past 30 days), orcurrent smokers (had smoked more than 100 cigarettesand had smoked in the past 30 days). A binary variablewas created with current smoker versus former/neversmoker. The participants also were asked, “Do you con-sider yourself a current smoker?” (The response optionswere “yes” or “no.”) There was excellent agreementbetween the classification of smoking status based onthe number of cigarettes smoked in the past 30 daysand the participants’ self-reported smoking status(Kappa = .97).Tobacco use patterns and practices were measured bydetermining the amount of tobacco smoked each day,the age of smoking initiation [18] and reasons fortobacco use [19]. Physical health consequences oftobacco use were assessed with the item, “Do you have,or have you had symptoms that you believe were causedor made worse by smoking?” [20]. Items also wereincluded to determine: the primary sources of tobaccoprocurement ("As you know, cigarettes are expensiveand people get them in different ways. Where do youget yours?”), average weekly expenditure on tobacco("About how much money do you spend on tobacco perweek?”), and type of cigarettes smoked ("What kind ofcigarettes do you smoke... store bought, roll your own,butts, other?”).Nicotine dependence was measured with the Fager-ström Test for Nicotine Dependence (FTND) [20]. Thistest is appropriate for the assessment of nicotine depen-dence in smokers with schizophrenia [21]. The codingalgorithm yields a total score of 0-10. Scores above 6are indicative of a high level of dependence. Althoughwidely used, the internal consistency for the FTND scalehas been borderline (Cronbach’s alpha .67) [22]; in thisstudy, the Cronbach’s alpha was .50. In addition tousing this scale, the participants were asked to rate theirtobacco addiction using a self-rated addiction scale of 0-10, where 0 was “not at all” addicted and 10 was “extre-mely” addicted. They also were asked about usingtobacco to manage their psychiatric symptoms: “Somepeople use smoking to cope with their symptoms, suchas having anxiety or hearing voices. How often do yousmoke to cope with symptoms?” The item was scoredwith a 4-point scale rated as “not at all,” “a little,”“somewhat,” or “a great deal.” Another open-endedquestion asked, “What symptoms do cigarettes help youmanage?”Substance UseComorbid substance use was assessed with items fromthe substance use section of the Addiction SeverityIndex (ASI), originally developed for clinical purposes[23], [24]. The ASI has seven sections measuring variousaspects of an individual’s life that may be affected bysubstance use. For research purposes, the use of indivi-dual items from the substance use section of the ASIhas been found to be reliable, valid, and valuable [25].The participants were asked, “How many days in thepast month (last 30 days) did you use...any alcohol?Alcohol to get drunk? Heroin (smack, junk)? Metha-done? Opium, codeine, or pain killers like Tylenol 3?Sedatives, hypnotics or tranquilizers like Valium orXanax? Cocaine or crack? Amphetamines, like speed, Eor meth? Marijuana (weed, pot)? Hallucinogens, likeLSD or mushrooms? Inhalants, like glue, paint thinneror gas? Any other substances? Specify.” The ASI resultswere reported as number of days and were categorizedinto “no, none” or “yes, 1 or more days” because of theparticipants’ infrequent regular use and the distribu-tional properties of their responses [26].AnalysisA total of 788 people participated in the study, whichrepresents approximately 20% of the clients who receivedcare from the 7 community mental health teams. Thedata from these clients were cleaned and screened beforeanalysis to ensure missing data were random in occur-rence and that all data were within their excepted ranges.Responses from 59 (7.5%) individuals were excludedbecause they did not have a clear psychiatric diagnosis.Descriptive analysis of the sample (N = 729) employedchi square tests to determine the associations betweenpsychiatric diagnosis and the categorical study variables.Independent sample t-tests employing Levine’s test forequality of variance were employed to examine the rela-tionships between psychiatric diagnosis and the continu-ous variables. We employed Hosmer and Lemeshow’sJohnson et al. BMC Psychiatry 2010, 10:101 4 of 12model-building process to determine the variables thatwere associated with current smoking status (currentsmoker vs. former/never smoker) [27]. First, weemployed univariate logistic regression analyses to iden-tify the study variables associated with smoking statusand conducted these analyses for the entire sample andfor men and women, separately. In the second step, vari-ables that were associated with smoking status at p ≤ .25were included in the multivariate logistic regression mod-els (all participants and gender-specific). To obtain themost parsimonious and stable models, we then trimmedthem by removing statistically non-significant variablessequentially by examining the Wald statistic and compar-ison of the likelihood ratios. If the likelihood ratio testwas significant when a non-significant variable wasremoved (i.e., p < .05), then the variable was added backto the model. Once the main effects models were fina-lized, all possible interactions between diagnostic cate-gory and the other variables were examined. All analyseswere conducted with IBM SPSS Statistics 18.ResultsDemographicsAbout one half (51.2%) of the participants were women;26.6% were of a racialised group; 76.5% had a highschool or better education; 63.0% reported being singleand never married; 71.0% lived in independent, privatehouses or apartments; 52.9% lived alone; and the major-ity (56.7%) received government disability benefits. Theaverage age of the participants was 47.4 years (SD =12.1) (see Table 1). To determine if those who providedaccess to their records differed from those who did not,we compared the two groups by the variables listed inTable 1 and found no statistically significant differences.Psychiatric Diagnostic CategoryThe majority (59.8%) of the participants had a diagnosisof schizophrenia spectrum disorder and the remainderhad mood (38.1%) or anxiety (2.1%) disorders. For thesubsequent analyses, we combined those with a mooddisorder or anxiety disorder into a single group. Theparticipants with schizophrenia spectrum disorder weremore likely to be male, single and never married, live ina residential facility or group residential home, andreceive social assistance (see Table 1).The mean BSI scores for the sample were: somatisa-tion = 10.8 (SD = 4.3), depression = 12.0 (SD = 5.4),and anxiety = 11.8 (SD = 5.3) (see Table 1). In terms of‘caseness’ of psychological distress, 12.2% of the partici-pants surpassed the GSI cutoff value of 63 or greater. Ingeneral, those with mood or anxiety disorders hadgreater symptomatology; 15.4% of this group, comparedwith 10.0% of those with schizophrenia spectrum disor-der met the ‘caseness’ criterion.Tobacco UseAlmost one half (46.8%) of the participants were currentsmokers (see Table 1); 57.5% of the men and 35.6% ofthe women were current smokers. The prevalence ofparticipants who reported “ever smoking” was 89.3%.Most (53.8%) of the participants began smoking at 15years of age or younger. Of those who currently smoked,the average number of cigarettes smoked daily was 20.2cigarettes (SD = 13.9), and the main reasons reportedfor smoking were addiction (36.8%) and anxiety (37.1%).The majority of current smokers reported smokingevery day (96.2%), had smoked for 30 years, on average,and were self-identified “chain smokers” (61.5%). Almostone third of the current smokers reported lighting a sec-ond cigarette while the first cigarette was still burning(27.4%). The current smokers’ median FTND score was6.0. In relation to their self-rated addiction, the meanresponse was = 7.4 (SD = 2.5) on a scale of 0 to 10.Although the self-rated addiction scores were not signif-icantly associated with the FTND scores (Spearman rho= .03, p = .70), they were associated with the averagenumber of cigarettes smoked per day (Spearman rho =.44, p < .001) and age of smoking initiation (Spearmanrho = -.12, p = .030). About one half (51.5%) of the par-ticipants revealed that they had experienced symptomsof a disease or illness that were caused or worsened bytheir smoking.Almost all (92.2%) of the current smokers reported“buying tobacco from a store,” which was the mostcommon method of procuring tobacco, although it wasnot exclusive to other methods including “receivingtobacco from friends” (53.3%), “bumming cigarettesfrom people” (39.6%), “sharing someone else’s” (39.5%),and “picking up butts” (30.5%) (i.e., picking up cigaretteends from sidewalks and ashtrays and smoking the endsor re-rolling the salvaged tobacco). The average amountof money spent per week on tobacco was (CAD) $40.50(SD = $25.70). Almost one half (41.2%) of the currentsmokers indicated that they had, on occasion, given upbuying food so that they would have enough tobacco.Many of the current smokers (68.8%) reported thatthey coped with their psychiatric symptoms by smokingand 30.3% reported doing this “a great deal.” Those whoanswered affirmatively indicated that cigarettes helpedthem manage multiple symptoms including anxiety/stress (95.9%), depression (20.6%), and hearing voices/delusions (10.0%).Bivariate associations with current smoking statusThe men with a schizophrenia spectrum disorder, in thesample, were 1.8 times more likely to be current smo-kers than were those men with a mood or anxiety disor-der (see Table 1). The association between diagnosticcategory and smoking status was not significant for theJohnson et al. BMC Psychiatry 2010, 10:101 5 of 12Table 1 Demographic Characteristics and Participants’ Substance Use by Diagnostic CategoryCharacteristic All SchizophreniaSpectrumDisorderMood orAnxietyDisorder1Differences(N = 729) (n = 436) (n = 293)(100%) (59.8%) (40.2%)f % f % f % c2 (df), sig.2Gender (n = 719) 8.0 (1), p = .005Male 351 48.8 228 53.3 123 42.3Female 368 51.2 200 46.7 168 57.7Racialised Group (n = 680) 0.3 (1), p = .592No (e.g., white/European) 499 73.4 300 74.3 199 72.1Yes (e.g., Aboriginal/Asian/South Asian/Black) 181 26.6 104 25.7 77 27.9Education (n = 723) 1.1 (1), p = .289Less than high school 170 23.5 108 25.0 62 21.3High school or more 553 76.5 324 75.0 229 78.7Marital Status (n = 719) 18.9 (3), p = <.001Single and never married 453 63.0 289 67.5 164 56.4Separated/Divorced 159 22.1 92 21.5 67 23.0Married (spouse or common law partner) 79 11.0 30 7.0 49 16.8Widowed 28 3.9 17 4.0 11 3.8Housing (n = 723) 28.5 (3), p <.0001Independent (private house or apartment) 513 71.0 279 64.6 234 80.4Residential facility (licensed/boarding) 102 14.1 81 18.8 21 7.2Semi-independent (subsidy/supportive care) 94 13.0 66 15.3 28 9.6Shelter/hostel/no housing 14 1.9 6 1.4 8 2.7Living Arrangement (n = 724) 26.4 (3), p <.0001Lives alone 383 52.9 240 55.6 143 49.0Lives with family 170 23.5 87 20.1 83 28.4Group home resident 101 14.0 76 17.6 25 8.6Lives with roommate/friend(s)/girlfriend/boyfriend 70 9.7 29 6.7 41 14.0Sources of Financial Support (multiple responses permitted, n = 714)Disability benefits (yes v. no) 405 57.0 235 55.8 170 58.8 0.7 (1), p = .397Canada Pension Plan or other pension (yes v. no) 165 23.1 102 24.1 63 21.7 0.4 (1), p = .525Earned income/paid work (yes v. no) 167 23.4 89 21.0 78 26.9 3.0 (1), p = .082Social assistance/welfare (yes v. no) 119 16.7 87 20.5 32 11.0 10.5 (1), p = .001Family contribution (yes v. no) 110 15.4 58 13.7 52 17.9 2.1 (1), p = .150Smoking Status (n = 729) 13.1 (2), p = .001Current 341 46.8 226 51.8 115 39.2Former 156 21.4 91 20.9 65 22.2Never 232 31.8 119 27.3 113 38.6Any Alcohol Intoxication (in past month) (n = 716) 2.9 (1), p = .088Yes 63 8.8 31 7.2 32 11.2No 653 91.2 399 92.8 254 88.8Any Cocaine Use (in past month) (n = 716) 0.0 (1), p = 1.000Yes 28 3.9 17 4.0 11 3.8No 688 96.1 412 96.0 276 96.2Any Cannabis Use (in past month) (n = 717) 2.3 (1), p = .128Yes 92 12.8 48 11.2 44 15.3No 625 87.2 382 88.8 243 84.7Johnson et al. BMC Psychiatry 2010, 10:101 6 of 12women. The GSI score (≥ 63 vs. < 63; ‘caseness’) wasnot statistically significantly associated with smoking(see Table 2).The men were 2.5 times more likely to smoke thanwere the women (see Table 2). For women, being youngwas a risk factor (those 17-29 years of age were 2.8times were more likely to smoke compared with those60+ years of age). For men, the age group with thegreatest risk of smoking was the 50-59 years of agegroup (OR = 2.4, 95% CI: 1.1-5.1).Being a member of racialised group was protectiveagainst smoking for the women only. White/Europeanwomen were 2.4 times more likely to smoke comparedwith racialised women. Education was only significantfor the men; those with less than high school educationwere about twice as likely to smoke compared withthose who were better educated. Compared with thosewho were married, men who were separated or divorcedwere 3.3 times more likely to smoke. Marital status andeducation were not risk factors for the women.The respondents who reported having no housing orwho lived in temporary shelters or hostels were verylikely to smoke (OR = 17.9; 95% CI: 2.3-13.7). Therewere too few cases of people without housing to providea breakdown by gender. Other forms of housing, how-ever, also placed the women at risk of smoking; specifi-cally, women in residential facilities were 2.7 times morelikely to smoke than were women who lived indepen-dently. Similarly, living with their family protected bothmen and women from smoking (see Table 2).The only form of financial support received that wasassociated with smoking status was social assistance orwelfare. Both men and women who received this formof support were thrice as likely to smoke compared withthose not on assistance.Other substance use was associated with smoking sta-tus. For men who used alcohol to intoxication in theprevious month, or who had used any cocaine or canna-bis in the past month, current tobacco smoking was alsolikely. For women, the only other substance use that wasassociated with their smoking status was cannabis use(OR = 5.2; 95% CI: 2.5-10.5) (see Table 2).Multivariate associations with current smoking statusThe multivariate gender-specific models revealed thefollowing. For the men, the significant predictors ofsmoking status, adjusted for confounding, were: havinga schizophrenia spectrum disorder vs. a mood or anxietydisorder (ORadjusted = 2.0; 95% CI: 1.2-3.3), having lessthan a high school education (ORadjusted = 1.8; 95% CI:1.0-3.1), being separated or divorced, rather than mar-ried (ORadjusted = 3.8; 95% CI: 1.2-11.4), receiving socialassistance or welfare (ORadjusted = 2.6; 95% CI: 1.3-5.4),and having used cannabis in the past month (ORadjusted= 4.6; 95% CI: 2.2-10.0) (see Table 3). Being a memberof a racialised group and having used cocaine in thepast month had odds ratios that spanned unity; retain-ing these variables in the model, however, improved themodel (the comparison of log-likelihood ratios for mod-els with and without these variables were statisticallysignificant). The Nagelkerke R2 for this model, withseven variables, was .23. The correct classification rateswere 63.8% for current smokers and 70.9% for non-smo-kers; the overall correct classification rate was 67.0%.For the women, the significant predictors of smokingstatus were: age (17-29 years vs. 60+ years; ORadjusted =2.8; 95% CI: 1.0-8.0), being white or of European origin(ORadjusted = 2.5; 95% CI: 1.4-4.6), living in a residentialfacility vs. independent living (ORadjusted = 2.7; 95% CI:1.3-5.8), receiving social assistance or welfare (ORadjusted= 3.3; 95% CI: 1.6-6.5), and having used cannabis inthe past month (ORadjusted = 3.2; 95% CI: 1.2-8.0) (seeTable 3). The Nagelkerke R2 for this model, with fivevariables, was .17. The correct classification rates were37.6% for current smokers and 86.9% for non-smokers;the overall correct classification rate was 69.5%.DiscussionIt is noteworthy that almost one half of the study parti-cipants were current smokers; this is almost three timesTable 1 Demographic Characteristics and Participants?’? Substance Use by Diagnostic Category (Continued)Mean SD Mean SD Mean SD t (df), sig.Age (years) (n = 721) 47.4 12.1 47.8 12.4 46.9 11.9 1.0 (719), p = .336Brief Symptom Inventory (n = 715)Somatic symptoms 10.8 4.3 10.6 4.1 11.1 4.6 -1.6 (566.1), p = .1003Depression 12.0 5.4 11.5 4.9 12.8 6.0 -3.1 (522.4), p = .0023Anxiety 11.8 5.3 11.3 4.9 12.6 5.8 -3.2 (533.1), p = .0023Global Severity Index 34.7 13.2 33.4 12.1 36.6 14.5 -3.1 (533.6), p = .00231 Composed of participants with a mood disorder or an anxiety disorder (38.1% and 2.1% of the total sample, respectively).2Continuity correction applied for crosstabulations with 1 degree of freedom.3Levene’s Test for Equality of Variances significant; thus, equal variances not assumed for t-tests.Johnson et al. BMC Psychiatry 2010, 10:101 7 of 12Table 2 Bivariate Relationships between Smoking Status (Current vs. Former/Never) and Diagnostic Category,Demographic Characteristics and Other Substance UseCharacteristic All Men WomenOdds Ratio 95% CI Odds Ratio 95% CI Odds Ratio 95% CIDiagnostic Category1Schizophrenia spectrum disorder 1.7** 1.2 - 2.3 1.8** 1.2 - 2.8 1.3 0.8 - 1.9Mood or anxiety disorder (referent) 1.0 – 1.0 – 1.0 –GenderMen 2.5*** 1.8 - 3.3 – – – –Women (referent) 1.0 – – – – –Age Group17-29 years 2.4* 1.2 - 4.7 1.6 0.6 - 4.3 2.8* 1.1 - 7.030-49 years 1.7* 1.1 - 2.6 1.8 0.9 - 3.7 1.1 0.6 - 2.050-59 years 2.3** 1.4 - 3.7 2.4* 1.1 - 5.1 1.8 0.9 - 3.460+ years (referent) 1.0 – 1.0 – 1.0 –Racialised GroupNo (e.g., white/European) 1.8** 1.3 - 2.6 1.4 0.9 - 2.4 2.4** 1.4 - 4.1Yes (e.g., Aboriginal/Asian/South Asian/Black) (referent) 1.0 – 1.0 – 1.0 –EducationLess than high school 1.8** 1.3 - 2.6 2.1** 1.3 - 3.4 1.2 0.7 - 2.1High school or more (referent) 1.0 – 1.0 – 1.0 –Marital StatusSingle and never married 1.6 1.0 - 2.7 2.0 0.8 - 4.8 0.9 0.5 - 1.7Separated/Divorced 2.0* 1.1 - 3.5 3.3* 1.2 - 8.9 1.4 0.7 - 2.8Widowed 1.2 0.5 - 2.9 4.7 0.4 - 52.1 1.0 0.4 - 2.7Married (spouse or common law partner) (referent) 1.0 – 1.0 – 1.0 –HousingIndependent (private house or apartment) (referent) 1.0 – 1.0 – 1.0 –Residential facility (licensed/boarding) 2.0** 1.3 - 3.0 1.4 0.7 - 2.5 2.7** 1.4 - 5.1Semi-independent (subsidy/supportive care) 1.4 0.9 - 2.1 1.2 0.7 - 2.3 1.5 0.8 - 2.9Shelter/hostel/no housing2 17.9** 2.3 -137.7 M – M –Living ArrangementLives alone 2.2*** 1.5 - 3.1 2.1* 1.2 - 3.6 1.9* 1.1 - 3.3Lives with family (referent) 1.0 – 1.0 – 1.0 –Group home resident 3.4*** 2.0 - 5.6 2.4* 1.1 - 5.1 4.3*** 2.1 - 8.8Lives with roommate/friend(s)/girlfriend/boyfriend 2.1* 1.2 - 3.7 2.7* 1.1 - 6.4 1.6 0.7 - 3.6Sources of Financial Support (multiple responses permitted)Disability benefits (yes v. no) 0.9 0.6 - 1.1 0.8 0.5 - 1.2 0.8 0.5 - 1.3Canada Pension Plan or other pension (yes v. no) 0.7 0.5 - 1.1 0.6 0.4 - 1.0 1.0 0.6 - 1.6Earned income/paid work (yes v. no) 0.8 0.5 - 1.1 0.7 0.4 - 1.1 0.8 0.5 - 1.3Social assistance/welfare (yes v. no) 3.3*** 2.1 - 5.0 3.1*** 1.7 - 5.8 3.2*** 1.7 - 5.9Family contribution (yes v. no) 0.8 0.5 - 1.2 0.9 0.4 - 1.9 1.0 0.6 - 1.6Any Alcohol Intoxication (in past month)Yes 2.2** 1.3 - 3.7 2.1* 1.0 - 4.2 1.4 0.5 - 3.5No (referent) 1.0 – 1.0 – 1.0 –Any Cocaine Use (in past month)Yes 7.5*** 2.6 - 21.8 8.4** 1.9 - 36.3 3.7 0.9 - 20.6No (referent) 1.0 – 1.0 – 1.0 –Any Cannabis Use (in past month)Yes 5.5*** 3.2 - 9.3 4.6*** 2.0 - 10.5 5.2*** 2.5 - 10.5No (referent) 1.0 – 1.0 – 1.0 –Brief Symptom Inventory (Global Severity Index)< 63 (referent) 1.0 – 1.0 – 1.0 –≥ 63 1.3 0.8-2.0 1.0 0.5 - 2.0 1.6 0.8 - 3.01 76 (38.0%) of the 200 women with schizophrenia spectrum disorders were current smokers. 55 (32.7%) of the 168 women with mood or anxiety disorders werecurrent smokers. 143 (62.7%) of the 228 men with schizophrenia spectrum disorders were current smokers. 59 (48%) of the 123 men with a mood or anxietydisorders were current smokers.2Treated as missing in gender-specific models because of small numbers.*p < .05; **p < .01; ***p < .001.Johnson et al. BMC Psychiatry 2010, 10:101 8 of 12Table 3 Multivariate Relationships between Smoking Status (Current vs. Former/Never) and Diagnostic Category,Demographic Characteristics and Other Substance UseCharacteristic All Men WomenAdjusted OddsRatio95%CIAdjusted OddsRatio95% CI Adjusted OddsRatio95%CIDiagnostic CategorySchizophrenia spectrum disorder 1.5* 1.0 -2.12.0* 1.2 - 3.3 NI1 –Mood or anxiety disorder (referent) 1.0 – 1.0 – – –GenderMen 2.0*** 1.4 -2.9– – – –Women (referent) 1.0 – – – – –Age Group17-29 years 2.6* 1.2 -5.8NI – 2.8* 1.0 -8.030-49 years 1.4 0.8 -2.5– – 1.0 0.5 -2.050-59 years 1.8* 1.0 -3.1– – 1.7 0.9 -3.560+ years (referent) 1.0 – 1.0 –Racialised GroupNo (e.g., white/European) 1.8** 1.2 -2.71.5 0.8 - 2.6 2.5** 1.4 -4.6Yes (e.g., Aboriginal/Asian/South Asian/Black)(referent)1.0 – 1.0 – 1.0 –EducationLess than high school – – 1.8* 1.0 - 3.1 NI –High school or more (referent) – – 1.0 – – –Marital Status2Single and never married 1.0 0.5 -1.71.7 0.6 - 4.4 NI –Separated/Divorced 1.8 1.0 -3.53.8* 1.2 -11.4– –Widowed 1.4 0.5 -4.1C – – –Married (spouse or common law partner)(referent)1.0 – 1.0 – – –HousingIndependent (private house or apartment)(referent)1.0 – NI – 1.0 –Residential facility (licensed/boarding) 1.8* 1.1 -3.1– – 2.7** 1.3 -5.8Semi-independent (subsidy/supportive care) 1.6 1.0 -2.6– – 1.9 0.9 -3.8Shelter/hostel/no housing M3 – – – M –Source of Financial SupportSocial assistance/welfare (yes v. no) 2.7*** 1.6 -4.42.6* 1.3 - 5.4 3.3*** 1.6 -6.5Any Cocaine Use (in past month)Yes – – 4.9 1.0 -24.0– –No (referent) – – 1.0 – – –Any Cannabis Use (in past month)Yes 4.5*** 2.5 -8.14.6*** 2.2 -10.03.2* 1.2 -8.0No (referent) 1.0 – 1.0 – 1.0 –1 Not included in the model because the bivariate relationship (unadjusted odds ratio) had a p value ≥ .25 (NI).2Widowed combined with separated/divorced in gender-specific models because of small numbers (C).3Treated as missing in gender-specific models because of small numbers (M).*p < .05; **p < .01; ***p < .001.Johnson et al. BMC Psychiatry 2010, 10:101 9 of 12the 2007 smoking rate of 14% in the province of BritishColumbia, Canada [28]. The participants tended to beheavy smokers who were highly dependent on nicotine.Other researchers also have reported very high rates oftobacco dependence among people with serious mentalillness [6], particularly those with schizophrenia [29].What is particularly troubling about our findings is thatVancouver is a region that has some of the strongesttobacco control measures in Canada [30]. Althoughthese measures have been instrumental in reducing thesmoking rate to one of the lowest in Canada, a moretailored approach with considerable support, includingpharmacological aid, social support and other resources,is needed for community-based people with seriousmental illness.We found that tobacco use rates varied by psychiatricdiagnosis (39.2% for those with mood and anxiety disor-ders and 59.8% for those with schizophrenia), and thatdiagnosis was only predictive of men’s smoking. Theoverall rate is lower than what has been reported else-where. It has been reported that, in Kentucky, the preva-lence of current daily smoking for patients with bipolardisorder and schizophrenia were 66% and 74%, respec-tively [31]. This may point to the importance of thesocial context in influencing the tobacco use of peoplewith serious mental illness. Kentucky, a tobacco produ-cing state in the USA, is reported to have the highestcurrent smoking prevalence rate in the USA [32].More men than women reported being current smo-kers and the predictors of tobacco use varied by gender,in the gender-stratified analysis we found differentialpredictors of current smoking status. These findingssuggest that while strategies need to be found for peoplewith mental health issues, in general, services need to begender sensitive. Gender has historically been a factor intobacco use; men have been more likely to smoke thanhave women. Although the gender gap in the generalpopulation’s smoking rate is narrowing, there remains asubstantial differential in the smoking rates of men andwomen with serious mental illness. More research isneeded of people with serious mental illness to untanglethe relationships among gender, psychiatric diagnosis,the social context, and smoking status.The specific needs of people with a diagnosis of schi-zophrenia spectrum disorder are unique. For example,they may require more support for cessation and theymay need education about how their negative symptomsmay interfere with some of the conventional methods ofcessation support such as group interaction. The findingthat smokers had higher rates of substance use than didthe non-smokers echoes the results of other researchersand magnifies the overlap between tobacco use andother substance use. Best practice guidelines recom-mend that treatment for these co-occurring disorders beintegrated [33]. Although movement towards theintegration of mental health and addiction services isgaining momentum, and more settings have begun tosuccessfully incorporate smoking cessation into theirpractice [34], there is still much dispute among clini-cians about whether tobacco use should be treated as anaddiction and considered part of the spectrum of sub-stance use within the context of dual disorder services.Many of the smokers in this study reported strategi-cally using tobacco to cope with their psychiatric symp-toms. Reports published elsewhere have discussed thecomplicated roles nicotine and tobacco play in the livesof people with mental illness [35]. The stimulating effectof nicotine is known to modulate social and interperso-nal factors to reduce anxiety and to relieve boredom.Nicotine also alters the neurochemistry of the brain andaffects the rate at which psychotropic medications aremetabolised [35]. Clearly the use of tobacco has seriousimplications for psychiatric recovery, which is a compel-ling reason to advocate strongly for the clinical monitor-ing of changes in tobacco use in clients.Tobacco cessation support is a service that should beoffered to all clients wanting to stop smoking, andsmoking cessation interventions have been shown to beeffective in mentally ill clients residing in the commu-nity [36]. The reason for the high smoking rates amongpersons with mental illness may, in part, be related tomental healthcare providers’ reluctance to integrateinterventions for tobacco reduction into their practice,and the lack of attention given to tobacco dependencein organizations providing services for the mentally ill.Integrated solutions must include preparing mentalhealth providers to support tobacco reduction andsmoking cessation efforts.It is clear that the economic costs of tobacco use placea significant burden on people with serious mental ill-ness, especially because many rely on government sub-sistence, which is well below the poverty line [37]. Atthe time of this survey, income from a disability pensionwas capped at $856.42 per month. Social assistance fora single person with a disability, provided by the Gov-ernment of BC, was 62% of the low-income cut offestablished by the federal government [38]. Smokers inthis study spent an average of $160 per month ontobacco; almost 20% of their monthly income. In addi-tion, many of the smokers made choices to smoke“butts” and to buy cigarettes instead of food. It is welldocumented that poverty is associated with poorerhealth outcomes and the extra burden of tobacco-related effects confounds these people’s already compro-mised health outcomes. Tobacco use treatments havebeen shown to be highly cost-effective [39]. Subsidizingnicotine replacement therapy (NRT) is efficacious in sig-nificantly increasing cessation rates and the number ofJohnson et al. BMC Psychiatry 2010, 10:101 10 of 12cessation attempts by smokers wanting to stop smoking[40]. In heavy smokers, higher doses of NRT have beenshown to increase cessation rates [41]. A way to reduceboth the physical and the economic burden of tobaccois for governments or third-party health insurers to pro-vide nicotine replacement therapeutic products free ofcharge for people with serious mental illness.These findings must be considered in light of severalmethodological limitations. First, the relatively low parti-cipation rate limits our ability to generalize to the com-munity-based mental health population as a whole.Other community-based studies of people with mentalillness have reported similar response rates [42,43].There are specific factors associated with seriously men-tally ill people’s willingness to engage in research[44,45]. Many of these factors affected our ability torecruit participants, including the lack of a supportiveresearch culture in the study settings and a reliance onmental health team staff for client referral. Client-speci-fic factors included a fear that the information providedwould not be kept confidential and would have animpact on their healthcare. The length of the question-naire may have been a barrier; many people believedthat they could not complete a 45-minute interview.The presence of some symptoms (e.g., paranoia) mayhave had an additional impact on recruitment. Anotherlimitation of the study relates to the accuracy of themedical diagnosis data; 19% of the participants did notpermit access to their medical records. Our reliance onself-reported diagnosis, for these case, may have resultedin misclassification bias. Additionally, some confidenceintervals for the odds ratios were very wide (i.e., cocaineuse, being widowed, and having no housing) indicating alack of precision in these estimates.ConclusionPeople with serious mental illness have very high ratesof tobacco use and levels of nicotine dependence, andbear a significant health and economic burden becauseof their tobacco use. Many of the factors that are asso-ciated with smoking vary by gender, and socio-environ-mental factors play a key role. Researchers havesuggested that smoking, particularly by those with schi-zophrenia, is likely the result of self-medication forsymptoms. Consistent with Srinivasan and Thara’s con-clusions, we found that social factors, including whereone lives, and one’s marital status, education, andsources of income are associated with smoking, whichsuggests a more multifacted explanation of tobacco usein the presence of mental illness is required [46]. Thefinding that gender is strongly associated with smokingstatus may be explained by a biological sex-based factoror it may represent further support for the hypothesisthat social determinants are significant factors at play.More work must be undertaken to better understandthe motivators and reinforcers of tobacco use in thispopulation and to develop appropriate tobacco cessationinterventions.AcknowledgementsThis research was supported by a grant from the National Cancer Institute ofCanada with funds from the Canadian Cancer Society (No. 016334). Dr.Ratner holds a Michael Smith Foundation for Health Research Senior Scholaraward.Author details1School of Nursing, University of British Columbia, T201 - 2211 WesbrookMall, Vancouver, BC, Canada V6T 2B5. 2BC Centre of Excellence for Women’sHealth, E311 - 4500 Oak Street, Box 48, Vancouver, BC, Canada V6H 3N1. 3BCMental Health & Addictions Research Institute, A3-113, 938 W. 28th Avenue,Vancouver, BC, Canada V5Z 4H4. 4Institute for Healthy Living and ChronicDisease Prevention, University of British Columbia Okanagan, 3333 UniversityWay, Kelowna, BC Canada, V1V 1V7. 5Cancer Nursing Research, Faculty ofNursing, University of Manitoba, Canada, Room 487 Helen Glass Centre forNursing, 89 Curry Place, Winnipeg, Manitoba, R3T 2N2.Authors’ contributionsJLJ was the principal investigator for the study and wrote the major sectionsof the paper. PAR completed the final data analysis and wrote severalcomponents of the paper. LAM assisted with data collection and preliminaryanalysis and contributed to writing the findings section. CTCO conductedsome data analysis. RMP assisted with planning the study and commentedon the paper. JLB contributed to the development of the project andoffered comments on the paper. MG aided in designing the recruitmentstrategy and offered comments on the paper. AS and MO were members ofthe research team and offered comments on the paper. All authors readand approved the final manuscript.Competing interestsThe authors declare that they have no competing interests.Received: 6 October 2009 Accepted: 30 November 2010Published: 30 November 2010References1. Lasser K, Boyd JW, Woolhandler S, Himmelstein DU, McCormick D, Bor DH:Smoking and mental illness: A population-based prevalence study. JAMA2000, 284(20):2606-2610.2. Dani JA, Harris RA: Nicotine addiction and comorbidity with alcoholabuse and mental illness. Nat Neurosci 2005, 8(11):1465-1470.3. 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Tob Control 2001, 10(2):175-180.41. Hatsukami D, Mooney M, Murphy S, LeSage M, Babb D, Hecht S: Effects ofhigh dose transdermal nicotine replacement in cigarette smokers.Pharmacol Biochem Behav 2007, 86(1):132-139.42. Dixon L, Medoff DR, Wohlheiter K, DiClemente C, Goldberg R, Kreyenbuhl J,Adams C, Lucksted A, Davin C: Correlates of severity of smoking amongpersons with severe mental illness. Am J Addict 2007, 16(2):101-110.43. Gerber GJ, Prince PN: Measuring client satisfaction with assertivecommunity treatment. Psychiatr Serv 1999, 50(4):546-550.44. Roberts LW, Warner TD, Brody JL, Roberts B, Lauriello J, Lyketsos C: Patientand psychiatrist ratings of hypothetical schizophrenia researchprotocols: assessment of harm potential and factors influencingparticipation decisions. Am J Psychiatry 2002, 159(4):573-584.45. Rosen C, Grossman LS, Sharma RP, Bell CC, Mullner R, Dove HW: Subjectiveevaluations of research participation by persons with mental illness. JNerv Ment Dis 2007, 195(5):430-435.46. Srinivasan TN, Thara R: Smoking in schizophrenia – all is not biological.Schizophr Res 2002, 56(1-2):67-74.Pre-publication historyThe pre-publication history for this paper can be accessed here: this article as: Johnson et al.: Gender-specific profiles of tobaccouse among non-institutionalized people with serious mental illness.BMC Psychiatry 2010 10:101.Submit your next manuscript to BioMed Centraland take full advantage of: • Convenient online submission• Thorough peer review• No space constraints or color figure charges• Immediate publication on acceptance• Inclusion in PubMed, CAS, Scopus and Google Scholar• Research which is freely available for redistributionSubmit your manuscript at et al. BMC Psychiatry 2010, 10:101 12 of 12


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